In this paper, a stationary problem for the reaction-diffusion equation with a discontinuous right-hand side is considered. Based on ideas from contrast structure theory, the asymptotic representations for eigenvalues and eigenfunctions are constructed by solving a Sturm-Liouville problem and an estimation of the remainder is obtained. Moreover, a sufficient condition which guarantees the stability of the solution to this task is established.
This paper explores the neighbor sum distinguishing list total coloring of graphs $G$ with maximum degree $\varDelta \left( G \right) \geqslant 8$ and maximum average degree ${\rm{mad}}\left( G \right) < \frac{{14}}{3}$ . By applications of the Combinatorial Nullstellensatz and discharge method, moreover, it is shown that the neighbor sum distinguishing total choice number of the graphs does not exceed $\varDelta \left( G \right) + 3$ .
Let $G = (V, E)$ be a simple graph. For any vertex set $S$ of V, if $G - S$ is acyclic, then $S$ is a decycling set of G; the minimum size of a decyling set is called the decycling number of G, denoted by $\phi \left( G \right)$ . In this paper, we consider the decycling problem of join graphs and obtain the exact value for the decycling number of some types of join graphs. Let ${G_m}$ and ${G_n}$ be simple connected graphs of the order m and n, respectively. Then the decycling number of the join graph ${G_m} \vee {G_n}$ satisfies: $\min \{ m,n\} \leqslant \phi ({G_m} \vee {G_n}) \leqslant $ $ \min \{ m + \phi ({G_n}),n + \phi ({G_m})\}$ . The results presented in this paper confirm that the upper bound for the above inequality is tight. In particular, if ${G_m}$ and ${G_n}$ are trees, then we can obtain the exact value for the decycling number of ${G_m} \vee {G_n}$ .
Using resorcinol-formaldehyde resin as the carbon source, melamine as the nitrogen source, and NaOH as the etchant, a nitrogen-doped carbon-coated silicon (Si@void@N-C) anode material with a yolk-shell structure was synthesized. The samples were characterized and tested by XRD, SEM and X-ray photoelectron spectroscopy, TEM, and electrochemical tests; the results confirmed that a Si@void@NC composite anode material with a yolk-shell structure was successfully synthesized. The material was found to have excellent electrochemical performance. The initial capacity reached 1282.3 mA/g after charging and discharging at a current density of 0.1 A/g. After 100 cycles, its specific capacity was as high as 994.2 mAh/g with a capacity retention of 77.5%, demonstrating good cycle performance. The nitrogen-doped carbon shell of the Si@void@N-C material helps with the electrical conductivity of the composite material. Meanwhile, the yolk-shell structure effectively alleviates the volume effect of silicon; this feature is beneficial to the formation of a stable SEI film and improves the cycle stability of the battery.
In this study, we used negative ion photoelectron spectroscopy (NIPES) combined with quantum chemical calculation to explore the electronic structures, micro-solvation effect, and stabilization mechanism of two compounds, SO3– and HSO3–, that are readily abundant in the atmosphere. Vertical detachment energies of (3.31 ± 0.02) and (3.91 ± 0.02) eV and adiabatic detachment energies of (3.02 ± 0.05) and (3.56 ± 0.05) eV were measured for SO3– and HSO3–, respectively. These results are reproduceable when using a nuclear ensemble approach and Dyson orbitals in the calculation. The typical density of states method, however, cannot demonstrate the nuclear vibration effect, ionization probability, and orbital relaxation effect during the ionization process. We studied the micro-solvation effect of HSO3–·(H2O)n (n = 0 ~ 5) and found that system stability was enhanced by an increase in the surrounding water molecules, whereby electrostatic interaction played a dominant role and the induction effect made an increasingly important contribution. We believe this work will help improve the modeling of atmospheric sulfate aerosols and provide a scientific basis for the effective control of haze formation.
Ag nanoparticles were first prepared using a seed-based thermal synthetic procedure. The monometallic particles were then transformed into bimetallic particles via a galvanic replacement reaction. A transmission electron microscope (TEM), scanning transmission electron microscope (STEM), and absorption spectrum were subsequently used for characterization. By controlling the amount of seed added, the ultrasonic exposure, and the centrifugal time, we can effectively tune the size of the particles and the localized surface plasmon resonance peak positions. The TDBC film can be wrapped on the surface of the metallic nanostructures by a ligand exchange reaction to achieve strong coupling between surface plasmon and molecular excitons.
Porous silicon/hard carbon composite (Si@CTS) was successfully fabricated using liquidphase encapsulation and a low-temperature pyrolysis process, in which silicon particles from cutting waste in the manufacturing of crystalline silicon solar cells was used as a raw material and chitosan as carbon source. In this paper, the electrochemical performance of Si@CTS and a mixture of Si@CTS and graphite (Si@CTS/G) as anode materials of a lithium-ion battery was studied. The Si@CTS electrode showed a high discharge specific capacity of 1672.8 mAh/g and a high initial coulombic efficiency of 84.45%. After 100 cycles, the Si@CTS retained a reversible capacity of 626.4 mAh/g. The discharge specific capacity of the Si@CTS/G composite was 698.1 mAh/g; hence, the discharge specific capacity of the Si@CTS/G composite was higher than that of Si@CTS and offered better stability. The findings are critical for mass manufacture and deployment of silicon/carbon anodes with high capacity and stability in lithium-ion batteries.
To better understand the diversity of bryophytes in East China, bryophytes were systematically investigated and collected in the Huangshan - Tianmu Mountain range and Xianxia - Wuyi Mountain range in the region. In the course of field investigation, eighteen new records of liverwort species were found in Zhejiang Province, belonging to 10 families and 13 genera, respectively. Notably, Hattoria is a new genus record for Zhejiang Province. In this paper, the habitats, geographical distributions, and main identifying features of these new records are provided. Moreover, illustrations of rare and endemic liverwort species in China are presented. These new findings further enrich the bryophyte flora of Zhejiang Province and provide new basic information about the flora of the province.
Two epiphyllous species, namely Cheilolejeunea chenii and C. obtusilobula, were newly recorded in Zhejiang Province. The specimens were collected from Gaoshanwan Village, Yushang Township, Qingyuan County, Zhejiang Province. It is worth noting that C. chenii has a very narrow distribution range in China and is a particularly rare species that should be protected as soon as possible. In this paper, the morphological characteristics, distribution, and habitats of the two species are briefly described and discussed. Given that the two species are not currently within the protection scope of the nature reserve, we also propose new ideas for in-situ conservation of epiphyllous liverworts.
In this study, the Shengli Coal Mine—a representative coal mine in the semi-arid grassland of Inner Mongolia—and the adjacent Beizi Temple, Botanical Garden, and Nanshan Reservoir were selected as the study areas. By studying the correlation between the characteristics of the bryophyte community and the physical and chemical properties of the soil, the influence of soil on the distribution of bryophytes was analyzed during the reclamation process. The results showed that there were 4 families, 6 genera, and 7 species of bryophytes in the Shengli mining area. The total coverage of bryophytes in each habitat could be ranked according to the following order: Beizi Temple > Botanical Garden > south dump of the mining area > Nanshan Reservoir > plantation in the mining area > north dump of the mining area. An analysis of the diversity index, moreover, showed that the Shannon-Wiener index at the south dump was the highest, indicating that the species complexity of the moss community at the south dump was relatively high. An analysis of the correlation between the coverage of the bryophyte community and the physical and chemical properties of the soil showed that the soil pH value, silt content, sand content, and gravel content all had significant effects on the distribution of bryophytes. Multivariate analysis of the bryophyte community structure and soil physical and chemical properties showed that the differences in bryophyte community structure between different study areas was related to the cumulative influence of environmental factors.
Spatial accessibility is an important reference index to determine whether the layout of various types of facilities is reasonable. Scientific evaluation of the spatial accessibility of service facilities is an important basis for judging regional service differences and optimizing the spatial allocation of resources. Using the basic potential model as a basis for our analysis, we introduced a demand-side threshold, designed a three-level service radius based on the scale requirements for elderly care institutions, and implemented regional priority matching principles. Taking Fengxian District of Shanghai as an example, we analyzed the spatial accessibility of elderly care institutions in the region based on the actual driving time between supply and demand points and ArcGIS spatial analysis technology. The results show that the spatial accessibility of nursing homes in Fengxian District is uneven, and the spatial accessibility in some areas of Nanqiao Town, Zhuanghang Town, and Jinhui Town is significantly higher. A trend of gradually decreasing accessibility can be observed from the city center to the periphery. In some towns and villages of the central region, there are dense elderly care institutions and relatively concentrated elderly resources. The improved potential model considers the influence of factors such as the service capacity of elderly care institutions and the needs of the elderly, which can evaluate the spatial accessibility of institutions more effectively. The research results provide a reference point and offer suggestions for scientific planning and decision-making of elderly care institutions.
In this study, we used the geographic weighted regression (GWR) method to explore the impact of compulsory education quality on housing prices. For the purpose of this analysis, we considered Wuhan in Hubei Province as the study area and collected school and housing price data from websites such as Sofang.com, Jzb.com, Whjyj.gov.cn, etc. We also used the inverse distance weighted (IDW) method for visual analysis. The results showed that: ① The quality of compulsory education has a positive impact on housing prices, and provincial demonstration schools, in particular, create a relatively high price premium on housing prices; ② The capitalization effect of the quality of junior high school is higher than that of elementary school; ③ The quality of primary and junior high school education in new urban areas has the greatest impact on housing prices. The quality of the combination of primary and junior high schools in the central and northern areas of Jiangan District had a significant impact on housing prices, while the quality of schools in the Wuchang, Qiaokou, and Jianghan districts had relatively less impact on housing prices as a whole.
A supply network is an important carrier for the spatial flow of industrial elements, and its structure and evolution can reflect the spatial clustering characteristics of related industries. In this study, we used thermal analysis, network analysis, and other methods to understand and analyze China’s automobile parts supply network at the national and regional levels in 2009, 2014, and 2019; in addition, we explored the evolution of spatiotemporal pattern characteristics of the network’s structure. The study found that: ① Automobile parts companies are primarily distributed across the eastern part of China, followed by the central and western regions. The companies form six clusters centered on the Yangtze River Delta region, Beijing-Tianjin-Hebei region, Northeast region, Pearl River Delta region, Central China region, and Chengdu-Chongqing region. ② The density of the network continues to increase. In 2019, the network density reached 0.5017, showing strong connectivity. Changchun had the highest extroversion in 2009, and Wuhan had the highest in 2014 and 2019. Shanghai has always maintained the highest introversion and continues to increase. More than 50% of the top ten cities are located in the Yangtze River Delta, and the remaining cities are located in the Beijing-Tianjin-Hebei, Northeast, Chengdu-Chongqing, and Central China regions. In addition, the automobile parts supply network has obvious hierarchical characteristics. The first-level links included Shanghai-Changchun in 2009, Shanghai-Changchun and Shiyan-Wuhan in 2014, and Shanghai-Changchun and Shanghai-Wuhan in 2019. ③ If we analyze the spatiotemporal characteristics of the supply network of China’s six major clusters with OEMs and automobile parts factories as nodes, we find that the Northeast region forms a vehicle-parts concentric inward supply network structure, the Yangtze River Delta and Chengdu-Chongqing regions form vehicle-parts concentric outbound supply network structures, the Beijing-Tianjin-Hebei and Pearl River Delta regions form vehicle-parts eccentric outbound supply network structures, and Central China forms a vehicle-parts eccentric inward supply network structure.
