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C-T Net: Remote sensing image change detection model integrating CNN and Transformer Yi WU, Shilin YUN J* E* C* N* U* N* S*    2025, 2025 (4): 49-60.   DOI: 10.3969/j.issn.1000-5641.2025.04.005 Abstract （1082）   HTML （16）    PDF（pc） （4156KB）（542）       Knowledge map    Save Due to factors such as differences in acquisition time, angle, and sensor characteristics, dual temporal remote sensing images often manifest various pseudo-changes. Moreover, certain changes may have an uninteresting nature and typically correlate with adjacent objects. However, the utilization of a fully convolutional neural network (FCN) may lead to the loss of long-range information. To address this issue, this study proposes a network that integrates convolutional neural networks (CNN) and Transformer (C-T Net), which has an overall network architecture consisting of a deep feature extraction section and a detection head section. The network backbone combines CNN and Swin Transformer. Additionally, two novel fusion modules, C-to-T and T-to-C, are designed to amalgamate local features and global features. The detection head section utilizes Transformer encoding and decoding to derive refined feature maps for discerning change regions. Comparative experiments with multiple change detection models validate the efficacy of C-T Net. On the LEVIR-CD and WHU-CD datasets, the proposed method achieves the highest F1_1 (90.63%, 86.24%) and $ {p}_{\mathrm{I}\mathrm{o}\mathrm{U}} \_1 $(82.87%, 75.81%). Results across both datasets affirm that our proposed algorithm outperforms existing methodologies from both visual and data-centric perspectives. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis of the status, hotspots, and trends of open-source innovation: A bibliometric study based on CNKI literature from 2005 to 2024 Rui WANG, Qiuyue LYU, Jia LIAO J* E* C* N* U* N* S*    2025, 2025 (5): 125-139.   DOI: 10.3969/j.issn.1000-5641.2025.05.012 Abstract （1044）   HTML （4）    PDF（pc） （1646KB）（371）       Knowledge map    Save This study systematically analyzes the evolutionary characteristics and research hotspots of open- source innovation in China. A dataset comprising 732 valid journal articles, with “open-source” in the title, was retrieved from the China National Knowledge Infrastructure (CNKI) for the period 2005–2024. A bibliometric approach was employed to examine such dimensions as annual publication volume, disciplinary distribution, keyword co-occurrence and clustering, burst keywords, and timeline evolution. The results indicate that research in this field has progressed through three stages, initial exploration, steady development, and rapid growth, with a significant surge in publications over the past five years. Disciplinary distribution analysis reveals a multidisciplinary landscape centered on library and information science, computer science, and industrial technology, which extends to fields such as education, management, and law. Keyword clustering analysis identifies nine core research areas, accompanied by a review of the representative literature within each cluster. Timeline evolution analysis suggests that future research will likely focus on the deep integration of artificial intelligence (AI) and open-source ecosystems, the evolution of collaboration and governance models in open-source communities, open-source software security and supply chain risk identification, and open-source law and intellectual property protection. On the basis of these findings, we propose several recommendations to foster the sustainable development of open-source innovation in China, including strengthening the synergistic governance of AI and open-source ecosystems, enhancing supply chain security systems, advancing innovations in legal and licensing frameworks, and constructing a digital open-source infrastructure oriented toward industrial and public services. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on the GitHub developer geographic location prediction method based on multi-dimensional feature fusion Sijia ZHAO, Fanyu HAN, Wei WANG J* E* C* N* U* N* S*    2025, 2025 (5): 1-13.   