The regioselective hydrodifluoroalkylation of alkenes has been identified as a straightforward and highly atom-economical approach for synthesizing value-added difluoroalkylated molecules that have important applications in the pharmaceutical and agrochemical industries, as well as in the field of materials science. This is because olefins are structurally diverse raw materials that are inexpensive and easily obtainable from a wide range of sources. Recent years have witnessed significant progress in this field with the rapid development of visible-light catalysis and novel difluoroalkylating reagents. This article review summarizes the latest advances in the regioselective anti-Markovnikov and Markovnikov hydrodifluoroalkylation of various olefins, which provides an inspirational locale for researchers to engage. Herein, we supplement ideas for designing and developing new reactions, difluoroalkyl reagents, and catalytic strategies.
This work reports a one-step protocol for the synthesis of β-hydroxyamides via amino-carbonylation of epoxides using aromatic amines as the ammonia source instead of silylamine. The reaction was catalyzed by Co2(CO)8 modified with bidentate O-containing ligand L2, which was prepared by the oxidation of Xantphos [9,9-dimethyl-4,5-bis(diphenylphosphino)xanthene] by H2O2 as a diphosphine-dioxide. Under mild conditions (60 °C, CO 3.0 MPa, 6 h), L2-modified Co2(CO)8 efficiently catalyzed the amino-carbonylation of propylene oxide with aniline to generate 3-hydroxy-N-phenylbutanamide with the yield of 64%. The developed catalytic system exhibited fair stability and general substrate scope.
Pesticides are important tools to control crop diseases and pest hazards, guaranteeing the crop harvest. Natural products and their derivatives are major sources of novel pesticides and play indispensable roles in various fields, such as insecticide, fungicide, plant growth regulation, immune regulation and so on. In recent years, numerous fields of biotechnology have made great progress, like genomics, proteomics and structural biology. And thus, the identification of pesticide targets based on natural products and the creation of novel pesticide molecules based on target structures developed rapidly. The concept, rational design, received more attention in pesticide creation. In this article, the discovery of active natural products based on existed targets or novel targets verifying by natural products were demonstrated by several cases, and the subsequent progress in the development of new pesticides were also discussed. The cases explained the important role of natural products in bridging new targets and novel pesticides.
As the main component of greenhouse gases, CO2 represents an inexpensive and readily available renewable C1 synthon. In the past few decades, great efforts have been made toward the development of chemical processes that use CO2 as a promising fossil fuel alternative for C1 feedstocks for the production of industrially attractive chemicals. This could provide access to materials of commercial interest from an abundant, nontoxic, renewable, and low-cost carbon source, thus offering interesting opportunities for the chemical industry, organic synthesis, and so on. Considering the importance of chiral heterocycles in organic synthesis and drug development, the development of highly stereoselective and efficient catalytic asymmetric reactions using CO2 as a C1 synthon for these chiral heterocycles has received considerable attention. Successful examples for chiral lactones, carbonates, and carbamates have already been demonstrated. In this paper, we summarize the recent advances in this field.
This study developed a one-step spraying method using an aqueous solution of SiO2 particles to prepare SiO2/PET fabrics with saturated and uniform structural colors. The color brightness and uniformity were significantly improved by optimizing the treatment temperature, pH of the colloidal solution, and by introducing a small amount of ethanol. This method would promote the applications of structural colored fabrics because using water would reduce both environmental pollution and production cost.
As essential signaling molecules in biological systems, carbohydrates are involved in several vital physiological and pathological processes via specific recognition by receptors. Hence, nanomaterials comprising carbohydrates are crucial for deciphering and regulating biological processes. A non-covalent assembly process can conveniently yield carbohydrate-based nanomaterials owing to the unique merits of simplicity and controllability of the process. This review summarizes the construction and application of glyco-based functional materials through host-guest interactions and coordination-driven self-assembly processes. Additionally, their potential challenges and future directions are highlighted with the aim of improving understanding on carbohydrate-related physiological and pathological processes.
Sulfate ionic liquids, a new type of ionic liquid, are formed by the combination of sulfate anions and organic cations. Sulfate ionic liquids are important because of their unique characteristics, such as a lack of halogen ions, extremely low vapor pressure, high thermal stability, a high viscosity index, low isothermal compressibility, selective solubility, and good biocompatibility. In this paper, the recent progress in sulfate ionic liquids is reviewed, with focus on one-step and two-step synthetic methods. The physicochemical parameters, such as the density, viscosity, refractive index, surface tension, isothermal compression coefficient, and sound conduction, are summarized, and the effects of the physicochemical properties of sulfate ionic liquids, such as the length of the alkyl chains with anionic and cationic groups, are discussed. The sulfate ionic liquids discussed in this review could be used in various fields as new environmentally friendly solvents, extractants, catalysts, lubricating materials, gas absorption materials, and to separate or dope composite materials.
In this study, the electron-deficient pyridinium group and the naphthalene fluorophore were simultaneously introduced to a bipyridyl-containing Zr(Ⅳ) metal-organic framework (MOF) by the N-alkylation post-synthetic modification. The obtained ionic MOF, Zr-bpy-MNap, is nonluminous in the pristine state but shows selective fluorescence turn-on responses to some solvents and ammonia. Test papers were prepared using the MOF for the identification of solvents and detection of ammonia in air. The fluorescence turn-on response is considered to be due to complex host-guest interactions and solvation effects. The MOF is non-fluorescent due to the quenching effects of the host-guest communications between the bromide ion and the framework. On exposure to some solvents or ammonia, the quenching mechanisms are weakened or destroyed by the solvation of bromide or host-guest interactions between ammonia and the framework, resulting in fluorescence turn-on responses. The fluorescence originates from the charge-transfer transition between the naphthyl group and the electron-deficient framework.
The design of efficient and stable supported metal catalysts to prevent metal species from sintering into large nanoparticles under harsh preparation and reaction conditions is key for various important processes, including the conversion of C1 resources and dehydrogenation of low carbon alkanes to C2 and C3 olefins. Zeolites with uniform subnano micropores and various three-dimensional crystalline structures have been proven as ideal supports for preparing highly efficient and stable metal catalysts via encapsulating subnanometric metal clusters within their pores, cages, and channels. Interactions between metal clusters and the zeolite skeleton can regulate their geometric and electronic structure. The development of zeolite-confined subnanometric cluster catalysts aims to take advantage of this joint confinement effect and induce synergy between guest metal species and active sites in host zeolite frameworks. This can further improve the catalytic activity of resultant composite catalysts, for applications in multiple catalytic reaction processes . In this review, typical preparation methods of zeolite-confined subnanometric clusters and their catalytic applications in selective hydrogenation of CO2 and alkynes, hydrogen generation by formic acid decomposition and ammonia borane hydrolysis, and propane dehydrogenation to propene are discussed.
A promising chemical looping-oxidative coupling of methane (CL-OCM) oxygen-carrier catalyst, Na2WO4/Mn7SiO12-SiO2, was obtained by adding extra Mn2O3 to Mn2O3-Na2WO4/SiO2 and in-situ activating in the reaction stream. After experiencing an induction period, the oxygen-carrier phase transformed from Mn2O3 to Mn7SiO12 in association with an improvement in C2-C3 selectivity but decreased CH4 conversion. The Na2WO4/Mn7SiO12-SiO2 oxygen-carrier catalysts could also be obtained by directly calcining the Na2WO4/Mn2O3-SiO2 precursor at 800 ℃ in air. At 750 ℃ and a CH4 residence time of 12 s, the catalyst achieved 12% (or 7%) CH4 conversion and 81.5% (or 90.0%) C2-C3 selectivity using a mCat/mCH4 weight ratio of 27 (or 13.5). Notably, only C3H6 was detected as C3 products, whose selectivity was about 5%. The CL-OCM reaction proceeded selectively through the redox cycle mode of Mn7SiO12 $ \leftrightarrow $ [MnSiO3 + MnWO4]. The lattice-oxygen mobility in Mn7SiO12 was much weaker than that in Mn2O3, which improved C2-C3 selectivity but decreased CH4 conversion. Our findings provide guidance for the exploration of more advanced catalytic oxygen-carrier catalysts toward efficient CL-OCM process.
Methyl glyoxylate is widely used in organic synthesis and chemical production. The application of traditional preparation methods is limited by high cost, low efficiency, and significant environmental pollution. During the coal to ethylene glycol process, methyl glycolate is produced as an intermediate product of the hydrogenation of dimethyl oxalate (DMO) to ethylene glycol. Methyl glycolate can be selectively obtained from DMO via hydrogenation, and therefore, has the potential to serve as raw material for methyl glyoxylate. However, only few studies have considered this process. Herein , the applications, traditional preparation methods, and state-of-the-art research progress of methyl glycolate oxidation are reviewed. Recent research on selective oxidation of related alcohols (such as ethanol) to aldehydes and ketones is also summarized.
The selective hydrogenation of cinnamaldehyde is an important model reaction for investigating the relationship between catalyst structures and regioselectivity. In recent years, researchers have designed and synthesized a series of better-performing, supported Pt-based catalysts for the selective hydrogenation of cinnamaldehyde, based on electronic, synergistic, and geometric effects to improve the selectivity. In this mini-review, we aim to summarize the recent progress in different supported Pt-based catalysts for the selective hydrogenation of cinnamaldehyde and discuss the performance of these supported catalysts to provide ideas for the design of novel better-performing Pt-based catalysts.
To improve the catalytic performance of copper-based catalysts in the electroreduction of CO2, nitrotriacetic acid (NTA) was used as an additive to prepare copper-based catalysts having a three-dimensional structure by applying electrodeposition. The prepared catalysts exhibited excellent selectivity and activity for the electroreduction of CO2 to multi-carbon (C2+) products. At –1.26 V vs. RHE, the faradaic efficiency of C2H4 and C2+ products over the Cu-0.5/CP electrode reached 44.0% and 61.6%, respectively, and the total current density reached 12.3 mA·cm–2. In addition, Pd- and Zn-based catalysts were prepared by employing electrodeposition; the results showed that their selectivity for CO was significantly improved, proving that NTA has a certain universality in the preparation of electrocatalysts by using electrodeposition.
The efficient fixation and utilization of CO2 under mild conditions is one of the key components of green carbon science. The electrocatalytic coupling of CO2 and organic compounds can produce value-added chemicals, which is beneficial to sustainable development. In this review, we summarize the current methods of synthesizing carboxylic acids, organic carbonates, carbamates, and other chemicals via electrocatalytic CO2 coupling with organic compounds. We also present the latest research progress and opportunities in this field, such as asymmetric electrocarboxylation to construct chiral molecules, electrochemical ring-opening carboxylation, electrochemical N-methylation, electrocarboxylation with non-sacrificial anodes, and paired electrosynthesis.
To achieve the national strategy of carbon neutralization, the electroreduction of carbon dioxide into usable reagents via renewable energy has caused widespread concern in the scientific community. Cu-based electrocatalysts can reduce carbon dioxide to high value-added multi carbon products, but the catalytic mechanism still needs to be studied to improve its selectivity and efficiency. Depending on the state of the Cu, Cu-based catalysts can be divided into Cu alloy/composite catalysts, single-atom, oriented crystalline, and oxidized Cu-based catalysts. This paper introduced the common preparation methods, structural characteristics, effect of electro catalytic reduction of carbon dioxide, and possible catalytic mechanism of the four types of Cu-based catalysts mentioned above.
