Search Results (343)

Establishing a mechanism for ecological product value realizing (EPVR) is a critical component of China’s ecological civilization strategy, aimed at translating the concept that “lucid waters and lush mountains are invaluable assets” into actionable economic policies. Although central government investments in the form of project for EPVR have increased significantly, surpassing CNY 700 billion by 2024, studies rarely focus on these projects and how to evaluate them. Evaluating the performance of EPVR projects is essential for optimizing resource allocation, enhancing project accountability, and ensuring the sustainable realization of ecological, economic, and social values. This study innovatively defines the conceptual connotation of EPVR projects and constructs a comprehensive performance evaluation system based on a “benefit-cost” analysis, comprising a multi-dimensional indicator system, quantifiable calculation methods, and explicit evaluation criteria. As water source protection projects are typical EPVR projects, the comprehensive water environment management project of Hongfeng Lake is selected for an in-depth empirical study. The results reveal that (1) the total annual benefits amount to CNY 923.66 million, dominated by ecological benefits (84.04%); (2) with an investment of CNY 1194.66 million, the project yields a net loss and a moderate performance index (PCPI = 0.77); (3) the project performance is primarily affected by weak economic value conversion stemming from restrictive zoning policies and underdeveloped market mechanisms for ecological services; and (4) integrated development pathways—such as ecotourism, eco-aquaculture, and ecological branding—are proposed to enhance the long-term sustainability of the project. The Hongfeng Lake case establishes a replicable framework for global assessment of analogous projects and delivers actionable insights for enhancing benefit–cost ratios in public ecological initiatives, with costs confined to data collection, modeling, and validation. Therefore, this study contributes a quantifiable and reproducible tool for the full lifecycle management of EPVR projects, thereby facilitating more informed government decision-making. Key findings reveal the following: (1) A comprehensive “Benefit-Cost” performance evaluation framework, pioneered in this study and tailored specifically for individual EPVR projects, surpasses regional-scale accounting methodologies like Gross Ecosystem Product (GEP). (2) A novel consolidated metric (PCPI) is introduced to integrate ecological, economic, and social dimensions with cost input, thus enabling direct cross-project comparison and classification. (3) The framework operationalizes evaluation by providing a detailed, adaptable indicator system with explicit monetization methods for 26 distinct benefits, thereby bridging the gap between theoretical value accounting and practical project assessment. (4) The empirical application to a drinking water source protection project addresses a critical yet understudied category of EPVR projects, offering insights into “protection-oriented” models. Full article

Joining technologies play a decisive role in the sustainability, circularity, and end-of-life performance of metal structures. Despite the increasing emphasis on low-impact manufacturing and Extended Producer Responsibility (EPR), the connection between joining methods and producers’ environmental obligations remains underexplored. This review provides a comprehensive assessment of conventional and emerging techniques, including fusion welding, solid-state welding, mechanical fastening, adhesive bonding, and hybrid and AM-assisted processes, examining how each technology influences material efficiency, durability, repairability, disassembly, and recyclability. Particular attention is devoted to the effects of joint characteristics on life-cycle impacts, waste generation, and the technical and economic feasibility of high-quality material recovery, using recent LCA evidence and industrial case studies from automotive, shipbuilding, aerospace, and consumer products. Building on this analysis, the review proposes qualitative checklists and semi-quantitative scoring schemes to compare joining options under EPR-relevant criteria and to identify best- and worst-case design scenarios. Finally, promising research directions are outlined, including reversible and debond-on-demand solutions, low-energy solid-state routes, joining strategies for multi-material yet recyclable structures, and the integration of digital twins and LCA-informed design tools, offering a roadmap for metal structures that align technical performance with EPR-driven end-of-life management. Full article

In recent years, hybrid polymer/POSS (Polyhedral Oligomeric Silsesquioxane) systems have attracted particular attention, combining the advantages of organic and inorganic components. This paper reports on the thermal and thermo-oxidative degradation and weathering processes of these materials, as well as their impact on mechanical, chemical, and morphological properties. The paper discusses the physical and chemical changes occurring during degradation, the mechanisms of autoxidation, and the influence of environmental factors such as UV radiation, temperature, and humidity. Particular attention is paid to the role of POSS nanoparticles in polymer stabilization—their barrier function, free radical scavenging, and oxygen diffusion limitation. Methods for analyzing ageing processes are presented, including thermogravimetry coupled with infra-red spectroscopy (TG-FTIR), mechanical property testing, and yellowness index assessment. Material durability prediction models and their importance in designing composite lifespans in the context of the circular economy are also discussed. It is demonstrated that the appropriate type and concentration of POSS (typically 2–6 wt.%) can significantly improve polymer composites’ resistance to heat, radiation, and oxidizing agents, extending their service life and enabling more sustainable lifecycle management of products. Full article