DOI: 10.3969/j.issn.1000-5641.2025.05.001 Abstract （999）   HTML （22）    PDF（pc） （1118KB）（93）       Knowledge map    Save The geographic location information of developers is important for understanding the global distribution of open source activities and formulating regional policies. However, a substantial number of developer accounts on the GitHub platform lack geographic location information, limiting the comprehensive analysis of the geographic distribution of the global open source ecosystem. This study proposed a hierarchical geographic location prediction framework based on multidimensional feature fusion. By integrating three major categories of multidimensional features—temporal behavior, linguistic culture, and network characteristics—the framework established a four-tier progressive prediction mechanism consisting of rule-driven rapid positioning, name cultural inference, time zone cross-validation, and a deep learning ensemble. Experiments conducted on a large-scale dataset built from 50000 globally active developers demonstrated that this method successfully predicted the geographic locations of 82.52% of the developers. Among these, the name cultural inference layer covered most users with an accuracy of 0.7629, whereas the deep learning ensemble layer handled the most complex cases with an accuracy of 0.7557. A comparative analysis with the prediction results from the Moonshot large language model validated the superiority of the proposed method in complex geographic inference tasks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Study on bacterial community structure in the intestine of Litopenaeus vannamei and its cultivation environment Qin JIN, Chuwen QIU, Xincheng YUAN J* E* C* N* U* N* S*    2025, 2025 (4): 104-113.   DOI: 10.3969/j.issn.1000-5641.2025.04.011 Abstract （750）   HTML （5）    PDF（pc） （2599KB）（775）       Knowledge map    Save The aim of this study was to compare the bacterial community structure of Litopenaeus vannamei in different cultivation environments. An Illumina MiSeq high-throughput sequencing-based method was used for detecting the 16S rRNA gene of the bacterial community in the intestine of L. vannamei and water and sediment samples from cultivation ponds. The results showed that the bacterial community in the intestine of L. vannamei and in the cultivation environment included 62 phyla, 175 classes, 381 orders, 631 families, 1141 genera, and 2035 species. Proteobacteria, Actinobacteriota, and Chloroflexi were the dominant bacterial phyla in the intestine of L. vannamei, with average percentages of 33.67%, 25.33%, and 12.77%, respectively. Proteobacteria, Chloroflexi, and Firmicutes were the dominant bacteria in the sediments, accounting for 28.33%, 17.33%, and 11.13%, respectively. The dominant bacteria in the water were Actinobacteria, Cyanobacteria, and Proteobacteria, with average percentages of 29.33%, 27.0%, and 21.33%, respectively. The abundance-based coverage estimator (ACE) and Chao diversity index of bacterial community in the intestine of L. vannamei were higher than those in the water, while lower than those in the sediments. The bacterial community richness in the intestine of L. vannamei was similar to that in water, but higher than that in the sediments. The Shannon index of the bacterial community in the intestine of L. vannamei was higher than that in the sediments, but lower than that in water. The Simpson index of the bacterial community in the intestine of L. vannamei was higher than that in the sediments but lower than that in the water. Nevertheless, some dominant bacterial species were similar in the intestine of L. vannamei and in the cultivation environment. At the operational taxonomic unit (OTU) level, 1492 identical units were detected in the intestines of L. vannamei and sediments, and 588 identical units were detected in the intestine of L. vannamei and water. Both cluster and principal coordinate analyses showed that the bacterial community in the intestine of L. vannamei was relatively similar to that in the sediments. This study explored the relationship between the bacterial community in the intestine of L. vannamei and its cultivation environment, providing valuable data for the scientific use of environmental probiotics, quality and production, and disease and epidemic prevention. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Interactive data structure and algorithm visualization based on AI agents Ruiyang PANG, Xuesong LU J* E* C* N* U* N* S*    2025, 2025 (5): 32-42.   DOI: 10.3969/j.issn.1000-5641.2025.05.004 Abstract （722）   HTML （14）    PDF（pc） （1065KB）（465）       Knowledge map    Save Data structures and algorithms (DSA), as a core course in computer science education, play a key role in cultivating programming skills and algorithmic thinking of students. Visualization can significantly enhance teaching effectiveness and deepen student understanding in DSA education. However, existing DSA visualization tools often rely on manually written visualization codes that lead to limitations such as limited coverage, high maintenance costs, and lack of interactivity; hence, the needs of dynamic demonstrations and personalized teaching are difficult to meet. With the outstanding performance of large language models (LLMs) in code generation, automated DSA visualization has become a promising possibility. Therefore, this study proposed an interactive visualization code generation method based on the reasoning and acting (ReAct) AI agent framework, aiming to address the low automation and insufficient interactivity of traditional visualization tools. By