Science, Engineering and Technology Proceedings
An international, peer-reviewed open-access platform dedicated to publishing high-quality conference proceedings that explore the intersections of science, engineering, and technology, and their transformative impact on societal advancement. The series aims to provide a comprehensive forum for researchers, scholars, and practitioners to share their latest findings, foster interdisciplinary collaboration, and address global challenges. Each submission undergoes a rigorous peer-review process to ensure the publication of relevant, impactful, and timely content that contributes to advancing scientific knowledge, engineering practices, and technological innovations for the betterment of society.
Contemporary Research on Interdisciplinary Professional Development Methods
Contemporary professional development increasingly demands interdisciplinary approaches that transcend traditional sectoral boundaries. This paper examines emerging methods and frameworks for enhancing professional competencies across diverse fields, with particular emphasis on technology-driven efficiency, project management innovation, and post-pandemic professional adaptation. The research investigates how digital transformation initiatives, collaborative learning environments, and competency-based training models contribute to workforce advancement in rapidly evolving industries. Through systematic analysis of contemporary practices, this study identifies critical success factors including technological integration, cross-functional collaboration, and adaptive learning strategies. The findings reveal that effective professional development programs incorporate blended learning modalities, real-time performance analytics, and industry-specific customization while maintaining transferable core competencies. This research contributes to understanding how organizations can design and implement sustainable professional development systems that respond to changing market demands, technological disruptions, and evolving workforce expectations. The implications extend to policy formulation, organizational strategy, and educational curriculum design for professional training programs.
Copyright (c) 2025 Ming Gao, Jie Huang, Rui Qian (Author) https://creativecommons.org/licenses/by/4.0
Application of Chemical Stabilizers to Sustain Urease Inhibition in Soil–Plant Systems
Nitrogen fertilizers play a crucial role in enhancing crop yield and quality in agricultural production. However, urea, as a major nitrogen source, is rapidly hydrolyzed by urease in soil, leading to nitrogen loss through ammonia volatilization or nitrate leaching. This not only reduces fertilizer use efficiency but also increases environmental pollution. Urease inhibitors can delay urea decomposition and improve nitrogen utilization, but their activity in soil is often rapidly diminished due to temperature, moisture, and microbial activity, limiting their long-term application. The introduction of chemical stabilizers provides a new strategy to prolong the effective period of urease inhibitors. In this study, urea and urease inhibitors were applied in a simulated soil–plant system, with the addition of different types of chemical stabilizers. The effects on urease activity, soil nitrogen dynamics, and plant growth and nitrogen uptake were systematically evaluated. The results showed that chemical stabilizers significantly slowed the degradation of urease inhibitors, maintaining low soil urease activity for an extended period. Correspondingly, ammonia volatilization was markedly reduced, and nitrate retention increased, indicating effective control of soil nitrogen loss. Meanwhile, plant nitrogen uptake and growth performance were improved, demonstrating that stabilizers can not only prolong the inhibitory effect but also promote crop growth. Mechanistic analysis suggested that chemical stabilizers may extend inhibitor activity by forming protective complexes with the inhibitors or by reducing microbial degradation rates. This strategy has significant potential for agricultural applications, as it can improve nitrogen use efficiency, reduce environmental pollution, and provide a theoretical basis for optimizing soil–plant nitrogen management. Future studies could further explore the effectiveness and applicability under different soil types, crop species, and long-term field conditions.
Copyright (c) 2025 Alexander Carter (Author) https://creativecommons.org/licenses/by/4.0
Metal-Catalyzed Reactions for Energy and Biomedical Applications
Metal-catalyzed reactions have emerged as cornerstone technologies bridging energy conversion systems and biomedical therapeutics through their remarkable selectivity, efficiency, and tunability. This review examines the dual convergence of metal catalysis in addressing critical challenges in sustainable energy production and advanced healthcare delivery. The discussion encompasses single-atom catalysts, dual-metal site architectures, and coordination polymer frameworks that enable transformative processes including electrocatalytic carbon dioxide reduction, fuel cell oxygen reduction, hydrogen evolution, and targeted therapeutic interventions. Recent advances in biohybrid catalysis and metal-organic frameworks have demonstrated unprecedented control over reaction pathways and biological interactions, creating opportunities for integrated solutions to energy sustainability and human health challenges. Through analysis of catalyst design principles, mechanistic understanding, and structure-activity relationships, this work illustrates how strategic manipulation of metal centers, ligand environments, and support materials drives performance optimization across both domains. The synthesis of knowledge from energy catalysis and catalytic biomedicine reveals common design strategies and emerging opportunities for cross-disciplinary innovation that will shape future development of multifunctional metal-based systems capable of addressing interconnected global challenges in climate, energy security, and healthcare accessibility.
