Search Results (6,219)

The expansion of renewables in modern power systems and the coordinated development of upstream and downstream industrial chains are promoting a shift on the utility side from traditional settlement by energy toward operation driven by data and models. Industrial electricity consumption data exhibit pronounced multi-scale temporal structures and sectoral heterogeneity, which makes unified long-term load and generation forecasting while maintaining accuracy, interpretability, and scalability a challenge. From a modern system identification perspective, this paper proposes a System Identification in Adaptive Bayesian Framework (SIABF) for medium- and long-term industrial load forecasting based on daily freeze electricity time series. By combining daily aggregation of high-frequency data, frequency domain analysis, sparse identification, and long-term extrapolation, we first construct daily freeze series from 15 min measurements, and then we apply discrete Fourier transforms and a spectral complexity index to extract dominant periodic components and build an interpretable sinusoidal basis library. A sparse regression formulation with ${\ell }_1$ regularization is employed to select a compact set of key basis functions, yielding concise representations of sector and enterprise load profiles and naturally supporting multivariate and joint multi-sector modeling. Building on this structure, we implement a state-space-implicit physics-informed Bayesian forecasting model and evaluate it on real data from three representative sectors, namely, steel, photovoltaics, and chemical, using one year of 15 min measurements. Under a one-month-ahead evaluation using one year of 15 min measurements, the proposed framework attains a Mean Absolute Percentage Error (MAPE) of 4.5% for a representative PV-related customer case and achieves low single-digit MAPE for high-inertia sectors, often outperforming classical statistical models, sparse learning baselines, and deep learning architectures. These results should be interpreted as indicative given the limited time span and sample size, and broader multi-year, population-level validation is warranted. Full article

Per- and polyfluoroalkyl substances (PFAS) are a large group of synthetic chemicals used in daily life and industrial production. Due to their widespread use, these compounds are frequently detected in environmental samples. Many studies have shown that PFAS pose a significant threat to both ecological environments and human health, leading to widespread public concern. This study developed and optimized an analytical method for the detection of 32 common PFAS compounds in chemical additives and environmental samples, including oil displacement agents, groundwater and soil, utilizing High-Performance Liquid Chromatography–Quadrupole-Orbitrap High-Resolution Mass Spectrometry (HPLC–Q-Orbitrap HRMS) technology. Applications in an eastern Chinese oilfield revealed significant PFAS accumulation, with ∑PFAS concentrations in groundwater and soil at the well site ranging from 212.29 to 262.80 ng/L and from 23.70 to 71.65 ng/g, respectively, exceeding background levels by 10-fold. The oil displacement agents used in oilfields are one of the important sources of PFAS, particularly p-perfluorous nonenoxybenzenesulfonate (OBS), a perfluorooctanesulfonic acid (PFOS) substitute. Soil analysis indicated greater mobility of short-chain PFAS, while long-chain compounds adsorbed more readily to surface layers. Molecular docking and quantitative structure–property relationship (QSPR) modeling suggest that the bioaccumulation potential of OBS is high and comparable to that of PFOS. Zebrafish embryo assays demonstrated that OBS induced significant concentration-dependent cardiac developmental toxicity, including pericardial edema and apoptosis, showing 1.5–2.4 times greater toxicity than PFOS across multiple endpoints. These findings reveal OBS as a pervasive contaminant with elevated environmental and health risks, necessitating urgent re-evaluation of its use as a PFOS substitute. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

Cross-border e-commerce, as an emerging trade format, offers new chances for optimizing industrial chains’ layout, enhancing economic resilience, and attaining high-quality development at the city level. In this context, treating the execution of the cross-border e-commerce comprehensive pilot zone (CBEC) as a quasi-natural experiment, this study subtly attests to how the CBEC affects urban entrepreneurship by using a difference-in-differences (DID) technique. The results exhibit that the CBEC greatly promotes urban entrepreneurship, which is supported by some robustness tests, including instrumental variable testing and placebo testing. Heterogeneity analysis reveals that in cities with more developed economies, stronger digitalization, richer cultures, sounder law rules, and better business environments, the benefit for the CBEC on entrepreneurship is more significant. Mechanism testing argues that the CBEC promotes urban entrepreneurship through talent aggregation and industrial upgrading. Precisely, the more concentrated high-quality talents are and the more advanced the industrial structure is, the higher the urban entrepreneurship. More importantly, the CBEC exhibits a spatial spillover effect on entrepreneurship, promoting local entrepreneurship while stimulating the motivation to imitate and learn in neighboring areas, thereby driving their entrepreneurship. The findings offer a viable decision-making guide for building a unified factor market and achieving regional coordinated development. Full article