Three-dimensional vegetation volume is a comprehensive index that can be used to represent the ecological benefits of urban vegetation. However, the challenge of how to accurately and quickly carry out three-dimensional vegetation volume monitoring in highly heterogeneous urban habitats is an urgent problem that requires attention. In this paper, we used Shanghai Botanical Garden as a case study. We acquired low-altitude, high-resolution images of Shanghai Botanical Garden through a UAV aerial photography system; after extracting the data, we calculated the surface elevation and canopy height models, estimated the three-dimensional vegetation volume, and analyzed the spatial distribution pattern. The results showed that: ① The overall plane and elevation accuracy of UAV images was better than 0.1 m, and the average error and standard deviation of the canopy height model accuracy was 0.27 m and 0.58 m, respectively. ② The vegetation volume of Shanghai Botanical Garden was distributed in a pattern from northeast low to southwest high, with a total vegetation volume of 3538944.50 m3. The average green density of the botanical garden was 6.51 m3/m2. The three gardens with the highest vegetation volume were: Peony Garden (289491.00 m3), Pinetum Garden (338322.10 m3), and the Green Space Attached to The Greenhouse (360587.50 m3). The three gardens with the lowest vegetation volume were: Recreational Green Space (24761.50 m3), Monocotyledon Botanical Garden (31621.40 m3), and Rose Garden (74607.30 m3). The three gardens with the highest vegetation volume density were: Tropical Orchid Room (9.23 m3/m2), Fern Garden (11.30 m3/m2), and Magnolia and Camphor Avenue (13.11 m3/m2). The three gardens with the lowest vegetation volume density were Recreational Green Space (1.57 m3/m2), Scientific Research Center Green Space (1.81 m3/m2), and Rose Garden (2.58 m3/m2). ③ The vegetation volume of each specialized garden was significantly related to the distribution area of the arbor community, the height of the constructive species, and the product thereof. The vegetation volume density of each specialized garden was significantly related to the proportion of the area of the arbor community in the specialized garden, the height of the constructive species, and the product thereof. This research can serve as a methodology reference for the quick estimation of urban vegetation volume, and provide basic data vegetation volume estimates and spatial pattern optimization for Shanghai Botanical Garden.
Estuarine cities are heavily influenced by anthropogenic activities. In turn, their water bodies often face serious eutrophication and pollution problems, thereby exerting significant pressure on the urban production and living environment. This study focuses on the water bodies in the city of Shanghai, an important estuarine megacity in China. Using the Sentinel-2 satellite and in situ measured water spectrum data, we built an inversion model for rapid identification of two critical parameters for eutrophication assessment, namely chlorophyll-a concentration and turbidity. We subsequently analyzed the spatial and temporal variability of these two parameters using time-series satellite data. Our results showed that the correlation coefficient (R2) of turbidity and chlorophyll-a concentration inversion based on remote sensing was 0.95 and 0.87, respectively; the root mean square error (RMSE) was 4.33 μg/L and 8.93 NTU, respectively. Time-series analysis from 2019 showed that both chlorophyll-a concentration and turbidity in different urban water bodies were highest in the summer and lowest in the winter in Shanghai. Specifically, chlorophyll-a concentrations across water bodies decreased in the following sequence: aqua-culture/planting ponds, permanent freshwater lakes, reservoir ponds, permanent rivers, and canals/transportation rivers. In the case of turbidity, the water bodies ordered from highest to the lowest followed the sequence: aqua-culture/planting ponds, permanent rivers, canals/water delivery rivers, permanent freshwater lakes, and reservoir ponds. Time series analysis of chlorophyll-a concentrations and turbidity from 2019 showed that in water bodies with less human disturbance, the correlation between chlorophyll-a concentration and turbidity was stronger than those that were heavily influenced by anthropogenic activities. The use of Sentinel-2 satellite images to retrieve the chlorophyll-a concentration and turbidity in water bodies can generally provide information on the eutrophication status of water bodies in Shanghai; the data, moreover, can serve as a reference for aquatic environmental monitoring of inland water bodies in other cities.
In this paper, the presence of the Tianzhuang fault was confirmed using a combination of petroleum geophysical exploration, geology, remote sensing, and other data. The study concluded that the fault originated from the west of Tiancun, Jinyuan District, Taiyuan City with a total length of about 35 km from Houjiazhai to Tianzhuang. The fault trends from west to east with the pattern EW-NEE-NE, and tends to the SE as a high-angle normal fault. The Tianzhuang fault is a concealed fault associated with the piedmont fault of the East and West Mountains of the Taiyuan Basin. Through the joint drilling exploration across the Tianzhuang fault, near the Ma Lianying Road, there were three distinct sedimentary cycles of river lake swamp facies found in the strata: in the 80 ~ 60 m section, the sedimentary environment tends to frequent gradually, and the sedimentary facies is lake→swamp; in the 60 ~ 30 m section, the sedimentary environment tends to be stable and frequent twice, and the sedimentary facies is river→swamp→river→lake→swamp→river; in the 30 ~ 0 m section, the sedimentary environment tends to be stable gradually, and the sedimentary facies is swamp→lake→swamp. The Quaternary strata in the site gradually thickens from north to south in the horizontal direction, and the coarse-grained deposits become thinner. There is a magnitude change in the borehole, ZK3←→ZK4←→ZK7, and the first layer is thick in the vertical direction. Particle deposition occurs at 20 ~ 30 m, and the floating is not large; the sedimentation cycle number is roughly “M” from deep to shallow, and the sedimentation number reaches a peak at 30 ~ 40 m and 50 ~ 60 m. From the perspective of detecting the strata, all the boreholes in the silty layer of the Holocene boundary were exposed, and the depth was relatively small. It is believed that the sampling rate of the bored sand layer is not the same and hence it is expected that the fault of the Tianzhuang fault is not broken. There are three primary sets of fault-breaking strata in the Tianzhuang fault, all of which are from the Late Pleistocene strata; these did not penetrate the Upper Pleistocene, and thus the target fault was determined to be the late Pleistocene active fault. From top to bottom, the offset of faulted strata increases gradually: 0.4 m, 3.5 m, and 7.2 m, in turn. There are two coseismic displacements of about 3 meters in the exposed depth of the borehole, which can be used to judge the occurrence of two main dislocation events in the identified layer. This provides reliable geological evidence for analyzing the seismic risk of the Tianzhuang fault.
Constructing 3-Pre-Lie algebras has always been a difficult problem; until now, there have been very few examples of 3-Pre-Lie algebras. In this paper, we use homogenous Rota-Baxter operators of weight zero on the infinite dimensional 3-Lie algebra $A_{\omega}=\langle L_m | m\in {\mathbb{Z}}\rangle$ to construct 3-Pre-Lie algebras $B_k,~0\leqslant k\leqslant 4$ , and we subsequently discuss the structure. It is shown that $B_2$ and $B_4$ are non-isomorphic simple 3-Pre-Lie algebras, $B_1$ is an indecomposable 3-Pre-Lie algebra with infinitely many one-dimensional ideals, and $B_3$ is an indecomposable 3-Pre-Lie algebra with finitely many ideals.
The discounted Hamilton-Jacobi equation (H-J equation) is a special form of the contact Hamilton-Jacobi equation; hence, study of the discounted H-J equation is important. In this article, we first study an expression of the viscosity solution $u_{\lambda}(x,t)$ for the discounted H-J equation in non-compact space. Then, we explore the convergence of the viscosity solution $u_{\lambda}(x,t)$ for a specific discounted H-J equation with $\lambda >0$ in non-compact space for the initial value in different cases.
Let $G$ be a simple graph. A total coloring $f$ of $G$ is called an IE-total coloring if $f(u)\neq f(v)$ for any two adjacent vertices $u$ and $v$ , where $V(G)$ denotes the set of vertices of $G$ . For an IE-total coloring $f$ of $G$ , the set of colors $C(x)$ (non-multiple sets) of vertex $x$ under $f$ of $G$ is the set of colors of vertex $x$ and of the edges incident with $x$ . If any two distinct vertices of $G$ have distinct color sets, then $f$ is called a vertex-distinguishing IE-total coloring of $G$ . We explore the vertex distinguishing IE-total coloring of complete tripartite graphs $K_{5,5,p}$ $(p \geqslant 2\;028)$ through the use of multiple methods, including distributing the color sets in advance, constructing the colorings, and contradiction. The vertex-distinguishing IE-total chromatic number of $K_{5,5,p}$ $(p \geqslant 2\;028)$ is determined.
By generalizing and using the normal form theory and center manifold theorem of delay differential equations, a class of high-codimension bifurcation problems of predator-prey systems with delay and Allee effect are investigated. Firstly, sufficient conditions for the existence of the positive equilibrium and the codimension 3 bifurcation at this positive equilibrium are established. Subsequently, the normal form of the system at the positive equilibrium is deduced. Finally, from the topological equivalence of the normal form and the original system, the bifurcation phenomenon of the original system at the positive equilibrium is analyzed.
Homotopy analysis method is an effective method for constructing approximate analytical solutions to strongly nonlinear problems. The technique has been widely applied to solve important problems in scientific research and engineering technology. Compared with other existing techniques, this method leverages auxiliary parameters and functions to adjust and control the convergence region and convergence speed of approximate analytical solutions. In this paper, we present a parameter selection algorithm based on machine learning techniques to determine the optimal values of convergence control parameters for homotopy analysis solutions. This marks the first time that homotopy analysis method and machine learning techniques have been combined to obtain approximate analytical method with better convergence for strongly nonlinear mathematical and physical equations. By applying the method to several examples, we show that the convergence of solutions using the proposed method is better than those obtained from existing homotopy analysis methods. In addition, our algorithm is both more universal and flexible.
Using double-layer networks, we constructed a coupled propagation model (Noisy Voter - Susceptible-Infected-Recovery) with different time evolution scales. This coupled spreading process can be characterized by numerical analysis method of microscopic Markov chain theory. We verified the accuracy of the proposed numerical analysis method using a large number of Monte Carlo simulation experiments. We found a crossover phenomenon of the phase transition type in the coupled model. Specifically, when the noise in the opinion formation process is relatively small, the information propagation scale and the proportion of positive opinions change discontinuously with the information transmission rate. At the same time, the hysteresis loop and bistability phenomenon appear, in which the phenomenon of global consensus can be observed. When the noise is large, the order parameters of these two dynamic processes vary continuously with the transmission rate.