Copyright (c) 2025 Jessica Lin, Michae Chen (Author) https://creativecommons.org/licenses/by/4.0
Modern Management Approaches for Complex Interdisciplinary Systems
Complex interdisciplinary systems present unprecedented management challenges requiring innovative approaches that transcend traditional project management paradigms. This paper examines contemporary management methodologies applicable across diverse domains including project evaluation frameworks, leadership practices, and performance optimization strategies. The investigation reveals that effective management of complex systems demands multidimensional evaluation criteria, adaptive leadership competencies, and integration of digital technologies with human expertise. Through systematic analysis of management practices in construction, software development, manufacturing, and professional training contexts, this study identifies convergent principles applicable across disciplinary boundaries. The findings demonstrate that successful management of interdisciplinary systems requires balance between standardized processes and contextual adaptation, emphasis on stakeholder engagement alongside technical excellence, and integration of quantitative metrics with qualitative assessment. Comparative analyses of leadership behaviors, project success factors, and performance evaluation methodologies reveal that contemporary management approaches prioritize flexibility, continuous learning, and collaborative problem-solving over rigid adherence to predetermined plans. These insights contribute to understanding how management frameworks can effectively address complexity, uncertainty, and rapid change characterizing modern interdisciplinary systems across professional domains.
Copyright (c) 2025 Yang Liu, Wei Zhang (Author) https://creativecommons.org/licenses/by/4.0
Data Analytics Applications in Diverse Professional Domains
Data analytics has emerged as a transformative force across diverse professional domains, fundamentally altering decision-making processes and operational methodologies. This paper examines the application of analytical frameworks in multiple sectors including business intelligence, manufacturing systems, construction management, and digital platforms. The investigation reveals that analytics-driven approaches enable organizations to extract actionable insights from complex datasets, optimize performance metrics, and enhance strategic planning capabilities. Through systematic analysis of implementation strategies across various professional contexts, this study identifies common challenges including data quality issues, analytical skill gaps, and organizational resistance to data-driven paradigms. The findings demonstrate that successful analytics adoption requires integration of technological infrastructure with human expertise and organizational culture transformation. Comparative analyses of analytics applications in domains such as supply chain optimization, user engagement enhancement, risk assessment, and performance evaluation reveal convergent methodologies that transcend disciplinary boundaries. These insights contribute to understanding how data-driven decision-making frameworks can be adapted and scaled across different professional environments, providing practical guidance for organizations seeking to leverage analytics capabilities for competitive advantage and operational excellence.
Copyright (c) 2025 Priya Singh, Daniel Wang, Marie Dubois (Author) https://creativecommons.org/licenses/by/4.0
Functional Coordination Polymers with Dual Catalytic Applications
Functional coordination polymers have emerged as remarkable materials exhibiting dual catalytic capabilities that enable simultaneous or sequential transformations in complex reaction systems. These crystalline porous materials, constructed from metal ions or clusters connected by organic linkers, possess unique structural features including high surface areas, tunable pore environments, and diverse metal coordination sites that facilitate multiple catalytic functions. The integration of different catalytic functionalities within a single framework enables cascade reactions, tandem transformations, and multifunctional catalytic processes that would be difficult to achieve using conventional catalysts. This review examines the design principles underlying dual-functional coordination polymers, emphasizing how structural architecture, metal center selection, and ligand design contribute to multifunctional catalytic behavior. The synthetic strategies for creating coordination polymers with controlled dual functionalities are discussed, along with characterization approaches that reveal structure-activity relationships. Applications in various catalytic processes including organic transformations, environmental remediation, electrochemical reactions, and biomimetic catalysis are comprehensively analyzed. Special attention is given to systems demonstrating synergistic effects where dual functionalities enhance overall catalytic performance beyond simple additive contributions. The challenges associated with maintaining structural stability during catalytic processes and strategies for optimizing dual catalytic activities are addressed. This comprehensive analysis demonstrates that functional coordination polymers represent a powerful platform for developing advanced catalytic systems with multiple integrated functionalities.
Copyright (c) 2025 Ming Patel, Sofia Zhang, Hiroshi Tanaka (Author) https://creativecommons.org/licenses/by/4.0
Multi-Metal Catalytic Systems for Selective Product Formation
Multi-metal catalytic systems have emerged as powerful platforms for achieving selective product formation in various chemical transformations. These systems leverage synergistic interactions between different metal centers to enhance catalytic activity, improve selectivity, and enable reactions that are challenging for single-metal catalysts. The strategic combination of metals creates unique electronic and geometric properties that facilitate targeted reaction pathways while suppressing undesired side reactions. This review examines the fundamental principles governing multi-metal catalysis, including electronic effects, geometric modifications, and cooperative mechanisms between metal sites. The application of bimetallic and trimetallic catalysts in hydrogenation reactions, carbon dioxide reduction, biomass conversion, and other industrially relevant processes is comprehensively discussed. Special attention is given to the design strategies that enable precise control over product distribution, including support selection, metal composition optimization, and surface engineering approaches. Recent advances in characterization techniques have provided deeper insights into the structure-activity relationships in multi-metal systems, facilitating rational catalyst design. The challenges associated with maintaining selectivity under various reaction conditions and strategies for catalyst stability enhancement are also addressed. This comprehensive analysis demonstrates that multi-metal catalytic systems represent a promising avenue for developing sustainable and efficient chemical processes with enhanced selectivity.