Global demand for food is expected to grow significantly by 2050, underlying the urgency of a sustainable transition in agriculture. In this context, the Circular Economy (CE) paradigm emerges as a promising strategy. This transition is still ongoing, underscoring the importance of sustainability assessment as the first crucial step in supporting this process effectively. Therefore, comprehensive and robust evaluation tools and methodologies are necessary to support effective decision-making processes in this context. This study addresses this topic by conducting a literature review focused on the main evaluation methodologies adopted to assess the sustainability of circular technologies in agriculture, as well as to identify emerging research trends and to identify current knowledge gaps. Therefore, the main objective of this research is to establish a well-defined framework that starting from existing researches, it will support the development of future research directions. The performed review identifies Life Cycle Assessment (LCA) as the most applied methodology for environmental impact assessment, due to its ability to analyze environmental impacts and resources consumption throughout the entire life-cycle of a product, followed by Multi-Criteria Analysis (MCA) and performances-based models for their capacity of integrating and managing many dimensions (environmental, economic, and social) within the evaluation process. Emerging trends highlight the increasing adoption of computational approaches, such as System Dynamics (SD), facilitating a more comprehensive assessment of complex agricultural systems. Despite this increasing attention, the review addresses the significant gap, or rather, the limited management of stakeholders’ conflicts and synergies. This gap will inform potential research directions within the Agritech project, especially regarding the development of Social Multi-Criteria Evaluation (SMCE) to integrate stakeholders’ perspectives in the sustainability assessment of circular technologies. Full article

Honey bees are essential both for many global ecosystems and apicultural production. The management of bee colonies remains labour-intensive, which drives a need for automated solutions. This work presents a proof-of-concept system to monitor honey bee activity by identifying the yearly lifecycle stages exhibited by the colony. A non-invasive vibration monitoring system was developed and placed on top of brood frames in Warsaw-type beehives to collect vibration data over a full apicultural season. The recorded vibration signals were analyzed using both Convolutional Neural Networks (CNNs) and classical machine learning approaches such as the extra trees method. Recursive Feature Elimination with Cross-Validation (RFECV) was performed to isolate the most important frequency bins for lifecycle period identification. The results demonstrate that the critical frequencies for recognizing yearly honey bee activity are concentrated below 1 kHz. The proposed machine learning models achieved a weighted accuracy score of over 95%. These findings have significant implications for future bee monitoring hardware design, indicating that sampling frequencies may be reduced to as low as 2 kHz without significantly compromising model accuracy. Full article

Smart Manufacturing Systems (SMSs) have evolved into intelligent, data-driven ecosystems that integrate cyber–physical systems, digital twins, and artificial intelligence to enhance efficiency, sustainability, and resilience. This review synthesises more than 250 recent studies across four domains: manufacturing technologies, systems management, sustainable production, and human–robot collaboration. In process optimisation, hybrid machine learning and genetic algorithms reduce surface roughness in machining by up to 35% and decrease energy use in additive manufacturing by 20–30%. In systems management, digital twins and reinforcement learning enable adaptive scheduling and predictive maintenance, increasing operational flexibility and reducing industrial downtime. Sustainability-oriented research shows that additive manufacturing can cut energy consumption by up to threefold compared with subtractive routes, while aluminium recycling and hot-forming processes lower life-cycle impacts. Furthermore, the integration of ISO 14001, ISO 50001, and ISO 14040 supports consistent environmental and energy performance assessment across sectors. Building on this evidence, the review critically examines recent developments in manufacturing technologies, systems management, sustainable practices, and human–robot collaboration, highlighting emerging paradigms such as explainable AI and human-centric design that strengthen safety, transparency, and resilience. Open challenges and research opportunities are outlined to guide future innovation toward intelligent, adaptive, and sustainable manufacturing systems. Full article