As a result of ongoing advances in artificial intelligence technology, the potential for learning analysis in teaching evaluation and educational data mining is gradually being recognized. In classrooms, artificial intelligence technology can help to enable automated student behavior analysis, so that teachers can effectively and intuitively grasp students’ learning behavior engagement; the technology, moreover, can provide data to support subsequent improvements in learning design and implementation of teaching interventions. The main scope of the research is as follows: Construct a classroom student behavior dataset that provides a basis for subsequent research; Propose a behavior detection method and a set of feasible, high-precision behavior recognition models. Based on the global features of the human posture extracted from the Openpose algorithm and the local features of the interactive objects extracted by the YOLO v3 algorithm, student behavior can be identified and analyzed to help improve recognition accuracy; Improve the model structure, compress and optimize the model, and reduce the consumption of computing power and time. Four behaviors closely related to the state of learning engagement: listening, turning sideways, bowing, and raising hands are recognized. The accuracy of the detection and recognition method on the verification set achieves 95.45%. The recognition speed and accuracy of common behaviors, such as playing with mobile phones and writing, are greatly improved compared to the original model.
In this paper, we propose the construction of a bi-directional fully connected structure for better extraction of context information. We also propose the construction of a bi-directional attention structure for compressing matrices containing rich text features into a vector. The bi-directional fully connected structure and the gated structure are then combined. This research demonstrates that the proposed combined structure has a net positive effect on text classification accuracy. Finally, by combining these three structures and a bi-direction long short-term memory, we propose a new text classification model. Using this model, we obtained competitive results on seven commonly used text classification datasets and achieved state-of-the-art results on five of them. Experiments showed that the combination of these structures can significantly reduce classification errors.
By combining information extraction technology, data matching technology, and one-time manual processing, mathematical subjective questions can be transformed into tree-shaped multiple-choice questions. In this study, an automatic correction system for college mathematics assignments was developed by combining modern information and network technology; the system was subsequently trialed in the teaching of entry-level college mathematics courses. The proposed system solves bottlenecks related to automatic grading of subjective mathematics questions, including multiple-choice questions, fill-in-the-blank questions, judgment questions, quiz questions, calculation questions, and proof questions. The system can correct routine exercises for mathematics courses of primary schools, middle schools, and universities so as to achieve more efficient completion. The idea, furthermore, can be applied to various aspects of mathematics teaching, such as previews before class, classroom exercises, reviews after class, preparations for examinations, online examinations, etc. The electronic data collected in the process of automatic correction can subsequently be used for data analysis, teaching guidance, teaching research, and the construction of educational informatization.
In this paper, the atomic structure, stability, electronic structure, and magnetism of two-dimensional transition metal phosphide MnTn+1 (M = V, Cr; T = P, As, and Sb) slices were systematically studied using the first-principles calculations based on density functional theory. By calculating the formation energy and phonon spectrum, it was determined that only V4As5, Cr2P3, Cr3P4, Cr4P5, Cr2As3, and Cr3As4 are stable two-dimensional magnetic multilayers. The results show that these stable two-dimensional magnetic materials are antiferromagnetic metals. In addition, the electronic structure and the magnetic coupling mechanism of these materials were further analyzed.
This paper explores a method for generating optically mediated entanglement between Bose-Einstein condensates (BECs). Using a quantum nondemolition Hamiltonian with BECs placed in a Mach-Zehnder configuration, it is shown that entangled states can be induced by performing measurement on light. In particular, the effects of the entangled state in the presence of decoherence were analyzed. The behavior of the entangled state was found to be sensitive to the atom-light interaction time. The entangled state is relatively stable when the dimensionless interaction time $ \tau \lesssim \frac{1}{\sqrt{N}} $ and relatively fragile when the time is greater.
In this paper, the motion trajectory of micro-nanoparticles is calculated based on the Euler-Richardson algorithm after the optical force exerted on the particles is determined using Mie scattering theory. The Euler-Richardson algorithm has better calculation accuracy and faster convergence speed than the Euler algorithm and the Euler-Kromer algorithm, and thus is an appropriate approach to describe the trajectory of particles. Hence, the motion trajectory of a nanoparticle in a periodic conservative optical force field is calculated based on the Euler-Kromer algorithm; the results confirm consistency with the physical analysis, further verifying the effectiveness and stability of the approach. The calculation method shown in this paper provides a high-efficiency approach to study optical trapping, transport, sorting of colloidal particles, and biological macromolecules as well as the cooling of macroscopic particles in optical micro-manipulation.
In this paper, the exterior solution for a spherically symmetric black hole surrounded by plasma is studied in detail. After deriving the fundamental governing equations, the analytic solutions under two approximate conditions, ${g_{tt}}{g_{rr}} = - 1$ and $p = 0$ , are investigated. Comparing the two results with the accurate numerical solution, we find that the former approximation offers superior accuracy. This provides a basis for studying the quasinormal modes of perturbations as well as the shadow and ring when the black hole is surrounded by plasma.
In this study, ultrafast spin dynamics on FM-AFM (ferromagnetism-antiferromagnetism) thin film were explored using pump-probe technology with circularly polarized and linear pump beams. Circularly polarized light generates an effective inducting magnetic field, which is called the inverse Faraday effect. The direction of the transient Kerr peak only depends on the angular momentum of photons. The amplitude of the Kerr peak depends on the thickness of the MnIr film. This may be attributed to the fact that the transient Kerr peak originates from the magnetization of paramagnetic electrons. This study may help further the understanding of spin dynamics in HD-AOS (Helicity-Dependent All Optical Switching).
In recent years, a new laser cooling method—named bichromatic adiabatic cooling—has been developed; however, the method offers low cooling efficiency. In this paper, numerical optimization of the bichromatic adiabatic cooling process has been performed. It is shown that there is an optimal pulse time for the optical fields to achieve the highest cooling efficiency. Moreover, a comparison of the cooling efficiency with the Gaussian light pulse and square pulse shows that the cooling efficiency is insensitive to the pulse shape. Because this cooling method relies on adiabatic evolution of the light field-atom system, it is shown that it is better to slow the speed of the atomic beam to maintain the adiabatic condition. Finally, the effect of spontaneous emission on bichromatic adiabatic cooling is studied. The results show that use of a long pulse significantly reduces cooling efficiency.
In this paper, the phase estimation limits of an active-related Mach-Zehnder interferometer with three port inputs and two different input states was studied using quantum Fisher information and quantum Fisher information matrix theory. In the case of an arbitrary light field input to a single port, the effect of the input field fluctuation on the limit of phase estimation is eliminated by the theory of phase averaging and the quantum Fischer information matrix. In the case of a dual port input coherent state, the effect of the fluctuating light field on the estimation limit cannot be eliminated, and the phase estimation limit depends on the initial phase of the two input coherent states.
Recently, research on multi-mode, multi-band transceivers has garnered significant interest; in this context, the Software-Define Radio (SDR) system is considered a good candidate. To reduce the negative influence of out-of-band interference on transceiver performance of the SDR system, a high out-of-band rejection IF (intermediate frequency) filter with tunable bandwidth and programable gain is proposed. The proposed filter consists of a biquadratic Gm-C filter, a gain-boosting stage, and a 5th-order elliptic filter. In the proposed filter, the variable gain is achieved using a biquadratic Gm-C filter and a gain-boosting stage, and the tunable bandwidth is achieved using capacitor arrays. In addition, a 5th-order elliptic filter is added to improve out-of-band rejection. The post-layout simulation shows that the bandwidth is tuned over a range of 1 MHz–30 MHz, and the minimum out-of-band rejection at twice the bandwidth reaches 44.56 dB. The gain control range is from –20 dB to 20 dB, and the power consumption and active area for the analog counterpart is 5.1 mW and 1.23 mm2, respectively. The proposed filter is suitable for the analog front-end of multi-mode communication terminals.
In recent years, white pollution caused by waste plastics has attracted widespread attention. Microplastics, which are smaller than 5 mm, are widely distributed in the marine environment. The organisms attached to microplastic surfaces include potential pathogenic bacteria that are harmful to marine life and even human health, as well as plastic-degrading bacteria that can reduce their pollution. Microplastics are difficult to degrade, so they can exist in the aquatic environment for a long time, and the microorganisms attached to their surface can also live stably. In addition, microplastics may pass through the food chain to organisms at higher nutritional levels, and may be eaten by fish and affect fish growth. This paper reviews the distribution of microplastics in the ocean and the potential effects of harmful substances contained or attached to the microplastic surface on organisms. The ecological effects of pathogenic microorganisms attached to the surface of microplastics and plastic decomposition microorganisms, as well as the potential of microplastic transmission to high nutritional levels through the food chain were discussed. The ecological risk of microplastic distribution and surface-attached organisms was analyzed. Furtherly, it is still necessary to understand the impact of plastic waste and microplastics on the marine ecosystem, so as to fully understand the ecological effects of marine microplastics and their attachments, and provide a scientific basis for marine plastic pollution control.
In this study, we analyzed the diversity of microbes across 45 collected soil samples using meta-barcoding. The analysis showed similar α diversity of soil microbes in a vertical forest (VF) building and the surrounding green area, but a high level of differentiation in the microbe community composition and β diversity between these two types of habitats. The results indicated that a VF can accommodate a large number of microbe species and provided evidence for the contribution of VFs to the conservation of urban biodiversity.
Based on the classical theory of island biogeography, thirteen remnant forest patches of fragmented urban habitats were chosen as islands, and the Lao Mountain as the mainland. Then, canonical correspondence analysis (CCA) was used to analyze the relationship between species composition and four factors — namely, patch area, distance to species pool, human disturbance intensity, and isolation degree. The lengths of vectors in CCA biplots were used as weights for each influencing factor to calculate the overall sum, named the isolated island index (III); an isolated island index evaluation system of forest patches in fragmented urban habitats was subsequently designed. The results indicated that the relationship between III and richness was significantly negative for native species, but not significant for alien species richness. There was a significant relationship found for bird dispersal and wind dispersal species. The linear function was determined to be the best simulating model.
In this study, an ecological survey of the lake water in Luxun Park from January to October 2019 was conducted to determine the community structure characteristics and health of phytoplankton in the water. In particular, the community composition, density, biomass, diversity, uniformity, and dominant species of phytoplankton were analyzed. A total of 83 genera of 8 phyla of phytoplankton were identified; of these, Cyanophyta, Chlorophyta, and Diatoms had the largest number of species. The annual average cell density was 14.17×106 ind/L, and the annual average biomass was 3.57 mg/L. Density and biomass typically increase with seasonal changes. The dominant phyla were the Cyanophyta, Chlorophyta, and Diatom phylum; meanwhile, Pseudo-Anabaena, Platychophyta, Scenedesmus, and Cyclotella were the major dominant species. Redundant analysis was used to further analyze the environmental factors in the lake water of Luxun Park. The results showed that pH, nitrate nitrogen, nitrous nitrogen, and permanganate index are the key factors affecting the structure of the phytoplankton community.
