Search Results (1,180)

Extreme hydro-meteorological events are intensifying under climate change, disproportionately disrupting last-mile healthcare in flood-prone geographies. In this study, flood-adaptive primary care clinics (FAPCCs) integrated with islandable smart microgrids and a rapid-deploy medical technology stack (MedTech) are developed and evaluated to ensure continuity of essential services (triage, maternal and child health, vaccination cold-chain, minor procedures, diagnostics, and telemedicine) during fluvial, pluvial, and coastal flooding. Evidence on resilient health facilities, microgrid architectures, distributed energy resources, and modular clinical systems is presented in a multi-layer systems design: (1) a modular, amphibious, and elevatable clinic chassis; (2) a photovoltaic–battery–diesel hybrid system with demand-aware energy management; (3) redundant connectivity long-term evolution/fifth-generation, satellite, and very high frequency; (4) a rapid-deploy MedTech kit including point-of-care diagnostics, low-temperature cold-chain, negative-pressure isolation, and sterilization modules; and (5) flood-aware logistics using unmanned aerial vehicle/unmanned surface vehicle. A mixed-integer linear programming sizing is formulated and dispatched with a continuity-of-care reliability metric that couples energy availability to clinical throughput. Simulation across three archetypal sites (peri-urban delta, inland riverine, coastal estuary) shows that FAPCCs achieve the service availability of higher than 99.5% across 7-day grid outage scenarios while reducing fuel use by 62–81% relative to diesel-only baselines, maintaining vaccine temperatures within 2–8 °C with <0.1% thermal excursion time, and sustaining telemedicine quality of service with <150 ms median uplink latency in hybrid networks. A life-cycle cost analysis indicates a 7.1–9.8 year discounted payback from fuel displacement and avoided service loss. Deployment playbooks and policy guidance are also proposed for Ministries of Health and Disaster Agencies in monsoon-impacted regions. Full article

Growing global environmental awareness has fueled a “green” market, but also a confusing array of information, raising risks of misinformation. In response, sustainability certifications and instruments have become crucial tools in the cosmetics industry. However, the rapid spread of these ecolabels has created new problems, including market fragmentation, consumer confusion, and heightened concerns about greenwashing. This study conducts a systematic comparative analysis of 24 prominent sustainability schemes within the cosmetics sector. We developed an analytical framework to assess each instrument across three dimensions: (i) value chain coverage (from sourcing to end-of-life), (ii) corporate sustainability scope (environmental, social, governance), and (iii) verification and transparency mechanisms. The results reveal a fragmented landscape with significant scope imbalances. Most instruments robustly cover upstream impacts (e.g., ingredient sourcing), but downstream phases—including consumer use, packaging, and circularity—are markedly under-addressed. At the corporate level, environmental criteria dominate, with social and governance dimensions inconsistently integrated. Verification rigor and transparency vary widely, with many relying on confidential audits or self-declaration. In conclusion, while valuable as market instruments, prevailing certifications are insufficient as standalone assurance tools. The findings highlight a misalignment with emerging regulations, underscoring the need for greater lifecycle integration, enhanced transparency, and alignment with comprehensive corporate sustainability frameworks. Full article

Advanced manufacturing is undergoing a profound transformation, with data quickly becoming its most strategic asset. The industry is pushing toward Industry 4.0 with its sights already on the human-centric Industry 5.0. Manufacturing firms are rapidly integrating AI, IoT, and advanced analytics to enable real-time decision making, predictive maintenance, and full manufacturing lifecycle optimization. However, this data-driven revolution exposes a critical vulnerability: the hidden direct costs and cascading downstream consequences of inaccurate, missing, or corrupt data. This paper provides an in-depth examination of the data quality crisis facing modern manufacturing, exploring its quantifiable impact on cost, safety, and strategic decision making; and identifies the tangible barriers preventing scalable AI in manufacturing today. We investigate how bad data undermines the digital thread, erodes both operational and strategic trust, and stalls the transition to autonomous systems. Supported by recent industry surveys, academic findings, and leading trends, we reveal that most manufacturers suffer from systemic data quality issues, with billions lost annually to inefficiencies, rework, and flawed decisions. Addressing this, the paper evaluates state-of-the-art solutions for real-time data validation, anomaly detection, and predictive imputation. Building upon this, we identify key gaps—including the lack of unified data quality frameworks, integration across legacy/modern systems, and actionable imputation under uncertainty—and propose a roadmap to bridge them. The paper concludes by outlining four research directions that support a seamless, scalable transition toward a trustworthy data foundation in manufacturing. Industry 4.0/5.0 is defined by data, insight, and actionable intelligence: only manufacturers that tame their data chaos will thrive. Full article