Copyright (c) 2025 David Chen, Yuki Nakamura (Author) https://creativecommons.org/licenses/by/4.0
Structural Engineering of Metal Sites for Catalysis
Structural engineering of metal sites has emerged as a pivotal strategy for advancing heterogeneous catalysis, enabling unprecedented control over catalytic activity, selectivity, and stability. The precise manipulation of metal coordination environments, metal-support interactions, and spatial arrangements of active sites provides opportunities to tailor catalytic properties for specific reactions. Single-atom catalysts represent a frontier in this field, offering maximum atomic efficiency and unique electronic structures that differ fundamentally from nanoparticles and bulk materials. The coordination geometry, oxidation state, and local environment of isolated metal atoms critically determine their catalytic performance in reactions ranging from electrocatalytic carbon dioxide reduction to hydrogen peroxide synthesis. Dual-metal site architectures introduce additional complexity and synergy, enabling tandem catalytic processes and cooperative substrate activation. This paper examines the principles and strategies of structural engineering for metal catalytic sites, exploring the relationships between atomic-scale structure and catalytic function. Recent advances in synthesis methods, characterization techniques, and theoretical understanding have enabled rational design of metal sites with optimized properties. The integration of experimental observations with computational predictions continues to drive progress in developing next-generation catalysts for energy conversion, environmental remediation, and chemical synthesis applications.
Copyright (c) 2025 Jian Liu, Anna Rodriguez, Kenji Yamamoto (Author) https://creativecommons.org/licenses/by/4.0
Electrocatalytic and Enzymatic Inhibition by Metal Complexes
Metal complexes have emerged as versatile compounds with significant applications in both electrocatalysis and enzymatic inhibition. The unique electronic properties and coordination geometries of transition metal complexes enable them to facilitate electron transfer processes in electrocatalytic reactions while simultaneously interacting with biological enzymes to modulate their activity. This paper explores the dual functionality of metal complexes in advancing electrocatalytic processes for energy conversion and environmental remediation, as well as their therapeutic potential through enzyme inhibition mechanisms. The coordination environment, oxidation states, and ligand architectures play crucial roles in determining the efficacy of metal complexes in both domains. Recent developments in copper-based coordination polymers, ruthenium catalysts, and zinc-containing metalloenzyme inhibitors demonstrate the growing importance of rational design strategies. Understanding the structure-activity relationships and mechanistic pathways is essential for developing next-generation catalysts and therapeutic agents. This review examines the current state of metal complex applications, highlighting key design principles, mechanistic insights, and future directions for research in this interdisciplinary field spanning materials chemistry, bioinorganic chemistry, and medicinal chemistry.
Copyright (c) 2025 Sarah Kim (Author) https://creativecommons.org/licenses/by/4.0
Enzyme Inhibitor Development Using Metal-Organic Framework Technology: Therapeutic and Agricultural Potential
Metal-organic frameworks (MOFs) have emerged as revolutionary platforms for enzyme inhibitor development, offering unprecedented opportunities in both therapeutic and agricultural applications. These crystalline porous materials, composed of metal nodes connected by organic linkers, provide unique structural properties that enable precise control over enzyme-inhibitor interactions. The high surface area, tunable pore sizes, and customizable chemical environments of MOFs facilitate the design of highly selective and efficient enzyme inhibitors. In therapeutic applications, MOF-based enzyme inhibitors show remarkable potential for treating various diseases by targeting specific enzymatic pathways while minimizing off-target effects. Agricultural applications demonstrate significant promise in developing environmentally sustainable enzyme inhibitors for pest control and crop protection. The versatility of MOF platforms allows for the incorporation of multiple inhibitory mechanisms within a single framework, enhancing overall efficacy. Recent advances in MOF synthesis and functionalization have enabled the development of stimuli-responsive inhibitor systems that can be activated under specific conditions. This comprehensive review examines the current state of MOF-based enzyme inhibitor development, highlighting key breakthroughs in design strategies, synthesis methodologies, and practical applications. The integration of computational modeling with experimental validation has accelerated the discovery of novel inhibitor-MOF combinations with enhanced selectivity and potency.