Using a combination of mechanical and representative sampling, plots in 13 cities of the Yangtze River Delta Region were surveyed in 2015—2016; the survey consisted of 449 plots across 76 urban parks, roads, and waterfront greenbelt areas. As part of this study, we analyzed the factors and drivers for species composition, distribution, size, coverage, and growth vigor of the predominant trees located in the urban plots; the data provides a scientific basis for the future construction of forest cities and the improvement of urban human settlements. The results show that: ① In total, 157 tree species were recorded in the surveyed plots, which belonged to 115 genera and 54 families. The dominant families belonged to the Rosaceae group (20 species across 12 genera), with a high proportion of single families and relatively dispersed species composition. ② The plots were characterized by significant temperate features (73 genera), showing that the tree species have typical temperate properties in application. ③ The main tree species of the urban man-made forests were Cinnamomum camphora and Platanus × acerifolia, with coverage reaching 27.45%; specifically, Cinnamomum camphora was the dominant species. The utilization of trees across different types of urban plots varied considerably. ④ It was found that tree species with 10 cm ≤ D≤ 30 cm constituted the majority of tall trees in the urban plots, including many species of native trees such as Sapindus saponaria, Koelreuteria bipinnata 'Integrifoliola', and Liquidambar formosana. The quantity and distribution of the trees, however, was found to be extremely unbalanced. The coverage of Cinnamomum camphora and Platanus × acerifolia in the area was as high as 45.18%, and the relative species diversity and community stability were found to be weak. We recommend that the coverage of other tree species is gradually increased in the future construction of urban plots.
Based on daily monitoring data of the Qingcaosha Reservoir water intake from 2010 to 2019, this paper analyzes the interannual and seasonal variation trends in the main physical and chemical water quality indicators, and explores the correlation between these indicators. The results show that: ① The dissolved oxygen at the intake of Qingcaosha Reservoir was consistently high, and the pH value was slightly alkaline. ② The concentration of ammonia nitrogen was low at the intake of Qingcaosha Reservoir, the concentration of nitrate-nitrogen was between 1.2 and 2.0 mg/L, the concentration of total phosphorus was between 0.1 and 0.2 mg/L, and the permanganate index was between 2.0 and 4.0 mg/L. These indexes, furthermore, all showed a downward trend after 2015, suggesting that the quality of incoming water subsequently improved. ③ Dissolved oxygen concentration, water temperature, and pH exhibited obvious seasonal variations, while total hardness, permanent hardness, conductivity, and chloride showed consistent variations under the influence of seawater intrusion. There was no significant seasonal variation in the other indicators. ④ The concentration of total phosphorus and the permanganate index increased with turbidity. The concentration of total phosphorus and nitrate nitrogen, moreover, decreased with an increase in water discharge at Datong.
In this study, follow-up monitoring over the course of two and a half years was carried out to analyze the long-term impact of sediment dredging on water quality, new sediments, benthos, and microorganisms of a river reach in Shandong Province. The results showed that the contents of CODCr and TP could be effectively removed by dredging, but little effect was observed on the contents of TN and NH4+-N. The C/N ratio in the new sediments decreased gradually over the observation period, which was beneficial to the recovery of microbial and benthic communities. Dredging can reduce the average biomass and density of tremididae in surface sediments to a certain extent, while Chironomidae density is less affected by dredging. Dredging did not change the microbial community structure significantly, which may be related to the depth of dredging. Exogenous pollutants and man-made drainage can affect the water quality and sediment pollutant content of the dredged river section; this effect, however, may recover in a short time after completion of the microbial community construction. In conclusion, the overall engineering effects of dredging were observed during the study period (2–4 years after dredging); in this time, the water quality and sediment pollution improved, which was conducive to the restoration of benthos and microbial diversity. However, with the passage of time and human interference, the net effect on water quality maintenance weakened.
In the Chongming Dongtan reclamation area, the soil carbon flux observation system (LI-8100A) and root removal method were used to continuously measure soil respiration (RS), heterotrophic respiration (RH), and autotrophic respiration (RA) of five land use types (Phragmites australis wetland, Imperata cylindrica wetland, young forest, middle-age forest, and cropland); the methods were also used to measure the soil temperature, volumetric water content, electrical conductivity, and other environmental factors in the 0-10 cm soil layer. In this study, the differences in soil respiration and its components among different land use types in the Chongming Dongtan reclamation area were systematically compared. The results showed that: ① RS in the young forest, middle-age forest, and cropland plot were significantly lower than those in the P. australis wetland and the I. cylindrica wetland plot; ② the proportion of RH found in the young forest, middle-age forest, and cropland was significantly higher than that observed in the P. australis wetland and the I. cylindrica wetland; ③ RS and its components showed a significant exponential relationship with soil temperature, but showed weak correlations with the soil volumetric water content and electrical conductivity. Compared with the residual wetlands, the different agroforestry utilization methods significantly reduced RS, but significantly increased RH, which may suggest that the soil organic carbon pool is still in a state of net loss after 20 years of reclamation. Thus, effective measures should be taken to improve the carbon sequestration capacity of the reclaimed soil in this area.
Density gradient centrifugation in combination with quantitative protargol stain (Ludox–QPS) is an important research method for the separation, extraction, and staining of microbenthos. Glutaraldehyde solution is the most widely used fixative for this method; however, it is a hazardous chemical and transportation by civil aviation is forbidden. Hence, biogeography studies using glutaraldehyde solution on a large scale are greatly limited. In contrast, samples fixed with formaldehyde solution can be transported by air consignment under certain conditions. To test whether the results of a study on microbenthic communities fixed with formaldehyde and glutaraldehyde are comparable, we collected microbenthic samples from three natural habitats (bare mudflat, Scirpus mariqueter meadow, sandy beach) and fixed them with either formaldehyde (final concentration is 1%) or glutaraldehyde (final concentration is 2%). Then, we tested the significance of differences in species composition, diversity, and biomass of the communities using an ANOSIM two-way crossed analysis (fixative type and habitat type). The results of this study demonstrate that although significant differences exist in the community structures among the three habitats, the species composition, diversity indices (number of species, number of individuals, evenness, Shannon-Wiener diversity index), and biomass of the microbenthic communities between the two fixative types have no significant differences. NMDS analysis also shows similar results (stress = 0.11). In conclusion, the data indicates that the microbenthic communities fixed by the two fixatives are comparable. Therefore, formaldehyde is also suitable for ecological research of microbenthos as a fixative.
In this paper, we explore the dynamic changes in photosynthesis and chlorophyll fluorescence in Kandelia candel leaves from restored mangroves in southern Zhejiang Province to provide a scientific basis for the ecological restoration and northward migration of Kandelia candel. Using a LI-6800F portable, automatic photosynthesis-fluorescence measurement system, the diurnal variation of photosynthesis and chlorophyll fluorescence of Kandelia candel were measured for one day each month in 2019; the data was then used to analyze the relationship between the two parameters. The results showed that, except for water use efficiency (WUE) and non-photochemical quenching (NPQ), the diurnal variation curves of the parameters were generally u-shaped or inverted u-shaped; examples of these parameters included transpiration rate (E), stomatal conductance (Gsw), electron transfer rate (ETR), and maximum photochemical reaction quantum efficiency ( $F_{\rm {v}}' $ / $F_{\rm {m}}'$ ). Some parameters, such as net photosynthetic rate (A), E, Gsw, and ETR, were found to be significantly higher in July and August than in November and December. In addition, except for WUE, the correlation between photosynthetic factors and fluorescence factors was significant on a daily basis, and the correlation between A, E, Gsw, WUE, and ETR was significant on a monthly basis. The results also demonstrated that the photosynthetic capacity of Kandelia candel leaves was the strongest around noon on any given day; at the monthly level, the strongest capacity was observed in the summer, followed by spring and autumn, and lastly winter. The correlation between photosynthesis and chlorophyll fluorescence of Kandelia candel leaves on individual days was higher than that at the monthly level.
To understand the stability of the estuarine ecosystem and nitrogen balance in the context of global climate change, it is important to investigate the temperature sensitivity of the microbial nitrogen fixation process. Until now, there have been few studies in the literature on the response of the nitrogen fixation process to temperature changes and its influencing factors. We selected six sampling sites around the Yangtze River Estuary (including four sites inside and two sites outside the Yangtze River Estuary) for the scope of the study; in particular, we explored the temperature sensitivity and influencing factors of the nitrogen fixation process on sediments of the Yangtze River Estuary using slurry incubation experiments and the 15N2 isotope tracer technique. The results showed that the in-situ temperature nitrogen fixation rate in the sediments of the Yangtze River Estuary ranged from 0.72 to 2.85 nmol·g–1·h–1. At 5 ~ 10 ℃ and20 ~ 30 ℃, the nitrogen fixation rate was inhibited by an increase in temperature. However, in the range of 10 ~ 20 ℃, the nitrogen fixation rate was significantly promoted with an increase in temperature. The sensitivity of the nitrogen fixation rate to temperature is relatively consistent, although the physical and chemical properties of the sediments vary significantly. Correlation analysis showed that the contents of sulfide, ferrous iron, nitrate, and total organic carbon were the main environmental factors affecting nitrogen fixation.
River regime changes have a substantial impact on estuary hydrodynamics and the incidence of saltwater intrusion. Based on measured water depth data in the North Branch of the Yangtze River Estuary from 2007 and 2016, we analyzed the river regime changes of the North Branch across a 10-year timespan; in addition, we numerically simulated and analyzed the influence of the river regime change on hydrodynamics and saltwater intrusion. The water volume increased by 4.4% in the upstream section, decreased by 8.8% in the midstream, and decreased by 20.5% in the downstream of the North Branch from 2007 to 2016. Overall, the data reflects an overall net erosion in the upstream section and deposition in the midstream and downstream sections of the North Branch. In fact, a new sand body with deposition thickness of 4 ~ 6 m appeared in the bifurcation of the North Brach and South Branch. The numerical simulation results, moreover, show that the new sand body caused a 15.0% increase in saltwater intrusion, a change in net water diversion ratio from –2.8% to –3.2% in the upstream section of the North Branch during spring tide, and clear enhancement of saltwater intrusion; if the new sand body continues to deposit up to 0.85 m, the saltwater intrusion will not increase further. At the water intakes of the three reservoirs in the South Branch, the new sand body caused the average salinity during spring tide to increase by 0.14 at Dongfengxisha reservoir, 0.12 at Chenhang reservoir, and 0.11 at Qingcaosha reservoir; similarly, the average salinity during the subsequent middle tide increased by 0.15 at Dongfengxisha, 0.11 at Chenhang, and 0.09 at Qingcaosha. The deposition in the downstream section of the North Branch led to tidal prisms cross section at the port of Lianxin during flood tide and ebb tide to decrease by 15.2% and 16.4%, respectively, in spring tide, and decrease by 21.2% and 19.0%, respectively, in neap tide. As we move further upstream along the North Branch: the amount of rising and falling tides decreases and shows a relative downward trend, the high tide level drops, the low tide level rises, and the tidal range decreases during spring tide. Moreover, the tidal prisms in the midstream and downstream sections of the North Branch decreased, and the saltwater spillover from the North Branch into the South Branch weakened. The deposition in the downstream section of the North Branch caused the salinity decrease at the water intakes of the three reservoirs of the South Branch. On the whole, the saltwater spillover from the North Branch to the South Branch weakened significantly due to the river regime changes in the North Branch from 2007 to 2016; the salinity decreased by 2 ~ 3 in the upstream section of the North Branch and 1 ~ 2 in the downstream section of the North Branch; the salinity decreased at the water intakes of the three reservoirs of the South Branch; and the average salinity during spring tide and the subsequent middle tide decreased by 0.41 and 0.21, respectively, at Dongfengxisha reservoir, decreased by 0.34 and 0.18, respectively, at Chenhang reservoir, and decreased by 0.28 and 0.17, respectively, at Qingcaosha reservoir.