The municipal solid waste management sector is a nationally significant greenhouse gas source in Sri Lanka, yet decision makers lack comprehensive, city-level life-cycle assessment of full waste management chains. This study quantifies and compares greenhouse gas emissions and mitigation potential of alternative waste management scenarios for Colombo and Kandy, supporting nationally determined contributions (NDC) 3.0. Using IPCC 2021 GWP100 V1.03 as the impact assessment method, six scenarios were assessed, including business-as-usual, recycling, composting, confined cover windrow composting, anaerobic digestion, refuse-derived fuel production, incineration, pyrolysis, co-processing in cement kilns, open dumping, and sanitary landfilling. The business-as-usual scenario, dominated by open dumping, resulted in the highest greenhouse gas emissions in both Colombo and Kandy. In contrast, the integrated waste management approach (Scenario 3), combining anaerobic digestion, confined cover windrow composting, refuse-derived fuel production, and enhanced recycling, converted both cities from net emitters to net carbon sinks. Over the projection period of 2026–2035, this transition is expected to deliver substantial cumulative emission reductions, contributing significantly toward achieving NDC 3.0 waste sector targets in Sri Lanka despite the relatively small share of national baseline emissions in the sector. These findings highlight the strong mitigation potential of integrated waste management systems for advancing low-carbon urban strategies. Full article

Dryland farming on the Loess Plateau faces significant challenges due to water scarcity and low nitrogen use efficiency. Although conventional urea sustains crop yields, it is associated with elevated greenhouse gas emissions and nitrogen losses. Despite growing interest in both slow-release fertilizers and plastic mulching, their environmental footprints remain insufficiently evaluated. This study, therefore, aimed to identify a management strategy that maximizes productivity while minimizing environmental impacts. Using a life-cycle assessment (LCA) framework, we compared four cultivation strategies, flat cultivation with urea (NU), flat cultivation with slow-release fertilizer (NS), mulching with urea (PU), and mulching with slow-release fertilizer (PS), each at nitrogen rates of 125, 225, and 325 kg ha⁻¹. The results demonstrated that PS reduced the carbon footprint per unit of net ecosystem economic benefits (NEEB) by 3.74–27.86% and the nitrogen footprint per unit of NEEB by 10.48–47.41%. At 225 kg ha⁻¹, PS increased grain yield and NEEB by 7.40% and 9.87%, respectively, compared to 125 kg ha⁻¹. Compared to 325 kg ha⁻¹, the 225 kg ha⁻¹ rate improved energy use efficiency by 19.81% while reducing carbon emissions, carbon, and nitrogen footprint per unit of NEEB by 10.29%, 14.36%, and 24.47%, respectively. In conclusion, mulching combined with slow-release fertilizer at 225 kg ha⁻¹ represents a balanced and regionally appropriate strategy, achieving strong agronomic performance alongside reduced environmental costs and improved economic returns. Full article

Elevated nutrient inputs exacerbate the conflict between the advancement of fruit production and environmental sustainability. Quantifying the emission-reduction potential of fruit production systems, predicting environmental impacts, and identifying key orchard management practices are critical to promoting the sustainability of fruit production. However, predictive models for orchard environmental impact are primarily based on machine-learning approaches and fail to adopt an efficiency-oriented perspective to quantify emission reductions in orchards with high yields and high partial factor productivity of nitrogen fertilizer (PFP-N). Therefore, this study adopts life-cycle assessment, a deep-learning predictive model, and a slack-based measure (SBM)-undesirable model to evaluate and forecast the environmental impacts of orchards, which encompasses global warming potential (GWP), reactive nitrogen losses (Nr), acidification potential (AP), and eutrophication potential (EP), while also identifying the mitigation potential of orchards. In addition, local sensitivity analysis reveals the extent to which each input variable affects the model predictions. The results indicated that the emission-reduction potential for the high yield and high PFP-N group was quantified as 53.31%, 52.28%, 50.54%, and 52.65% for GWP, Nr, AP, and EP, respectively. The application amount of nitrogen fertilizer is the largest contributing factor among the four environmental impacts (GWP, Nr, AP and EP). These findings are helpful for assessing and predicting environmental impacts, quantifying emission-reduction potential, and determining the relative importance of agricultural input factors associated with environmental impacts, thereby providing potential theoretical support for promoting sustainable orchard development. Full article