Bio-based 2,5-furandicarboxylic acid (FDCA) is expected to partially replace petroleum-based terephthalic acid (PTA) for the synthesis of high-performance polymer materials. This review article summarizes the latest achievements on the various synthesis routes of FDCA from 5-hydroxymethylfurfural (HMF), furoic acid, furan, diglycolic acid, hexaric acid, 2,5-dimethylfuran, and 2-methylfuran. In particular, the direct oxidation, heterogeneous thermal catalytic oxidation, photoelectric catalytic oxidation of HMF and furoic acid carboxylation, disproportionation, carbonylation, and other routes to synthesize FDCA are reviewed in detail. Based on the comparative analysis of the advantages and disadvantages of each route, the HMF route and the furoic acid route are considered the most promising candidates for the large-scale production of FDCA. Further exploration and future research should be carried out to improve the catalytic production and separation efficiency of FDCA, simplify the reaction process, and reduce production wastes.
Plastics are widely used in daily life owing to their light weight, portability, and affordability. However, post-consumer-waste plastics do not degrade easily in the natural environment, making plastic pollution a new global environmental issue. Thus, exploration in the field of plastic degradation has increased in recent years. To promote the treatment of plastic waste and provide a scientific reference for environmental protection and sustainable development, this study describes the current state of plastic pollution. It also systematically summarizes various research fields of plastic degradation and presents the development prospect of photocatalysis and bio-based plastics in the future.
The simple rule of base-pairing of nucleic acids enables nucleic acid structures to be designed via powerful energy-based prediction tools. Thus, nucleic acid structures have attracted considerable attention owing to their ability to fold into a variety of synthetic structures. However, the lack of chemical diversity of nucleic acid bases makes nucleic acid structures less functionally diverse than proteins, thereby limiting their practical applications. This review focuses on the underlying technology of nucleic acid molecular engineering, especially on the studies of nucleic acid structures and their molecular interactions. As nucleic acid structures are fully spatially addressable, a diversity of particles could be linked to designated positions on the surface of nucleic acid structures. Additionally, the intermolecular reaction kinetics of nucleic acids could be continuously fine-tuned by rational design of nucleic acid sequences. This review also summarizes the development of synthetic molecular networks, dynamic molecular machines, and nucleic acid-based biomaterials, as well as the application of these as green biomedical devices in biomolecular recognition, cell surface engineering, and biocatalysis.
Photothermal therapy has attracted attention as a novel cancer treatment with high specificity, minimal invasiveness, and low toxicity. In this study, a facile, effective, and green method was developed to prepare a novel supramolecular photothermal material. Grinding a commercial dye, dibenzotetrathiafulvalene (DBTTF, with absorption <400 nm) with cucurbit[8]uril (CB[8]) in air with a small amount of water leads to the oxidation of DBTTF to radical cations. Furthermore, DBTTF dimerizes, assembles into the cavity of CB[8], and forms a ternary supramolecular complex with strong absorption in the visible and near-infrared regions, where the longest absorption wavelength exceeds 1000 nm. The photothermal conversion efficiency of the supramolecular system is 18.7%. The system exhibits good photothermal stability and biocompatibility, and has been successfully applied in the photothermal ablation of live tumor cells. This supramolecular material has potential applications in photothermal therapy and other photothermal conversion fields.
A graph is called a two-degree graph if its vertices have only two distinct degrees. A two-degree tree of order at least three have two degrees, $ 1 $ and $ d $ for some $ d\geqslant 2; $ such a tree is called a $ (1,d) $ -tree. Given a positive integer $ n, $ we determine: (1) the possible values of $ d $ such that there exists a $ (1,d) $ -tree of order $ n; $ (2) the values of $ d $ such that there exists a unique $ (1,d) $ -tree of order $ n $ , and (3) the maximum diameter of two-degree trees of order $ n. $ The results provide a new example showing that the behavior of graphs may sometimes be determined by number theoretic properties.
In this paper, we consider the inverse problem of quadratic eigenvalue for a Hermitian R-antisymmetric matrix. By using the matrix block method, singular value decomposition, vector straightening, and the Moore-Penrose inverse, we prove the existence of a Hermitian R-antisymmetric solution. In addition, we provide the general expression for a Hermitian R-antisymmetric solution, and discuss the best approximation thereof. Finally, an example is offered to validate the theory.
In this paper, we introduce the notion of strongly Gorenstein weak flat modules, and we subsequently provide homological characterizations of strongly Gorenstein weak flat modules. It is shown that a Gorenstein weak flat module is a summand of a strongly Gorenstein weak flat module.
The complete convergence of sequences of random variables under sublinear expectation was studied. Using the properties of extended negatively dependent (ND) sequences, under the condition that the $ \lambda $ -order Choquet integrals of the random variable are finite, the complete convergence of the weighted sums for extended ND sequences under a sublinear expectation was proved. The results generalize and improve the results of independent sequences in the classical probability space.
The dynamical behavior of a class of second-order semilinear singularly perturbed Neumann boundary value problems with a turning point are studied. Firstly, we establish sufficient conditions for the exchange of stabilities near the turning point. By correcting the regularized equation of the degenerate problem, the accuracy of the asymptotic solution to the original problem is improved. Secondly, the Nagumo theorem is used to prove the existence of a smooth solution. Finally, a specific example is used to verify the validity of the results.
This paper considers a Neumann boundary value problem of a singularly perturbed delay reaction-diffusion equation with a nonlinear reactive term. By using the boundary layer function method, contrast structure theory, and contraction mapping principle, the asymptotic expansion of the solution is constructed, and the existence of a uniformly valid solution is proven. Finally, an example is presented to show the effectiveness of our result.
The precision (inverse covariance) matrix generated by the periodic vector autoregressive model is a sparse block tridiagonal matrix. Based on this precision matrix, a new block trace function is proposed for testing the equality of block traces of two precision matrices, the asymptotic behavior under the null hypothesis is investigated. Numerical experiments show that the proposed testing procedure has both appropriate size and good power.
In real-world scenarios, various events in the news are not only too nuanced and complex to distinguish, but also involve multiple entities. To address these problems, previous event-centric methods are designed to detect events first and then extract arguments, relying on imperfect performance for event trigger detection; this process, however, is unfit to deal with the sheer volume of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from an event-centric to a target-centric view. This paper proposes a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extractions first and subsequently the detection of dozens of event types; this approach can be applied to data from massive corporations. Experimental results on a real-world Chinese financial dataset demonstrated that our model outperformed previous methods, particularly in complex scenarios.
In the era of big data and with the continuous expansion of data, there are significant challenges with efficient access to data. Hence, designing an efficient index structure is of great significance. ALEX (updatable adaptive learned index) is a learned index that uses a machine learning model to replace the traditional B-tree index structure. Although it offers good time and space performance, it suffers from frequent page faults. In order to solve this problem and further improve the performance of ALEX, a memory pre-allocation strategy based on huge pages is proposed, on the basis of ALEX, that can help reduce the rate of memory page faults and improve the overall performance of ALEX. In the memory allocation phase, the pre-allocation strategy is adopted, and the memory free phase adopts a delayed release strategy. Experiments on the Longitudes dataset show that this strategy offers good performance.
Traditional virtual terrain modeling commonly uses a procedural generation method based on manual design, which cannot be used for competent simulation modeling tasks that need to restore real environments, such as in military applications. In this paper, we proposed a landscape simulation modeling method based on remote sensing images. The core of our proposed method is a landscape blended texture generation network (LBTG-Net); this method uses a blended texture generator (BTG) to generate landscape blended textures with the supervision of a style discriminator (SD) and multi-stage classification loss. Then, we procedurally build the complete virtual environment based on the blended texture generated by LBTG-Net. Our method has two main features: (1) accurate land-cover classification ability of remote sensing image inputs; and (2) high quality landscape blended texture outputs to guarantee virtual landscape modeling quality. We used multispectral image data from the Sentinel-2 satellite as the experimental dataset. The experimental results showed that our method offered high performance under mainstream land-cover classification evaluating indicators and can accurately reproduce the environmental distribution of input remote sensing images while completing high-quality virtual terrain simulation modeling.
In this paper, we propose a method for fast splicing of three-dimensional point clouds based on the lock pin model on a container terminal using high overlapping views. This experiment first uses an Azure Kinect depth camera to collect scene point clouds, and subsequently preprocesses the point cloud. The target point cloud is thus obtained. For lock pins with slightly different views, the sample consensus initial algorithm (SAC-IA) is used on the basis of the classic iterative closest point (ICP) algorithm to determine the overlapping position relationship of the two point clouds. In the overall splicing process, the relative size of the bounding box area projected by the lock pin in the z-direction of the camera is adopted to estimate the general shape of the lock pin; the relative size of the bounding box area is also used to select an appropriate number of point cloud views with high overlap in order to ensure the accuracy of registration and reduce processing time by comparing the difference between the area of adjacent views. The experimental results show that the proposed method has a lower relative registration error for the lock pin, and can quickly establish a workpiece model suitable for type matching.
The popularity of positioning devices has generated a large volume of vehicle driving data, making it possible to use historical data to predict the driving time of vehicles. Vehicle driving data consists of two parts: the sequence of road segments that the vehicle travels through, the departure time, the total length of the path, and other external information. The questions of how to extract sequence features in road segments and how to effectively fuse sequence features with external features become the key issues in predicting the travel time. To solve the aforementioned problems, a transformer-based travel time prediction model is proposed, which consists of two parts: a road segment sequence processing module and a feature fusion module. First, the road segment sequence processing module uses the self-attention mechanism to process the road segment sequence and extract the road segment sequence features. The model can not only fully consider the spatiotemporal correlation of road speeds between each road segment and other road segments, but also ensures the parallel input of data into the model, avoiding the low efficiency problem caused by sequential input of data when using recurrent neural networks. The feature fusion module fuses the road segment sequence features with external information, such as departure time, and obtains the predicted travel time. On this basis, the number of road segments connected by the intersection is determined by the upstream and downstream intersection features of the road segment, and the input model is combined with the road segment characteristics to further improve the prediction accuracy of the driving time. Comparative experiments with mainstream prediction methods on real data sets show that the model improves prediction accuracy and training speed, reflecting the effectiveness of the proposed method.
With the development of the Internet of Things, a large number of sensor devices can be connected to a network. Anomaly detection of data generated by these devices is related to the stability of system services. A time series database is a database system optimized for time series data. As an important component of a monitoring system, time series databases are responsible for storing and querying continuous streams of time series data. The current time series database, however, cannot fully utilize system computing resources and cannot meet the latency requirements when coping with queries from multiple data sources. To address these drawbacks, we redesigned the query execution model of a time series database based on the well-known InfluxDB, and we proposed InfluxDB-PP (parallel processing) as a method to address the aforementioned problems. The experimental results show that InfluxDB-PP reduces query latency by about 85.7% compared to InfluxDB for real-time anomaly data query scenarios.