Reverse engineering (RE) has been increasingly adopted in metal manufacturing to digitize legacy parts, connect “as-is” geometry to mechanical performance, and enable agile repair and remanufacturing. This review consolidates scan-to-simulation workflows that transform 3D measurement data (optical/laser scanning and X-ray computed tomography) into simulation-ready models for structural assessment and manufacturing decisions, with an explicit focus on sustainability. Key steps are reviewed, from acquisition planning and metrological error sources to point-cloud/mesh processing, CAD/feature reconstruction, and geometry preparation for finite-element analysis (watertightness, defeaturing, meshing strategies, and boundary condition transfer). Special attention is given to uncertainty quantification and the propagation of geometric deviations into stress, stiffness, and fatigue predictions, enabling robust accept/reject and repair/replace choices. Sustainability is addressed through a lightweight reporting framework covering material losses, energy use, rework, and lead time across the scan–model–simulate–manufacture chain, clarifying when digitalization reduces scrap and over-processing. Industrial use cases are discussed for high-value metal components (e.g., molds, turbine blades, and marine/energy parts) where scan-informed simulation supports faster and more reliable decision making. Open challenges are summarized, including benchmark datasets, standardized reporting, automation of feature recognition, and integration with repair process simulation (DED/WAAM) and life-cycle metrics. A checklist is proposed to improve reproducibility and comparability across RE studies. Full article

To meet 2050 climate targets, the construction sector must reduce CO₂ emissions and transition toward circular material flows. Recycled aggregates (RA) derived from construction and demolition waste (CDW) and industrial byproducts such as oil shale ash (OSA) show potential for use in concrete, although their application remains limited by standardisation and performance limitations, particularly in structural uses. This study aims to develop and evaluate low-strength, resource-efficient concrete mixtures with full replacement of natural aggregates (NA) by CDW-derived aggregates, and partial or full replacement of cement CEM II by OSA–metakaolin (MK) binder, targeting non-structural 3D-printing applications. Mechanical performance, printability, cradle-to-gate life cycle assessment, eco-intensity index, and transport-distance sensitivity for RA were assessed to quantify the trade-offs between structural performance and global warming potential (GWP) reduction. Replacing NA with RA reduced compressive strength by ~11–13% in cement-based mixes, while the aggregate type had a negligible effect in cement-free mixtures. In contrast, full cement replacement by OSA-MK binder nearly halved compressive strength. Despite the strength reductions associated with the use of waste-derived materials, RA-based cement-free 3D-printed specimens achieved ~30 MPa in compression and ~5 MPa in flexure. Replacing CEM II with OSA-MK and NA with RA lowered GWP by up to 48%, with trade-offs in the air-emission, toxicity, water and resource categories driven by the OSA supply chain. The cement-free RA mix achieved the lowest GWP and best eco-intensity, whereas the CEM II mix with RA offered the most balanced multi-impact profile. The results show that regionally available OSA and RA can enable eco-efficient, structurally adequate 3D-printed concrete for construction applications. Full article

The sustainability of the beef and dairy industry requires a systems approach that integrates environmental stewardship, social responsibility, and economic viability. Over the past two decades, global genetics consortia have advanced data-driven germplasm programs (breeding and conservation programs focusing on genetic resources) to enhance sustainability across cattle systems. These initiatives employ multi-trait selection indices aligned with consumer demands and supply chain trends, targeting production, longevity, health, and reproduction, with outcomes including greenhouse gas mitigation, improved resource efficiency and operational safety, and optimized animal welfare. This study analyzes strategic initiatives, germplasm portfolios, and data platforms from leading genetics companies in the USA, Europe, and Brazil. US programs combine genomic selection with reproductive technologies such as sexed semen and in vitro fertilization to accelerate genetic progress. European efforts emphasize resource efficiency, welfare, and environmental impacts, while Brazilian strategies focus on adaptability to tropical conditions, heat tolerance, and disease resistance. Furthermore, mathematical models and decision support tools are increasingly used to balance profitability with environmental goals, reducing sustainability trade-offs through data-driven resource allocation. Industry-wide collaboration among stakeholders and regulatory bodies underscores a rapid shift toward sustainability-oriented cattle management strategies, positioning genetics and technology as key drivers of genetically resilient and sustainable breeding systems. Full article