Under high-speed wind conditions, cross-polarization synthetic aperture radar (SAR) is not affected by signal saturation. Hence, SAR can be used to observe expansive, high-speed wind fields under all-weather, day- and night-time conditions and offers great potential for monitoring typhoons. Sentinel-1, which was launched by the European Space Agency (ESA), is one of the few available SAR satellites in orbit at present that can provide cross-polarization data. Based on Sentinel-1 cross-polarization data, seven different cross-polarization models, including the C-band cross polarization ocean model (C-2PO), C-band cross-polarization coupled-parameters ocean model (C-3PO), and quad-polarization stripmap cross-polarization model (QPS-CP), developed from 2011 to 2021 were used to estimate the typhoon wind fields of Higos and Molave. A denoising method was applied to remove the noise from extra wide (EW) mode SAR images. The results show that the denoising method can effectively reduce the noise and improve the retrieved wind fields. The C-3PO model performs well in monitoring high-speed winds, but does not obtain reliable results for low- to moderate-speed winds compared with the Sentinel-1 Level-2 Ocean (OCN) product. By merging results from the cross-polarization model and the OCN wind product, the combined wind field can effectively reproduce the inner high-speed winds and outer relative low-speed winds. This study is of significant value for forecasting, data assimilation, and research of typhoon disasters.
Sand, gravel, and crushed rock—together referred to as construction aggregates—are the world’s most extracted solid materials by mass. China’s annual consumption of construction aggregates reached over 20 billion tons in 2018, accounting for nearly half of global consumption. This article provides an overview of the use of sand and gravel in China, including current supply and demand conflicts and the impacts of mining, transportation, and use. We highlight that: ① the national demand for sand and gravel has continued to grow in the last two decades; crushed rock has become the main source of construction aggregates, whereas the supply of river sand has significantly declined; and ② there are significant environmental, economic, and social challenges associated with sand and gravel mining, transportation, and use, including the emergence of illicit supply networks. We then discuss opportunities to ensure sand and gravel supply, minimize mining impacts, and promote sustainable trajectories for the Chinese aggregates industry. First, the quantification of the material flows and stocks of construction aggregates that includes geological and anthropogenic stocks is crucial to identify supply bottlenecks and ensure more efficient use of resources. This requires establishing a reliable data monitoring system. Second, the government should increase investment and establish relevant institutions to optimize supply systems and minimize their impacts, strengthen the regulatory framework, promote the uptake of alternative materials, and establish standards and implement best practices in the aggregates industry. Finally, interdisciplinary integrated research is needed to analyze the existing challenges associated with the supply of sand and gravel resources as well as the potential and risks of adaptation strategies.
Alternating direction iteration (ADI) scheme is an effective method for solving real positive definite linear systems; in many cases, however, the method requires that all the direction matrices involved are multiplication exchangeable, which severely limits the scope of application. In this paper, new revised alternating direction iteration (RADI) schemes are proposed, that do not stipulate the multiplication exchangeable requirement, thereby expanding the application scope. In parallel, measures to improve the efficiency of RADI schemes are also discussed.
A family of linear operators $\{N_{h};h\in\mathcal{P_{+}}(\mathbb{N})\}$ in $L^{2}(M)$ are defined. Firstly, we prove that $N_{h}$ is a positive, densely defined, self-adjoint closed linear operator. In general, $N_{h}$ is not bounded, hence, we explore the sufficient and necessary conditions such that $N_{h}$ is bounded. Secondly, we consider the dependence of $N_{h}$ on $h$ : $N_{h}$ is strictly increasing with respect to $h$ , and the operator-valued mapping $N_{h}$ is an isometry from $l^{1}_{+}(\mathbb{N})$ to the subspace of bounded generalized number operators on $L^{2}(M)$ , where $l^{1}_{+}(\mathbb{N})$ is the space of the summable function on $\mathbb{N}$ . We consider the conditions such that $\{N_{h_{n}};n\geqslant1\}$ is strongly and uniformly convergent. If $\{h_{n};n\geqslant1\}$ is convergent monotonically to $h$ , the domain of $\{N_{h_{n}};n\geqslant1\}$ and $N_{h}$ have some interesting properties, we show, furthermore, that a convergent family of $\{N_{h_{n}};n\geqslant1\}$ can be obtained. We prove that $\{N_{h};h\in\mathcal{P_{+}}(\mathbb{N})\}$ is commutative observable on $\mathcal{S}_{0}(M)$ .
In this paper, we study complete gradient shrinking K?hler-Ricci solitons with a vanishing fourth-order Bochner tensor (i.e. $\text{div}^{4}(W)=\nabla_{\bar{k}}\nabla_{j}\nabla_{\bar{i}}\nabla_{l}W_{i\bar{j}k\bar{l}}=0$ ), and obtain the corresponding classification results.
Neural architecture search algorithms aim to find more efficient neural network structures in a huge neural network structure space using computer heuristic search instead of manual search. Previous studies have addressed the problem of inefficient and time-consuming search for early neural network structures by introducing various constraints on the search space. While constraints on the search space can improve and stabilize the performance of the model, they ignore potentially efficient model structures. Hence, in this study, we constructed a recursive model search space that focuses more on the macroscopic structure of neural networks. We proposed a neural architecture search algorithm that explores this search space through a step-by-step incremental search approach. Experiments showed that the algorithm can efficiently perform neural architecture search tasks in complex search spaces, but still fell slightly short of the latest constrained search space-based neural architecture search algorithms.
With the development and iteration of financial technology(FinTech) software programs, the size of test suites will gradually increase, which may introduce inherent redundancy. In order to effectively quantify test redundancy, a test redundancy evaluation metric called MVI (Most Valuable Item) is proposed in this study. To verify the validity of the MVI metric, the MVIR (Most Valuable Item Reduction) test case reduction algorithm is proposed. Experimental results show that the MVIR can achieve a test case reduction ratio of more than 89.88% assuming the test performance loss is less than 9.20%, this demonstrates that the MVI metric is valid.
The identification and classification of garment patterns are important technologies for intelligent clothing production and management. This paper proposes a method to convert garment patterns into graphic data and subsequently proposes a lightweight graph neural network GPC-GCN (Garment Pattern Classification Graph Convolutional Network) that can process this graphic data. The proposed graph data modeling method can not only maintain information on the shape of each component in the garment pattern but also deal with the arbitrariness of the position of components in garment patterns. Experiments show that the proposed graph neural network GPC-GCN achieves a better result for the classification of garment patterns compared to convolutional neural networks and graph convolutional networks.
With the rapid development of the Internet of Things, embedded hardware products face great challenges in data security. The AES (Advanced Encryption Standard) algorithm has the advantages of strong attack resistance, fast operation speed and flexible block length in the field of data encryption and decryption. The speed of this algorithm on microcontroller platforms is far inferior to general-purpose CPUs (Central Processing Units) which have an extended instruction set for AES encryption. To solve this problem, a speed optimized AES algorithm in CTR (Counter) mode based on the Cortex-M4 core instruction set is implemented using assembly language. The kernel’s unique barrel shifter and three-stage pipeline are used to optimize the round transformation of the algorithm, and the number of instruction cycles is reduced. Testing on an FRDM-K82F development board shows that the assembly optimization of the algorithm is substantially more efficient than the code implemented using the C language, and it offers more advantages in both cost and power consumption compared to hardware encryption based on the coprocessor.
Battlefield environments are combat spaces that contain geographic elements such as terrain and roads. Road modeling and simulation is an important part of battlefield simulation and plays a key role in complex combat decision-making. Traditional road modeling is unable to handle the complex terrain conditions present in the field; hence, this paper proposes road modeling and simulation method for field environments. In particular, in order to support road modeling and simulation of complex terrain environments, road construction designs oriented to typical battlefield environments are proposed. This method divides the road network into different sub-models according to their characteristics and models them separately, improving the demand for realism in battlefield simulation. Then, the proposed method uses OpenStreetMap geographic information data to drive road network construction. The model offers real-time, high accuracy road information content and complete classification that can meet the needs of military operations and modeling simulations for typical battlefield environments. Secondly, using terrain elevation data, road construction rules, and other auxiliary information, the road height is adjusted to adapt to the complex terrain conditions of the battlefield and possible multi-level road network structures. Lastly, the introduction of a $ {G}^{2} $ continuous Hermite interpolation spline can flexibly represent the center line of the road and improves the reusability of the road model through grid deformation. Experiments show that the proposed simulation method can more reliably restore the real details of a road network to effectively fit complex terrain and improve the reusability of road models. Finally, it provides a feasible analysis angle and modeling method for researching geographic elements in battlefield environments.
The (2 + 1)-dimensional Dirac oscillator is a fundamental model used to study the relativistic extensions of quantum effects and principles. Due to the influence of relativistic effects, including the non-equidistant and negative excitation spectrum and the spin-orbit coupling, the eigenstates are complicated dressed states composed of spin and angular momentum state vectors; in turn, this renders theoretical research difficult. In this work, we decouple the spin and angular momentum state vectors and separate the spin-up and -down components into positive- and negative-energy states, respectively, using the Foldy-Wouthuysen (F-W) transformation. The Hamiltonian and eigenstates of the Dirac oscillator are then largely simplified in the F-W representation; nevertheless, we find the forms of the operators for spin and angular momentum in the same representation with complex combinations of each other. The results are useful in advancing research in relativistic quantum mechanics and spin-orbit coupling.