Big data and artificial intelligence technologies are driving a paradigm shift in smart farming, yet intelligent decision-making faces critical bottlenecks. At the data level, challenges include fragmentation, high acquisition costs, and inadequate secure sharing; at the model level, issues involve regional heterogeneity, weak adaptability, and insufficient explainability. To address these, this paper systematically reviews global research to establish a theoretical framework spanning the entire production cycle. Regarding data governance, trends favor federated systems with unified metadata and layered storage, utilizing technologies like federated learning for secure lifecycle management. For decision-making, approaches are evolving from experience-based to data-driven intelligence. Pre-harvest planning now integrates mechanistic models and transfer learning for suitability and variety optimization. In-season management leverages deep reinforcement learning (DRL) and model predictive control (MPC) for precise regulation of seedlings, water, fertilizer, and pests. Post-harvest evaluation strategies utilize spatio-temporal deep learning architectures (e.g., Transformers or LSTMs) and intelligent optimization algorithms for yield prediction and machinery scheduling. Finally, a staged development pathway is proposed: prioritizing standardized data governance and foundation models in the short term; advancing federated learning and human–machine collaboration in the mid-term; and achieving real-time, ethical edge AI in the long term. This framework supports the transition toward precise, transparent, and sustainable smart agriculture. Full article

The global shift towards sustainable development and low-carbon growth has intensified the need for efficient management of natural resources. This study proposes an integrated economic assessment framework to evaluate how ESG (Environmental, Social, and Governance) integration and circular economy strategies influence resource productivity and long-term economic performance. The research focuses on the water–energy–land nexus as a critical driver of global economic systems. Using a combination of multi-criteria decision analysis (AHP/TOPSIS), material flow analysis (MFA), life-cycle assessment (LCA), and panel econometric modeling on a broad dataset of countries (2018–2023), we examine the relationship between resource efficiency, ESG adoption, and economic competitiveness. The results indicate that circular business models and strong ESG practices significantly reduce resource intensity, enhance total factor productivity, and strengthen economic resilience. Scenario modeling demonstrates that transitioning from linear to circular resource flows can yield substantial economic and ecological benefits, including a ~1–3% rise in GDP and a ~15–20% drop in resource intensity under a high-circularity scenario. These findings provide actionable insights for policymakers and businesses, emphasizing that sustainable resource governance is not only an environmental necessity but also a key driver of global economic transformation. Full article

The transition to electric vehicles (EVs) plays a critical role in reducing global carbon emissions. However, the end-of-life management of electric vehicle batteries (EVBs) presents significant sustainability and operational challenges. This study proposes a blockchain-based framework that enables full lifecycle tracking of EVBs, from production to disposal or reuse, while addressing issues of transparency, efficiency, and regulatory compliance. The framework incorporates a multi-criteria decision model to guide data-driven end-of-life routing—whether for second-life reuse or direct recycling—based on technical, environmental, and economic indicators. By integrating smart contracts with a hybrid web/mobile platform, the system ensures tamper-proof documentation, stakeholder accountability, and compliance with the EU battery passport regulation. A detailed cost analysis of deploying the framework on Ethereum is also presented. The proposed solution aims to enhance the sustainability of EVB management, reduce environmental impact, and promote circular economy practices within the EV industry. Full article

Over-the-air (OTA) firmware updating has become a fundamental requirement in modern Internet of Things (IoT) deployments, where thousands of heterogeneous embedded devices operate in remote and distributed environments. Manual firmware maintenance in such systems is impractical, costly, and prone to security risks, making automated update mechanisms essential for long-term reliability and lifecycle management. This paper presents a unified OTA update architecture for ESP32-based IoT devices that integrates centralized version control and multi-protocol communication support (Wi-Fi, BLE, Zigbee, LoRa, and GSM), enabling consistent firmware distribution across heterogeneous networks. The system incorporates version-compatibility checks, rollback capability, and a server-driven release routing mechanism for development and production branches. An analytical model of timing, reliability, and energy consumption is provided, and experimental validation on a fleet of ESP32 devices demonstrates reduced update latency compared to native vendor OTA solutions, together with reliable operation under simultaneous device loads. Overall, the proposed solution provides a scalable and resilient foundation for secure OTA lifecycle management in smart-industry, remote sensing, and autonomous infrastructure applications. Full article