leveraging the code generation capabilities of LLMs and integrating with the data structure visualization (DSV) platform interface, the proposed method transformed Python-based DSA code into interactive, executable, and dynamically visualized code, thereby enhancing teaching clarity and learning experience. To systematically evaluate the effectiveness of the method, we constructed a dataset of 150 pairs of DSA code and corresponding DSV visualization code and compared three approaches—direct prompting, chain-of-thought prompting, and the ReAct AI agent approach—across several mainstream LLMs. The experimental results showed that the proposed ReAct AI agent-based method significantly outperformed the other approaches in terms of the compilation rate, execution rate, and usability rate, with the best performance observed in the DeepSeek-R1 model. This demonstrated notable improvements in the accuracy and interactivity of generated visualization code. This research confirms the feasibility and advantages of integrating LLMs with agent frameworks in DSA visualization teaching, offering a novel path toward building efficient, personalized, and automated tools for computer programming education. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Open source evaluatology: A framework and methodology for evaluating open source ecosystems based on evaluatology Shengyu ZHAO, Wei WANG, Fanyu HAN, Jiaheng PENG, Lan YOU J* E* C* N* U* N* S*    2025, 2025 (5): 151-161.   DOI: 10.3969/j.issn.1000-5641.2025.05.014 Abstract （692）   HTML （7）    PDF（pc） （670KB）（408）       Knowledge map    Save The open source ecosystem, as a critical component of the modern software industry, has garnered increasing attention from both academia and industry regarding its evaluation challenges. However, existing evaluation methods face issues such as inconsistent evaluation standards, lack of theoretical grounding, and poor comparability of evaluation results. Guided by foundational theories of evaluatology, this study introduced a novel interdisciplinary research domain, open source evaluatology, for the first time. It established a theoretical framework and methodological system for evaluating the open source ecosystem. The primary contributions of this paper include the following. Developing the theoretical foundation of open source evaluatology based on the five axioms of evaluatology and defining fundamental concepts, evaluation dimensions, and standards for open source ecosystem evaluation. Designing an evaluation conditions framework comprising five levels: problem definition, task instances, algorithm mechanisms, implementation examples, and supporting systems. A hybrid evaluation model combining statistical and network metrics was proposed. Based on the experiments conducted using the GitHub dataset, this study validated the proposed method from three dimensions: open source repositories, developers, and communities. The results demonstrated that the proposed evaluation model exhibited strong applicability and explanatory power in open source scenarios. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Synergy between large language models and open source ecosystems in AI education Lijun XU, Li YANG, Ziyi HUANG J* E* C* N* U* N* S*    2025, 2025 (5): 66-75.   DOI: 10.3969/j.issn.1000-5641.2025.05.007 Abstract （637）   HTML （9）    PDF（pc） （1061KB）（79）       Knowledge map    Save To address the challenges of outdated teaching resources, insufficient practical skills, and a lack of value-oriented guidance in education, this study constructs an innovative pedagogical model driven by the dual-engine of large language model (LLM) and open source ecosystem. The model is designed to bridge the gap between theoretical knowledge and real-world engineering practice by integrating open-source tools, dynamic code repositories, and authentic project scenarios into the curriculum. Meanwhile, LLMs are employed as intelligent teaching assistants to enable personalized learning paths, generate automated feedback, and support immersive ideological and ethical modules. This research was implemented in the course “Artificial intelligence and its applications”, where a mixed-method evaluation was conducted. Quantitative metrics such as attendance, interaction frequency, repository contributions, and assignment performance were used to measure student engagement and learning effectiveness. Additionally, a set of custom-designed assessment formulas was used to evaluate cross-platform transferability and community participation. Experimental results from 90 undergraduate students showed that learners engaged in open-source collaboration and LLM-assisted learning achieved significantly higher scores in both technical proficiency and value cognition than those in the control group. The study demonstrates that the integration of LLMs and open-source collaboration can effectively enhance student autonomy, promote engineering skills, and reinforce ethical awareness. This dual-driven model not only offers a feasible approach for modernizing AI education but also contributes to the broader goal of cultivating socially responsible and technically competent AI talents. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on challenges and optimization of large multimodal model applications in treefall scenarios Lei FENG, Chaonan LI, Chunjie SHENG, Yuxing SHI, Yicheng HUANG, Jianhong JIN, Yun XU, Yuzhou DU, Nina ZHOU, Sihao MIAO J* E* C* N* U* N* S*    2025, 2025 (5): 53-65.   DOI: 10.3969/j.issn.1000-5641.2025.05.006 Abstract （608）   HTML （11）    PDF（pc） （1399KB）（618）       Knowledge map    Save To address the limited robustness of large multimodal models (LMMs) in complex visual scenarios, such as identifying responsibility for fallen trees, which emanates from their reliance on single-path reasoning. This study proposes a novel reasoning optimization method based on Beam Search Chain-of-Thought (BS-CoT). Conventional models often fall into a “first-impression” trap, in which an initial incorrect inference leads to an irreversible analytical failure. The proposed BS-CoT method counteracts this by exploring and evaluating multiple potential inference paths in parallel. It maintains a diverse set of hypotheses about the scene, continuously pruning less likely hypotheses, which effectively overcomes the tendency to commit to a single, fallacious line of reasoning. This significantly enhances visual decision-making capabilities in complex and noisy environments. To validate its efficacy, we constructed a specialized dataset capturing a wide array of treefall incidents in urban governance. Experimental results demonstrated that the proposed method achieved substantial improvements in both event recall and key information capture rates compared with baseline models. This research not only provides a reliable technical solution for visual decision-making challenges in urban public safety but also introduces a new, more robust paradigm for improving the reasoning reliability of large models in critical applications. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A high robust fast video steganography algorithm in lossy channels Zheng HU, Chang HUANG J* E* C* N* U* N* S*    2025, 2025 (4): 38-48.   DOI: 10.3969/j.issn.1000-5641.2025.04.004 Abstract （596）   HTML （3）    PDF（pc） （1438KB）（40）       Knowledge map    Save High-definition videos lose hidden information when compressed by lossy channels during transmission. Concurrently, efficient video steganography algorithms require hardware implementation to achieve low-power, high-speed, and reliable real-time processing. To ensure real-time and reliable transmission of secret data, the steganography algorithm requires low complexity and high robustness. Therefore, this study proposes an efficient and robust steganography algorithm for hardware platform implementations. The algorithm employs the generation principle and distribution characteristics of DC (Direct Current) coefficients to modify the pixel values directly in the spatial domain rather than in the discrete cosine transform domain. Therefore, the proposed algorithm exhibits both the simplicity of the spatial-domain steganography algorithm and the high robustness of the transform-domain steganography algorithm. Experimental results show that the proposed algorithm has low computational complexity and exhibits excellent robustness and visual quality in local lossy channel compression. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Application and evaluation of large language models in open source project topic annotation Dexin HE, Fanyu HAN, Wei WANG J* E* C* N* U* N* S*    2025, 2025 (5): 14-24.   DOI: 10.3969/j.issn.1000-5641.2025.05.002 Abstract （593）   HTML （11）    PDF（pc） （795KB）（66）       Knowledge map    Save With the rapid development of open source communities, the number of GitHub projects has increased exponentially. However, a considerable portion of these projects lack explicit topic labels, creating challenges for developers in technology selection and project retrieval processes. Existing topic generation methods rely primarily on supervised learning paradigms that suffer from strong dependencies on high-quality annotated data and other limitations. This study addresses the accuracy and efficiency issues in open source community project topic annotation by conducting the first comprehensive study on the application effectiveness of large language models in GitHub project topic prediction tasks. We constructed a dataset containing 3000 popular GitHub projects that were selected based on a quantitative metric specifically designed to evaluate the activity and influence of open source projects, encompassing multidimensional features including repository names, README documents, and