In this paper, corrections to the octet baryon masses based on the strange quark contribution are calculated using the SU(3) covariant chiral effective theory. We find that the items violating chiral power counting rules are local and can be subtracted by local counterterms which leads to extended minimal subtraction ${(\text{E}}\overline {{\text{MS}}})$ scheme. In addition to the chiral contribution, relativistic correction items are also retained, which is meaningful for accurately calculating the corrections to baryon masses and analytical extrapolation.
In this paper, bandgap tuning of C3N through the stacking pattern, layer number, and external electric field were investigated by employing first-principles density functional theory (DFT) calculations. Four stacking structures—namely AA-1, AA-2, AB-1, and AB-2—were investigated in our study; the calculation results showed that the AB-2 structure was the most energetically favorable. Accurate calculations of the bandgap by the HSE06 hybrid functional revealed a large bandgap difference between the C3N bilayers with AA and AB stacking; specifically, structures with AA stacking had much smaller bandgap than those with AB stacking. Moreover, we found that the bandgap of C3N decreases from 1.21 eV for a single layer to 0.69 eV for the AB-2 bulk structure. By applying a vertical electric field, the bandgap of a C3N bilayer, tri-layer, and four-layer with AB-2 stacking can be tuned to a nearly metallic state.
In this study, we investigated the dynamic behavior of quantum Fisher information (QFI) for the qubit-qutrit system suffering from noisy environments by considering quantum memory; the qubit is located near the event horizon of the Garfinkle-Horowitz-Strominger (GHS) dilation black hole and the qutrit stays at the asymptotically flat region. We proposed an effective strategy to protect QFI under the influence of noise by employing weak measurement and reversal measurement. The results show that QFI decays as the amplitude damping strength increases; meanwhile, QFI is nearly constant with an increase in the phase damping strength. QFI can be improved with the selection of appropriate values for measurement strengths and reversal strengths.
In this paper, a method for testing the Bell correlation between two spatially separated two-mode squeezed Bose-Einstein condensates (BECs) is proposed. Using the referenced method, violation of the Clauser-Horne-Shimony-Holt (CHSH) Bell inequality can be observed. First, the method for producing the required physical states is introduced, and then the Bell correlation is tested by calculating the relevant factors using the normalized expected value of the particle number operator. It is shown that violation of the Bell inequality can be observed when $r \lesssim 0.49$ . One of biggest violations occurs, furthermore, when $r \to 0$ and $B = 2\sqrt 2 $ . The method is highly robust in the presence of noise.
In this paper, we present a new type of atom-light accelerometer (ALA) based on use of an atom-light hybrid interferometer. At present, all-optical accelerometers are the most commonly used and most stable accelerometers on the market, owing to their small size and high accuracy. However, due to measurement bandwidth limitations, their practical application range is limited. Hence, we designed a new type of accelerometer to address this challenge. The atom-light hybrid interferometer is first constructed in the atomic system through the stimulated Raman scattering (SRS) process, and the elastic mass of the accelerometer is formed by a mirror. When the mass is subjected to acceleration on the experimental platform, it will perceive the change in external displacement, thereby introducing the phase into the interferometer. Through the change of the interference fringe, the change of the external phase and the displacement can be determined; hence, the magnitude of the acceleration can be obtained. The primary advantage of the atom-light accelerometer is that the Stokes field generated by the SRS process is phase related to the atomic spin-wave, which ensures the stability of the device phase. Secondly, the adjustable bandwidth of the device increases its scope of application. Finally, theoretical calculations show that its measurement accuracy exceeds the standard quantum limit (SQL) under ideal conditions.
Research on complex networks has given birth to models for understanding evolution dynamics and structure formation; their respective degree growth fluctuations, however, behave very differently. To test the validity of existing models, we carry out an empirical study on two real networks. The results show that both their fluctuation exponents decrease linearly with the observation interval, presenting an interval-dependent picture that has not been predicted by any of the existing models. By exploring the response of the fluctuation to shuffling data, we deduce the interval dependence from the reinforcement of the internal temporal correlation. These results reveal not only the limitations of the existing models, but the complex dynamics of the correlation itself, which is significant for further understanding the underlying mechanism of network evolution.
This paper studies the use of quantum nondemolition (QND) measurement to produce a spin squeezed atomic Bose-Einstein condensate (BEC) in a double-well trap. The spin squeezed atomic Bose-Einstein condensate is performed by putting the BECs of a double well in the two arms of a Mach Zehnder interferometer and performing a QND measurement. The dynamics of the light-atom system are solved using an exact wave-function approach, in contrast to previous approaches where approximations were made using techniques like the Holstein-Primakoff approximation. The backaction of the measurement on atoms is minimized by monitoring the condensate at zero detection current and the identical coherent beams. At the weak atom-light interaction limit, we find that the average spin direction is relatively unaffected by observing the conditional probability distribution and the Q function distribution. The spin variance is squeezed along the axis of optical coupling.
In this study, photoluminescence spectra are studied in perovskite quantum dot superlattices based on two-photon absorption processes at 10 K. The dynamics of excitons is obtained using a time-resolved photoluminescence detection system. The sample exhibits typical superfluorescence characteristics in the single-photon excitation case: When the pumping power increases, the transient peak intensity increases nonlinearly, and the radiation lifetime decreases rapidly. Meanwhile, the intensities of the two-photon absorption fluorescence spectra are proportional to the square of the excitation power, and the dynamics of excitons under the two-photon absorption case exhibits the same characteristics as those in the single-photon excitation case. Thus, when the excitation density reaches a certain intensity, two-photon absorption can also induce a superfluorescence process.
As a new type of production factor, data elements are becoming key to enterprise development. As an important component of national economic production, the chemical material industry must upgrade their data information system according to the needs of its construction. In this regard, a tailored data governance module is proposed for managing experimental formula data in the chemical material industry. The data governance module proposes the use of corresponding data standards and specifications according to the current business scenario of the enterprise. The system obtains data from the front end, improves its quality, stores it in a database, evaluates the data from the back end, and finally returns it to the front end for display, thereby creating a closed-loop negative feedback system.
The material research industry generates a wide variety of data from a wide range of sources. Notably, the data islanding issue pertaining to this scenario demands an application platform that integrates data collection, management, and application so that the value of the data can be better utilized for development. For this purpose, this study proposes a data management system that handles the entire data management lifecycle from collection to application for the material research industry, including thematic database, formula performance prediction, and formula correlation analysis capabilities. Itis expected that this development will promote data-driven innovations and improvements within the industry.
A hybrid transaction analytical processing (HTAP) system must concurrently support both transaction processing and query analysis. To eliminate interference between them, HTAP systems also typically assign different copies of data to both workloads, handling online transaction processing (OLTP) and online analytical processing (OLAP) requests separately, and synchronizing data between the copies based on a log replay. An HTAP system is committed to efficiently synchronizing OLTP data to OLAP, thereby providing a fresher data access service. In addition, the speed of sending and replaying the logs of the tables to be queried is a key factor affecting the freshness of the data. In this paper, using the table grouping based log parallel replay method and the characteristics of the HTAP load, a log sending and replay method is proposed based on the query frequency of the OLAP side. To ensure data consistency, this method improves the processing priority of high-frequency query table logs and achieves efficient log sending and replay capabilities along with a targeted priority display of high-frequency query table data, thereby ensuring the freshness of the HTAP system.
This study investigates the application of consortium blockchain technology to steel inspection certificates to facilitate the issuing of trustworthy, accountable, transferable, divisible, and electronic certificates for industrial internet users. With the help of blockchain technology, steel plants provided authentic certificate data to Ouyeel. They authorized Ouyeel to produce and issue electronic inspection certificates based on the trustworthy certificate data and online business transactions for internet users. The smart contracts were developed for quality data recording, ownership transferring, certificate history, and so on. The blockchain-based steel inspection certificates were tamper-proof and could be verified by scanning the 2D barcode on the certificates. Thus, the costs of printing, mailing, and archiving for manufacturers, distributors, and end users are significantly reduced. The experiments prove the high efficiency and availability of the system through sufficient function and performance tests.
Join order selection, i.e., the determination of the cheapest join order from available alternatives, is one of the most critical tasks in query optimization. The enormous search space of a join order makes it difficult to find an optimal join order in an efficient manner. Although there are many optimization algorithms for join order selection, existing benchmarks are unsuitable for evaluating these join order selection strategies because they cannot configure the depths of the joins or cover all join styles. To effectively evaluate the quality of join order selection algorithms used in an optimizer, a generic evaluation tool for join order selection is implemented in this study. The tool takes the primary key-based deterministic data generation method for portable application scenario migration, a join order sampling algorithm to reduce the investigated join spaces, and a result-guided parameter instantiation algorithm to support a valid query generation. We applied the tool on OceanBase and PostgreSQL, and the experiment results show its effectiveness in evaluating the performance of join order selection in query optimizers in a generic and efficient manner.
As the internet drives toward “digital transformation”, education equity and data trust-worthiness pose significant challenges in development. Blockchain, as a distributed ledger technology with tamperproof data, is jointly maintained by multiple parties and can solve equity and trustworthiness issues in scenarios such as educational resource allocation, intellectual property rights, and student information authentication. Although blockchain is capable of addressing the core education problems, its data immutability and transparency properties limit the upgradation process of smart contracts and disclosure of sensitive data in blockchain applications. Hence, updating educational applications and creating low privacy security of educational data becomes strenuous. To address the problem of limited smart contract upgrades, this study proposes an efficient and fully decoupled blockchain smart contract architecture. The as-proposed architecture aids in decoupling the contracts into proxy logical contracts, proxy data contracts, logical contracts, and data contracts, achieving an average reduction of 28.2% in upgradation costs compared with traditional methods. Moreover, we combined on- and off-chain collaboration to optimize transactions under the decoupled contract architecture and reduce data migration while updating contracts by integrating the underlying blockchain storage tree, optimized to reduce latency by half. To solve the problem of privacy protection, we propose a privacy data protection scheme based on permission management and LDP (Local Differential Privacy) to improve data privacy security while reducing the negative impact on blockchain performance. Finally, these solutions were integrated and implemented into an educational platform comprising a trusted knowledge exchange community and student growth system.
This study proposes a comprehensive and general database environment simulation tool that can achieve the accurate, efficient and dynamic simulation of a normal runtime environment and broken resource situations from multiple dimensions. This tool can help users customize required test scenarios, reduce the difficulty of database benchmarking, improve testing efficiency, and achieve a better referential ability of benchmark results. Experiments under customized runtime environment demonstrate the superiority of this tool.