Copyright (c) 2025 Liang Chen (Author) https://creativecommons.org/licenses/by/4.0
Urease Inhibition Mechanisms in Soil-Plant Systems Using Coordination Polymers for Sustainable Agriculture
The increasing demand for sustainable agricultural practices has intensified research into enzyme inhibition mechanisms that can optimize nitrogen utilization efficiency in soil-plant systems. Urease, a critical enzyme responsible for urea hydrolysis in soils, plays a pivotal role in nitrogen cycling but often leads to significant nitrogen losses through ammonia volatilization when not properly regulated. This study investigates the application of coordination polymers as innovative urease inhibitors in soil-plant systems, focusing on their mechanisms of action, environmental stability, and agricultural implications. Coordination polymers, characterized by their unique structural properties and tunable chemical compositions, offer promising solutions for controlled enzyme inhibition while maintaining soil health and supporting plant growth. The research examines various copper-based coordination polymers and their effectiveness in prolonging urease inhibition compared to conventional chemical stabilizers. Results demonstrate that coordination polymers exhibit superior performance in maintaining enzyme inhibition over extended periods, with minimal adverse effects on beneficial soil microorganisms and plant development. The study also evaluates the impact of these inhibitors on soil carbon, nitrogen, and phosphorus dynamics, revealing enhanced nutrient retention and improved fertilizer use efficiency. Furthermore, the investigation explores the relationship between coordination polymer structure and inhibition selectivity, providing insights into the design of next-generation agricultural amendments. These findings contribute to the development of environmentally sustainable approaches to nitrogen management in agricultural systems, offering potential solutions to reduce greenhouse gas emissions while maintaining crop productivity.
Copyright (c) 2025 James Hu (Author) https://creativecommons.org/licenses/by/4.0
Design of Metal–Organic Frameworks with V-Shaped Auxiliary Ligands: Insights into Structure and Function
Metal-organic frameworks (MOFs) represent a revolutionary class of crystalline materials that have garnered significant attention due to their exceptional structural diversity and multifunctional properties. The incorporation of V-shaped auxiliary ligands in MOF design has emerged as a powerful strategy for controlling structural architecture and enhancing functional capabilities. This comprehensive review examines the role of V-shaped ligands in directing the formation of three-dimensional hybrid networks, influencing pore characteristics, and enabling specific applications ranging from gas adsorption to catalysis and drug delivery. The geometric constraints imposed by V-shaped ligands provide unique opportunities for engineering breathing behaviors, flexibility, and selective binding properties. Recent advances demonstrate that strategic use of V-shaped auxiliary ligands significantly improves thermal stability, enhances sorption properties, and creates highly efficient catalytic systems. Furthermore, these ligands enable MOFs with specialized functionalities, including electrochromic properties, supercapacitor applications, and enzyme inhibition capabilities. This review synthesizes current understanding of structure-property relationships in V-shaped ligand-based MOFs, highlighting key design principles and emerging applications.
Copyright (c) 2025 Yang Chen (Author) https://creativecommons.org/licenses/by/4.0
Independent Predictive Factors for Positive Margins after Conization of Cervical HSIL in Women of Childbearing Age and the Necessity of Secondary Surgery
This study aims to analyze the high-risk factors for positive surgical margins after loop electrosurgical excision procedure (LEEP) and the necessity of secondary surgery in women of childbearing age (20-40 years) with high-grade squamous intraepithelial lesions (HSIL). Through retrospective analysis of 526 HSIL patients who underwent LEEP at the Third Affiliated Hospital of Sun Yat-sen University from 2015 to 2019, the positive margin rate was found to be 22.43%. Multivariate analysis indicated that TCT result of HSIL (OR = 3.414, 95%CI = 1.793-6.500, P < 0.001) and lesions involving ≥5 points (OR = 8.567, 95%CI = 4.439-16.533, P < 0.001) were independent risk factors for positive margins. Among 34 patients who underwent secondary surgery within six months after initial conization, 32 had positive margins, and the residual disease rate among them was only 21.88% (7/32), indicating that positive margins do not necessarily lead to residual lesions. Therefore, the decision for secondary surgery in patients with positive margins should be individualized based on factors such as age, fertility needs, lesion characteristics, complications, and personal preferences, rather than reflexively performing secondary surgery.
