Search Results (3,893)

Although previous studies composited response of sea surface temperature (SST) to typhoon sea surface wind (SSW) forcing around typhoon center, how SST responded spatiotemporally along the typhoon track over the ocean remains unclear. Through Empirical Orthogonal Function (EOF) analysis, several isolated typhoons in the Western North Pacific (WNP) from 2021 to 2024 were investigated. Two SSW forcing modes and two SST response modes were identified. The first SSW mode spatially reflects the overall distribution of SSW along the track, centering at its maturation position. And the first SST mode exhibits a high spatial correlation ($\left|R\right|>0.85$) with this SSW mode. The second SSW mode displays a distinct track-long scale dipole pattern along the path of the typhoon, representing its intensity variation during the “development–maturation–decay” lifecycle. Similarly, the second SST response mode shows a significant but lower correlation with this second SSW mode. Both corresponding SST response modes typically lag behind their respective wind-forcing by approximately 2 to 4 days, indicating that these SST response modes are direct reactions to SSW forcing. These cases implies that two track-long scale SSW modes are generally present during the lifecycle of typhoons and that their corresponding SST responses are dominated accordingly. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. Physics-informed machine learning, as an emerging “gray box” paradigm, effectively integrates the advantages of both by embedding physical laws (such as control equations) into machine learning models in the form of constraints, priors, or residuals. This article systematically elaborates on the core fusion mechanism of physics-informed machine learning (PIML) in bridge engineering, innovative applications throughout the entire lifecycle of design, construction, operation, and maintenance, as well as its unique data augmentation strategy. Research has shown that PIML can significantly improve the accuracy and robustness of damage identification, load inversion, and performance prediction, and is the core engine for constructing dynamic and predictive digital twin systems. Despite facing challenges in complex physical modeling, loss function balancing, and engineering interpretability, PIML represents a fundamental shift in bridge health monitoring towards intelligent and predictive maintenance by combining advanced strategies such as active learning and meta learning with IoT technology. Full article

Rising quantities of a broad spectrum of contaminants due to high industrial and residential wastewater effluent loads have further raised the stakes with respect to environmental and health concerns. These demands, coupled with limitations in existing wastewater treatment solutions, have culminated in innovative supplementary solutions in the form of alternative wastewater treatments that, in general, encompass physical, chemical, or biological methods. By quantifying the resource consumption, pollution emissions, and ecological effects across the life-cycle in wastewater treatments, Life-Cycle Assessment (LCA) has proven valuable as a fundamental methodology for assessing and quantifying environment-related sustainability in wastewater treatments. Although valuable in its current applications, LCA is limited in its assessment of the relevant data related to the impacts of construction activities, novel contaminants emerging in wastewater treatment plants, and sludge disposal options. By considering pollutant type, wastewater treatment options, and important LCA methodological considerations, all encompassed within a structured framework including synthesis tables and comparative figures, our hope is that this study will prove valuable to rigorous decision-making processes based on related notions underpinning sustainability concerns in this domain. Full article

This paper presents a digital twin-based lifecycle framework aimed at improving the safety and security of instrumentation and control (I&C) systems for nuclear power plants and small modular reactors. The approach formalizes DT components, functions, and stakeholder interactions across the entire lifecycle, enabling continuous V&V, accelerated commissioning, proactive fault detection, cyber-resilience, and faster and safer modification of I&C algorithms. The methodology is validated through case studies involving DT-supported V-model testing and Markov-based modeling of the intelligent diagnostic system of an NPP pump. The results show that the proposed DT-enabled lifecycle methodology increases test coverage, shortens verification time, and enhances proactive safety and security capabilities of I&C systems. The study outlines future research directions toward adaptive, explainable, and regulation-ready DTs for next-generation nuclear systems. Full article

The UK’s net-zero by 2050 commitment necessitates urgent housing sector decarbonisation, as residential buildings contribute approximately 17% of national emissions. Post-1950 construction prioritised speed over efficiency, creating energy-deficient housing stock that challenges climate objectives. Current retrofit policies focus primarily on technological solutions—insulation and heating upgrades—while neglecting broader sustainability considerations. This research advocates systematically integrating Circular Economy (CE) principles into residential retrofit practices. CE approaches emphasise material circularity, waste minimisation, adaptive design, and a lifecycle assessment, delivering superior environmental and economic outcomes compared to conventional methods. The investigation employs mixed-methods research combining a systematic literature analysis, policy review, stakeholder engagement, and a retrofit implementation evaluation across diverse UK contexts. Key barriers identified include regulatory constraints, workforce capability gaps, and supply chain fragmentation, alongside critical transition enablers. An evidence-based decision-making framework emerges from this analysis, aligning retrofit interventions with CE principles. This framework guides policymakers, industry professionals, and researchers in the development of strategies that simultaneously improve energy-efficiency, maximise material reuse, reduce embodied emissions, and enhance environmental and economic sustainability. The findings advance a holistic, systems-oriented approach, positioning housing as a pivotal catalyst in the UK’s transition toward a circular, low-carbon built environment, moving beyond isolated technological fixes toward a comprehensive sustainability transformation. Full article

