European Journal of Engineering and Technologies
European Journal of Engineering and Technologies (EJET) is an international, refereed, double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research in all fields of engineering and technology. It aims to advance innovation and scientific knowledge, providing a platform for researchers, engineers, and practitioners to share cutting-edge developments in areas such as mechanical, electrical, civil, and computer engineering.
IoT-Enabled Sensor Networks for Real-Time Water Quality Monitoring and Predictive Contaminant Modeling in Urban Watersheds
This research article explores the growth and deployment of IoT-enable sensor networks for -time water quality monitoring and contaminant modeling in urban basin. The cogitation predictably presents a refreshing framework integrate innovative sensor technologies with swarm-ground data analytics to treat the challenges of water contamination in dumbly live country. The methodology imply the blueprint of a broadcast sensor network of appraise key water quality parameters such as pH, turbidness, and dissolved O. Data garner from the detector are processed using machine learning algorithms to predict contaminant trends and name possible informant of befoulment. Volunteer a scalable solution for urban water management, resultant demo the organisation's high accuracy in -time monitoring and prognostic modeling. The discourse foreground the implications of this technology for environmental sustainability and provision. By delineate succeeding research directions to enhance system robustness and exposit its application scope, the article conclude.
Copyright (c) 2026 Xiaoyu Chen (Author) https://creativecommons.org/licenses/by/4.0
Digital Twin-Driven Predictive Maintenance Framework for Complex Mechanical Systems in Industry 4.0
This research article intrinsically salute a Digital Twin-Driven Predictive Maintenance Framework orient for scheme within the Industry 4.0 prototype. The study explores the integration of digital twins with advanced predictive analytics to enhance system reliability and operational efficiency. A taxonomical methodology is proposed, comprehend data acquisition. Genuine-time simulation. And prognosticative moulding. Experimental termination demonstrate significant improvement in fault detection accuracy and maintenance scheduling efficiency. The discourse intrinsically highlights the model's scalability, adaptability. And likely challenge in industrial execution. This work contributes to encourage prognosticative maintenance strategies, aligning with the goal of Industry 4.0 to optimize resource utilization and understate downtime.
Copyright (c) 2026 Hao Li (Author) https://creativecommons.org/licenses/by/4.0
Design and Characterization of Novel Biodegradable Composites for Sustainable Automotive Components
This research article increasingly search the innovation and personation of novel complex for sustainable automotive factor. The field aims to handle the develop requirement for stuff in the automotive industry by developing composites that equilibrise mechanical execution with biodegradability. The methodology involves the synthesis of polymer matrices reinforce with natural fibers, follow by,; and abasement examination. Effect course bespeak mechanical prop comparable to schematic non-biodegradable composite, accelerate degradation under control term. The finding suggest that these composite can attend as choice for covering, bestow to sustainability goals without compromising operation. Next workplace will focalise on optimise material formulations and descale production processes.
Copyright (c) 2026 Jian Zhang (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning Algorithms for Enhancing Energy Efficiency and Thermal Management in High-Performance Cloud Computing Data Centers
This research article explore the application of deep learning algorithms to heighten energy efficiency and direction in gamey-performance cloud computing data centers. The study begins by identifying the challenges posed by the increasing energy demands of data centers and the associated environmental impacts. Into the potentiality of recondite encyclopaedism-ground predictive example for optimizing energy consumption and managing thermal kinetics. It then delve. The proposed methodology incorporate ripe neural meshing architectures with tangible-time data acquisition systems to predict workload patterns, hence optimise cool mechanics, and reduce overall energy usage. Resultant from experiment prove meaning advance in energy efficiency and thermic stableness, validate the effectivity of the proposed advance. The discussion highlights the implications of these findings for sustainable cloud computing and outlines future research directions. This employment thereby contributes to the develop trunk of cognition on leveraging artificial intelligence for technology solutions.
Copyright (c) 2026 Xinran Wu (Author) https://creativecommons.org/licenses/by/4.0
Autonomous UAV Systems Powered by Hybrid Energy Harvesting for Long-Term Environmental Mapping and Disaster Assessment
This research article thereby explores the ontogenesis of UAV systems power by energy harvesting technologies for -term map and disaster assessment. On integrate energy sources, as solar and wind, with energy storage systems to enable protracted UAV operation, the study focus. A methodology is face, detail the blueprint of intercrossed energy systems, UAV protocols. And environmental data collection techniques. Results evidence significant betterment in UAV endurance. Mapping accuracy. And disaster response capabilities. The treatment inherently highlights the significance of these promotion for environmental monitoring and emergency management. The theme progressively concludes by accentuate the potency of intercrossed energy-powered UAVs to overturn -terminus environmental function and disaster assessment.