The use of the software supply chain has been continuously interspersed throughout the software system development process. In recent years, security incidents related to the software supply chain have frequently occurred, and its security has become a global issue. Software maintainability, as one of the important attributes of software quality, reflects the difficulty of software maintenance activities. Although the trend of an open source software supply chain has gradually become popular in recent years, research into its maintainability remains extremely limited. Based on the above considerations and combined with a traditional research approach to software maintainability risk, this paper explores a unique analysis perspective regarding the maintainability risk of open source software and proposes a quality model of open source supply chain software maintainability. The model measures nine software attributes, including team health, activity, dependency influence, test integrity, external dependency, and understandability, based on 16 metrics for reflecting the maintainability of the open source software supply chain. At the same time, based on the GitHub hosting platform and npm sub-ecological data (this includes software information, dependencies, behavioral data generated during the development of each software, and so on), the maintainability indicators of different projects at the same time and within different time periods for the same project are compared and calculated, confirming the rationality of the proposed method. Using the model proposed herein, the quality maintainability of the open source software supply chain can be effectively evaluated, thereby guiding software design and reconstruction and the development of a higher quality software system.
Based on logistics-field blog post data from Weibo from November 2019 to May 2022, the user behaviors of express logistics services in the context of the Coronavirus epidemic are profiled. Using grounded theory and abstract clustering methods, five user behaviors and 22 subject contents are abstracted, and the corresponding user profile is generated. This paper further discusses the subject contents, the subject evolution, and the analysis of group differences. The results show that user satisfaction with logistics services was similar, and the dissatisfaction was diversified with obvious escalation. Variables of transportation efficiency and logistics guarantee were the main factors affecting the evaluation, and the development of the epidemic affected the concerns and attitudes of the subject contents, which had obvious group differences at different degrees.
Convolutional neural networks have made remarkable achievements in artificial intelligence, such as blockchain, speech recognition, and image understanding. However, improvement in model performance is accompanied by a substantial increase in the computational and parameter overhead, leading to a series of problems, such as a slow inference speed, large memory consumption, and difficulty of deployment on mobile devices. Knowledge distillation serves as a typical model compression method, and can transfer knowledge from the teacher network to the student network to improve the latter’s performance without any increase in the number of parameters. A method for extracting representative knowledge for distillation has become the core issue in this field. In this paper, we present a new knowledge distillation method based on intermediate correlation operation, which with the help of data augmentation captures the learning and transformation process of image features during each middle layer stage of the network. We model this feature transform procedure using a correlation operation to extract a new representation from the teacher network to guide the training of the student network. The experimental results demonstrate that our method achieves the best performance on both the CIFAR-10 and CIFAR-100 datasets, in comparison to previous state-of-the-art methods.
Text semantic matching is the basis of many natural language processing tasks. Text semantic matching techniques are required in many scenarios, such as search, question, and answer systems. In practical application scenarios, the efficiency of text semantic matching is crucial. Although the representational learning semantic-matching model is less accurate than the interactive model, it is more efficient. The key to improve the performance of learning-based semantic-matching models is to extract sentence vectors with high-level semantic features. On this basis, this paper presents the design of a feature-fusion module and feature-extraction module based on the ERINE model to obtain sentence vectors with multidimensional semantic features. Further, the performance of the model is improved to obtain semantic information by designing a loss function of semantic prediction. Finally, the accuracy on the Baidu Qianyan dataset reaches 0.851, which indicates good performance.
Instance segmentation is an important task in computer vision. In recent years, the development of meta- and few-shot learning has promoted the combination of computer vision learning tasks, which has overcome the bottleneck of detection and classification with regard to objects that are difficult to manually label and those with high labeling costs. Although great progress has been made with few-shot semantic segmentation and object detection, instance segmentation based on few-shot learning has not become a research hotspot until very recently. Beginning with an overview of few-shot instance segmentation, existing approaches are divided into categories of anchor-based and anchor-free algorithms. The architectures and primary technologies behind those approaches are respectively discussed, and common datasets and evaluation indices are described. Additionally, advantages and disadvantages of algorithm performance are analyzed, and future development directions and challenges are presented.
Vehicle stowage and route planning are common problems for various delivery methods related to supermarket distribution. In order to resolve these problems, we propose capacitated route planning for supermarket order distribution based on order splitting. We construct the problem model with the goal of minimizing the total cost of delivery. Combined with real cases, an improved gray wolf optimization algorithm adding a genetic mutation operation is proposed. The effectiveness of the model and algorithm is verified by comparing performance with the genetic algorithm. The results show that when the total demand of supermarkets is close to an integer multiple of the vehicle capacity, our proposed planning approach is better. This is mainly reflected in the fact that the order splitting plan can make full use of vehicle capacity, reduce the empty driving rate for vehicles, and reduce the total distribution cost.
Capacity prediction plays an important role in smart logistics, and its results are important for improving the accuracy of capacity scheduling and truck-cargo matching. Existing researches on capacity prediction in urban road networks aim to determine the number of available vehicles in future periods, while the problem of capacity prediction in bulk logistics aims at predicting the information on the trucks (e.g. the truck’s identity document (ID)) to carry certain types of goods for different flows, which is closely related to whether the trucks can return to the steel plant within the expected time (called capacity accessibility). In the case of bulk logistics, it is necessary to take into account the impact of the time spent on the two trips from the steel plant to the customer’s business and back to the steel plant. Since trucks need to stop several times in the long-distance transportation process but the length of stopping time varies, the uncertainty of stopping time makes the accurate prediction of transportation delivery time difficult. In addition, the freight platform only assigns capacity to one-way transport tasks (i.e. from the steel plant to the customer’s business), and the return trip (i.e. back to the steel plant) is determined by the truck drivers, which leads to the lack of return trajectory and poses a challenge to predict the return time of trucks to the steel plant. In order to solve the above challenges, based on the data sets of waybills, trucks, trajectories, and transport endpoints of logistics enterprises, we extract the stay behavior features, transport endpoint features, and environmental features. Then, the self-attention mechanism is introduced to obtain the weights of different features on the time consumption of two trips respectively to further improve the accuracy of capacity accessibility prediction. On this basis, a truck capacity prediction method based on self-attention mechanism is proposed, including capacity candidate set generation based on historical flow similarity, capacity accessibility prediction based on self-attention mechanism, and capacity carrier flow prediction based on long short-term memory (LSTM). Finally, the experimental results of comparison experiments on real logistics datasets show that the proposed method has higher prediction accuracy and can provide powerful decision support for the optimization of capacity scheduling in bulk logistics.
Trajectory data are typically large in scale and require frequent updates; hence, there are high performance requirements for trajectory queries. In order to improve the query efficiency of trajectory data, a two-level trajectory partition algorithm is presented herein. In the first partition, trajectory data was divided into sub-trajectories based on optimized minimum bounding rectangle (MBR) to improve the approximate effect of trajectory data. In the second partition, the sub-trajectories were grouped by the grid structure according to spatio-temporal characteristics. A packing method of R-tree was proposed based on the partition algorithm, and the divided trajectory data was packed into the R-tree from bottom to top. Finally, compared with a method based on the average number or average size of trajectory segments, experimental results show that the proposed method offers better query performance than the two other methods based on the average number of trajectory segments and combined movement features; in fact, the query efficiency is improved by 43% and 30.5%, respectively.
With digital transformation and the development of iron and steel logistics, the scale of iron and steel logistic data has rapidly expanded, and traditional relational databases can no longer meet the storage and query needs. Considering that a distributed not only structured query language (NoSQL) database has a simple expansion capability, fast reading and writing speeds, and low cost, in this study, distributed cloud storage and NoSQL technologies are used to store and build indexes for massive steel logistic data, improving the accuracy of the storage capacity and query performance of the logistic data. First, Spark is used to associate and fuse the data from different sources, and then store and manage the historical and real-time data generated by the freight platform in a hierarchical manner. It then builds spatiotemporal and attribute indexes for the three types of queries mainly involved in steel transportation to achieve an efficient query of multi-source logistic data. Finally, the experimental results based on real steel logistic data show that the proposed scheme is superior to traditional relational database methods in terms of data writing, storage, and querying, and can effectively support the storage and querying of massive logistic data.
A long short-term memory (LSTM)-based network employing a fault prediction algorithm and tree-structured network employing a minimal cost repair generation algorithm are proposed in this study to predict possible anomalies using a large amount of historical data for the effective identification of fault treatments. In addition, the minimal cost repair operation sequence was generated based on dynamic programming; the sequence of valid operation orders could be quickly generated. The results of this study indicate that the proposed networks could effectively reduce the dispatch error rate, improve the dispatch efficiency, and reduce the failure time of power grid systems, and therefore can be used to reduce the economic loss caused by the aforementioned factors.
As the core components of aircraft, engines play a vital role during flight. Accurate prediction of the remaining useful life of the aeroengine can help prognostics and health management, thus preventing major accidents and saving maintenance costs. In view of the lack of consideration of different time steps and the relationship between different sensors and operating conditions in existing methods, a remaining useful life prediction method based on the Transformer was proposed, which fuses multi-feature outputs from different encoder layers. This method selects two input data with different time steps, analyzes the relationship between the sensors using permutation entropy, and extracts features independently from the operating condition data. The experimental results on the public aeroengine dataset CMAPSS (Commercial Modular Aero-Propulsion System Simulation) show that the proposed method is superior to other advanced remaining useful life prediction methods.
There are rare $q $ -congruences on double series in the literature. In this paper, we present several $q $ -congruences involving double series. When $q $ tends to 1, the proposed approach provides the corresponding conclusions for congruences.
In this paper, de Moivre’s theorem for a matrix representation of a class of hyperbolic split quaternions is studied. Firstly, the study of hyperbolic split quaternions is transformed into the study of a matrix representation of hyperbolic split quaternions. Secondly, by using the polar representation of a hyperbolic split quaternion, the three forms of de Moivre’s theorem for a matrix representation of the hyperbolic split quaternion are obtained, and Euler’s formula is extended. Thirdly, the root-finding formula of the hyperbolic split quaternion matrix representation equation is obtained. Finally, the validity of the conclusions is verified with some examples.
In this paper, we explore the blow-up of solutions to a class of nonlocal porous medium systems with space-dependent coefficients and inner absorption terms under nonlinear boundary conditions in ${\mathbb{R}}^{n}\left(n \geqslant 3\right)$ . By constructing an energy expression and using the differential inequality technique, we obtain sufficient conditions for the global existence of solutions to the problem. Then, upper bound and lower bound estimates of the blow-up time are derived by means of the Sobolev inequalities and other differential methods when blow-up occurs.
In this paper, the blow-up problem of a parabolic equation with a nonlinear gradient term in finite time is studied. By constructing an auxiliary function, using the method of energy estimation and the differential inequality technique, the lower bound of blow-up time is obtained. After limiting the parameters of the equation, the existence of a global solution is proved.
This paper explores the existence of anti-periodic solutions for a class of nonlinear discrete dynamical systems with summable dichotomy. Using the Banach fixed-point theorem, sufficient conditions for the existence and uniqueness of anti-periodic solutions for nonlinear discrete dynamical systems are established. Lastly, an example is presented to illustrate the main results.