Cucumber (Cucumis sativus L.) is a globally important crop valued for both fresh consumption and processing, particularly in the United States. It was the first specialty crop among horticultural crops with a publicly available draft genome, providing a foundation for molecular breeding and trait discovery. However, cucumber production faces significant yield losses due to a wide range of biotic stresses. The crop is highly susceptible to fungal, viral, and bacterial pathogens throughout its lifecycle. To combat these challenges, breeders deploy conventional and contemporary breeding strategies to develop disease-resistant cultivars. Advances in high-throughput sequencing and genomic tools, such as quantitative trait loci mapping, genome-wide association studies, and genomic selection, have accelerated the identification and subsequent integration of resistance genes and loci into elite cucumber germplasm. This review highlights recent progress in resistance breeding for biotic stress management in cucumber, with a focus on major diseases caused by fungal, viral, and bacterial pathogens. It emphasizes the role of genomic tools, the discovery of key resistance genes and QTLs, and the potential of modern breeding approaches to improve crop resilience. Continued innovation and integration of emerging technologies will be essential for developing durable, broad-spectrum resistance in future cucumber cultivars. Full article

Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications. Full article

Unmanned Aerial Vehicles (UAVs), particularly multirotor drones, require rigorous structural monitoring to ensure safe and reliable operation. Visual inspections are often inefficient and may miss early signs of damage. Even when faults are detected visually, effective repair requires contextual knowledge such as past repairs, part specifications, and supplier information. This study presents an implemented and experimentally validated closed-loop Product Lifecycle Management (PLM) system that integrates vibration-based structural health monitoring (SHM) with UAV maintenance workflows. A physical quadcopter platform is utilized to collect vibration data for training and testing under eight physically induced single-fault scenarios, including damaged propellers and loosened components. Deep learning models are trained on time-domain vibration data collected from onboard sensors to learn fault patterns and are then deployed in the proposed system for real-time fault classification. The GRU (Gated Recurrent Unit) model is selected for deployment due to its superior performance and lower computational cost and is integrated with a custom-developed UAV data repository within the Aras Innovator PLM platform. Experimental validation shows that the GRU model achieves 99.26% classification accuracy and a macro F1-score of 0.9917, confirming the reliability of the vibration-based fault detection approach. This end-to-end integration enables not only real-time fault detection but also lifecycle traceability, digital documentation, and data-driven maintenance decisions. Experimental validation across test runs confirms that the proposed system accurately detects structural faults and enables automated safety protocols and maintenance workflows. The system improves inspection efficiency and demonstrates how closed-loop PLM can move beyond static documentation to actively monitor, diagnose, and manage UAV health throughout its operational lifecycle. Full article

Soil degradation, declining fertility, and rising greenhouse gas emissions highlight the urgent need for sustainable soil management strategies. Among them, biochar has gained recognition as a multifunctional material capable of enhancing soil fertility, sequestering carbon, and valorizing biomass residues within circular economy frameworks. This review synthesizes evidence from 186 peer-reviewed studies to evaluate how feedstock diversity, pyrolysis temperature, and elemental composition shape the agronomic and environmental performance of biochar. Crop residues dominated the literature (17.6%), while wood, manures, sewage sludge, and industrial by-products provided more targeted functionalities. Pyrolysis temperature emerged as the primary performance driver: 300–400 °C biochars improved pH, cation exchange capacity (CEC), water retention, and crop yield, whereas 450–550 °C biochars favored stability, nutrient concentration, and long-term carbon sequestration. Elemental composition averaged 60.7 wt.% C, 2.1 wt.% N, and 27.5 wt.% O, underscoring trade-offs between nutrient supply and structural persistence. Greenhouse gas (GHG) outcomes were context-dependent, with consistent Nitrous Oxide (N₂O) reductions in loam and clay soils but variable CH₄ responses in paddy systems. An emerging trend, present in 10.6% of studies, is the integration of artificial intelligence (AI) to improve predictive accuracy, adsorption modeling, and life-cycle assessment. Collectively, the evidence confirms that biochar cannot be universally optimized but must be tailored to specific objectives, ranging from soil fertility enhancement to climate mitigation. Full article