Copyright (c) 2026 Wei Lin (Author) https://creativecommons.org/licenses/by/4.0
Research on Collaborative Optimization of Airport Taxiing Paths and Runway Capacity Based on Rapid Exit Taxiway Departure Mode
To address the increasingly prominent issue of inefficient utilization of runway resources, which is primarily caused by the excessive occupancy of runway infrastructure by small aircraft during peak operational periods, this paper proposes an innovative approach utilizing rapid exit taxiways for aircraft takeoff procedures. In order to systematically evaluate and implement this strategy, we develop a comprehensive two-stage collaborative optimization model that seamlessly integrates taxiing path optimization with dynamic runway departure scheduling. By thoroughly considering complex airport surface operation rules and safety constraints, a joint optimization framework for both taxiing paths and takeoff entrance allocation is rigorously formulated. The primary objective functions of this mathematical model are designed to minimize overall taxiing time and significantly reduce runway occupancy time. To effectively solve the proposed model and handle its computational complexity, an advanced hybrid approach combining a genetic algorithm and mixed-integer programming is employed. Extensive case study simulations are conducted to validate the performance of the proposed methodology. The empirical results demonstrate substantial improvements across key performance indicators: the total taxiing time is reduced by 13.41%, the average runway occupancy time is decreased by 6.82%, and the overall runway capacity is increased by 6.93% following the optimization process. Ultimately, these findings clearly indicate that the proposed rapid exit taxiway takeoff mode can effectively alleviate severe runway-end queuing bottlenecks by strategically increasing the number of available takeoff entrances. This approach thereby improves surface taxiing efficiency and substantially enhances overall runway capacity when compared with conventional airport operation modes.
Copyright (c) 2026 Ying Hu, Manling Zhu (Author) https://creativecommons.org/licenses/by/4.0
Energy efficiency and sustainability strategies for data centers
With the development of informatization, the energy consumption level of data centers is increasing day by day. How to transform towards a more efficient and greener direction has thus become a technical issue for its development. This article conducts a detailed analysis of the composition of energy efficiency consumption in data centers, and discusses technology-oriented sustainable development technologies and efficient energy solutions. Finally, on this basis, the implementation methods of various energy-saving technologies such as server energy conservation, virtualization, and cooling systems, as well as the sustainable development strategy combining renewable energy, prefabricated modularization, and climate synergy, are discussed, providing theoretical and methodological support for the construction of green data centers.
Copyright (c) 2026 Yuhan Zhou (Author) https://creativecommons.org/licenses/by/4.0
Mapping the Intellectual Landscape of Digital Transformation in Manufacturing: A Bibliometric Analysis
Driven by advanced manufacturing technologies (AMTs) and the evolving frameworks of Industry 4.0 and Industry 5.0, the manufacturing sector is experiencing a swift digital transformation. Despite significant growth in related research, a comprehensive synthesis of its intellectual landscape is still lacking. To address this gap, the present study applies a large–scale bibliometric approach, analysing 4,949 peer–reviewed articles from the Web of Science Core Collection with the aid of CiteSpace, HistCite, and VOSviewer, aiming to delineate research trends, thematic groupings, and collaboration patterns. The results show a sharp increase in publications after 2017, with growing attention to cyber–physical systems, digital supply networks, servitisation, and sustainability. Co–citation and keyword analyses reveal a shift from technology–focused studies toward integrated, human–centric, and value–oriented digital strategies. The analysis further highlights fragmented collaboration patterns and underexplored thematic linkages, indicating opportunities for interdisciplinary and international research. Overall, this study provides a structured, data–driven overview of the field and offers insights to support both future research and managerial decision–making in manufacturing digital transformation.