cervical high-grade squamous intraepithelial lesion
Copyright (c) 2025 Yingying Huang (Author) https://creativecommons.org/licenses/by/4.0
Length-Weight Relationship Modeling Applications in Aquatic Species Biomass Estimation Methods
Length-weight relationships represent fundamental biometric tools in aquatic ecology, providing essential methods for biomass estimation across diverse marine and freshwater species. These allometric relationships enable researchers to convert easily measured morphometric parameters into biomass values, facilitating population assessments and ecosystem monitoring. Contemporary approaches integrate traditional morphometric measurements with advanced technologies including metabarcoding, machine learning, and in situ imaging systems. The application of length-weight models spans multiple taxonomic groups, from zooplankton communities to large crustaceans and fish populations. Recent developments emphasize spatiotemporal modeling approaches that account for environmental variability and seasonal dynamics in aquatic systems. Machine learning algorithms have enhanced biomass estimation accuracy by incorporating multiple morphometric variables beyond simple length measurements. Metabarcoding techniques now enable species-specific biomass calculations from genetic material, revolutionizing plankton community assessments. The integration of these methodologies addresses critical challenges in aquatic biodiversity monitoring and fisheries management. This review synthesizes current applications of length-weight relationship modeling, examining methodological advances in biomass estimation techniques across aquatic ecosystems. The analysis encompasses traditional regression approaches, contemporary technological innovations, and emerging computational methods that improve estimation precision. Understanding these relationships remains crucial for sustainable fisheries management, ecosystem health assessment, and biodiversity conservation in aquatic environments.
Copyright (c) 2025 Omar Siddiqui (Author) https://creativecommons.org/licenses/by/4.0
Migration Infrastructure Design Impacts on Riverine Crustacean Population Dynamics Studies
The proliferation of anthropogenic barriers in riverine systems has fundamentally altered the population dynamics of aquatic organisms, particularly migratory crustaceans. This paper examines how migration infrastructure design influences crustacean population dynamics through comprehensive analysis of barrier effects, passageway effectiveness, and population fragmentation patterns. River fragmentation disrupts natural migration patterns essential for crustacean life cycles, leading to population isolation and demographic changes. The construction of migration passageways represents a critical intervention strategy, yet their design parameters significantly influence success rates. This study synthesizes current understanding of barrier impacts on crustacean populations, evaluating infrastructure design principles that promote population connectivity. Key findings indicate that passageway design features including gradient, substrate composition, and flow characteristics directly influence migration success. Population dynamics modeling reveals that even partial connectivity restoration can substantially improve population stability in fragmented systems. The research highlights the importance of species-specific design considerations, particularly for commercially important species. Furthermore, the temporal aspects of migration infrastructure operation significantly affect population recruitment patterns. This comprehensive analysis provides essential guidance for designing effective migration infrastructure that supports sustainable crustacean populations while accommodating human development needs. The findings emphasize the critical role of adaptive management approaches in optimizing infrastructure performance for long-term population viability.
Copyright (c) 2025 Liang Ming (Author) https://creativecommons.org/licenses/by/4.0
Carapace Measurement Techniques for Determining Growth Rates in Cultured Crustaceans
Accurate measurement of carapace dimensions is fundamental for assessing growth rates in cultured crustaceans, providing essential data for aquaculture management and biological research. This paper examines various carapace measurement methodologies employed in crustacean cultivation, analyzing their precision, applicability, and effectiveness across different species. Traditional morphometric approaches, including direct caliper measurements and photographic analysis, are evaluated alongside modern digital imaging techniques and automated measurement systems. The study reviews measurement protocols for major cultured species including mud crabs, red king crabs, lobsters, and freshwater crayfish, examining how environmental factors such as temperature influence growth patterns and measurement accuracy. Standardized measurement techniques are critical for establishing reliable length-weight relationships and determining size at maturity parameters essential for aquaculture optimization. The analysis reveals that carapace width measurements generally provide more consistent growth indicators than length measurements, particularly in brachyuran species. Temperature-dependent growth responses necessitate controlled measurement environments and standardized protocols to ensure data comparability across studies. Integration of morphometric data with molting frequency observations enhances growth rate calculations and provides comprehensive assessment tools for aquaculture practitioners. Recommendations include adoption of standardized measurement protocols, implementation of digital imaging systems for improved accuracy, and establishment of species-specific measurement guidelines to optimize growth monitoring in commercial crustacean culture operations.
Copyright (c) 2025 Ravi Mahadevan (Author) https://creativecommons.org/licenses/by/4.0
Freshwater Ecosystem Management Strategies for Commercially Important Decapod Species Populations
Freshwater decapod crustaceans represent economically vital components of aquatic ecosystems worldwide, yet their populations face unprecedented threats from anthropogenic pressures including habitat degradation, pollution, and climate change. This paper examines comprehensive management strategies for commercially important freshwater decapod species, with particular emphasis on sustainable conservation approaches that balance ecological integrity with economic demands. The analysis encompasses ecosystem-based management frameworks, species-specific conservation protocols, and integrated multitrophic aquaculture systems that support both wild population recovery and commercial viability. Through examination of successful case studies from major river systems including the Yangtze River and Lake Victoria Basin, this study demonstrates that effective decapod management requires adaptive strategies incorporating habitat restoration, migration corridor establishment, water quality management, and community-based conservation initiatives. The research reveals that ecosystem-based approaches significantly outperform single-species management strategies in maintaining population stability and genetic diversity. Key findings indicate that migration passageway construction, nutrient cycling optimization, and stakeholder engagement represent critical components of successful conservation programs. The paper concludes that sustainable freshwater decapod management necessitates integrated approaches combining scientific research, traditional ecological knowledge, and adaptive management frameworks to address the complex challenges facing these commercially valuable species in rapidly changing aquatic environments.