With the continuous development of industrial intelligent inspection technology, the equipment element state recognition system based on digital image processing is widely used. In order to improve the accuracy of power distribution cabinet(PDC) equipment element state recognition in a distribution room, a ResNet(residual networks)-based equipment element state recognition method is proposed. Firstly, the data acquisition system is set up and the data set is constructed. Then, for the PDC image, the preset device component target area is cropped to generate the device component image. For device component images, a ResNet-based component state recognition model was constructed and trained, and the trained model was used to identify component states. Taking the data set for power distribution cabinet equipment element in substation distribution rooms as the research object, a network of single prediction heads is adopted as the component with complex features, and the network of multiple prediction heads is adopted as the component with simple features. Then, the compact and pruning model compression method is used to reduce the number of parameters and the calculation amount under the condition of less accuracy loss. Finally, the architecture design of the inspection system is introduced. A JetSon Nano edge terminal is used as the running hardware of the algorithm module to reduce the communication cost.
With the continuous development of China’s electric power system, the security and reliability of power supply directly affects regional production output and people’s economic life. As an important part of the power dispatch system, traditional fault locations rely on the cumulative experience and manual judgment of dispatchers. Faced with increasing demands, fault locations that rely solely on the traditional method are likely to result in an increase in misjudgment rates and pose a threat to the stable operation of the power system. This paper proposes a Boolean equation based on Kirchhoff’s law and the grid fault location algorithm to address this challenge. The fault location issue can effectively be converted to Boolean linear mixed programming problems and combined with simulated annealing algorithms. When these genetic algorithms are applied to the idea of a network and realized in the grid for fast positioning of small faults, the scheduling error rate can be reduced and the time difference from fault occurrence to fault isolation and fault processing can be shortened; in turn, this saves human resources and improves scheduling efficiency.
Cricket fighting is a traditional Chinese game that has lasted for more than 1000 years. Before competitions, Xishuai grass (stimulant grass) is commonly used to hit a cricket’s head and antennae to prepare the insects for battle. In “Flora of China” and flora of Chinese provinces, Xishuai grass is referenced as the Chinese common name of Eleusine indica (L.) Gaertn. In “Flora of Beijing” and “Flora of Hebei”, Xishuai grass is referenced as the Chinese common name of the genus Eleusine. In this paper, however, we demonstrate that Xishuai grass should actually be Digitaria spp. Given the mistakes in the above flora references, this paper aims to clarify the correct classification for future reference.
In this study, the MS2 phages were used as an indicator microorganism to test the function of seven disinfectants with different components: hydrogen peroxide, ethanol, fermented lactic acid disinfectant, iodine disinfectant, quaternary ammonium disinfectant, chlorine-containing disinfectant, and peracetic acid disinfectant. Our results showed that the virus disinfection rate varied notably between the selected disinfectants. The iodine disinfectant exhibited the strongest disinfection effect, followed by the quaternary ammonium salt disinfectant and the peracetic acid disinfectant, while the disinfection effects of hydrogen peroxide, ethanol, and fermentation lactic acid disinfectant were inadequate. The test results provide a reference for the efficient utilization of various disinfectants to eliminate harmful microorganisms in the environment.
Aluminum (Al) used for hydrogen generation and aqueous contaminant removal has been widely studied given its abundance and low redox potential; the reduction ability of Al, moreover, is restricted by the passive surface oxide film on Al particles. In addition to common Al surface treatment methods, such as acid/alkali washing, alloying, and mechanical ball-milling, Al surface modification technology arising in recent years has also been confirmed as an efficient Al activation method given its economical cost and benign manufacturing process. In this study, the merits and disadvantages of surface modification relative to other Al surface treatment methods were highlighted by reviewing existing research on the application of Al surface modification in hydrogen generation and aqueous contaminant removal. In addition, the paper presents an outlook on Al surface modification technology used for hydrogen generation and aqueous contaminant removal to promote the study of related processes.
The development of city-scale vegetation maps is helpful for vegetation management and conservation. Vegetation classification systems in China mainly consider natural vegetation and most classification systems operate at the national or provincial scale, making them unsuitable for city-scale classification. Until now, the lack of a classification system designed specifically for urban vegetation has limited the studies on urban vegetation. Based on the origin, disturbance, and function, our classification system divides urban vegetation into natural, secondary, and cultivated vegetations. Based on the function, cultivated vegetation is further divided into artificial forest land, landscape green land , and urban agricultural vegetations. Based on the Classification System of China’s Vegetation in 1980 and the three newly proposed preliminary guideline documents for classification of natural vegetation, we establish a new urban vegetation classification system. We applied the principles of this new urban system in Shanghai and other areas in China, to further refine the system and ensure it has both academic and practical values. This work provides the theoretical basis for compiling information about urban vegetation and provides technical support for the recognition, protection, construction, and management of urban vegetation.
In this study, we obtained tree species from 72 Taoist temples across China. We subsequently documented the tree species composition, distribution pattern, and impact factors in different regions to determine the role of Taoist temples in biodiversity protection. The results showed that: ① Among 72 Taoist temples sampled across China, we observed a total of 354 species of trees, belonging to 85 families and 208 genera; ② The tree species in the Taoist temples were mainly native species, and the mean value for the proportion of native species in each Taoist temple was 62.5%±19.8% (mean ± standard deviation). Most of the Taoist temples (77.8%), moreover, housed threatened tree species; ③ Taoist tree species originated largely from subtropical regions, with the Yangtze River Basin being the most represented, especially in the southwest and south-central regions where a relatively large proportion of ethnic minorities reside; ④ The main factors affecting the distribution of Taoist tree species were geography and climate, but their composition was indistinguishable within each climate zone. As the climatic zone moves northward, there is no religious tree species replacement phenomenon (i.e., replacing the original religious tree species by native tree species with similar morphology or cultural meaning). The above results indicate that Taoist temples are rich in plant resources, which are potential biodiversity treasures. Thus, they play an essential role in protecting and maintaining biodiversity, with the potential to serve as a reference for regional ecological restoration and urban green space construction.
We collected soil samples from Maogang Town (Songjiang District, Shanghai) and used high-throughput sequencing techniques to reveal the diversity and community composition of soil bacteria and soil fungi in this peri-urban area. We found a total of 632 bacteria OTUs (operational taxonomic units) and 593 fungi OTUs in Maogang, in which the dominant microbial groups were Proteobacteria (bacteria) and Ascomycota (fungi). The Shannon Wiener index of soil bacteria was found to be in the range of 4.41 to 4.71, and the soil fungi’s Shannon Wiener index was found to be in the range of 1.99 to 2.60. Land-use type insignificantly affected α diversity of soil bacteria and soil fungi; however, land-use type significantly affected β diversity of soil bacteria (R = 0.32, p = 0.001) and soil fungi (R = 0.22, p = 0.001). Lastly, there were significant correlations observed between three soil environmental factors—namely, soil pH, porosity, and moisture—and community composition and β diversity of soil bacteria and soil fungi.
In this study, the effects of floating-bed planting on the retention characteristics of heavy metals by emergent hydrophytes in Jinze of Shanghai was studied. A series of sampling sites were set up in the reservoir at the bankside and the floating bed area, respectively.Cyperus alternifolius, Canna indica, andIris tectorum were selected as the representative emergent hydrophyte species to analyze the effects of floating-bed planting on the retention of Cu, Zn, Fe, Cr, Cd, and Pb in plants. The results showed that the biomass of C. indica and I. tectorum that were planted on the floating bed was significantly higher than those planted on the bankside (p < 0.05), while the biomass of C. alternifolius planted on the floating bed was significantly lower than those that were planted on the bankside (p < 0.05). The Cu, Fe, and Pb contents of C. indica planted on the floating bed, moreover, were significantly higher than those that were planted on the bankside (p < 0.05). The Cu, Zn, and Cd contents of C. alternifolius and I. tectorum planted on the floating bed were significantly higher than those that were planted on the bankside (p < 0.05). The retention of Cu, Fe, Cd, and Pb by C. indica; Cu, Zn, Fe, Cd, and Pb by I. tectorum; and Cd by C. alternifolius planted on the floating bed was significantly higher than those that were planted on the bankside (p < 0.05). In summary, correlations between the retention of different heavy metals for the same hydrophyte varied under different planting conditions; these differences were also observed among different species. Above all, it was shown that floating-bed planting can improve the growth and heavy metal absorption capacity of emergent hydrophytes to a certain extent, which has an important effect on the corresponding retention characteristic of heavy metals. Based on the results of our research, it is recommended that C. indica and I. tectorum would be preferred as floating bed plants. For the purpose of long-term monitoring, further systematic and comprehensive studies should be carried out in the future.
In this study, sedimentary component analysis and radioactive dating of sediment core from Krossfjorden were used to evaluate the vertical distribution of grain sizes and sedimentary changes since the 1930s. The analysis helps us to understand the environmental changes that have taken place in fjords of the high-latitude glacier front over the past few decades. The results show that the sedimentation rate (0.35 cm/a) has increased significantly since the 1990s and is about twice the rate observed historically before the 1990s (0.16 cm/a). After the 1990s, the mean and median grain size increased and sediment coarsening appeared. Moreover, the composition of grain sizes changed significantly, including a decrease in the sorting coefficient, increase in kurtosis fluctuation, and positive skewness change. Changes in the sedimentary characteristics indicate that the environment has changed in the Krossfjorden during the last thirty years. Climate change has caused rapid glacier melting, which has significantly increased the proliferation of terrigenous materials in fjords of the high-latitude glacier front.
Based on a field investigation of eleven villages in Shanghai across four seasons and statistical analysis of photos and observation data, 62 perennial herbs species, belonging to 30 families and 52 genera, were identified. Gramineae, Compositae, and Asparagaceae accounted for the majority of species observed; meanwhile, native plants accounted for a relatively low proportion, representing only 15.63% of the total. Challenges for the plant landscape include monotonous dominant species, similar landscapes, and poor growth conditions. Cluster analysis was used to classify the characteristics of the investigated perennial herbs and to select potential substitutes for underperforming species. The data can be used to optimize the allocation of plant communities and to provide a theoretical basis for improving the rural plant landscape of Shanghai.
This study investigated the combined application of water-retaining agents and organic fertilizers to enhance the soil water-holding capacity of waste dumps in open pit coal mines by measuring the anti-evaporation capacity and water content of man-made soil. The results showed that the anti-evaporation capacity of water-retaining agents in the gel state was superior to that in the dry state. Among the tested water-retaining agents, we found that France Essen at a concentration of 5% was the optimal choice. The soil water-holding time could be prolonged by the combined application of water-retaining agents and organic fertilizers, resulting in improved soil water-holding capacity. The optimum ratio of water-retaining agents to organic fertilizer was 3‰∶20% or 5‰∶10%. The water retention time of field capacity in this area would be improved by more than one time. Our findings provide valuable insights for the scientific community regarding soil remediation in waste dumps within open pit coal mines in Northwest China.