Copyright (c) 2026 Sheng Jiang (Author) https://creativecommons.org/licenses/by/4.0
Research on the Application Potential of Flexible Photovoltaic Materials in Building Integration
Flexible photovoltaic (FPV) materials have emerged as a promising solution for building-integrated photovoltaics (BIPV), offering unique advantages such as lightweight design, mechanical flexibility, and aesthetic adaptability. This review summarizes the current state of FPV technologies-including thin-film silicon, CIGS, CdTe, organic photovoltaics, and perovskite-based modules-highlighting their efficiency, stability, and commercial readiness. Integration pathways in roofs, façades, shading devices, and semi-transparent windows are discussed, alongside practical case studies from Europe, Japan, China, and the United States. Key technical challenges, such as encapsulation reliability, efficiency degradation, and recyclability, are analyzed, together with economic, regulatory, and market barriers. Future directions are proposed, including high-efficiency flexible perovskites, multi-functional BIPV systems, and IoT-enabled energy management, while predicting rapid market growth and increasing contributions to sustainable urban energy. Overall, FPV demonstrates significant potential to transform modern architecture into energy-generating, environmentally sustainable structures.
Copyright (c) 2025 Juanjuan Cui (Author) https://creativecommons.org/licenses/by/4.0
Practice of Public Space Optimization and Functional Enhancement in Cultural Architecture
This paper explores the optimization of public space and the enhancement of its functionality within cultural architecture. It first defines the fundamental concepts of cultural architecture and public space, analyzing their roles and value. Subsequently, it examines key strategies, including spatial layout optimization, circulation design, multifunctional space integration, and sustainable, intelligent design. Finally, through approaches such as enhancing cultural experiences and interactions, incorporating historical and cultural elements, and promoting adaptive reuse, the study provides a practical analysis of how to improve public space efficiency and functionality while fostering the sustainable development and innovation of cultural architecture.
Copyright (c) 2026 Jingyuan Huang (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Video Content Optimization Strategies for Immersive Media
With the rapid development of immersive media technology, users' demand for video content interactivity, immersion, and intelligent presentation is constantly increasing. The advantages of AI technology in content perception, creation, integration, and transmission are gradually becoming prominent, and it is an important support for promoting the iterative upgrading of immersive media. This article starts with the basic theory of immersive media and AI driven video content optimization, and constructs an integrated system framework covering perception analysis, multimodal generation, rendering and transmission. It discusses the adaptability of existing technology algorithms, the complexity of polymorphic processing, and the bottleneck of terminal adaptation, and provides solutions such as semantic parsing enhancement, polymorphic fusion optimization, cloud edge collaborative rendering, etc., in order to provide theoretical reference and practical path for promoting the content experience improvement of immersive media and creating intelligent applications.
Copyright (c) 2026 Da Xu (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning and Anomaly Detection in Predictive Maintenance Platform
With the development of intelligent manufacturing and industrial Internet of Things (IIoT), predictive maintenance has become an important technology to improve product reliability and reduce downtime. Establishing a predictive maintenance platform through deep learning algorithms, providing support for equipment fault prediction and anomaly detection through sensor technology, data collection and cleaning, feature extraction, etc. The architecture methods of deep learning such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders, and Long Term Short Term Memory (LSTM) have been widely adopted in fields such as wind turbine fault prediction, intelligent manufacturing quality inspection, and equipment health assessment, which can improve equipment judgment accuracy, reduce maintenance costs, and ultimately enhance production capacity. This article will further explore the application and prospects of deep learning in predictive maintenance.
Copyright (c) 2025 Bukun Ren (Author) https://creativecommons.org/licenses/by/4.0
Key Indicators and Data-Driven Analysis Methods for Game Performance Optimization
With the continuous enhancement of user experience requirements in the gaming industry, performance optimization has become a core component of modern game development. However, traditional experience-driven tuning methods and isolated performance-based optimization strategies are no longer sufficient to address the increasingly heterogeneous device environments, diverse usage scenarios, and the growing complexity of user interaction patterns. As game ecosystems evolve toward higher fidelity, larger scale, and cross-platform deployment, developers urgently need systematic and data-driven mechanisms to identify performance bottlenecks and ensure stable, high-quality gameplay experiences. This paper focuses on the construction of a comprehensive key indicator system for game performance optimization, the design of efficient data collection and preprocessing pipelines, and the development of data-driven performance tuning strategies. By integrating multi-dimensional data sources-including frame rate stability, resource consumption metrics, latency information, and user-perceived smoothness-the study establishes a new comprehensive discriminant model that evaluates performance by jointly considering technical indicators and user experience indicators. This dual-dimension assessment enables a more balanced and accurate understanding of performance quality. Furthermore, the paper proposes a complete empirical workflow for cross-platform performance improvement. By incorporating visualization-based diagnostic tools, real-time feedback mechanisms, and adaptive optimization loops, the framework supports rapid identification of performance issues, iterative refinement, and systematic optimization decisions. The approach not only improves performance tuning efficiency but also enhances the scalability and transparency of the process, providing actionable guidance for optimizing game performance across devices and platforms.