This paper establishes a coarse version of finite asymptotic property C-decomposition complexity in the context of coarse spaces. In particular, permanence properties of finite asymptotic property C-decomposition complexity are studied, and it is shown that finite coarse asymptotic property C-decomposition complexity implies coarse property A. In addition, the paper explores coarse property C and coarse decomposition complexity.
This paper aims to address the challenge of seeking an optimal safe path for a UAV (unmanned aerial vehicle) from an initial position to a target position, while avoiding all obstacles in a three-dimensional environment. An improved APF (artificial potential field) method combined with the regular hexagon guidance method is proposed to solve unreachable and local minimum problems near obstacles as observed with traditional artificial potential field methods. First, we add a distance correction factor to the repulsive potential field function to solve problems associated with unreachable targets. Then, a regular hexagon-guided method is proposed to improve the local minimum problem. This method can judge the environment when the UAV is trapped in a local minimum point or trap area and select the appropriate planning method to guide the UAV to escape from the local minimum area. Then, 3D modeling and simulation were carried out via Matlab, taking into account a variety of scenes involving complex obstacles. The results show that this method has good feasibility and effectiveness in real-time path planning of UAVs. Lastly, we demonstrate the performance of the proposed method in a real environment, and the experimental results show that the proposed method can effectively avoid obstacles and find the optimal path.
The concept of knowledge tracking involves tracking changes in a student’s knowledge level based on historical question records and other auxiliary information, and predicting the result of a student’s subsequent answer to a question. Since the performance of existing neural network knowledge tracking models needs to be improved, this paper proposes a deep residual network based on a stacked gated recurrent unit (GRU) network named the stacked-gated recurrent unit-residual (S-GRU-R) network. The proposed solution aims to address over-fitting caused by too many parameters in a long short-term memory (LSTM) network; hence, the solution uses a GRU instead of LSTM to learn information on the sequence of questions. The use of stacked GRU can expand sequence learning capacity, and the use of residual connections can reduce the difficulty of model training. Experiments on the Statics2011 data set were completed using S-GRU-R, and AUC (area under the curve) and F1-score were used as evaluation functions. The results showed that S-GRU-R surpassed other similar recurrent neural network models in these two indicators.
Distant supervision relation extraction captures noisy instances while reducing the burden of manual annotation, which hinders the training and testing process. To alleviate this problem, we proposed a de-noising method based on the influence function. The influence function measures the influence of each training point; the influence of one training point is defined as the change in test loss after removing the training point. We observed that this property could be used to determine whether a training instance involves noisy data. First, we designed a scoring function based on the influence function. Then, we integrated the scoring function into a bootstrapping framework to obtain the final denoising dataset from a small clean set. Using this preprocessing method, every distantly supervised dataset could be denoised by our method. Experimental results showed that the proposed denoised dataset can achieve good performance on a public dataset.
Adaptive learning is an educational method that uses computer algorithms to coordinate interaction with learners, and provides customized learning resources and learning activities to address the unique needs of each learner. With the impact of COVID-19, adaptive learning has become increasingly important. One of the challenges with adaptive learning is how to provide personalized learning resources for learners—i.e., how to generate personalized recommendation for learners from a large set of learning resources. Existing methodologies mainly generate recommendations based on a learner’s knowledge level; however, this approach has some limitations. Firstly, when assessing a learner’s knowledge level, learners’ forgetting phenomenon has to date not been well modeled. Secondly, recommendations are generated separately from knowledge tracing tasks, ignoring the interconnectedness between these aspects. In addition, learners’ preferences for the type of learning resources and learning strategies is normally ignored if the knowledge level alone is used. To solve the aforementioned problems, this paper proposes a knowledge and personality incorporated multi-task learning framework (KPM) to boost course recommendations (i.e., the above-mentioned learning resources); the proposed method regards an enhanced knowledge tracing task (EKTT) as an auxiliary task to assist the primary course recommendation task (CRT). Specifically, using EKTT, we design a personalized forgetting controller to enhance the deep knowledge tracing model for accurately assessing a learner’s knowledge level. With CRT, we combine the learner’s knowledge level and sequential behavior with their personality adapted to the specific context to obtain learner’s profile; this data is subsequently used to generate a course recommendation list. Experimental results on real-world educational datasets demonstrate the superiority of our proposed method in terms of hit ratio (HR), normalized discounted cumulative gain (NDCG), and precision, indicating that our method can generate more personalized recommendations.
The issue of how to unfold an irregular cylindrical mural from the top surface of a cave corridor into a panorama is a challenge for researchers involved with ancient mural protection and secondary development. This paper presents a method of dividing cylindrical murals into many overlapping small areas for sampling firstly, and then stitching these sampled images into a panorama. The constituents of this method include the following key elements: ① Reconstructing the 3D model with the sampled image set; ② Mapping the image texture to the 3D model; ③ Fitting the reconstructed irregular 3D cylindrical surface to the ideal cylindrical surface which is closest to the original form; and ④ Projecting the mural of the ideal cylindrical surface to a panorama. The method proposed in this paper was verified on an actual cave image set. The experimental results showed that the proposed method can generate the panorama in full; moreover, there was no evidence of stitching traces or texture deformation on the panorama. The proposed method offers practical value for mural protection.
In this research, we carried out a survey based on one fixed transect for Tettigonioidea and Grylloidea insects distributed in Tianmu Mountain from April to October of 2019. The results showed that there were 28 species of Tettigonioidea and 19 species of Grylloidea in Tianmu Mountain. Among them, six species of Tettigonioidea and eight species of Grylloidea were recorded in Tianmu Mountain for the first time. The insects became adults in August, September, and October. The insects distributed at lower altitudes tended to become adults earlier than those at higher altitudes. The number of species declined initially with increasing altitude, and subsequently increased. The Tettigonioidea species are distributed at various altitudes while Grylloidea species are primarily distributed at low altitudes. Because Truljalia tylacantha, Ruidocollaris truncatolobata, Goniogryllus punctatus and some other species are only distributed in a narrow scope at high altitudes, they can be used as indicator species for climate change in Tianmu Mountain.
Plagiochila is one of the genera with the largest number of species among liverworts. A study was conducted to understand the diversity and distribution of the Plagiochila genus in Anhui Province. Based on the results of the study, there were 23 species of Plagiochila found in the region, of which seven species and one subspecies were first observed in Anhui Province. Floristic analysis showed that tropical Asian elements were the most abundant, accounting for 47.83%, and East Asian elements were the second most abundant, accounting for 34.78%. In the future, the accuracy of bryophyte identification can be further improved; meanwhile, the scope and depth of future investigations should be reinforced.
In this work, micro-scale porous carboxyl polystyrene microspheres (PS) were prepared using seed polymerization. Quantum dots (QDs) were used as fluorescent molecules to synthesize QDs with different emission wavelengths and successfully loaded into porous microspheres to form fluorescent coding microspheres (QDs@PS). Subsequently, serum samples of patients with acute leukemia (AL) were detected, and the antigen in the serum was quantitatively analyzed using flow cytometry. Scanning electron microscope (SEM) and flow cytometry images showed that the microspheres were regular and uniform in size. Fluorescence microscopy showed that the QDs permeated uniformly into the whole microsphere. In addition, QDs@PS showed good fluorescence stability, no QD leakage was observed, and the QDs@PS maintained its fluorescence for a period of at least two weeks. The use of fluorescence spectroscopic analysis for the detection of human immunoglobulin G (IgG) showed that the carboxyl groups on the surface of fluorescent microspheres are beneficial for the efficient covalent binding of biological macromolecules, which can be used for sandwich immunosandwich reaction coupling with leukemic high expression antigen interleukin 6 (IL-6). Combined with serum samples from leukemia patients, the fluorescence of QDs was detected by flow cytometry, and the mean fluorescence intensity (MFI) was calculated to determine the content of IL-6 in the serum. These results indicate that the designed optically-encoded microcarrier can be successfully applied to high-throughput and multichannel biomolecular analysis and has great potential in blood disease detection and diagnosis.
Exploring the remediation effect of native plants on soil contaminated with heavy metals has significant value for real-world applications. In this study, two native plants—reed and metasequoia—were selected for remediation of heavy metal arsenic in the soil of a woodland in Shanghai, and changes in soil indexes before and after phytoremediation were monitored. The results showed that: ① The arsenic content in rhizosphere soil of Ph. australis and Metasequoia was 52.4% and 28.6% lower, respectively, than the arsenic content in non-rhizosphere soil. The arsenic content in non-rhizosphere soil, moreover, was lower than the screening value for soil environmental risk. ② After comparing the microecological characteristics of rhizosphere between reed and metasequoia, it was found that metasequoia had a better nutrient enrichment effect than reed, demonstrating that metasequoia would have a better restoration effect in terms of soil fertility.
A I-TiO2/Sr2MgSi2O7:Eu,Dy composite photocatalyst was prepared via hydrolysis for efficient degradation of organic pollutants in the absence of light. In this paper, the photocatalytic degradation of Rhodamine B (RhB) by the composite photocatalyst was studied. The results show that the degradation ability of I-TiO2/Sr2MgSi2O7:Eu,Dy composite photocatalyst with a I-TiO2 ratio of 30% is better, and the degradation efficiency of RhB pollutants reached 31.9% in 6 h without a light source. These results indicate that a Sr2MgSi2O7:Eu,Dy composite photocatalyst, supported by long afterglow phosphor, can absorb light energy and become a new light source in light-free or low-light environments for the photocatalytic reaction of I-TiO2 in order to achieve 24-hour catalytic purification.
With the present global warming scenario, the erosion of intertidal flats in estuarine zones often occurs due to rising sea levels and an increase in human activities. Intertidal flats have an important ecological function and economic value, including for carbon sequestration, preventing flooding, water purification, attenuating waves, and tourism development. Hence, it is of great theoretical and practical significance to study the stability of the wetland ecosystem for intertidal flats. Previous studies mainly focused on the stability of intertidal bare flats, while the stability of salt marsh ecosystems has attracted relatively less attention. The mechanisms of their respective influencing factors are, as of yet, poorly understood. In this study, we took a typical muddy intertidal zone of Chongming Dongtan in the Yangtze River Estuary as an example and made a comparative analysis on sediment stability for both the salt marsh zone and the adjacent bare flat using in-situ sampling and laboratory tests. The results indicate that: ① Sediment stability improves with an increase of clay content in the bare flat. ② Sediment stability in the salt marsh zone is significantly higher than that in the adjacent bare flat because of the “reinforcing” effect of the root system. ③ Underground biomass determines sediment stability for the same type of vegetation. The sediment becomes more stable with an increase of the underground biomass in vegetation. The sediment stability of different vegetation is determined by characteristics of the vegetation root system. The sediment stability of Spartina alterniflora vegetation zone with coarser roots was worse than that of Scirpus mariqueter with finer roots. Our results not only advance theoretical research on sediment stability in intertidal flats, but also provide scientific guidance for the construction of Green Sea Defence and other coastal green protection measures.