Modular construction is generally defined as a typical offsite construction approach that can improve environmental sustainability throughout the building project lifecycle. Based on this situation, identifying the strengths, weaknesses, opportunities, and threats (SWOT) while promoting this sustainable construction method effectively during the urbanisation process is essential. Generally, modular construction is a sustainable building approach that can improve project sustainability, considering the environmental, social, economic, and technological aspects. A comprehensive understanding of the basic situation of prefabricated construction is worthwhile to ensure the widespread adoption of this offsite building method. By employing the SWOT analytical framework, this study adopts a literature review approach to conduct the investigation. In terms of the project results, the core strengths of using modular construction include improving environmental sustainability, enhancing management effectiveness, and improving construction safety and quality. The major weaknesses, on the other hand, are a lack of expertise and research, excessively high initial costs, and difficulties in stakeholder coordination. On the other hand, the major opportunities include promoting the SDGs and other policies, the Industrial Revolution 4.0, and urbanisation and building demands. The main threats, however, include substitute construction technologies, imperfect building codes and standards, and a lack of social and market acceptance. Further research can increase the sample size and collect more accurate firsthand data to validate the results of the current investigation, which can increase the effectiveness of promoting modular construction in the targeted regions. Full article

Bovine viral diarrhea virus (BVDV) causes severe mucosal inflammation in cattle, and effective treatment options remain limited. Dysregulated activation of the NLRP3 inflammasome, driven by NF-κB and STAT3 signaling, may exacerbate disease pathogenesis, highlighting this axis as a potential therapeutic target. Although traditional Chinese medicine has shown promise in antiviral and anti-inflammatory applications, it remains unclear whether it can inhibit BVDV replication via the NF-κB/STAT3-NLRP3 pathway. The present study aimed to clarify the inhibitory effect of luteolin on bovine viral diarrhea virus (BVDV) replication, and to elucidate its underlying mechanisms from two perspectives: interference with viral internalization and replication processes, as well as regulation of the NF-κB/STAT3-NLRP3 inflammasome pathway. Collectively, this work intended to provide experimental evidence and theoretical support for the development of luteolin as a natural anti-BVDV agent. To this end, BVDV-infected MDBK cells were treated with gradient concentrations of luteolin, followed by quantification of viral load using qRT-PCR and Western blot assays. Meanwhile, the activation status of the NF-κB/STAT3-NLRP3 signaling pathway was evaluated via immunofluorescence staining and luciferase reporter gene assays. Our results demonstrate that luteolin exhibits potent dual antiviral activity against cytopathic BVDV-1m in MDBK (Madin-Darby Bovine Kidney) cells, effectively suppressing both viral replication and inflammatory responses. At non-cytotoxic concentrations, luteolin specifically inhibited the internalization and replication stages of the viral lifecycle, accompanied by reduced NS5B polymerase activity. Importantly, luteolin disrupted the NF-κB/STAT3-NLRP3 axis by suppressing phosphorylation of p65 (Ser536) and STAT3 (Ser727), downregulating NLRP3 and pro-caspase-1 expression, and inhibiting caspase-1 cleavage (p20) as well as maturation of IL-1β and IL-18. Consequently, it attenuated the overexpression of TNF-α and IL-8. To our knowledge, this is the first report of a single compound simultaneously targeting multiple stages of the BVDV lifecycle and counteracting NLRP3-mediated immunopathology, offering a strategic basis for developing flavonoid-based therapies against Flavivirus infections. Full article