Reproductive performance in livestock and poultry is a core determinant of economic efficiency in the animal industry. While traditional research has primarily focused on genetics, endocrinology, and immune regulation, emerging microbiome studies reveal that commensal microbiota within the gut and reproductive tracts play an underestimated yet pivotal role in host reproductive health. This review systematically synthesizes recent advances regarding the relationship between the microbiome and reproductive functions in major livestock species (cattle, pigs, sheep, and chickens). We first delineate the theoretical basis and mechanisms of the “gut-reproductive axis,” highlighting cross-system communication mediated by microbial metabolites, including short-chain fatty acids (SCFAs), indoles, and bile acids. Subsequently, we provide an in-depth comparative analysis of the microecological features of both female (vagina/uterus) and male (semen/epididymis) reproductive systems, examining their impacts on fertility, sperm quality, and pregnancy outcomes. Furthermore, we explore the molecular and systemic mechanisms governing microbial regulation of reproduction, encompassing the modulation of the hypothalamic-pituitary-gonadal (HPG) axis, the balance of local mucosal immunity and inflammation, and epigenetic regulation. Finally, we address current challenges—such as causal validation and the scarcity of multi-species databases—and propose future directions, including spatial multi-omics, AI-integrated analysis, and microbial intervention strategies. Ultimately, this review aims to offer a theoretical foundation and translational insights for elucidating reproductive regulatory networks and developing microbiome-driven precision strategies to enhance reproductive performance. Full article

As transparency and sustainability gain strategic importance, the mass balance approach under chain of custody (MB-CoC) has become a central mechanism for assessing product carbon footprints (PCFs) in complex chemical value chains. The MB-CoC enables the attribution of renewable and recycled feedstock characteristics via certified bookkeeping when physical segregation or molecular tracing is infeasible—thus complementing PCF methodologies based on ISO 14067 and the LCA standards ISO 14040/44. However, the methodological integration of the MB-CoC into ISO-conformant PCFs remains insufficiently defined and empirically underexplored. This paper systematically reviews the interaction between the MB-CoC and PCF/LCA frameworks. It (i) synthesizes the allocation rules of ISO 14040/44/67 and the attribution principles of the MB-CoC according to ISO 22095 and key industry initiatives; (ii) analyzes academic publications, guidelines, and corporate applications; and (iii) identifies methodological tensions concerning system boundaries, allocation logic, residual mixes, treatment of biogenic and recycled carbon, and risks of double counting. Our review reveals five recurring insights across the literature: the need for certification and standardization; the importance of primary data and residual mixes; the requirement for ISO conformity; the necessity of transparent reporting of conventional versus alternative inputs; and the lack of independent empirical case studies. Addressing these gaps through harmonized rules, residual mix development, and comparative applications will be essential for establishing the MB-CoC as a robust instrument for circularity, decarbonization, and regulatory compliance, developed by interdisciplinary research and industry approaches. Full article

Improving the thermodynamic and economic performance of industrial refrigeration systems is essential for reducing energy consumption and enhancing cold chain sustainability. This study presents an integrated energy, exergy, and exergoeconomic assessment of a full-scale two-stage ammonia (R717) vapor compression refrigeration system operating under real industrial conditions in Türkiye. Experimental data from 33 measurement points were used to perform component-level thermodynamic balances under steady-state conditions. The results showed that the evaporative condenser exhibited the highest heat transfer rate (426.7 kW), while the overall First Law efficiency of the system was 63.71%. Exergy analysis revealed that heat exchangers are the dominant sources of irreversibility (>45%), followed by circulation pumps with a notably low Second Law efficiency of 11.56%. The exergoeconomic assessment identified the circulation pumps as the components with the highest loss-to-cost ratio (2.45 W/USD). An uncertainty analysis confirmed that the relative ranking of system components remained robust within the measurement uncertainty bounds. The findings indicate that, although the existing NH₃ configuration provides adequate performance, significant improvements can be achieved by prioritizing pump optimization, maintaining higher compressor loading, and implementing advanced variable-speed fan control strategies. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

The transition toward a Circular Economy (CE) has received significant attention from academia, industry, and policymakers; however, manufacturing practices remain predominantly linear, generating waste and inefficiencies. This study addresses the lack of accessible sustainability assessment methods by introducing the Circularity Calculator (CC), a novel tool for evaluating circular strategies during the early phases of process development. Unlike existing assessment frameworks, which often require extensive data and customization, the CC can be integrated directly to existing processes to combine environmental and economic impact into a streamlined evaluation process for early decision-making. The research involves collaboration with a leading German automotive manufacturer. Site visits and interviews enabled the identification of material flows and primary waste streams, which informed the definition of relevant indicators. The CC generates a dimensionless index, enabling comparison and prioritization of proposed scenarios without relying on supply-chain-wide data, which is often unavailable at early stages. Implications demonstrate the adaptability of the CC across industrial contexts, supporting conceptual planning and operational phases. Its intuitive design facilitates adoption by practitioners without extensive expertise in sustainability. The tool represents an advance in CE assessment, contributing to Sustainable Development Goals (SDGs) 9, 12, and 17 by promoting sustainable industrial practices, resource circularity, and collaborative evaluation frameworks. Full article