The remote sensing inversion of crop canopy water content is a valuable for assessing drought stress of wheat fields and implementing precision irrigation. This study aimed to quickly obtain the canopy water content during the growth period for winter wheat in North China by using multi-temporal remote sensing images of Landsat-8 OLI and Sentinel-2 MSI from January to May 2017. The regression relationship was constructed with NDWI and measured water content in a wheat field via the mixed pixel decomposition model. The genetic algorithm was then used to inverse the canopy water content. The proposed method demonstrated better performance compared to ground-truth data, with the coefficient of determination (R2) and the root mean square error (RMSE) of 0.567 and 5.6%, respectively. Additionally, the error was reduced by more than 20% when compared to the direct inversion based on NDWI. This study indicates that quantification of different linear mixing ratios of wheat canopy and background soil can effectively eliminate the influence of soil on wheat water content inversion, and is crucial for the application of remote sensing to wheat growth monitoring.
In response to the need for high timeliness and accuracy monitoring for inundation region during flood disaster, a new extraction method of water areas based on SAR texture and LightGBM was proposed. Compared with other methods, such as the SDWI water index, SVM, RF and GBDT methods, it shows that the accuracy of water extraction of river, lake and flooded area is beyond 98% and higher than other methods. Meanwhile, the operating efficiency of the proposed method is 20 ~ 100 times higher than other methods, which greatly improves the timeliness of inundation emergency monitoring during flood disaster.
The process of change and factors influencing dissolved CH4 concentration and flux in the coastal wetlands of Jiuduansha (JDS) and Xisha (XS) in the Yangtze Estuary were explored. The concentration of dissolved CH4 varied significantly during the sampling period, with the highest in JDS wetland being (0.30±0.19) μmol·L–1 during autumn, while that in XS wetland being (1.16±1.52) μmol·L–1 during summer. The average dissolved CH4 concentration in XS wetland ((0.56±0.91) μmol·L–1) was slightly higher than that in JDS wetland ((0.18±0.17) μmol·L–1). Principal component analysis revealed that the temporal and spatial variations in CH4 were mainly related to seasonal variation and tidal cycling in coastal wetlands. The CH4 emission under low-temperature, high-salinity, and oxygen-rich water environments was limited. The fluxes of dissolved CH4 also showed seasonal and regional variations. The water-to-air diffusion of CH4 was the largest in autumn in JDS wetlands ((0.45±0.43) nmol·m–2·s–1) and in summer in XS wetlands ((3.34±5.21) nmol·m–2·s–1). The lateral fluxes of dissolved CH4 were maximum in autumn in JDS wetlands ((2.32±9.32) nmol·m–2·s–1) and in summer in XS wetlands ((1.66±5.06) nmol·m–2·s–1). Use of water quality parameters and dissolved CH4 concentration to fit a multiple regression equation produced a high-frequency and continuous CH4 concentration. The annual average lateral transport flux (JDS wetland: 1.46 mg·m–2·d–1; XS wetland: 0.34 mg·m–2·d–1) and annual average vertical diffusion flux (JDS wetland: 1.85 mg·m–2·d–1; XS wetland: 2.90 mg·m–2·d–1) of dissolved CH4 was calculated. The results show that dissolved CH4 in coastal wetlands is an important sources of CH4 in the atmosphere and coastal waters.
Heterosigma akashiwo is one of the species of harmful algal blooms (HABs) threatening marine ecosystems and the fishing industry across coastal waters worldwide. With increasing global levels of atmospheric CO2 and global warming, HABs have increased in prevalence, duration, and geographic span; this phenomenon has been further stressed by intensified anthropogenic influences, such as eutrophication. Through controlled experiments with different nutrients, CO2 concentrations, and temperatures, our study aimed to understand the response of H. akashiwo to different nutrients with changes in climate. In all simulated CO2 and temperature scenarios, both cell density and the specific growth rate of H. akashiwo in the low phosphorus groups were significantly lower than those in the high phosphorus groups. Furthermore, the maximum cell density and specific growth rate of H. akashiwo were significantly enhanced by increased CO2, while the specific growth rate was accelerated by the dual effect of increased CO2 levels and temperature. The growth response of H. akashiwo to CO2 and temperature was similar between different nutrient treatments. Taken together, the results indicate that phosphorus concentration could be the major factor controlling the growth of H. akashiwo, and the intensity and risk of H. akashiwo blooming in the future is increasing. Hence, controlling the increase of nutrients, particularly phosphate, could be a critical pathway to decrease the occurrence of H. akashiwo blooms. In summary, our case study provides scientific support for marine ecological management of HABs.
To understand the metabolic potential and environmental adaptation mechanisms of Nitrospira in tidal flat wetlands, 14 high-quality Nitrospira genomes were constructed from five tidal flat wetlands along the coast of China using metagenomic binning and assembly methods. Phylogenetic analysis showed that among these genomes, three belonged to Comammox (complete ammonia oxidizer), nine belonged to lineage II and IV of Nitrospira, and two belonged to lineage III, which has yet to be discovered; taken together, this data suggests that abundant and diverse Nitrospira are present in China’s tidal flat wetlands. Metabolic reconstruction revealed that these Comammox and Nitrospira contained cyanase, urease, and other enzymes involved in the degradation of nitrile compounds and amide compounds; hence, they may utilize the organic nitrogen as energy by coupling with ammonia-oxidizing microorganisms. In addition, Nitrospira possessed flexible strategies to resist environmental stresses such as viral attack and osmotic changes. These results provide insights on the diversity, ecological function, and environmental adaptation mechanisms of Nitrospira from tidal wetlands.
In February 2014, a persistent and strong northerly wind caused an extremely severe saltwater intrusion event in the Changjiang Estuary, which posed a threat to the safety of water intake from this source. Increasing river discharge from upstream reservoirs in the river basin is a method to combat severe saltwater intrusion. To simulate and analyze the effects of various river discharges on saltwater intrusion, we used the Unstructured quadrilateral grid, Finite-differencing, Estuarine and Coastal three-dimensional Ocean numerical Model (UnFECOM). By taking into account realistic river discharge and wind conditions, the model accurately reproduced the extremely severe saltwater intrusion process that occurred in February 2014. Our findings indicated that the net water flux (NWF) across the section at the upper reaches of the North Channel (NC) remained landward during the most critical period of saltwater intrusion from February 10 to 13, 2014, despite the increase in river discharge. However, the magnitude of NWF tended to decrease with increasing river discharge. The net water diversion ratio (WDR), NWF (Net Water Flux), and salt flux increased with the increase in river discharge. Under realistic river discharge conditions, WDR was –29% (the negative sign indicates that the NWF is landward), NWF was –2300 m 3/s, and the net salt flux (NSF) was –68 t/s, indicating that the NWF and NSF were landward due to the landward Ekman transport effect induced by the persistent severe northly wind. When the river discharge increased by 3000 m 3/s, WDR and NWF across the section were nearly zero, and NSF was –34 t/s. When the river discharge increased by 8000 m 3/s, WDR was 21.5% and NWF was seaward, at 3550 m3/s. NSF was –6 t/s and landward. At the water intake of Qingcaosha Reservoir, the longest continuous unsuitable water intake time decreased slightly when the river discharge increased by less than 4000 m 3/s. When the river discharge increased to 5000 m3/s, the longest continuous unsuitable water intake time decreased significantly to 10.5 days. It may be challenging to achieve the necessary value and duration of river discharge increase required for the actual operation of the Three Gorges Reservoir. To ensure the safety of water intake, implementing an early warning and forecasting system for saltwater intrusion and storing water into the reservoir at a high level before the intrusion occurs are recommended as effective methods.
This study analyzed the spatial and temporal variations of depth-averaged residual currents of Eulerian, Lagrangian, and Stokes during a tidal cycle in the eastern waters of Macao. Data were collected for 15 days during the summer from two field stations using a method specifically designed to deal with stratified residual flow. Our findings revealed similar dynamical characteristics during flood and ebb tides in the southeast and northeast waters of Macao, with currents being more pow-erful in the southeast. We found that the material carried by the tidal current tends to travel northwest at the station A6 and southeast at A7, with Lagrange residuals being around 2.2 cm/s and 5.1 cm/s, respectively, which is slightly smaller than Eulerian residuals. Influenced by the southwest monsoon, the directions of Euler, Lagrangian, and Stokes surface residual currents on the surface generally pointed northeast. Furthermore, our results showed that the depth-averaged residuals during a tidal cycle pointed towards the open sea during upstream flooding, towards nearshore in northeast waters, and towards the east in southeast waters under non-flooding conditions. The intensity of Stokes drift in southeast waters was primarily influenced by wind on the water surface, while the effect of wind on the northeast waters was limited and weakened by enhanced runoff. The intensity and direction of transportation in both southeast and northeast waters were related to wind speeds on the sea surface. During summer, analysis of net tidal flux indicated a relatively stable counterclockwise residual current circulation in the waters downstream to the outlet of the Macao waterway. This circulation was driven by the strong northeast littoral current outside the Pearl River Estuary, leading to the formation of an east-west flowing structure compensated in the east waters of Macao. During this season, part of the tidal sediment was intercepted by this unique dynamic structure discharged by east four mouths in the Lingdingyang Estuary upstream and re-entered the Macao waters with rising tidal water, which leaded to sedimentation of beaches and troughs and poor water exchange.
Yingwuzhou Wetland is an artificially restored coastal salt marsh wetland aimed at improving ecosystem services. Development of the wetland has restored the original damaged coastal ecosystem through comprehensive coastline ecological engineering measures. The birds in the study site have been investigated and researched using the route survey method since 2018, and changes in the bird population and species diversity have been analyzed to evaluate the effectiveness of coastal zone ecological restoration projects and the impact of different wetland habitat types on bird diversity. The results showed that 67 bird species were recorded in the wetland, belonging to 13 orders and 32 families, with the largest number of birds belonging to Passeriformes, including 42 species belonging to 18 families. There were 35 species of resident birds, 24 species of winter migratory birds, 10 species of summer migratory birds, and 8 species of migratory birds. Among these, one species of national class I and seven species of class II are in the List of Key Protected Wild Animals in China, respectively. Remiz consobrinus, Gallinula chloropus, Acridotheres cristatellus, Tachybaptus ruficollis, Spodiopsar cineraceus, Hirundo rustica, and Passer montanus were the dominant species. The number of wetland bird species increased annually. There were significant differences in the bird species, quantity, and Shannon–Wiener indexes among different seasons. The declining trends of bird species, quantity, and Shannon–Wiener index were in the orders of fall > winter > spring > summer, fall > winter > summer > spring, and fall > spring > winter > summer, respectively. The bird numbers and species were the highest in the natural wetland complex area. Declining trends of the Shannon–Wiener index in different habitat areas were observed for the natural wetland complex area, salt marsh wetland restoration area, clear water conservation area, lawn activity area, and wetland purification exhibition area. The ecological restoration of the coastline has enriched the bird diversity of the wetland. Habitats with rich patch types and high patch mosaic have a markedly positive impact on bird diversity. The results of this study can provide a scientific basis for the coastal ecological restoration and sustainable development of coastline wetlands.