description information. Comparative experiments were conducted using several mainstream large language models from domestic and international sources including Claude 3.7 Sonnet, DeepSeek-V3, Gemini 2.0 Flash, GPT-4o, and Qwen-Plus. The results demonstrated that Claude 3.7 Sonnet achieved optimal performance across most evaluation metrics, and as the dataset scale expanded, the performances of all models tended to stabilize. The experiments proved that large language models exhibited excellent applicability in project topic annotation tasks, although significant performance differences existed among different models. These findings provide an important reference foundation for open source community project management and intelligent annotation system design. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis of erosion–deposition processes and mechanisms during flood–ebb and spring–neap tides over the mouth bar shoal on the northern side of Chongming Dongtan Zijie TAN, Shujie NIU, Maotian LI, Weihua LI, Xiaoqiang LIU, Yan SONG, Huikun YAO, Wenyan ZHANG, Dan PENG, Xinjie CHEN J* E* C* N* U* N* S*    2025, 2025 (4): 147-157.   DOI: 10.3969/j.issn.1000-5641.2025.04.015 Abstract （573）   HTML （18）    PDF（pc） （1528KB）（112）       Knowledge map    Save The erosion-deposition processes of estuarine bars are the basis of estuarine geomorphology and channel evolution. Most studies revealed the annual or seasonal change in deposition-erosion of estuarine bars based on chart data and sedimentary dating. However, understanding of erosion-deposition changes during the flood-ebb tide and spring-neap tide cycles is insufficient. This study conducted high-resolution in-situ observations of hydrodynamics and bedform changes on the tidal flat front of northern Chongming Dongtan, using bottom-mounted tripods over 15 flood–ebb tide cycles and one spring–neap tide cycle. The study found increases and decreases in velocity in four erosion-deposition phases over one flood-ebb cycle: flood tide erosion, flood tide deposition, ebb tide erosion, and ebb tide deposition. In addition, the flood flow originating from offshore shoals caused a high suspended sediment peak (SSP), which settled rapidly and formed the major silting layer after flood slack. The ebb flow caused another SSP and formed the major scouring layer due to long-term scouring by high velocity flow during the rapid ebb flow stage. From neap tide to spring tide, the bed exhibited a scouring trend due to the generally low suspended sediment concentration (SSC) of flood flow from offshore shoals. However, from spring tide to neap tide, the bottom showed a depositional trend due to a high and increasing SSC of flood flow from shoals. The above findings provide a reference for further understanding of estuarine erosion-deposition and evolution processes. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on video question answer for the development of theory of mind Yuanyuan MAO, Xin LIN, Qin NI, Ciping DENG, Yiming MA J* E* C* N* U* N* S*    2025, 2025 (6): 46-52.   DOI: 10.3969/j.issn.1000-5641.2025.06.006 Abstract （570）   HTML （5）    PDF（pc） （664KB）（130）       Knowledge map    Save In recent years, with the continuous development of machine theory of mind (ToM), research has found that the development of machine ToM differs significantly from the triangular model of children’s ToM development. Consequently, we propose a machine-oriented theory of mind triangular model. This model elucidates the relationships among various tools in the process of developing machine ToM. Additionally, we introduce an evaluation dataset suitable for the dynamic assessment of machine ToM. Finally, this paper designs a VideoQA(video question answer) model, named FOMemNet (fact and observer memory network), specifically tailored for cognitive reasoning—a model addressing belief, desire, and intention reasoning. Considering that models in cognitive reasoning tasks need to infer from the observer’s perspective, we incorporate the FOEM (vision fact and observer perception encoder module) module in FOMemNet for the fusion of multimodal features, thereby obtaining visual factual features and observer features. Subsequently, the model utilizes the FOF (fact and observer fusion) module and two memory modules to integrate features from both perspectives for obtaining a global representation. FOMemNet results in a 2.27% improvement of BDIQA. Our experiments demonstrate the effectiveness of the concept of fact and observer perception in enhancing cognitive reasoning abilities in VideoQA. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis of the effectiveness of small-scale habitat construction for wild animals in Shanghai: A case study of a bird habitat in Wusong River Yunlu QIAO, Jiayi Wang, Hongwei WANG, Weiyu YU, Yiyun YUAN J* E* C* N* U* N* S*    2025, 2025 (4): 84-92.   