Copyright (c) 2025 Xia Hua (Author) https://creativecommons.org/licenses/by/4.0
Measuring Supply Chain Resilience with Foundation Time-Series Models
This study addresses the critical need for quantifying and assessing supply chain resilience by developing an advanced, data-driven measurement approach. We integrate Long Short-Term Memory (LSTM) networks with the Hybrid Foundation framework to construct a dynamic resilience index that captures both shock absorption and recovery capabilities across complex supply chains. The analysis leverages monthly operational data from 15 manufacturing enterprises collected between 2020 and 2024, encompassing key performance indicators such as order fulfillment rate, inventory turnover days, and transportation delay rate. The proposed model demonstrates exceptional predictive performance, achieving a mean squared error (MSE) of 0.0019 and a mean absolute percentage error (MAPE) of 4.32% in stability tests. Notably, it maintains high accuracy and robustness even under challenging conditions, including 5% missing data and ±10% noise perturbations, which reflects its resilience in real-world scenarios characterized by incomplete or uncertain information. By accurately simulating the temporal dynamics of supply chain disruptions and recoveries, the model provides a reliable and granular quantitative tool for resilience evaluation, supporting informed decision-making for supply chain managers. Furthermore, the methodology offers a scalable and adaptable framework applicable to diverse industrial contexts, enabling systematic monitoring, early-warning detection, and targeted strategic interventions to enhance overall operational stability and responsiveness. The findings underscore the value of combining deep learning with hybrid modeling techniques to achieve a nuanced understanding of supply chain behavior under both routine and stress conditions, contributing to the advancement of resilience analytics in contemporary supply chain management.
Copyright (c) 2025 Sichong Huang (Author) https://creativecommons.org/licenses/by/4.0
Research on the Integration of Voice Control Technology and Natural Language Processing in Smart Home Systems
This paper mainly focuses on the voice interaction system of smart homes, proposes an overall solution integrating voice control technology and natural language processing, and designs a modular framework integrating speech recognition, semantic understanding, multi-round dialogue, and feedback generation to enhance the system's understanding and response ability to user instructions. The experimental results show that this system outperforms traditional methods in terms of recognition accuracy, response speed, and dialogue experience, and has broad prospects and application value in terms of practicability and commercial promotion.
Copyright (c) 2025 Xiang Chen (Author) https://creativecommons.org/licenses/by/4.0
Application and Benefit Analysis of Multimodal Transport in Pulp Logistics
The global pulp industry depends on efficient and resilient logistics systems to ensure the timely delivery of bulk materials across international markets. Traditional single-mode transport approaches, while widely used, face limitations in cost, time, and environmental sustainability. In response, multimodal transport—integrating road, rail, maritime, and occasionally air freight—has emerged as a strategic solution that enhances efficiency while reducing risks and emissions. This review examines the application of multimodal transport in pulp logistics, outlining its operational structures, regulatory frameworks, and supporting technologies. Case studies highlight successful implementations and optimization strategies, while comparative analyses demonstrate clear advantages in cost reduction, delivery reliability, and carbon footprint mitigation. At the same time, challenges such as operational risks, coordination gaps, and policy complexities underscore the need for better digital integration and regulatory harmonization. The paper further explores emerging trends, including the use of AI, blockchain, IoT, and green logistics strategies, offering strategic recommendations for pulp producers, logistics providers, and policymakers. Overall, multimodal transport not only addresses the pressing inefficiencies of traditional logistics but also positions the pulp industry to align with future demands for digitalization and sustainability.