Under the dual influence of human activities and natural factors, the coastal zone patterns are prone to rapid changes which can directly or indirectly affect the structure, function, and sustainable development of the coastal ecosystem. Using the coastal zone of Nanhui Dongtan in Shanghai as a typical research area, we used remote sensing interpretation, sea chart digitization, and field investigation to analyze changes in spatial patterns and changes in coastal zones over the last 20 years (from 2000 to 2020). In addition, the effects of coastal engineering (including reclamation engineering and siltation promotion engineering) and S. alterniflora invasion on coastal pattern dynamics were analyzed. The results showed that: ① Since 2000, under the influence of coastal engineering and biological invasion, the land use types of Nanhui Dongtan coastal zone changed from a simple pattern dominated by coastal wetlands to a complex pattern which included multiple land use types (i.e. coastal wetlands, inland wetlands, constructed wetlands, farmland, and construction land). ② Coastal reclamation engineering decreased 11894.7 hm2 of coastal wetlands in the Nanhui Dongtan coastal zone from 2000 to 2005. The reclaimed coastal wetlands were transferred into land use types such as rice fields, ponds, and farmland due to human activities; the reclamation engineering promoted deposition of sediment in the estuary and tidal mudflat (above 0 m) and the intertidal salt marsh developed with increased rates of 320.5 hm2/a and 110.9 hm2/a, respectively; meanwhile, the siltation rate decreased to 286.8 hm2/a and 15.7 hm2/a, respectively, after 2015. After 10 years (2005—2015) of natural recovery, the area of coastal wetlands did not reach the levels seen before reclamation in Year 2000. ③ Two types of siltation promotion engineering—hard siltation promotion engineering and biological siltation promotion engineering—have both significantly promoted the rapid development of coastal wetlands in Nanhui Dongtan. Hard silting promotion engineering with propagation rates of 516.9 hm2/a in tidal mudflats (above 0 m) and 915.7 hm2/a in intertidal salt marshes, respectively, was 5.4 times and 13.9 times higher than rates observed in non-siltation areas; hence, the effects were more significant than biological siltation promotion engineering which only resulted in pattern changes in a limited area between the seawall and the wave dissipation dike by planting S. alterniflora. ④ After introducing S. alterniflora in Nanhui Dongtan, it became the most dominant plant in the Nanhui Dongtan salt marsh, accounting for 56% of the total area; this significantly changed the ecological structure and function of coastal wetlands in 2020. Coastal engineering and biological invasion have a great impact on coastal zone patterns. Although the coastal ecosystem showed a certain resilience to coastal human activities, changes in the wetland type, area, and function were difficult or impossible to recover at a great cost in the future. How to integrate the ecological functions of coastal wetlands and inland wetlands through the implementation of coastal zone restoration, ecological protection, and other measures to achieve the sustainable and healthy development of coastal zones is an important problem for future land and sea planning.
In the context of rising sea-levels under global warming and fluvial sediment load decline, the risk of coastal erosion is increasing in global deltas. Evaluating the delta morphological change and its vulnerability to erosion in the future and analyzing land use exposure to coastal erosion, is of great significance for spatial planning, disaster prevention and mitigation, and the sustainable development of coastal regions. In this study, Pudong New Area of Shanghai, which is located in the Yangtze River Delta, was selected to compare its coastal erosion vulnerability in 2016 and 2035 using bathymetry of 2016 and modelled bathymetry of 2035 under a scenario of fluvial sediment load of 125 Mt/a and a cumulative sea-level rise of 16.5 cm. A comparison of land use exposure is made between the pattern in 2019 and the pattern projected for 2035 by the government. The results show that the spatial pattern of coastal erosion vulnerability is controlled by the morphological evolution of the delta over the last 1000 years. Coastal sections with high and very high categories of land use exposure and erosion vulnerability in the recent past (2016—2019) total 32.3 km in length, which accounts for 31.1% of the coast of the Pudong New Area. These figures are projected to be 47.5 km and 45.8%, respectively, in 2035, under the condition of reduced fluvial sediment load and planned land usage. In 2035, coastal sections along the Pudong Airport and Luchaogang will face an increase of erosion vulnerability category. According to the current plan, these two sections also show an increase in land use exposure due to airport expansion and new city development. The results suggest that these sections require erosion monitoring and erosion management during implementation of the plan. The methods used in this study can provide references for exposure assessment of coastal erosion and the formulation of territorial planning in coastal regions elsewhere.
Linear traveling-wave transformations are usually applied when constructing exact traveling-wave solutions for nonlinear evolution equations. Herein, for the first time, specific nonlinear traveling-wave transformations are introduced to extend the $N$ -soliton decomposition algorithm and an inheritance-solving strategy to a variable-coefficient nonlinear evolution equation. Two higher-dimensional nonlinear evolution equations with time-dependent coefficients, the Boiti-Leon-Manna-Pempinelli (BLMP) equation and the cylindrical Kadomtsev-Petviashvili (cKP) equation, are solved. The direct algebraic method and inheritance-solving strategy are used to construct several different types of multiwave-interaction solutions for the BLMP equation, specifically, the horseshoe-like solitons and their interaction with lump as well as different periodic waves. Using the $N$ -soliton decomposition algorithm, the higher-order interaction solutions between the horseshoe-like solitons, breathers, and lump waves of the cKP equation are established. These new multiwave-interaction solutions contribute to the existing solutions of nonlinear evolution equations with variable coefficients, enriching the repository of solutions to a certain extent.
LaSalle’s invariance principle is an important tool for studying the stability of stochastic systems. Considering the influence of time delay and pure-jump path on the stability of the system and using the convergence theorem for special semi-martingale, the LaSalle’s invariance principle for a class of stochastic delay differential equations driven by $\alpha$ -stable processes is established in this study. The sufficient conditions for the asymptotic stability of a class of delay equations are given by LaSalle’s invariance principle.
In this paper, blow-up of solutions to a class of weakly coupled semilinear double-wave systems with nonlinear terms of derivative type is considered. By choosing suitable functionals and using an iteration technique, the weakly coupled phenomena are studied in-depth for the case when $ p\ne q $ . For the case when $ p=q $ , the solution is degenerated to a single semilinear double-wave equation with a nonlinear term of derivative type. Furthermore, the nonexistence of global solutions to the Cauchy problem in the subcritical case is proven. Meanwhile, the upper bound estimate of the lifespan of solutions is also derived.
This paper presents an investigation of the weighted Drazin inverse $A^{d, W}$ of matrices based on the weighted core-EP decomposition of the pair $\{A, W\}$ . Some characterizations and representations of the weighted Drazin inverse are presented using the weighted core-EP decomposition of the pair $\{A, W\}$ . Further, the limit representations and the integral representations of the weighted Drazin inverse are discussed. Furthermore, an example is presented.
In recent years, the relativity between domains with specific metrics and complex Euclidean spaces has been a topic of interest in the study of complex variables. Two Kähler manifolds are called relatives if they admit a common Kähler submanifold with their induced metrics. A Cartan-Egg domain is a type of bounded non-homogeneous domain. Its Bergman kernel function can be constructed as an explicit expression using the expansion principle. In this paper, the relativity between a Cartan-Egg domain with Bergman metrics and a complex Euclidean space with canonical metrics is explored. In relation research of complex Euclidean spaces, the working premise is that a Bergman kernel function is a Nash function. However, the Bergman kernel function of Cartan-Egg domains are not necessarily Nash functions. Therefore, existing methods cannot be used directly. By analyzing the algebraic properties of a Bergman kernel function’s partial derivative function of a Cartan-Egg domain, we show that a Cartan-Egg domain with Bergman metrics is not related to a complex Euclidean space with canonical metrics.
Acute kidney injury is a clinical disease with a high morbidity rate, and early identification of potential patients can facilitate medical interventions to reduce morbidity and mortality. In recent years, electronic health records have been widely used to predict an individual’s potential risk. Most of the existing acute kidney injury prediction models tackle the issue of sparsity and irregularity in the physiological variables data by aggregating data or imputing the missing value, but ignore the patient’s health status implied by the missing information. Moreover, they do not consider the characteristics of and correlation between the various modalities. To solve the above issues, we present a multi-modal disease prediction model for acute kidney injury. The proposed model considers a variety of modal data, including physiological variables, disease, and demographic data. A new mask and time span based long short term memory (LSTM) network is designed to learn the time span and missing information of individual Physiological variables, and furthermore, to capture their numerical changes and frequency changes. The multi-head self-attention mechanism is introduced to promote interaction learning of each modality representation. Experiments on the real-world application of acute kidney injury risk prediction and mortality risk prediction demonstrate the effectiveness and rationality of the proposed model.
In this study, the electronic structure and magnetism of the Heusler alloy Fe2CrGe are investigated using first-principle calculations. Results show that the ground state of Fe2CrGe is antiferromagnetic metal in which Fe ion and Cr ion are in low- and high-spin states of $ S=0 $ and $ S=1 $ , respectively. The energy of the antiferromagnetic state is approximately 0.103 eV less than that of the ferromagnetic state. In addition, when a tetragonal strain is applied to Fe2CrGe, a transition from antiferromagnetic to ferromagnetic material occurs at +1.7% and –1.7% strains, and Fe2CrGe becomes a ferromagnetic half-metal. A half-metal energy gap of approximately 0.2 eV occurs when the strain reaches ±5%. The Curie temperature of Fe2CrGe is estimated to be 393 K, which is much higher than room temperature, indicating that Fe2CrGe may be a potential candidate for spintronic applications.
For a polar molecule subjected to an external electric field, its molecular axis will oscillate around the direction of the electric field, forming pendular states. Taking the two lowest-lying pendular states with magnetic quantum number $M=0 $ as qubit states, we study quantum entanglement of polar molecular arrays trapped in a one-dimensional optical lattice. We evaluate pairwise concurrence and global entanglement as functions of three dimensionless variables related to external field intensity–permanent dipole moment, a rotation constant, dipole-dipole interaction, and temperature —thus revealing the properties of the entangled molecular dipole arrays.
Plasma gratings are important in physics because they do not break down in strong fields. Using particle-in-cell (PIC) simulations, a new mechanism to generate plasma grating was developed based on the interactions between picosecond intense laser pulses (the magnitude of $ I $ is ${10}^{15}\;\mathrm{W}/{\mathrm{c}\mathrm{m}}^{2}$ ) and overcritical solid-density plasma (particle number density $n \approx 10{n}_{\rm{c}}$ ). This plasma grating results from the interference of plasma waves excited by strong laser fields in solids. Hence, one laser beam is sufficient for generating the gratings. The method produced a nanometer spatial period, which is significantly different from the micrometer spatial period produced by traditional methods that use two counter-propagating lasers in gas-density plasma. This finding may be useful for manipulating strong x-band frequency laser fields.
Based on multiple scattering theory, the effect of the configuration of complex unit cells on transmissivity in the negative first diffraction order is studied when negative transmission occurs in metagratings with a complex unit cell; that is, when transmitted beams lie on the same side of the normal as incident beams occurs in such metagratings. The complex unit cells are composed of two dielectric nanorods of different radii that constitute a metagrating when they are arranged in a line. Our calculations show that no stringent requirements on the radius of the smaller rods and their position in a complex unit cell are required in order for the negative transmission to be perfectly efficient. This implies that the configuration of complex unit cells is robust to the negative transmission, and hence, that it is easier to construct a high-efficiency dielectric metagrating with a complex unit cell.