In this study, the technical, economic, and environmental performances of a Linear Fresnel Reflector (LFR) integrated with an Organic Rankine Cycle (ORC), designed with a non-storage approach, and a monocrystalline photovoltaic (PV) system were comparatively evaluated in meeting a building’s 10 kW electricity demand. Solar-based electricity generation systems play a critical role in reducing carbon emissions and increasing energy self-sufficiency in buildings, yet small-scale, storage-free LFR-ORC applications remain relatively underexplored compared to PV systems. The optimal areas for both systems were determined using the P₁–P₂ methodology. The electricity generation of the LFR-ORC system was calculated based on experimentally measured thermal power output and ORC efficiency, while the production of the PV system was determined using panel area, efficiency, and measured solar irradiation data. System performance was assessed through self-consumption and self-sufficiency ratios, and the economic analysis included life cycle savings (LCS), payback period, and levelized cost of electricity (LCOE). The results indicate that the PV system is more advantageous economically, with an optimal payback of 4.93 years and lower LCOE of 0.053 €/kWh when the economically optimal panel area is considered. On the other hand, the LFR-ORC system exhibits up to 35% lower life-cycle CO₂ emissions compared to grid electricity under grid-connected operation (15.86 tons CO₂-eq for the standalone LFR-ORC system versus 50.57 tons CO₂-eq for PV over 25-year lifetime), thus providing superiority in terms of environmental sustainability. In this context, the study presents an engineering-based approach for the technical, economic, and environmental assessment of small-scale, non-storage solar energy systems in line with the United Nations Sustainable Development Goals (SDG 7: Affordable and Clean Energy and SDG 13: Climate Action) and contributes to the existing literature. Full article

This study systematically examines the policy and technological pathways for the sustainable development of China’s 3D printing industry under the “Dual Carbon” goals. A three-dimensional sustainability framework is developed, integrating resource efficiency, environmental performance, and socio-economic value. Based on this framework, the study conducts a full-process analysis covering design, material preparation, manufacturing, post-processing, use, and recycling stages. The analysis identifies key carbon-reduction mechanisms of 3D printing, including material savings, reduced energy consumption, lightweight-enabled emission reduction, and distributed manufacturing. A comparative analysis of China, the European Union, and the United States reveals major constraints in China’s 3D printing sector, particularly in top-level policy design, standardization systems, legal frameworks, industrial coordination, and low-carbon core technologies. Based on these findings, the study proposes a dual-driven development pathway integrating policy optimization and technological innovation. From an institutional perspective, this pathway emphasizes green policy incentives, including strategic planning, standard setting, green finance, and collaborative governance. From a technological perspective, it highlights the importance of low-carbon material development, refined energy-efficiency management, life-cycle carbon accounting platforms, and value creation across the product life cycle. Overall, the study demonstrates that effective policy–technology synergy is essential for transforming theoretical carbon-reduction potential into scalable and practical outcomes, providing a systematic analytical framework for academic research and actionable guidance for policymakers and industry stakeholders. Full article

Agricultural biomass has potential as a renewable and versatile carbon feedstock for developing eco-friendly and biodegradable polymers capable of replacing conventional petrochemical plastics. To address the growing environmental concerns associated with plastic waste and carbon emissions, lignocellulosic residues, edible crop by-products, and algal biomass were utilized as sustainable raw materials. These biomasses provided carbohydrate-, lipid-, and lignin-rich fractions that were deconstructed through optimised physical, chemical, and enzymatic pretreatments to yield fermentable intermediates, such as reducing sugars, organic acids, and fatty acids. The intermediates were subsequently converted through tailored microbial fermentation processes into biopolymer precursors, primarily polyhydroxyalkanoates (PHAs) and lactate-based monomers. The resulting monomers underwent polymerization via polycondensation and ring-opening reactions to produce high-performance biodegradable plastics with tunable structural and mechanical properties. Additionally, the direct extraction and modification of naturally occurring polymers, such as starch, cellulose, and lignin, were explored to develop blended and functionalized bioplastic formulations. Comparative evaluation revealed that these biomass-derived polymers possess favourable physical strength, thermal stability, and biodegradability under composting conditions. Life-cycle evaluation further indicated a significant reduction in greenhouse gas emissions and improved carbon recycling compared to fossil-derived counterparts. The study demonstrates that integrating agricultural residues into bioplastic production not only enhances waste valorization and rural bioeconomy but also supports sustainable material innovation for packaging, farming, and consumer goods industries. These findings position agriculture-based biodegradable polymers as a critical component of circular bioeconomy strategies, contributing to reduced plastic pollution and improved environmental sustainability. Full article