DOI: 10.3969/j.issn.1000-5641.2025.04.009 Abstract （564）   HTML （9）    PDF（pc） （661KB）（72）       Knowledge map    Save This study used the renovated Wusong River bird habitat in Shanghai to explore the effectiveness of constructing small-scale habitats for wild animals in Shanghai. The diversity and seasonal dynamics of birds in the Wusong River habitat for birds were analyzed using line transect method and direct counting methods. Fifty-four bird species in 10 orders and 29 families were recorded, including 11 species of waterfowl, 38 species of finches, and one species of bird protected at national level Ⅱ. Among the various residence types of birds, 28(51.9%), 15(27.8%), 6(11.1%), and 5(9.3%) species of resident, wintering, summering, and traveling birds, respectively, were recorded. Among the various bird lineages, 23(42.6%) and 17(31.5%) were found at the Palearctic and Oriental boundaries, respectively, while 14(25.9%) species were widespread. The dominant species were the Chinese blackbird, light-vented bulbul, spotted dove, [tree] sparrow, Japanese white-eye, silky starling, yellow-throated bunting, and long-tailed shrike. There was a low population of both summer residents and travelers in the habitat. The bird diversity index was highest in autumn (migration period), followed by winter (overwintering period), spring (migration period), and summer (breeding period), whereas the evenness index was highest in autumn (migration period), followed by spring (migration period), winter (overwintering period), and summer (breeding period). The results confirmed the effectiveness of constructing small-scale habitats for wild animals in Shanghai, and recommendations for the construction and management of such habitats are proposed. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Effects of soil conditioners on soil properties and vegetable growth in an open-pit coal mine dump Xiaoliang JIAO, Xinxin YU, Heyi GONG, Kun SONG, Liangjun DA J* E* C* N* U* N* S*    2025, 2025 (6): 106-115.   DOI: 10.3969/j.issn.1000-5641.2025.06.012 Abstract （559）   HTML （6）    PDF（pc） （760KB）（537）       Knowledge map    Save This study addresses the challenge of water and nutrient deficiencies in the planted soil of spoil dumps in open-pit coal mines by investigating the effectiveness of soil amendments containing a water-retaining agent, organic fertilizer, and microbial inoculant in enhancing soil properties and promoting plant growth. A pot experiment was conducted using Brassica rapa var. chinensis as the model plant to evaluate the effects of different amendment dosages. Key soil parameters were analyzed, including pH, electrical conductivity, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and organic matter content. Plant growth indicators such as plant height, leaf number, and dry biomass were measured. The findings indicated that applying the water-retaining agent and organic fertilizer significantly improved soil properties and promoted plant growth, whereas the microbial inoculant did not produce statistically significant effects. The optimal combined effect of the amendments on soil properties and plant growth was achieved at a water-retaining agent dosage of 5‰ and an organic fertilizer dosage of 30%. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Dynamics model in two-layer networks and case study on generative artificial intelligence bias cognition propagation Hongmiao ZHU, Xiaodong ZHAO, Huimin ZHOU, Jiayin QI J* E* C* N* U* N* S*    2025, 2025 (5): 191-201.   DOI: 10.3969/j.issn.1000-5641.2025.05.018 Abstract （549）   HTML （10）    PDF（pc） （1263KB）（1624）       Knowledge map    Save This study developed a model to understand the communication dynamics of generative artificial intelligence (GAI) bias cognition within a two-layer network comprising enterprise managers and ordinary employees. The model integrated the effects of communication between different levels and the impact of cognitive training. Using the next-generation matrix method, the study accurately calculated the propagation threshold, R0, that served as a crucial quantitative foundation for effective governance. Specifically, when R0<1, deviant cognition tended to disappear spontaneously, whereas when R0>1, a risk of biased cognition spreading existed. Additionally, the study compared and evaluated two intervention strategies through numerical simulations, providing a comprehensive analysis of the mechanisms that drove the generation and dissemination of deviant cognition within enterprises, supported by case studies. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Parallax image mosaic based on matching points and thin plate splines function model optimization Jiefang CHEN, Chang HUANG J* E* C* N* U* N* S*    2025, 2025 (4): 15-27.   