The growing adoption of life cycle assessment (LCA) across productive sectors has yet to be systematically examined in terms of its capacity to drive environmental transformation beyond methodological assessment. This systematic review (2018–2024) explores how LCA functions as a catalyst for environmental change in products, processes, and systems. Following PRISMA 2020 guidelines, 657 records from Scopus, Web of Science, and ScienceDirect were screened, yielding 50 high-quality studies assessed using the Critical Appraisal Skills Programme (CASP) tool; bibliometric network analysis via VOSviewer complemented qualitative thematic synthesis. Findings reveal a shift from conventional standardized life cycle assessment methodologies toward integrated frameworks such as LCSA, incorporating regionalized characterization factors, uncertainty quantification, and digital technologies. Applications across energy, agri-food, manufacturing, construction, and waste management support SDGs 12, 13, and 9 by identifying hotspots, comparing technologies, and informing policy. However, inconsistencies in functional units, system boundaries, and impact methods, alongside limited social and economic integration, restrict cross-study comparability. The evidence indicates that LCA is evolving from an assessment tool into a deliberative decision-making infrastructure, requiring harmonized yet context-specific methodologies and robust social indicators for equitable implementation. This review offers original value by combining bibliometric and critical methodological synthesis to map how life-cycle thinking induces environmental transformation, revealing the gap between evaluative capacity and transformative implementation. Full article

Phase change materials (PCMs) have emerged as a key enabler of high-performance, low-carbon buildings through latent heat-based thermal energy storage. This paper presents a systematic and critical synthesis of advances in PCM technologies for building applications published between 2022 and 2025, analyzing over 300 peer-reviewed studies to evaluate thermal performance, economic viability, environmental impact, and climate adaptability across three integration approaches: passive, active, and hybrid systems. The studies analyzed show that passive envelope integration employing macroencapsulated or form-stable PCMs in walls, roofs, and glazing is reported to deliver 15–45% energy savings with payback periods of 8–15 years, primarily through enhanced thermal inertia and indoor temperature stabilization. Active systems, which couple PCMs with HVAC, heat pumps, or air handling units, are found to achieve 20–40% energy reductions and shorter payback periods (3–8 years) by enabling load shifting, peak shaving, and improved coefficient of performance (COP). Hybrid configurations integrating passive and active strategies with AI-driven control demonstrate, in the literature, the highest potential, with reported energy savings of up to 50%, though they entail greater complexity and capital cost. The review further highlights material-level innovations, including ternary composite PCMs, bio-based alternatives, and nano-enhanced formulations that address intrinsic limitations such as low thermal conductivity (0.1–0.3 W/m·K for organics) and cycling instability. Despite significant progress, critical gaps persist in standardized testing protocols, long-term field validation, comprehensive lifecycle assessments, and real-world scalability, particularly in tropical and cold climates. By bridging material science, building physics, and energy system engineering, this work provides a forward-looking roadmap to accelerate the deployment of PCM-based solutions in the global decarbonization of the built environment. Full article