Copyright (c) 2025 Nguyen Thi Lan (Author) https://creativecommons.org/licenses/by/4.0
Crustacean Morphological Adaptations in Controlled Aquaculture Environments and Feeding Systems
Crustacean aquaculture has experienced unprecedented growth in recent decades, necessitating comprehensive understanding of morphological adaptations in controlled environments. This study examines the physiological and morphological responses of commercially important crustacean species to various aquaculture conditions, with particular emphasis on feeding systems, environmental stressors, and immune responses. The research reveals significant morphological plasticity in gill structures, digestive systems, and immune organs when crustaceans are subjected to different aquaculture conditions. Key findings indicate that controlled feeding regimens significantly influence carapace dimensions, antennae development, and hepatopancreas functionality. Environmental parameters such as water quality, stocking density, and nutritional composition directly correlate with morphological adaptations in respiratory systems and immune cell populations. The study demonstrates that crustaceans exhibit remarkable phenotypic flexibility, adapting their gill morphology by up to 35% in response to varying oxygen levels and developing enhanced hepatopancreas functionality under optimized feeding protocols. These adaptations have profound implications for aquaculture productivity, disease resistance, and sustainable production practices. Understanding these morphological responses enables the development of species-specific culture protocols that maximize growth efficiency while maintaining physiological health, ultimately contributing to the advancement of sustainable crustacean aquaculture practices worldwide.
Copyright (c) 2025 Jason McAllister (Author) https://creativecommons.org/licenses/by/4.0
Spatiotemporal Modeling of Soil Moisture in Humid Areas by Integrating Transformer Architecture and Remote Sensing Data
Soil moisture variation in humid regions presents high-frequency nonlinearity and is strongly influenced by surface conditions, making it difficult for traditional time series models to effectively capture long-range dependencies. In this study, a multimodal Transformer network architecture is proposed, integrating Sentinel-1 VV/VH radar data, MODIS NDVI, and meteorological variables to predict daily 0–10 cm soil moisture content at a 1 km² grid scale in Jiangning District, Nanjing. Data from 2021 were used for model training, and 2022 data were used for testing. Verified by in-situ measurements, the monthly average of 0–10 cm soil moisture in Jiangning during 2021 ranged from 0.20 to 0.32 m³/m³, with a standard deviation of 0.035 m³/m³, reflecting its high-frequency nonlinear characteristics. The model achieved an average RMSE of 0.022 m³/m³, which was lower than that of LSTM (RMSE = 0.029) and traditional SVR (RMSE = 0.034). The model's interpretability module (attention map) showed that vegetation cover and rainfall in the previous six days contributed 41.2% and 27.8%, respectively. This study provides an AI-based approach for modeling soil–vegetation–hydrology interactions driven by remote sensing data.
Copyright (c) 2025 Haojun Li, Rebecca S. Miller, Daniel T. Nguyen, Christopher J. Roberts (Author) https://creativecommons.org/licenses/by/4.0
Leveraging NLP for Semantic and Numerical Inconsistency Detection in Tax Submissions
This study presents an innovative method for identifying tax fraud through the application of natural language processing (NLP) to uncover irregularities within tax documents. Departing from conventional approaches that rely primarily on numerical analysis, the proposed framework combines domain-specific BERT embeddings with bidirectional LSTM architectures to effectively capture nuanced contextual information. A hybrid ensemble architecture is developed to process both structured data and free-text components within tax returns, facilitating the identification of semantic associations among financial entities and exposing numerical inconsistencies. The system was evaluated on a dataset comprising 15,000 tax documents, of which 8.5% were identified as fraudulent. The proposed model achieved superior performance, with an F1-score of 0.868 and an AUC of 0.931 — marking a 7.6% enhancement over leading existing models. Detection effectiveness varied by document category: individual income tax filings yielded an F1-score of 0.889, outperforming business-related filings, which scored 0.818. Further examination reveals that semantic features are particularly effective for identifying fraud in corporate tax documents, while numerical coherence indicators are more significant for personal filings. Although the approach requires higher computational resources compared to conventional techniques, its capacity to detect complex fraud schemes — especially those that disguise manipulation within textual content while maintaining plausible numeric data — offers a significant improvement to current tax fraud detection systems.