The Kraus operator-sum representation method for mixed-state evolution was used to analyze the change in the fidelity and von Neumann entropy of the final state after decoherent time evolution. The analysis was based on the Jaynes-Cummings model for the initial state set in the depolarization mode. The results show that the fidelity of the quantum state undergoing decoherent evolution exhibits decaying oscillations with time until it becomes stable, while the von Neumann entropy exhibits oscillations of decreasing amplitude with time.
Temporal modes are a set of orthogonal wave-packet modes that can be used to characterize temporal multi-mode quantum light fields. They provide a complete alternate theoretical framework for the description of quantum systems. This study is based on light-induced seeding as an input to stimulated Raman scattering (SRS), whose output, the Stokes field, is the input seed field of the next SRS ; thus, the process of a continuous iterative SRS system is realized. The pump light field is then fixed in a Gaussian waveform and super-Gaussian waveform, and the temporal waveform evolution characteristics of the output Stokes light field under the input of Gaussian waveform seed light with various structures are studied. The seed light injection can obtain the same stable waveform output through iteration, and the FWHM (full-width at the half of the maximum) of the output light field waveform depends on the pump light field. Furthermore, Schmidt mode decomposition is applied to the final stable output waveform, and the eigenvalues of the final output Stokes field are all concentrated in the fundamental mode by numerical calculation. The research on the temporal mode properties of light presented in this paper provides theoretical guidance and experimental reference for the further development and utilization of the quantum resource of temporal modes.
Based on the Monte Carlo algorithm, photon detection during ultrafast evolutionary light emission was simulated and analyzed in this study. In addition, a method for calculating the time-resolved photon second-order correlation function was developed, and the effects of various errors on the photon second-order correlation function were investigated. The results show that the synchronous time jitter (initial time drift) significantly increases the time-resolved second-order correlation function when the light intensity sharply increases at the initial time, and the value of the time-integrated second-order correlation function is high at any delay. The existence of background photon counts causes the value of the zero-delay second-order correlation function of thermal light to approach unity. This study proposes a simplified simulation method for the theoretical study of photon second-order correlation functions in complicated light fields. Furthermore, this study provides theoretical support and numerical analysis methods for the subsequent experimental measurements of second-order correlation functions with ultrahigh time resolution.
The optical response characteristics of a subwavelength lithium niobate film guided-mode resonance metasurface were investigated via simulations. The influences of parameters such as the period, filling factor and etching depth of the etched micro–nano structure on the transmission spectrum were examined, and the effects of light sources with different polarization states and incidence angles on the spectral linewidth were imvestigated. Because of the asymmetric grating structure design, the bound states in the continuum (BIC) decay into a quasi-BIC mode with a high Q value (>10 000), and the second harmonic conversion efficiency of the subwavelength lithium niobate film increases by five orders of magnitude as a result of the local field enhancement effect of the bound state. The simulation results show that a high-efficiency conversion of the second harmonic can be realized in the ultraviolet band when the peak power density of the incident fundamental wave is on the order of ~1 GW/cm2, that is, the ultraviolet second harmonic conversion efficiency emitted after a single pass through the subwavelength lithium niobate film is up to 10–3 orders of magnitude. This study affords ideas and design schemes for improving the nonlinear response characteristics of a micro–nano structure and optical table interface system.
Based on the closed-form t matrix of a two-body system in low-energy short-distance effective field theory, the approximate closed-form three-body T matrix for a zero-spin three-boson system is obtained using the Faddeev equation under two-body contact interactions. In momentum representation, the contact potentials are polynomials, and the Lippmann-Schwinger equation can be simplified to algebraic equations using a factorization trick, facilitating nonperturbative renormalization. However, it is impossible to apply such a factorization trick directly to the Faddeev equation. Therefore, the momenta dependence of the T matrix is “split” such that the factorization trick can still be applied. The closed-form T matrices are then obtained as nonperturbative approximate solutions of the Faddeev equation under the leading and next-to-leading order contact potentials with verified consistency. As in a two-body case, such a closed-form T matrix also facilitates the convenient implementation of the nonperturbative renormalization.
Electron vortex beams were first discovered in systems that have a conservable orbital angular momentum; for systems where orbital angular momentum is not conserved, the existence of the electron vortices is uncertain. This article takes the electrons in the central field as examples and, in case of relativity, constructs a case where the orbital angular momentum is not conserved while the total angular momentum is conserved. When the electrons that carry a fixed total angular momentum propagate along the z-axis, the perturbation solution of the electron vortex beams corresponding to the system at this time is calculated and combined with the Foldy-Wouthuysen (F-W) transformation. Accordingly, we can prove, in the case of relativity central field, that the vortex solution does exist when the electrons with orbital angular momentum propagate along the z-axis. Consequently, the corresponding vortex wave solution and spiral isophase surface are shown in this article.
Theories of gravity with torsion indicate that spin-torsion coupling impacts the propagation of spin particles in a torsion field background. This paper encompasses the Dirac action in a curved spacetime with torsion, generalized geometrical hydrodynamics method from the flat Riemann spacetime to Robertson-Walker spacetime, and obtained semiclassical equations including the flow conservation equation, dynamic equation, and spin evolution equation, which describe the behavior of a spin 1/2 particle in arbitrary curved spacetime with torsion. These equations show that spin-torsion interaction usually causes the particles to deviate from the geodesic. Moreover, a solution for the cosmological background is found with definitive cosmological torsion; it concludes that the motion of the particle under the torsion field is a spiral motion along the propagation path, which the particle would take without torsion. This effect, subsequently, verifies the cosmological models established on theories of gravity with torsion.
Based on previous studies on the scattering of Majorana and Dirac fermions in Schwarzschild spacetime and the effects of the torsion on the scattering of the two fermions, under the weak field approximation of gravity and the lowest order approximation of the perturbation of the gravitational field scattering of fermions, this study decomposes the spin connection into a vector-like part under parity transformation and separately analyzes the effects of the two parts on the scattering matrix elements of the two fermions. A difference is found to exist between the general gravitational field on the quantum scattering matrix elements of the two fermions, where the difference derives from the vector-like part. These findings are then verified in the context of the Kerr gravitational field, where the difference between the scattering matrix elements of the two fermions is determined to be related to the mass and angular momentum of the gravitational source. The difference diminishes in the case of Schwarzschild spacetime when the angular momentum is zero.
The upsurge in cloud-native databases has been drawing attention to shared architectures. Although a shared cache architecture can effectively address cache consistency issues among multiple read-write nodes, problems still exist, such as slow persistence speed, high latency in maintaining cache directories, and timestamp bottlenecks. To address these issues, this study proposes a shared cache architecture-based solution that is combined with novel persistent memory hardware, to realize a three-layer shared architecture database—TampoDB, which includes memory, persistent memory, and storage layers. The transaction execution process was redesigned based on this architecture with optimized timestamps and directories, thereby resolving the aforementioned problems. Experimental results show that TampoDB effectively enhances the persistence speed of transactions.
In HTAP (hybrid transactional and analytical processing) database, resource isolation and data sharing is a difficult problem. Although different vendors achieve resource isolation through different architectures, the freshness of user concerns, that is, the gap between online transactional processing (OLTP) write and online analytical processing (OLAP) read versions, is determined by the consistency model of data sharing. However, existing HTAP databases apply only one consistency synchronization model for an easy implementation, which is contradictory to the multiple consistency requirements of user applications, and the overall system performance is sacrificed for the highest consistency upward compatibility. In this paper, by constructing a cost model of freshness and performance tradeoff, proposing a consistency switching algorithm and a processing strategy for synchronized data before and after switching, and realizing an HTAP database prototype with adaptive switching between sequential consistency synchronization and linear consistency synchronization, which makes it possible to support query loads with different consistency (freshness) requirements and maximize the system performance without adjusting the HTAP architecture. The effectiveness of adaptive switching is also verified by extensive experiments.s of adaptive switching is also verified by extensive experiments.
The architecture of storage-compute separation has emerged as a solution for improving the performance and efficiency of large-scale data processing. However, there are notable performance bottlenecks in this approach, primarily due to the low access efficiency of object storage and the significant network overhead. Additionally, object storage exhibits low storage efficiency for small-sized files. For instance, ClickHouse, a MergeTree-based database, generates a plethora of small-sized files when storing data. To address these challenges, HG-Buffer (hybrid granularity buffer) is introduced as an SSD (solid state driver)-based caching management solution for optimizing the storage-compute separation in ClickHouse and S3, while also tackling the small-file issue in object storage. The primary objective of HG-Buffer is to minimize network transmission overhead and enhance system access efficiency. This is achieved by introducing SSD as a caching layer between the compute and storage layers and organizing the SSD buffer into two granularities: object buffer and block buffer. The object buffer granularity corresponds to the data granularity in object storage, while the block buffer granularity represents the data granularity accessed by the system, with the block buffer granularity being a subset of the object buffer granularity. By statistically analyzing data hotness information, HG-Buffer adaptively selects the storage location for data, improving SSD space utilization and system performance. Experimental evaluations conducted on ClickHouse and S3 demonstrate the effectiveness and robustness of HG-Buffer.
To address the deficiencies of ClickHouse, including underutilization of hardware resources, lack of flexibility, and slow node startup, this paper proposes metadata management strategies under the storage-compute separation architecture, which focuses on the description of data information through Part metadata. Part metadata are the most crucial component of metadata. To effectively manage data on remote shared storage, this study collected all Part metadata files and merged them. After key-value mapping, serialization, and deserialization processes, the merged metadata were stored in a distributed key-value database. Furthermore, a synchronization strategy was designed to ensure consistency between the data on remote shared storage and the metadata in the distributed key-value database. By implementing the above strategies, a metadata management system was developed for Part metadata, which effectively addressed the slow node startup issue in ClickHouse and supported efficient dynamic scaling of nodes.
Database management systems play a vital role in modern information systems. Isolation level testing is important for database management systems to ensure the isolation of concurrent operations and data consistency to prevent data corruption, inconsistency and security risks, and to provide reliable data access to users. Fuzzy testing is a method widely used in software and system testing. By searching the test space and generating diverse test cases, it explores the boundary conditions, anomalies and potential problems of the system to find possible vulnerabilities. This article introduces SilverBlade, a tool for fuzzy testing of database isolation levels, that aims to improve the diversity of generated test cases and explore the isolation level test space in depth-wise. To effectively search the huge test space, this study designed a structured test input that splits the test space into two subspaces of concurrent transaction combination and execution interaction modes for searching. To test the isolation-level core implementation test space more comprehensively, an adaptive search method based on depth and breadth was also designed for effective mutation test cases. The experimental results show that SilverBlade is able to generate diverse test cases and provide broader coverage of the core implementation code of the database isolation level in the popular database management system PostgreSQL. Compared to similar tools, SilverBlade performed better at improving test coverage in critical areas of the isolation level.
Feature management plays an important role in the AI(artificial intelligence) pipeline. Feature stores are designed to offer effective versioning of features during the model training and inference stages. Feature stores must ensure real-time feature updates and version management to collaborate with the upstream data ingestion tasks and power the model serving system. In AI-powered online decision augmentation applications, the model serving system responds to requests in real time to provide better user experience, and feature stores face the challenge of low-latency online feature retrieval. Focusing on this challenge, we developed FeaDB, an in-memory based multi-version online feature store, which adopts a time series model and provides feature versioning semantics to automatically manage features from ingestion to serving. Moreover, an append-write operation was applied to ensure ingestion performance, and version indexing was optimized to improve read operations. A snapshot mechanism is proposed, and it was experimentally proven that snapshot read operations improve performance of lookup and range lookup.