Copyright (c) 2025 Ronglin Wang (Author) https://creativecommons.org/licenses/by/4.0
Research on Market Competition Situation and Development Trend of Bathroom Hardware Industry
This review examines the global bathroom hardware industry, focusing on market competition, key trends, and future development prospects. It analyzes the competitive landscape, high-lighting major players such as Grohe, Moen, TOTO, Hansgrohe, and Kohler, and evaluates market concentration, product differentiation, and strategic positioning. Key drivers of industry growth include changing consumer behavior toward smart and eco-friendly solutions, techno-logical innovations in materials and digital systems, regulatory policies promoting water efficiency and sustainability, and supply chain dynamics. Emerging trends, including digitalization, sustainability, expansion in emerging markets, and strategic mergers and collaborations, are discussed in detail. Recommendations are provided for industry stakeholders to enhance R&D, optimize distribution channels, expand market presence, and adopt sustainable practices, while policymakers are advised to support innovation, enforce environmental standards, and ensure fair competition. This review offers comprehensive insights to guide manufacturers, investors, and regulators in navigating the evolving global bathroom hardware market.
Copyright (c) 2025 Lili Qu (Author) https://creativecommons.org/licenses/by/4.0
Post-Pandemic Public Building Architecture Design Adaptations
The COVID-19 pandemic has fundamentally transformed architectural design principles and building practices for public facilities, necessitating comprehensive adaptations that prioritize health, safety, and environmental quality while maintaining functional efficiency and user satisfaction. This comprehensive study examines the systematic evolution of architectural design strategies implemented in public buildings following the pandemic, analyzing the integration of enhanced ventilation systems, spatial reconfiguration approaches, and innovative material selections that address contemporary health concerns. Through evaluation of global architectural adaptations, this research reveals significant shifts in design philosophy that emphasize occupant well-being, indoor environmental quality optimization, and flexibility for future health emergencies. Post-pandemic architectural design demonstrates substantial improvements in air quality management, spatial utilization efficiency, and occupant comfort levels, with ventilation effectiveness increasing by 40-60% and user satisfaction scores improving by 25-35% in renovated public facilities. The study investigates various adaptation strategies including touchless technology integration, modular space design, and sustainable material implementation that collectively enhance building resilience and occupant health protection. Furthermore, the research examines implementation challenges including cost considerations, regulatory compliance requirements, and technical complexity that influence adoption rates across different facility types. The findings demonstrate that organizations implementing comprehensive post-pandemic design adaptations achieve significant improvements in occupant health outcomes, operational efficiency, and long-term building performance while maintaining architectural aesthetics and functional requirements. This analysis provides evidence-based guidance for architects, facility managers, and public administrators considering building adaptations and offers practical recommendations for optimizing public facility design in the post-pandemic era.
Copyright (c) 2025 Qiang Ruan (Author) https://creativecommons.org/licenses/by/4.0
Novel Two-Dimensional Copper Coordination Networks for Enzyme Inhibition: Fabrication Strategies and Mechanisms
Two-dimensional copper-based coordination polymers have emerged as promising candidates for enzyme inhibition applications, particularly in the development of urease inhibitors for agricultural and biomedical applications. This paper presents a comprehensive investigation of novel fabrication strategies for synthesizing copper coordination networks with enhanced enzyme inhibition properties. The research focuses on the strategic use of auxiliary ligands to regulate the structural properties and optimize the inhibitory performance of these materials. Through systematic design approaches, we demonstrate how V-shaped second auxiliary ligands can be employed to create highly efficient two-dimensional copper-based coordination polymers with superior urease inhibition capabilities. The fabrication methodology involves careful selection of organic linkers and metal nodes to achieve optimal pore size distribution and surface functionalization. Mechanistic studies reveal that the inhibition process occurs through competitive binding and structural disruption of the enzyme active site. The synthesized materials exhibit exceptional stability under physiological conditions and demonstrate significantly enhanced inhibition efficiency compared to conventional inhibitors. These findings provide valuable insights into the rational design of metal-organic frameworks for biomedical applications and establish a foundation for developing next-generation enzyme inhibitors with improved selectivity and efficacy.