Copyright (c) 2025 Jiayu Liang, Jiayan Fan, Zhen Feng, Jing Xin (Author) https://creativecommons.org/licenses/by/4.0
A Fault Diagnosis Method for the Charging and Discharging Water System of a Certain Ship Lift Based on Historical Data Analysis
This paper takes the hydraulic action of the discharge pipeline in the filling and discharging water system of a Certain ship lift as a case study. Based on analysis of operational data from different historical stages, a mathematical model is established to determine whether the equipment in the filling and discharging water system shows trends toward abnormal conditions. The proposed model will provide early warnings for equipment maintenance and reduce the frequency of timeout fault alarms for pipeline water extraction.
Copyright (c) 2025 Binbin Zou, Yansong Lei, Gengbang Huang, Lingzhi Yan (Author) https://creativecommons.org/licenses/by/4.0
Study on Whole-Life-Cycle Management of Green Building Projects
With the deepening of global sustainable development concepts, green buildings have demonstrated significant value in energy conservation, emission reduction, efficient resource utilization, and environmental friendliness. However, traditional project management often focuses on a single phase and lacks systematic control spanning from planning and design through operation and decommissioning. Based on Life Cycle Management (LCM) theory, this paper constructs a whole-life-cycle management framework covering four stages — planning and design, construction implementation, operation and maintenance, and decommissioning and recycling — and proposes key methods such as target setting, performance monitoring, cost control, and risk management. Through case studies of representative domestic and international green building projects, the effectiveness of the proposed indicator system and management strategies in controlling energy consumption, reducing carbon emissions, and optimizing economic benefits is demonstrated. The study shows that whole-life-cycle management not only enhances the overall performance of green buildings, but also promotes industry standardization and technological innovation, providing decision-making references and practical guidelines for governments, owners, and design-build teams.
Copyright (c) 2025 Tiantian Jia (Author) https://creativecommons.org/licenses/by/4.0
Measurement of Energy Loss and Coefficient of Restitution in Inelastic Collisions Using Tracker and Phyphox
Modern smartphones incorporate a variety of high-precision, highly sensitive sensors, and companion apps are being developed to leverage these capabilities. This experiment explores how the phyphox app can enhance traditional methods for measuring energy loss and the coefficient of restitution during inelastic collisions involving spheres with different elastic properties. Using Tracker’s automatic object-tracking and data-analysis tools, we analyze collision videos to investigate how energy loss and restitution vary with the number and type of intermediate layers. Our results indicate that, for spheres of identical elasticity, the residual energy decreases as the number of layers increases and as sphere mass increases, and the coefficient of restitution likewise diminishes with more layers. Under different materials, energy loss and restitution depend on the medium: for example, a table-tennis ball’s rebound speed after a given number of bounces decreases with drop height, with the greatest energy retention on a wooden board, then on a concrete floor, and least on an iron plate; its restitution coefficient follows the same order. Comparing spheres of different elasticity, those with higher elastic moduli retain more energy and exhibit higher restitution coefficients. Notably, restitution depends only on the sphere’s material, not on its size or mass: both large and small elastic spheres have almost identical restitution values, both exceeding that of the table-tennis ball, while an aluminum sphere shows the lowest restitution.
Copyright (c) 2025 Ruiqi Sha (Author) https://creativecommons.org/licenses/by/4.0
A Framework for Post-Mining Area Restoration Based on Social-Ecological System Analysis
Mining activities have had lasting negative impacts on the ecological and socio-economic environments of resource-dependent areas. Although numerous practices and studies have focused on sustainable mining and mine restoration, few have examined how social factors influence ecological recovery. Addressing the interdisciplinary and cross-system challenges brought by post-mining impacts requires a deeper understanding of human-environment interactions. This study aims to explore sustainable restoration and reconstruction pathways for social and ecological systems in post-mining areas. Grounded in social-ecological systems (SES) theory, the research employs a literature review, case studies, and network analysis. We first define post-mining SES concepts, establish SES prototypes and an analytical framework, characterize system features, and conceptualize dynamic trajectories. Based on these findings, adaptive management strategies are proposed for post-mining areas. The results indicate that identifying systemic pressures and vulnerabilities, while aligning SES multi-level dynamics, is essential for restoration. Cross-scale governance involving multiple stakeholders can enhance SES network resilience.