China has the advantages of scale and diversity in data resources, and mobile internet data applications, which generate massive amounts of data in diverse application scenarios, recommendation systems have the capability to extract valuable information from this massive amounts of data, thereby mitigating the problem of information overload. Most existing research on recommendation systems focused on centralized recommender systems, training the data on the cloud centrally. However, with increasingly prominent data security and privacy protection issues, collecting user data has become increasingly difficult, making centralized recommendation methods infeasible. This study focuses on privacy-preserving cloud-end collaborative training in a decentralized manner for personalized recommender systems. To fully utilize the advantages of end devices and cloud servers while considering privacy and security issues, a cloud-end collaborative training method named FedMNN (federated machine learning and mobile neural network) is proposed for recommender systems based on federated machine learning (FedML) and a mobile neural network (MNN). The proposed method was divided into three parts: First, cloud-based models implemented in various deep learning frameworks were converted into general MNN models for end-device training using the ONNX (open neural network exchange) intermediate framework and a MNN model conversion tool. Second, the cloud server sends the model to the end-side devices, which initialized and obtain local data for training and loss calculation, followed by gradient back-propagation. Finally, the end-side models are fed back to the cloud server for model aggregation and updating. Depending on different requirements, the cloud model was deployed on end-side devices as required, achieving end-cloud collaboration. Experiments comparing power consumption of the proposed FedMNN and FLTFlite (flower and TensorFlow lite) frameworks on benchmark tasks identified that FedMNN is 32% to 51% lower than FLTFlite. Using DSSM (deep structured semantic model) and deep and wide recommendation models, the experimental results demonstrated the effectiveness of the proposed cloud-end collaborative training method.
The high parallelism and throughput of graphics processing unit (GPU) can improve the performance of on-line analytical processing (OLAP) queries in databases. However, openGauss currently cannot take advantage of the benefits of heterogeneous computing hardware such as GPU. Therefore, in this study, we explore using GPU to accelerate the OLAP processing in the system and achieve higher performance. The focus is on how to implement and optimize GPU acceleration modules for openGauss. To address the difference in execution granularity between openGauss and PostgreSQL, we propose a CPU (central processing unit)-GPU collaborative parallel solution based on chunked reading and key distribution. This solution can reduce the I/O (input/output) time of the GPU Scan operator to reduce idle waiting time, and run multiple instances of GPU Join to support multi-GPU environments. To address the architectural differences between openGauss and PostgreSQL, a heterogeneous operator acceleration technology compatible with vectorized engines is proposed. A custom operator framework is implemented that can embed a vectorized execution engine, and a vectorized GPU Scan operator capable of processing openGauss columnar data is employed based on this framework. A prototype system is implemented to verify the effectiveness of the proposed approach.
User interfaces (UIs) play a vital role in the interactions between an application and its users. The current popularity of mobile Internet has led to the large-scale migration of web-based applications from desktop to mobile. Web front-end development has become more extensive and in-depth in application development. Traditional web front-end development relies on designers to give initial design drafts and then programmers to write the corresponding UI code. This method has high industry barriers and slow development, which are not conducive to rapid product iteration. The development of deep learning makes it possible to automatically generate web front-end code based on UI images. Existing methods poorly capture the features of UI images, and the accuracy of the generated code is low. To mitigate these problems, we propose an encoder–decoder model, called image2code, based on the Swin Transformer, which is used to generate web front-end code from UI images. Image2code regards the process of generating web front-end code from UI images as an image captioning task and uses Swin Transformer with a sliding window design as the backbone network of the encoder and decoder. The sliding window operation limits the attention calculation to one window, which reduces the amount of calculation by the attention mechanism while simultaneously ensuring that feature connections remain across windows. In addition, image2code generates Emmet code, which is much simpler and can be directly converted to HTML code, improving the efficiency of model training. Experimental results show that image2code performs better than existing representative models, such as pix2code and image2emmet, in the task of web front-end code generation on existing and newly constructed datasets.
In heterogeneous federated learning systems, among a variety of edge devices such as personal computers and embedded devices, resource-constrained devices, i.e. stragglers, reduce the training efficiency of the federated learning system. This paper proposes a heterogeneous coded federated learning (HCFL) system to ① improve the training efficiency of the system and speed up the training of heterogeneous federated learning (FL) for multiple stragglers, ② provide a certain level of data privacy protection. The HCFL scheme designs scheduling strategies from the perspective of client and server to satisfy the accelerated calculation of multiple stragglers model in the general environment. In addition, a linear coded computing (LCC) scheme is designed to provide data protection for task distribution. The experimental results show that HCFL can reduce training time by 89.85% when the performance difference between devices is large.
With the continuous development of network attack methods, it is becoming increasingly difficult to protect the security of power communication networks. Currently, the detection accuracy of abnormal traffic in distribution communication networks is insufficient and the efficiency of abnormal traffic detection is low. To address these issues, a new method for abnormal traffic detection in distribution communication networks is proposed, in which feature extraction and traffic classification are improved. The proposed method utilizes a time-frequency domain feature extraction method, using an adaptive redundancy boosting multiwavelet packet transform to quickly extract frequency-domain features, while time-domain features are extracted using the communication characteristics of the distribution network. To improve traffic classification and detection, a parallel deep forest classification algorithm is proposed based on a distributed computing framework, and the training and classification task scheduling strategies are optimized. The experimental results show that the false alarm rate of the proposed method is only 2.63% and the accuracy rate for the detection of abnormal traffic in distribution networks is 98.29%.
Next-generation power grids is the main direction of future smart grid development, and the accurate prediction of power loads is an important basic task of smart grids. To improve the accuracy of load prediction in smart power systems, this work characterized the load dataset based on an Autoformer, a prediction model with an autocorrelation mechanism; adds a feature extraction layer to the original model; optimized the model parameters in terms of the number of coding layers, decoding layers, learning rate, and batch size; and achieved cycle-flexible load prediction. The experimental results show that the model performs better in prediction, with an MAE, MSE, and coefficient of determination of 0.2512, 0.1915, and 0.9832, respectively. Compared with other methods, this method has better load prediction results.
In industrial scenarios such as substations, video-based visual smoke detection has been adopted as a new environmental monitoring method to assist or replace smoke sensors. However, in industrial applications, visual smoke detection algorithms are required to maintain a low false detection rate while minimizing the missed detection rate. To address this, this study proposes a smoke detection algorithm based on spatial and frequency domain methods, which perform smoke detection in both domains. In the spatial domain, in addition to extracting smoke motion characteristics, this study designed a method for extracting smoke mask characteristics, which effectively ensures a low missed detection rate. In the frequency domain, this study combined filtering and neural network modules to further reduce the false detection rate. Finally, a fusion domain post-processing strategy was designed to obtain the final detection results. In experiments conducted on a test dataset, the smoke detection algorithm achieved a false detection rate of 0.053 and missed detection rate of 0.113, demonstrating a good balance between false alarms and missed detections, which is suitable for smoke detection in substation industrial scenes.
In the process of government and enterprise evolution, as information technology deepens from informatization into digitization, the data generated by various applications are becoming increasingly multimode, multisource, and massive, thereby posing new challenges to data management. To address these challenges, many new technologies and concepts have emerged in the field of data management. Data Fabric is a method that integrates distributed data storage, processing, and applications into a whole, providing a set of visual interfaces for management. First, we analyzed the technical architecture, characteristics, value, and complete process of managing and applying the multimode data of Data Fabric. Subsequently, we proposed anomaly monitoring methods based on time series indicators as well as log data for multimode and multisource data, whereby the processing speed improved by 33.3% and 42.2%, and F1 score improved by 12.2 pps (percentage points) and 14.8 pps, respectively, using Data Fabric technology. This further demonstrates the efficiency and application value of Data Fabric technology in the newly proposed methods.
Advanced metering infrastructure is an important component in the construction of new power systems; however, advanced measurement systems rely on network information infrastructures and present major security issues. This study evaluates the network security posture of advanced metering systems by combining a hidden Markov model with an artificial immunity algorithm. First, a counting algorithm is used to obtain the security observation data in the power network. Subsequently, the Markov model is used to describe the change in the network security state, whereby the artificial immunity algorithm calculates the transfer probability matrix between different states. The state transfer matrix is then modified based on the state assessment error. Upon calculating the probability of being at different security states at different times and combining it with the risk loss vector, the final value of safety posture is obtained. The experiments demonstrate that the method proposed in this study has a good assessment effect and can capture the safety defects in the system more accurately, ensuring the safe operation of the advanced measurement system by accurately identifying the relevant safety defects in the system for a safe, smooth, and reliable operation of the new grid environment.
Power theft seriously endangers power equipment and personal safety, and causes significant economic losses for energy suppliers. Hence, it is important for these suppliers to accurately identify instances of power theft to reduce losses and increase efficiency. In this paper, based on the residual network (ResNet) structure, a 2D convolutional neural network is combined with a depthwise separable convolution enhanced self-attentive (DSCAttention) mechanism to improve the number of correctly-classified electricity theft users. In addition, electricity theft data often contains missing values, outliers, and positive and negative sample imbalance. Each of the above problems are treated separately using the zero-completion method, quantile transformation, and hierarchical splitting method, respectively. The proposed model has been extensively tested using real power theft data sets. The results show that the area under curve (AUC) index of the proposed model reaches a value of 91.92%, while mean average precision values MAP@100 and MAP@200 are measured reaching 98.58% and 96.77%, respectively. Compared with other electricity theft classification models, the proposed model performs the electricity theft classification task better. The method in this paper can be extended to electricity theft intelligent identification.
We give a survey on the contribution of our research group to the cell theory of weighted Coxeter groups. We present some detailed account for the description of cells of the affine Weyl group $ \widetilde{C}_n $ in the quasi-split case and a brief account for that of the affine Weyl group $ \widetilde{B}_n $ in the quasi-split case and of the weighted universal Coxeter group in general case.
The trajectory optimization of cycling is hindered by the errors of positioning equipment, riding habits of non-motor vehicles, and other factors. It leads to quality problems, such as abnormal data and missing positioning information in the riding trajectory, impacting the application of trajectory-based riding-map inference and riding-path planning. To solve these problems, this paper creates a framework for improving the quality of cycling-trajectory data, based on the construction of a grid index, screening of abnormal trajectory points, elimination of wandering trajectory segments, elimination of illegal trajectory segments, calibration of drift trajectory segments, and recovery of missing trajectory. Comparative and ablation experiments are conducted by using a real non-motor-vehicle cycling-trajectory dataset. The experimental results verify that the proposed method improves the accuracy of cycling-map inference.
Shared-nothing distributed databases are designed for the high scalability and high availability request of Internet-based applications. There have been significant achievements in shared-nothing distributed databases, but for some shared-nothing databases with stateless computation layers, long conflict-detection paths challenge database performance under high-contention workloads. To solve this problem, we design two methods, pre-lock and local cache, together with a high-contention detection module that allow high-contention to be quickly detected and the corresponding high-contention-handling strategy applied. Experiments show that our design and optimization for high-contention transaction-processing architecture can improve the performance of distributed databases under high-contention workloads.