DOI: 10.3969/j.issn.1000-5641.2025.04.002 Abstract （543）   HTML （10）    PDF（pc） （7373KB）（53）       Knowledge map    Save To solve the redundancy of matching points and local distortion of image after mosaic in intelligent driving, a parallax image mosaic algorithm based on optimization of matching points and improvement of thin plate spline function model is proposed. First, a sparse matrix is constructed according to the distribution positions of image matching points. Second, the number of mesh constrained matching points is used to eliminate redundant matching points, which reduces the time of calculating thin plate spline function model. Finally, an improved thin plate spline function model is used for image registration. The experimental results indicate that the proposed algorithm eliminates the redundancy of matching points and improves the image distortion problem, which has certain superiority and effectiveness. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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OSS Insight: A platform for open source ecosystem spatiotemporal data analysis and insights Xiaowei CHEN, Wei WANG, Fanyu HAN, Guanglei BAO, Fei DONG, Hao HUO, Chen LIU J* E* C* N* U* N* S*    2025, 2025 (5): 170-182.   DOI: 10.3969/j.issn.1000-5641.2025.05.016 Abstract （541）   HTML （3）    PDF（pc） （1447KB）（533）       Knowledge map    Save An open source ecosystem abounds with valuable data, yet extracting insights requires innovative data infrastructure and analytical methods. To address this, OSS Insight was developed that innovatively used the hybrid transactional analytical processing(HTAP) database for efficient storage and query of billions of GitHub event data and offered real-time exploration via a visual interface. It delved into spatiotemporal data analysis, modeling developer behaviors and ecosystem evolution, such as visualizing global contribution patterns. Integrated with large language models(LLMs), it enabled natural language to structured query language(SQL) conversion for intelligent querying. A case study of Kubernetes showcased its capabilities in analyzing developers, project evolution, and organizational collaboration. Experiments proved that OSS Insight efficiently analyzed large-scale open source data, and its LLM-driven interaction simplified data analysis and provided automated insights. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Effects of decomposed sheep manure combined with chemical fertilizer on the soil fertility and yield of highland barley fields Guoying SONG, Zhuoma BIANBA, Guoyi LIU J* E* C* N* U* N* S*    2025, 2025 (4): 77-83.   DOI: 10.3969/j.issn.1000-5641.2025.04.008 Abstract （537）   HTML （2）    PDF（pc） （591KB）（558）       Knowledge map    Save To study the effects of the combined application of decomposed sheep manure organic fertilizer and chemical fertilizer on the soil fertility and yield of highland barley fields (currently treated with conventional fertilization for highland barley planting in the Lhasa agricultural area), four different treatments were prepared for the field experiment by increasing the application of potassium chloride and decomposed sheep manure, and reducing the amount of urea and diammonium phosphate (DAP). The treatments follow: T1 conventional fertilization (sheep manure 5250 kg/hm2+ urea 180 kg/hm2 + DAP 180 kg/hm2), T2 (sheep manure 5250 kg/hm2 + urea 150 kg/hm2+ DAP 120 kg/hm2 + potassium chloride 30 kg/hm2), T3 (sheep manure 10500 kg/hm2 + urea 150 kg/hm2 + DAP 120 kg/hm2 + potassium chloride 30 kg/hm2), and T4 (sheep manure 15750 kg/hm2 + urea 135 kg/hm2 + DAP 75 kg/hm2 + potassium chloride 30 kg/hm2). The results showed that soil organic matter, total nitrogen, and total potassium decreased after harvesting compared with the situation before planting according to different fertilization modes. The available nitrogen, phosphorus, potassium, and pH of T3 increased slightly after harvesting compared with their levels before planting. Total and available phosphorus in T1 also increased after harvesting compared with their levels before planting. The height and biomass of highland barley were higher in the mature stage after applications of T1 and T4 treatments. With an increase in the proportion of decomposed sheep manure instead of chemical fertilizer, the yield of highland barley initially increased and then decreased. When the fertilizer substitution rate for T3 reached 16.67%, the yield of highland barley reached 5323.65 kg/hm2. Study findings indicate that the combined application of decomposed sheep manure and chemical fertilizer is beneficial for improving the fertilizer supply capacity of soil and increasing the yield of highland barley. This study provides a reference for rational fertilization and the reduction of non-point source pollution in highland barley fields in Tibet. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Evaluation of biochar-desulfurized gypsum mixed application on saline-alkali soil improvement and N2O emission reduction in coastal areas Ziyan CHEN, Rongrong YANG, Yi WU, Lijun HOU, Xia LIANG J* E* C* N* U* N* S*    2025, 2025 (4): 134-146.   