The increasing complexity of spacecraft On-Board Software (OBSW) necessitates advanced development and testing methodologies to ensure reliability and robustness. This paper presents a digital twin approach for the development and testing of embedded spacecraft software. The proposed electronic digital twin enables high-fidelity hardware and software simulations of spacecraft subsystems, facilitating a comprehensive validation framework. Through real-time execution, the digital twin supports dynamical simulations with possibility of failure injections, enabling the observation of software behavior under various nominal or fault conditions. This capability allows for thorough debugging and verification of critical software components, including Finite State Machines (FSM), Guidance, Navigation, and Control (GNC) algorithms, and platform and mode management logic. By providing an interactive and iterative environment for software validation in nominal and contingency scenarios, the digital twin reduces the need for extensive Hardware-in-the-Loop (HIL) testing, accelerating the software development life-cycle while improving reliability. The paper discusses the architecture and implementation of the digital twin, along with case studies based on a modular OBSW architecture, demonstrating its effectiveness in identifying and resolving software anomalies. This approach offers a cost-effective and scalable solution for spacecraft software development, enhancing mission safety and performance. Full article

Dynamic stress data are essential for evaluating structural fatigue life. To address the challenges of complex test data formats, low data reading efficiency, and insufficient visualization, this study systematically analyzes the .raw and .sie file formats from IMC and HBM data acquisition systems and proposes a unified parsing approach. A lightweight .dac format is designed, featuring a “single-channel–single-file” storage strategy that enables rapid, independent retrieval of specific channels and seamless cross-platform sharing, effectively eliminating the inefficiency of the .sie format caused by multi-channel coupling. Based on Python v3.11, an automated format conversion tool and a PyQt5-based visualization platform are developed, integrating graphical plotting, interactive operations, and fatigue strength evaluation functions. The platform supports stress feature extraction, rainflow counting, Goodman correction, and full life-cycle fatigue damage assessment based on the Palmgren–Miner rule. Experimental results demonstrate that the proposed system accurately reproduces both time- and frequency-domain features, with equivalent stress deviations within 2% of nCode results, and achieves a 7–8× improvement in file loading speed compared with the original format. Furthermore, multi-channel scalability tests confirm a linear increase in conversion time (R² > 0.98) and stable throughput across datasets up to 10.20 GB, demonstrating strong performance consistency for large-scale engineering data. The proposed approach establishes a reliable data foundation and efficient analytical tool for fatigue life assessment of structures under complex operating conditions. Full article

This work advances a 6G-ready, micro-granular SDN fabric that unifies high-performance edge data planes with intent-driven, multi-domain orchestration and cloud offloading. First, edge and cell-site whiteboxes are upgraded with Smart Network Interface Cards and embedded AI accelerators, enabling line-rate processing of data flows and on-box learning/inference directly in the data plane. This pushes functions such as traffic classification, telemetry, and anomaly mitigation to the point of ingress, reducing latency and backhaul load. Second, an SDN controller, i.e., ETSI TeraFlowSDN, is extended to deliver multi-domain SDN orchestration with native lifecycle management (LCM) for whitebox Network Operating Systems—covering onboarding, configuration-drift control, rolling upgrades/rollbacks, and policy-guarded compliance—so operators can reliably manage heterogeneous edge fleets at scale. Third, the SDN controller incorporates a new NFV-O client that seamlessly offloads network services—such as ML pipelines or NOS components—to telco clouds via an NFV orchestrator (e.g., ETSI Open Source MANO), enabling elastic placement and scale-out across the edge–cloud continuum. Together, these contributions deliver an open, programmable platform that couples in-situ acceleration with closed-loop, intent-based orchestration and elastic cloud resources, targeting demonstrable gains in end-to-end latency, throughput, operational agility, and energy efficiency for emerging 6G services. Full article

Digital Twin technology is transforming how buildings are designed, operated, and optimized, serving as a key enabler of smarter, more energy-efficient, and sustainable built environments. By creating dynamic, data-driven virtual replicas of physical assets, Digital Twins support continuous monitoring, predictive maintenance, and performance optimization across a building’s lifecycle. This paper provides a structured review of current developments and future trends in Digital Twin applications within the building sector, particularly highlighting their contribution to decarbonization, operational efficiency, and performance enhancement. The analysis identifies major challenges, including data accessibility, interoperability among heterogeneous systems, scalability limitations, and cybersecurity concerns. It emphasizes the need for standardized protocols and open data frameworks to ensure seamless integration across Building Management Systems (BMSs), Building Information Models (BIMs), and sensor networks. The paper also discusses policy and regulatory aspects, noting how harmonized standards and targeted incentives can accelerate adoption, particularly in retrofit and renovation projects. Emerging directions include Artificial Intelligence integration for autonomous optimization, alignment with circular economy principles, and coupling with smart grid infrastructures. Overall, realizing the full potential of Digital Twins requires coordinated collaboration among researchers, industry, and policymakers to enhance building performance and advance global decarbonization and urban resilience goals. Full article