The diagnostic method based on dual-view fundus imaging is widely used in diabetic retinopathy (DR) screening. This method effectively solves the problems of image occlusion and limited field of view under single-view. This paper proposes a learning method of feature fusion between dual-view images based on the attention mechanism to improve the accuracy of DR classification by effectively integrating different view information. Due to the small proportion of lesions in fundus images, the self-attention mechanism was introduced to enhance the learning of local lesion features. Moreover, a cross-attention mechanism is proposed to effectively utilize information between dual-view images to improve the classification of dual-view fundus images. Experiments were performed on the internal DFiD dataset and public DeepDRiD dataset. The proposed method can effectively improve the accuracy of DR classification and can be used for large-scale DR screening to assist doctors in achieving an efficient diagnosis.
The popularity of intelligent devices such as smartphones and surveillance cameras has led to serious face privacy problems. Face de-identification is considered an effective tool for protecting face privacy by concealing identity information. However, most de-identification methods lack explicit control and controllable changes in identifying de-identified face images, resulting in de-identified images that are inapplicable to face authentication and retrieval and other identity-related tasks. Therefore, this study proposes an identity inter-relationship-consistent face de-identification task in which the identity inter-relationship between two arbitrary de-identified faces maintained the same as before de-identification. To this end, a task-driven identity inter-relationship consistent generative adversarial network is introduced to generate de-identified faces with a consistent identity inter-relationship. A rotation-based de-identifier was designed to modify the original identity features to be de-identified with identity inter-relationship consistency. In addition, identity control loss is introduced to guarantee a precise identity generation using a de-identified generator. Qualitative and quantitative results show that our method achieves improvements compared with exiting methods for de-identifying de-identified faces as well as for maintaining their identity inter-relationship consistent.
We propose a neural network training framework called momentum-updated representation with reconstruction constraint for 3D (three-dimensional) object recognition using 2D (two-dimensional) images without angle labels. First, self-supervised learning is employed to address the lack of angle labels. Second, we use momentum updating based on a dynamic queue to maintain the stability of the object representation. Furthermore, the reconstruction constraint is applied to the learning process with an auto-encoder module, which enables the representation to capture more semantic information of the objects. Finally, during training, a dynamic queue reduction strategy is proposed for handling the imbalanced data distribution. Experiments on two popular multi-view datasets, ModelNet and ShapeNet, demonstrate that the proposed method outperforms existing methods.
Review text contains comprehensive user and item information and it has a great influence on users’ purchase decision. When users interact with different target items, they may show complex interests. Therefore, accurately extracting review semantic features and modeling the contextual interaction between items and users is critical for learning user preferences and item attributes. Focusing on enhancing the personalization capture and dynamic interest modeling abilities of recommender systems, and considering the usefulness of different features, we propose a hierarchical description-aware personalized recommendation (DAPR) algorithm. At the word level of review text, we design a personalized information selection network to extract important word semantic features. At the review level, we design a neural network based on a cross-attention mechanism to dynamically learn the usefulness of reviews, concatenate review summaries as descriptions, and devise a co-attention network to capture rich context-aware features. The analysis of five Amazon datasets reveal that the proposed method can achieve comparable recommendation performance to the baseline models.
Mainstream sentence classification algorithms rely on a single word vector model to obtain the feature vector representation of text, which leads to insufficient text mapping ability. Therefore, a multi-kernel learning method is used to fuse multiple text representations based on different word vectors to improve the accuracy of sentence classification. In the process of fusing different kernel functions, traditional kernel function coefficient optimization methods often lead to long training time and difficulty in finding a local optimum. To address this problem, a new kernel function coefficient optimization method that continuously approximates the optimal kernel function coefficient value based on parameter space segmentation and breadth first search was developed. In this study, a support vector machine (SVM) was used as a classifier to perform classification experiments on seven text datasets, and the experimental results showed that the multi-kernel learning classification results were significantly better than those of single-kernel learning. Moreover, the proposed optimization method performed better than traditional methods with less training cost.
Chinese sentiment analysis is one of important researches in natural language processing, which aims to discover the sentimental tendencies in the Chinese text. In recent years, research on Chinese text sentiment analysis has made great progress in efficiencies, but few studies have explored the characteristics of the language and downstream task requirements. Therefore, in view of the particularity of Chinese text and the requirements of sentiment analysis, using the Chinese text sentiment analysis method that integrates multi-granularity semantic features, such as characters, words, radicals, and part-of-speech is proposed. This introduces radical features and emotional part-of-speech features based on character and word features. Additionally, this integration uses bidirectional the long short-term memory network (BLSTM), attention mechanism and recurrent convolutional neural network (RCNN). The softmax function is used to predict the sentimental tendencies by integrating multi-granularity semantic features. The comparative experiment results on the NLPECC (natural language processing and Chinese computing) dataset showed that the F1 score of the proposed method was 84.80%, which improved the performance of the existing methods to some extent and completed the Chinese text sentiment analysis task.
In this study, Gly→Ala was introduced into three chains of the heterotrimeric model (abc); seven mutants were subsequently constructed, and the local structure and global motion changes were analyzed. DSC results showed that the Tm value of the single point mutation was reduced by about 15°C, while the double and triple point mutations did not form triple helical structures. MD simulation trajectories were analyzed by ladder models; the results showed that the value of the step parameter changes near the mutation point, indicating an unfolding of the triple helix structure. An elastic function was introduced to quantify the degree of collagen structure change. It was found that the hydrogen bond energy was highly correlated with the structural deformation fraction ( $ R^2=0.76 $ ), indicating that the mutation not only destroyed the hydrogen bond force, but also resulted in changes in the bending and motion states of the molecule. This study, combined with calculations and experiments, helped quantify the effects of glycine mutation on the overall structure and movement pattern of collagen. Hence, the study provides a theoretical basis for clarifying the pathogenic mechanism of glycine mutation.
This research study focuses on Tettigoniidea and Gryllidea insects distributed across Shanghai Pujiang Country Park, with data collected twice a month from April to December of Year 2020 and 2021. The results show that 8 species of Tettigonioidea, 16 species of Grylloidea, and 1 species of Gryllotalpidae live in Shanghai Pujiang Country Park. The adult phase and voltinism in their life cycles, moreover, were found to be stable. Most Tettigoniidea and Gryllidea tend to overwinter in soil as diapause eggs, and a proportion of them overwinter as nymphs. The research suggests, furthermore, that using the calling songs of Tettigoniidea and Gryllidea can be a simple and effective way to carry out studies about phenology and ecology of singing insect.
To investigate the influence of urban environmental differences on the leaf-unfolding phenology of trees, we extracted the leaf unfolding information of nine tree species using remote sensing data and analyzed their relationships with temperature, precipitation, and nighttime light in a pure forest in Shanghai. The results showed that : ① there were significant differences in the average onset of the leaf-unfolding phenology among species, from 95th to 104th day of the year; contrastingly, intra-species variation in leaf-unfolding date was greater, ranging from 69th to 138th day of the year. ② Different species exhibited different leaf phenology in response to environmental factors. Triadica sebifera was the most sensitive to precipitation changes, Taxodium distichum var. imbricatum was sensitive to precipitation changes and urbanization, and Koelreuteria bipinnata was sensitive to precipitation and climate changes. Other species were not sensitive to any environmental changes. ③ For species sensitive to environmental changes, the leaf-unfolding date was 45 days earlier when the average precipitation increase from 48 mm to 64 mm, and delayed by three days for every 1°C increase in average temperature before the growing season. The study showed that urban forest construction can be reasonably configured according to the response characteristics of a species to the environment, so that plants can better adapt to the environment and fulfill their roles in the ecosystem.
In this study, a benthic macroinvertebrates survey was conducted in the western Qingpu District. The Benthic Index of Biotic Integrity (B-IBI) was used to assess the regional water ecological health. The relationship between B-IBI and water quality indicators, including related indices, was analyzed, and the applicability of B-IBI in lake-swamp areas was explored. The results showed that the water ecological health of the study area was good overall, the proportion of healthy and sub-healthy sample sites was 67.7%, and B-IBI indexes of the lake-swamp areas are better than those of rivers. The B-IBI index can effectively indicate organic pollution and eutrophication in water bodies. In addition, a significant correlation among the biological indices was found, despite the differences in evaluation results. We conclude that the B-IBI index has good applicability for the water ecology assessment of lake-swamp areas.
The balance between supply and demand of ecosystem services is essential for sustainable development, while differentiated partitioning is important for optimal resource allocation. Based on the carnegie-ames-stanford-approach (CASA), the water balance equation, and the revised universal soil loss equation (RUSLE), this study depicts the supply and demand of carbon fixation and water and soil conservation services and their relationship in the Yangtze River Delta integration demonstration zone from 2000 to 2020. Self-organizing map (SOM) K-means two-order clustering technology is used to identify ecosystem service clusters dividing the ecological management zone. Management strategies are then proposed. The results show that: ① The supply of carbon fixation services continued to decrease but the demand increased. Additionally, the supply and demand of water and soil conservation services showed an increasing trend, and supply was less than demand. ② The supply-demand ratio of carbon fixation and soil conservation services showed an upward trend, whereas the supply-demand ratio of water conservation services showed a downward trend. Significant spatial differences were observed in the supply-demand ratio of ecosystem services. ③ Cluster analysis divided the demonstration area into different types of ecological regulation zones. The urban clusters in Wujiang and Shengze town districts are mainly to improve the capacity of water conservation services. The urban cluster in Qingpu District is to promote the water conservation and carbon fixation services capacity. The pilot start-up area and urban cluster in Jiashan City have a small gap between supply and demand, which focus on comprehensive protection. This study can provide decision-making support for resource allocation, ecological compensation, and coordinated development of regional integration in the demonstration area.
Herein, based on existing standards for the measurements of material degradation rates and the degradation abilities of microorganisms, four methods were designed to determine material degradation rates. These four methods included two standard methods (inoculums: composting, vermiculite+composting leachate) and two experimental methods (inoculums: vermiculite+Bacillus, vermiculite+thermophilic bacteria). For this, the raw paper and plastic film (polylactic acid, PLA) components of environmentally friendly tape, as well as the finished tapes, were used as test materials to compare the material degradation rates using the above methods. Throughout the 60-day test cycle, both the PLA films and raw paper presented high degradation rates according to the four methods. The degradation rate of finished tape products increased gradually under the composting and vermiculite+composting leachate treatment and marginally rapidly under the vermiculite+Bacillus treatment. Additionally, under the vermiculite + thermophilic bacteria treatment method, the finished tape materials displayed a markedly higher degradation rate than that produced by other methods (roughly 1.7 ~ 7.5 times). Thus, the addition of microorganisms, particularly thermophilic bacteria, enhances the testing efficiency of material biodegradation rates. Therefore, we suggest that the optimization of degradation cultures can improve the testing efficiency of material degradation parameters, allowing manufacturing enterprises to shorten the research and development cycles of biodegradable products.
中国综合性科技类核心期刊(北大核心)