The automotive industry’s shift to a Circular Economy for global sustainability is vital, but it faces challenges when establishing efficient Reverse Supply Chains. Reverse Supply Chain implementation is dependent on multiple barriers and enablers, including eco-nomic, managerial, technological, regulatory, and social domains, thus making single-factor solutions ineffective. The purpose of this review is to conduct a systematic literature review to understand how these interconnected barriers and enablers can collectively shape Reverse Supply Chain implementation and performance, specifically within the automotive sector, which remains little known. The PRISMA framework was utilised, which resulted in 129 peer-reviewed articles being selected for review. Findings showed that the literature focuses primarily on Electric Vehicle batteries within developing economies, particularly China. Reverse Supply Chain implementation is governed not only by isolated barriers but by complex systemic interdependencies between enablers as well. This complex inter-relationship between barriers and enablers can be categorised into five key dimensions: economic and financial; managerial and organisational; technological and infrastructural; policy and regulatory; and market and social. The study reveals two systemic patterns driving the transition: technology–policy interdependence and the conflicting relationship between large-scale production and value extraction. Our findings also presented a research agenda focusing on strategic value creation through material streams of automotive electronics, plastic, and composites with high potential value, and further insights are needed in regions such as the Middle East, Oceania, and the Americas. Organisations should consider Reverse Supply Chain as a strategic approach for securing critical material supplies, while policymakers could leverage the use of digital tools as the foundational infrastructure for subsidies allocation and prevent fraud. Full article

With the development of the Internet of Things (IoT) and smart industry, the demand for high-precision indoor positioning is becoming increasingly urgent. Ultra-ideband (UWB) technology has become a research hotspot due to its centimeter-level ranging accuracy, good penetration, and high multipath resolution. However, in complex environments, it still faces problems such as high cost of anchor node layout, gross errors in observation data, and difficulty in eliminating systematic errors such as electronic time delay. To address the aforementioned problems, this paper proposes a comprehensive UWB indoor positioning scheme. By constructing virtual reference stations to enhance the observation network, the geometric structure is optimized and the dependence on physical anchors is reduced. Combined with a gross error elimination method under short-baseline constraints and a double-difference positioning model including virtual observations, it systematically suppresses systematic errors such as electronic delay. Additionally, a quality control strategy with velocity constraints is introduced to improve trajectory smoothness and reliability. Static experimental results show that the proposed double-difference model can effectively eliminate systematic errors. For example, the positioning deviation in the Xdirection is reduced from approximately 2.88 cm to 0.84 cm, while the positioning accuracy in the Ydirection slightly decreases. Overall, the positioning accuracy is improved. The gross error elimination method achieves an identification efficiency of over 85% and an accuracy of higher than 99%, providing high-quality observation data for subsequent calculations. Dynamic experimental results show that the positioning trajectory after geometric enhancement of virtual reference stations and velocity-constrained quality control is highly consistent with the reference trajectory, with significantly improved trajectory smoothness and reliability. In summary, this study constructs a complete technical chain from data preprocessing to result quality control, effectively improving the accuracy and robustness of UWB positioning in complex indoor environments, and exhibits promising engineering application potential. Full article

Intensive livestock and aquaculture systems require high-quality feeds with the correct nutritional composition. The decrease in wild fish proteins has led to demands within the feed supply chain for new alternatives to fulfil the growing demand for protein. In this context, edible insects like the yellow mealworm (Tenebrio molitor) have the greatest potential to become a valid alternative source of proteins. This study evaluated the growth performance and nutritional profile of yellow mealworm larvae reared under laboratory conditions on eight different agro-industrial by-products: wheat middling, durum wheat bran, rice bran, hemp cake, thistle cake, dried brewer’s spent grains, dried tomato pomace, and dried distilled grape marc. The quantitative and qualitative impacts of rearing substrates on larvae were compared. The results showed that larvae adapt well to different substrates with different nutritional compositions, including the fibrous fraction. However, substrates affect larval growth feed conversion and larval macro composition. Hemp cake stood out for its superior nutritional value, as reflected by its high protein content and moderate NDF (Neutral Detergent Fiber) levels, which determine fast larval growth. On the contrary, imbalanced substrate lipid or carbohydrate content (rice bran), as well as the presence of potential antinutritional compounds (thistle cake), appeared to negatively affect growth performances. Full article