The high-penetration integration of renewable energy into port power systems is challenged by the stochastic volatility of wind–solar generation and dynamic load demands. To address this, this study proposes a data-driven bi-level coordinated planning framework for port wind–solar-storage systems, integrating a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and hybrid secretary bird optimization algorithm (SBOA) for solution seeking. The WGAN-GP-K-Means++ framework is adopted to capture the high-dimensional spatiotemporal correlations under the uncertainty of source ports and loads, and to generate the wind and solar resource scenarios for typical day. Subsequently, a bi-level planning model is constructed: the upper layer optimizes the siting and sizing of distributed generation and energy storage to minimize the life-cycle net present value, while the lower layer minimizes annual operating costs through multi-scenario dispatch. To resolve the resulting complex mixed-integer programming problem, a nested SBOA-Gurobi algorithm is developed. Case study of a Guangxi port demonstrates that the proposed approach reduces life-cycle cost by 44.94% relative to the baseline grid-connected scheme and exhibits superior convergence stability compared with GA, GRSO, and WOA. Additionally, sensitivity analysis quantifies the impact of electricity pricing policies, shore power utilization rates, and discount rate on the system’s economic benefits. This study provides a decision-support tool for the low-carbon transition and economic planning of port energy systems. Full article

This study investigated an intense and unusual summer transboundary dust storm event that occurred between 21 and 23 June 2024. By integrating remote sensing observations, reanalysis data, WRF-Chem simulations, and LAGRANTO trajectory tracking, we systematically revealed the dust emission, transport, deposition, and formation mechanisms of this event. The dust primarily originated from the Gobi region of southern Mongolia, where concentrations exceeded 10,000 µg m⁻³, and decayed exponentially as the Mongolian cyclone moved southeastward. Post border-crossing into China, the event transitioned to blowing and floating dust, with concentrations decreasing significantly. During transport, dry deposition dominated the source area and the frontal part of the transport path in the early stages, while wet deposition was associated with the precipitation system of the Mongolian cyclone and concentrated north and east of the cyclone’s track. On 21 June 2024, the average wind speed in the source region reached 11.35 ms⁻¹, the highest recorded in the past 45 years. This was attributed to surface anomalies, including reduced soil moisture, poor vegetation cover, higher temperatures, and decreased precipitation relative to the multi-year average. The comprehensive application of multi-source data and models in this work elucidates the full lifecycle of this rare summer dust event, providing scientific insights into the atmospheric processes governing extreme dust events and their transboundary impacts. Full article

Thorium (Th), a naturally occurring actinide, is gaining renewed attention due to its dual role as a strategic nuclear resource and a potential environmental contaminant. This review critically reassesses thorium valorization pathways by integrating extraction technologies, environmental behavior, toxicological risks, and regulatory constraints. While thorium is primarily recovered as a by-product of rare earth element (REE) processing, conventional hydrometallurgical methods—though mature—generate significant secondary waste and pose environmental challenges. Emerging technologies, such as functionalized adsorbents, membrane systems, and biohydrometallurgy, show promise but remain largely confined to laboratory-scale studies due to scalability and stability issues. A key finding is that thorium’s environmental mobility and toxicological impact are directly influenced by the extraction processes used, creating species with distinct bioavailability and risk profiles. This work highlights the disconnect between high laboratory efficiencies and real-world applicability, emphasizing the need for integrated approaches that consider lifecycle impacts, waste minimization, and occupational safety. We propose a circular economy framework for sustainable thorium management, connecting green primary processing, secondary recovery from industrial residues, smart environmental stewardship, and supportive policy. The review concludes that successful thorium valorization depends not on incremental efficiency gains but on holistic designs that reconcile technological performance with environmental and health safeguards. Full article

The environmental impact and overall sustainability of buildings is commonly evaluated through life-cycle assessments. Yet, building professionals often lack a clear understanding of the magnitude and distribution of associated impacts. Therefore, this study analyses the embodied impact in particular, as the relative share of the embodied impact increases in the context of highly energy-efficient buildings. This paper draws on a dataset of 108 single-family dwellings, 10 multi-family buildings, and 8 non-residential buildings. First, the balance between embodied and operational impacts is quantified. Subsequently, the study identifies the contribution of building elements and bill-of-quantities categories, and the influence of material choices. For single-family dwellings, this is followed by a stepwise reduction analysis to demonstrate how material choices can mitigate embodied impacts. Finally, the cases are benchmarked against European thresholds. Results show that an average share of 62–72% and 47% originates from embodied emissions, for residential and non-residential buildings, respectively. Floors represent the largest contributors across all building types. The slab on grade dominates in SFHs and internal floors in multi-storey buildings. In addition, interior finishes and structures also account for significant embodied impacts. Furthermore, for single-family dwellings, informed material selection can reduce embodied impacts by up to 60%. Full article