Copyright (c) 2025 Zheng Cao, Jian Tang (Author) https://creativecommons.org/licenses/by/4.0
Preparation and Capacitive Performance Study of Graphene/Carbon Nanotubes
Graphene and carbon nanotubes, as representatives of novel carbon materials, have demonstrated immense application potential in the field of energy storage owing to their distinctive structures and excellent physicochemical properties. Graphene boasts high electrical conductivity, a large specific surface area, and favorable mechanical properties, while carbon nanotubes are renowned for their high aspect ratio and outstanding electrical performance. Combining the two not only enables the utilization of their respective advantages but also further enhances the comprehensive performance of the material through a synergistic effect. As a highly efficient energy storage device, the performance of a supercapacitor largely depends on the characteristics of the electrode material. The graphene/carbon nanotube composite material, with its large specific surface area, excellent electrical conductivity, and good electrochemical stability, emerges as an ideal choice for supercapacitor electrode materials. This paper investigates the preparation methods of graphene/carbon nanotubes and their capacitive performance, which not only contributes to a profound understanding of the structure-property relationship of the material but also provides theoretical basis and technical support for the development of high-performance supercapacitors.
Copyright (c) 2025 Yutong Zhu (Author) https://creativecommons.org/licenses/by/4.0
Technological Integration in Urban Emergency Management: Challenges, Innovations, and Pathways to Resilient Systems
As urbanization accelerates and crises become more complex, urban emergency management faces increasing challenges, necessitating improvements in key areas and the adoption of new technologies. This study systematically examines technological innovations and their applications in urban emergency management. The research identifies critical obstacles, including fragmented data integration, insufficient risk prediction accuracy, delayed emergency response, uneven adoption of AI-driven solutions, fragile communication networks, and interdepartmental coordination gaps. This study categorizes key technologies into three main areas: Data Collection, which includes IoT sensors, UAVs, and GIS for real-time monitoring; Data Processing, leveraging AI/ML models for predictive analytics and 5G for rapid communication; and Information Integration & Decision Support, utilizing smart city platforms for centralized coordination. The practical effectiveness of these technologies is exemplified by applications such as AI-enhanced flood prediction, GIS-based evacuation routing, and crowdsourced crisis mapping. The advantages of these technologies lie in their ability to improve accuracy in risk prediction, optimize evacuation routes, and harness collective intelligence for crisis management. However, their application gives rise to challenges including technological complexity, data overload, and resource disparities—underscoring the need for accessible AI tools, adaptive governance frameworks, and equitable technology deployment. By bridging technological potential with practical implementation strategies, this work provides actionable insights for building resilient, tech-driven emergency management systems tailored to evolving urban risks.
Copyright (c) 2025 Ziming Wang (Author) https://creativecommons.org/licenses/by/4.0
Autonomous Driving Design Based on Deep Learning
In today's world, globalization and rapid technological progress are driving deeper urbanization. Along with the explosive increase in population and the number of vehicles, urban transportation systems face unprecedented challenges, including severe traffic congestion, frequent traffic accidents, and worsening air quality. According to international statistics, traffic accidents cause million deaths annually and result in direct economic losses exceeding 0.1 trillion USD. In this context, intelligent vehicle technology, especially autonomous driving cars, is widely regarded as one of the key technologies to alleviate these issues. This thesis delves into the application of deep learning and machine vision in intelligent vehicle systems, focusing particularly on their practical effects in path planning and obstacle recognition. By systematically analyzing and evaluating existing intelligent vehicle technologies, this paper proposes an innovative algorithmic solution that combines an enhanced A* search algorithm with advanced real-time image processing techniques. Experimental results demonstrate that this new algorithm significantly outperforms traditional methods in enhancing navigation efficiency and accuracy, providing a new solution for safe navigation of intelligent vehicles in complex environments. Moreover, this research not only advances the development of autonomous driving technology but also supports the theoretical and practical implementation of future intelligent transportation systems.
Copyright (c) 2025 Zetao Yu (Author) https://creativecommons.org/licenses/by/4.0
Evolution and Trends in the Electron Microscopy Data Bank
Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) have transformed structural biology by enabling the visualization of biological macromolecules at near-atomic resolution. Since its establishment in 2002, the Electron Microscopy Data Bank (EMDB) has played a pivotal role in this “Resolution Revolution,” serving as a central repository for cryo-EM data. Over the past decade, major advances in detector technology, image processing, and analytical methodologies have led to a surge in cryo-EM structure depositions. This study provides a comprehensive analysis of temporal trends in the EMDB, highlighting the growing number of entries, improvements in resolution, and the evolution of detector usage. It emphasizes the rise of single-particle analysis (SPA) as the dominant cryo-EM technique and examines how high-resolution detectors have enhanced data quality. Additionally, it explores resolution standards across leading scientific journals, underscoring the role of EMDB not only as a data archive but also as a catalyst for innovation in structural biology.
Copyright (c) 2025 Duoyan Hu (Author) https://creativecommons.org/licenses/by/4.0