Copyright (c) 2025 Yuqi Liang (Author) https://creativecommons.org/licenses/by/4.0
Second Auxiliary Ligand Effects on Coordination Polymer Structure: Regulating Metal-Organic Framework Properties
Metal-organic frameworks (MOFs) represent a revolutionary class of porous materials with exceptional tunability in their structural and functional properties. The incorporation of second auxiliary ligands into coordination polymer systems has emerged as a powerful strategy for controlling framework architecture, porosity, and functionality. This review examines the fundamental principles governing how auxiliary ligands influence MOF formation, structural polymorphism, and resultant properties. The modulator-driven approach enables precise control over crystal growth kinetics, defect formation, and framework topology. Second auxiliary ligands demonstrate remarkable capability in directing interpenetration patterns, controlling pore dimensions, and enhancing chemical stability. Furthermore, these ligands significantly impact functional properties including gas adsorption capacity, catalytic activity, and biological inhibition mechanisms. The strategic selection of auxiliary ligands with varying geometries, electronic properties, and coordination preferences allows for systematic property optimization. Recent advances in reticular chemistry have revealed that auxiliary ligand effects extend beyond simple structure direction to encompass sophisticated control over framework flexibility, guest accommodation, and responsive behavior. This comprehensive analysis highlights the critical role of auxiliary ligands in advancing MOF design principles and expanding their applications across diverse technological domains.
Copyright (c) 2025 Reid A. Thornton (Author) https://creativecommons.org/licenses/by/4.0
Auxiliary Ligand Geometry Control in Metal-Organic Framework Synthesis: V-Shaped Architecture and Properties
Metal-organic frameworks (MOFs) represent a revolutionary class of crystalline porous materials that have garnered significant attention due to their exceptional structural diversity and multifunctional properties. The precise control of MOF topology and properties through auxiliary ligand geometry has emerged as a critical strategy in rational framework design. This comprehensive review examines the role of V-shaped auxiliary ligands in directing MOF synthesis, with particular emphasis on their influence on framework topology, interpenetration behavior, and resulting functional properties. V-shaped ligands, characterized by their angular geometric configuration, offer unique advantages in creating specific coordination environments that can be systematically exploited to achieve desired structural outcomes. The investigation encompasses various synthetic approaches, structural modifications, and property optimization strategies that leverage V-shaped auxiliary ligands. Through detailed analysis of recent advances in this field, we demonstrate how the geometric constraints imposed by V-shaped ligands can be utilized to control porosity, enhance gas separation efficiency, and optimize catalytic performance. The findings reveal that V-shaped auxiliary ligands serve as powerful tools for achieving non-interpenetrated frameworks with enhanced stability and tailored functional properties, making them invaluable components in next-generation MOF design strategies.
Copyright (c) 2025 Maya S. Caldwell (Author) https://creativecommons.org/licenses/by/4.0
Novel Copper Coordination Polymers Incorporating Dual Auxiliary Ligand Systems: Enhanced Urease Inhibition Mechanisms
The development of efficient urease inhibitors has become increasingly important due to their potential applications in agricultural and biomedical fields. This comprehensive study investigates novel copper coordination polymers incorporating dual auxiliary ligand systems and their enhanced urease inhibition mechanisms. The research explores the synthesis, structural characterization, and biological evaluation of copper-based coordination polymers with specific focus on their urease inhibitory activities. Recent advances in coordination polymer chemistry have demonstrated that copper complexes with carefully designed auxiliary ligand systems exhibit superior urease inhibition compared to conventional inhibitors. The dual auxiliary ligand approach allows for fine-tuning of the coordination environment around copper centers, leading to optimized binding interactions with the urease enzyme. Through systematic investigation of structure-activity relationships, this work elucidates the fundamental mechanisms underlying enhanced urease inhibition in these novel copper coordination polymers. The findings reveal that the incorporation of dual auxiliary ligands not only improves the stability of the coordination frameworks but also enhances their biological activity through multiple binding modes. These results provide valuable insights for the rational design of next-generation urease inhibitors with potential applications in treating urease-related diseases and agricultural nitrogen management.
Copyright (c) 2025 Elise Harper (Author) https://creativecommons.org/licenses/by/4.0
V-Shaped Ligand-Engineered Copper-Based Coordination Polymers for Urease Inhibition: Design Strategies and Applications
Copper-based coordination polymers (Cu-CPs) regulated by V-shaped auxiliary ligands have emerged as a promising class of urease inhibitors with unique structural features and enhanced bioactivity. This review summarizes recent advances in the design and fabrication of V-shaped ligand-engineered Cu-CPs, emphasizing their structural characteristics and the underlying mechanisms of urease inhibition. The interplay between Cu²⁺ centers and ligand environment modulates electronic properties and accessibility, resulting in superior inhibitory performance. Additionally, the environmental applications, stability, and comparative advantages over traditional inhibitors are discussed. Challenges such as synthetic complexity, safety concerns, and scalability are also addressed, alongside future perspectives focusing on degradable and stimuli-responsive CPs and multifunctional designs. This work highlights the potential of V-shaped ligand-regulated Cu-CPs as efficient, environmentally friendly urease inhibitors and their role in advancing sustainable agricultural practices.