DOI: 10.3969/j.issn.1000-5641.2025.04.014 Abstract （537）   HTML （6）    PDF（pc） （923KB）（335）       Knowledge map    Save The effects of biochar and desulfurized gypsum (FGD) on the improvement of coastal saline-alkaline soils and the emission of nitrous oxide (N2O) from soils were quantitatively evaluated using different application ratios of biochar and FGD under laboratory conditions and combined with stable isotope and molecular biology methods. The results showed that compared with the treatment with biochar or FGD gypsum alone, soil exchangeable eodium saturation percentage (ESP) decreased significantly to 11.6% ~ 13.0% whereas soil organic carbon content increased by about 34 times after the application of the mixture. Compared with the control and the individual applications, cumulative soil N2O emission in the mixture treatment decreased by about 127.7%. This is mainly due to the fact that the mixture application of biochar and FGD gypsum can significantly increase the nitrification and denitrification potential of soil as well as the consumption of soil N2O, thereby effectively reducing the soil N2O emission. The results of this study provide data support and a theoretical basis for the development of coastal saline soil improvement technology and the management of soil greenhouse gas emissions. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Carbon emissions accounting and carbon emissions reduction benefits of sponge city construction based on life cycle assessment Xingyan BAO, Ruihui CHENG, Sheng XIE, Zhaokang WU, Haiyan KUAI, Boxiao ZHANG, Bowen LYU, Kai YANG J* E* C* N* U* N* S*    2025, 2025 (6): 94-105.   DOI: 10.3969/j.issn.1000-5641.2025.06.011 Abstract （534）   HTML （7）    PDF（pc） （1503KB）（538）       Knowledge map    Save In response to the new requirements for systematic sponge city development under China’s “carbon peaking and carbon neutrality” strategy, scientifically evaluating the carbon emissions and carbon emission reduction benefits of sponge city construction holds significant theoretical and practical value. This study took Wuhu, a national sponge demonstration city, as an example. Taking on the perspective of the whole life cycle, and combining the emission factor method and the Technical Guidelines for Carbon Emission Accounting for Sponge City Construction in Anhui Province, this study utilized the carbon emission accounting method for sponge cities that is applicable to engineering in practice, and evaluated the carbon emission and emissions reduction results of four types of typical sponge projects in 2022. Based on this, the study took a residential community as a representative example and employed the NSGA-Ⅱ algorithm to explore strategies for achieving synergistic carbon emissions reduction by optimizing the configuration of multiple types of sponge facilities. The results indicate the following. (1) The carbon emissions from sponge city projects in Wuhu are primarily concentrated in the construction phase, with total emissions of 11438.6 t. Among these, material production and transportation account for 53% and 36%, respectively, indicating considerable potential for emissions reduction. (2) During the operational phase, sponge cities largely rely on sustained natural processes to function, with the carbon reduction effects being relatively concentrated in this stage. The total carbon emissions during this phase are approximately –242.3 t. Assuming that existing operational conditions are maintained without any new facilities, sponge facilities are expected to achieve a cumulative carbon reduction of 7269.7 t in 30 years. Although the annual carbon reduction is relatively limited, long-term operation can gradually offset carbon emissions from the construction phase, demonstrating strong carbon neutrality potential. (3) In terms of specific facility types, green stormwater infrastructure such as grassed swales (4.95 kg/m2) and sunken green spaces (11.35 kg/m2) exhibit relatively low carbon emissions intensities during the construction phase. The carbon reduction benefits of sponge facilities in the operation stage are significantly influenced by their functional characteristics and the scale of implementation. (4) Taking a residential community as an example, and based on the annual total runoff control rate requirement, the coordinated carbon reduction capacity of sponge facilities can be enhanced by reasonably adjusting the scales of sunken green spaces, permeable pavements, rain gardens, and grassed swales. This study provides a quantitative evaluation of multi-facility sponge city systems from a holistic perspective, offering methodological support and a theoretical reference for the development of low-carbon urban drainage systems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）