The triplex reciprocating drilling pump is a critical piece of equipment in drilling platforms, and the operational condition of its core component—the valve body—directly affects the pump’s performance and the stability of the entire system. Therefore, accurate prediction of the valve body’s Remaining Useful Life (RUL) is of great significance for ensuring the safe operation of drilling pumps and enabling predictive maintenance. However, achieving this goal involves two major challenges: (1) The complex degradation process of the valve body, which involves strong impact loads, nonlinear wear, and coupling effects between fluid and mechanical systems, makes it difficult to establish a stable degradation model and achieve accurate RUL prediction. (2) There is a lack of publicly available real-world datasets for research purposes. To address these challenges, we propose CEEMDAN-BWO-optimized Bidirectional LSTM for Remaining Useful Life prediction (CB²-RUL). The method first applies Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to the raw vibration signals for decomposition and denoising, thereby improving signal stationarity and enhancing feature representation. Next, the Black Widow Optimization (BWO) algorithm is employed to automatically tune key hyperparameters of a Bidirectional Long Short-Term Memory (BiLSTM) network. Finally, the optimized BiLSTM captures the temporal evolution patterns of valve-body degradation and produces high-accuracy RUL estimates. Finally, to verify the effectiveness of the proposed approach, we constructed a real-world dataset named VB-Lifecycle, which comprises ten valve bodies from different positions within the equipment and spans the complete lifecycle from pristine condition to failure. Extensive experiments conducted on the VB-Lifecycle dataset demonstrate that the proposed method provides accurate RUL prediction for valve bodies. Full article

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle (ICL) software suite. The approach automates the import of urban geometry from OpenStreetMap and integrates geolocated weather data, enabling users to efficiently test scenarios involving NBSs and surface material modifications. Outdoor thermal comfort is quantified using the Universal Thermal Climate Index (UTCI), with results visualized through an interactive cloud-based 3D platform to support participatory urban planning. The methodology is demonstrated in Meunierstraat, Leuven (Belgium), where three planning alternatives are compared across seasonal extremes. Simulations show that targeted NBS interventions, particularly temporary participatory measures, can improve thermal comfort under extreme heat. However, the benefits are seasonally dependent and spatially heterogeneous, emphasizing the value of high-resolution, scenario-based analysis. This integrated workflow enhances both technical evidence and stakeholder engagement. While the tool is capable of linking outdoor comfort improvements with building energy performance and carbon emissions, the present paper focuses solely on the outdoor thermal comfort results, leaving indoor–outdoor coupling analysis as a direction for future work. Full article

Digitalisation is reshaping shipyard production, yet its methodological foundations remain fragmented across simulation, optimisation, Artificial Intelligence (AI), and Digital Twin (DT) research streams. This paper presents a domain-specific methodological review of shipyard production modelling from 2010 to 2025, synthesising advances in Discrete-Event Simulation (DES), multi-objective optimisation, hybrid simulation–optimisation architectures, Machine Learning (ML), reinforcement learning (RL), and DT-enabled cyber-physical systems. Using an explicit evaluative framework based on integration depth, validation basis, and decision scope, the review differentiates between analytically mature but execution-decoupled DES/optimisation approaches and integration-rich yet variably validated DT and AI-driven systems. The analysis shows that hybrid DES-optimisation frameworks currently represent the most operationally credible class of methods, delivering measurable production improvements under structured conditions, whereas many DT and AI contributions prioritise architectural integration and data synchronisation over longitudinal yard-wide KPI validation. A comparative assessment of simulation platforms, optimisation engines, and manufacturing execution system/enterprise resource planning/product lifecycle management infrastructures highlights the central role of structured product–process–resource data and execution-layer connectivity, while severe confidentiality constraints and the scarcity of openly available industrial datasets continue to limit reproducibility and benchmarking. Overall, shipyard production research is progressing toward increasingly integrated and cyber-physical systems, but sustained yard-scale validation and shared benchmark development remain critical prerequisites for translating architectural sophistication into demonstrable operational impact. Full article