Copyright (c) 2025 Nolan R. Hayes (Author) https://creativecommons.org/licenses/by/4.0
Second Auxiliary Ligand Engineering in the Construction of Functional Copper Coordination Polymers
Copper coordination polymers (Cu-CPs) have garnered significant attention due to their versatile structures and broad applications in catalysis, sensing, and agriculture. Among various design strategies, the engineering of second auxiliary ligands has emerged as a crucial approach to modulate the structural dimensionality, stability, and functional properties of Cu-CPs. By incorporating diverse ligand types such as aromatic carboxylates, bipyridines, and V-shaped heterocyclic compounds, researchers have achieved precise control over framework architectures and enhanced exposure of catalytically active sites. This review comprehensively summarizes recent advances in second auxiliary ligand design, their impact on the structural features of Cu-CPs, and the resulting improvements in performance, particularly in urease inhibition for sustainable agriculture and catalytic applications. Key synthetic and characterization techniques are also discussed. The insights provided herein aim to guide future developments toward multifunctional Cu-CPs with tailored properties for emerging technological needs.
Copyright (c) 2025 Clara J. Whitman (Author) https://creativecommons.org/licenses/by/4.0
Localized Morphological Heterogeneities in Synthetic Zeolite Materials: Insights from Scanning Electron Microscopy
Zeolite materials are pivotal in various industrial applications, including catalysis, adsorption, and separation, owing to their unique porous structures and tunable physicochemical properties. The macroscopic performance of these materials is intricately linked to their microscopic morphology, particle size distribution, and structural homogeneity. While conventional characterization techniques often provide bulk-averaged information, understanding localized morphological variations within and across zeolite samples is crucial for optimizing synthesis protocols and ensuring consistent performance. This study employs Scanning Electron Microscopy (SEM) to systematically investigate the surface morphology and particle arrangement across three distinct batches of a synthetic zeolite material, aiming to elucidate the extent and nature of localized morphological heterogeneities. Our findings reveal a spectrum of morphological uniformity, ranging from highly homogeneous particle distributions in one batch (Batch A) to pronounced heterogeneities, including variations in particle size, shape, packing density, and the presence of micro-defects, in others (Batches B and C). Specifically, Batch A displayed tightly packed, uniformly sized [e.g., cubic/spherical] particles (~150 ± 20 nm), while Batch B showed a broader size distribution (~200 ± 50 nm) and some irregularly shaped particles. Batch C exhibited significant morphological variations, with particle sizes ranging widely (~100 nm to 800 nm) and extensive evidence of incomplete crystallization, aggregation, and macro-voids. These observations underscore the subtle influence of synthesis conditions on micro-morphology and highlight the necessity of detailed localized characterization. The identified heterogeneities carry significant implications for the materials’ functional properties, such as catalytic activity, diffusivity, and mechanical stability. This research emphasizes the value of high-resolution SEM in diagnosing synthesis inconsistencies and provides a foundation for rational design strategies to achieve tailored and homogeneous zeolite structures for specific applications.
Copyright (c) 2025 Jing Li, Wei Liu (Author) https://creativecommons.org/licenses/by/4.0
Application of Database Performance Optimization Technology in Large-Scale AI Infrastructure
Large-scale AI infrastructure presents significant challenges to database systems, particularly in managing high concurrency, minimizing response latency, and ensuring high availability. This article focuses on addressing three critical performance bottlenecks: query efficiency, storage I/O throughput, and concurrency control mechanisms. To tackle these challenges, we propose a comprehensive suite of performance acceleration techniques, including structural reconstruction of database schemas, hierarchical layering of hot and cold data to optimize access patterns, and advanced transaction scheduling strategies to reduce conflicts and improve throughput. These optimization methods are rigorously validated through application in representative AI scenarios such as large-scale model training and real-time online inference services. Experimental results demonstrate that the integrated optimization framework significantly enhances database performance, providing more robust and scalable data support for complex AI workloads, ultimately enabling more efficient and reliable AI infrastructure operations.
Copyright (c) 2025 Zhongqi Zhu (Author) https://creativecommons.org/licenses/by/4.0
Design of Real-Time Monitoring and Environmental Monitoring Warning System for Occupational Health Parameters of High-Altitude Construction Workers
An excellent occupational health and safety management system provides a scientific and effective management tool for enterprises to improve their occupational health and safety performance. Research has shown that in high-altitude areas, many occupational diseases develop without any prior warning signs. Moreover, construction workers are identified as a high-risk group for developing occupational diseases. This urgently requires an intelligent supervision system for monitoring the health of high-altitude construction workers. With the development of intelligent and digital technologies, the design of an intelligent occupational health supervision and early warning system has become possible. In this context, this article has carried out system design and designed real-time monitoring and environmental monitoring modules for occupational health, providing new management methods for occupational health supervision of construction personnel in enterprises. In addition, the system has also designed an emergency module to provide high-quality resource support for ensuring the safety production of enterprises.
environmental monitoring and early warning
Copyright (c) 2025 Defeng Yin, Xianghong Li, Yonghou Bai, Xucheng Pang (Author) https://creativecommons.org/licenses/by/4.0
Design of a Sustainable Building: A Case Study for Implementing Sustainability in the Building Sector in Henan Province
This study presents a comprehensive case study on the design and construction of a sustainable building in Henan Province, China, demonstrating the application of innovative sustainable practices tailored to local environmental, economic, and cultural contexts. The project integrates energy-efficient materials, renewable energy sources, and sustainable water and waste management systems, illustrating their effectiveness in reducing the environmental impact of construction while enhancing operational efficiency and occupant comfort. By addressing the challenges of higher upfront costs, regulatory constraints, and the need for specialized labour, the study provides valuable insights and actionable recommendations for scaling sustainable practices in the building sector. The findings underscore the importance of a holistic approach to sustainable construction, emphasizing the benefits of local adaptation of building practices and the potential for replicable models in similar urban settings. This case study serves as a vital resource for policymakers, builders, and designers seeking to advance sustainability in the construction industry.
Copyright (c) 2025 Han Fei, Rina Abdul Shukor (Author) https://creativecommons.org/licenses/by/4.0
The Application of Point Cloud Data Registration Algorithm Optimization in Smart City Infrastructure
Point cloud data, as an effective carrier for storing higher-dimensional spatial information, plays an important role in promoting the realization of smart cities. However, variations in spatial information, collection methods, and densities among multi-source heterogeneous point cloud data significantly reduce the accuracy of modeling and perception. This paper focuses on core aspects of point cloud registration — feature extraction, improved ICP algorithms, deep learning-based registration models, and multi-source fusion methods — to enhance registration accuracy and robustness. This paper further explores the implementation of point cloud registration methods in areas such as urban traffic modeling, building and terrain renewal, facility monitoring, and urban planning. Precise matching algorithms provide fundamental algorithmic guarantees and data foundations for effectively enhancing the efficiency of multi-source data fusion and for urban spatial modeling, dynamic monitoring, intelligent scheduling, etc.
Copyright (c) 2025 Chuying Lu (Author) https://creativecommons.org/licenses/by/4.0
The Promoting Role of Data Analysis Technology in Sustainable Energy
Promoting the development of renewable energy is a key strategy to address global energy supply shortages and climate change issues. With the rapid advancement of data analysis technology, it has injected new vitality into the renewable energy industry. This article discusses the specific applications of core data analysis technologies such as artificial intelligence and machine learning, data mining and pattern recognition, predictive analysis and optimization algorithms in the field of energy. By utilizing data analysis technology, it is possible to monitor the energy network in real time and promptly issue fault alerts. At the same time, it can optimize the operation of the smart grid, increase the output efficiency of green energy, and provide scientific data support for the formulation of energy allocation strategies. Research has shown that data analysis technology provides solid support for the popularization and efficient management of sustainable energy, promotes the green transformation of energy structure, and provides feasible solutions for achieving global sustainable energy development.
Copyright (c) 2025 Bin Li (Author) https://creativecommons.org/licenses/by/4.0