Search Results (45,349)

Climate risk increasingly threatens regional industrial chain resilience, while artificial intelligence (AI) offers new tools for mitigating its effects. Using panel data for 167 Chinese prefecture-level cities from 2008 to 2023, this study examines the impact of climate risk on regional industrial chain resilience and the moderating role of AI. Climate risk significantly weakens resilience, whereas AI mitigates this adverse effect. Both effects are significant in eastern, northeastern, and coastal regions and in large and industrial cities. AI’s moderating role is more pronounced in cities with lower AI development, while heat and drought cause greater damage. The mechanism analysis shows that climate risk significantly inhibits industrial structure upgrading, total factor productivity, and industrial co-agglomeration. AI can alleviate these negative effects. Climate risk also generates negative cross-regional spillovers, whereas AI produces positive moderating spillovers. Both effects are concentrated within the first three years after a shock. These findings clarify how climate risk propagates through industrial chains and demonstrate AI’s value in risk governance, highlighting the need for timely and context-specific deployment of intelligent technologies. Full article

This article presents a critical review of the development of window systems used in civil engineering, interpreting them not as discrete construction products, but as complex technical and material systems whose actual value emerges from the interdependence of structural configuration, material composition, thermal performance, durability, and manufacturing and implementation constraints. The review discusses the evolution of windows from simple envelope elements providing daylight, ventilation, and weather protection into advanced building-envelope systems associated with energy efficiency, occupant comfort, in-service durability, and environmental responsibility. Particular attention is given to the principal families of window systems, including PVC-U, aluminium, timber, steel, façade, hybrid, and composite-based solutions. The analysis shows that improving the thermal insulation of a single component is not, in itself, a sufficient criterion for evaluating system quality. Declared performance may be constrained by thermal bridges at the installation interface, ageing of sealing systems, imperfections in joining processes, material deformation, and difficulties related to repair, disassembly, and recycling. From the perspective of sustainable construction, window systems should therefore be assessed across their whole life cycle, taking into account energy effectiveness, in-service stability, technological feasibility, renovation potential, and the possibility of closing material loops. The review also identifies the need for further research into integrated assessment methods, the long-term durability of advanced frame systems, the role of the window-to-wall interface, and verifiable strategies for circularity. Full article

Confronted with severe global challenges, including water scarcity, excessive use of chemical fertilizers and pesticides, and heavy metal contamination in soils, conventional agricultural technologies exhibit marked limitations in integrated water–fertilizer management and non-point source pollution control. Leveraging their excellent water retention capacity, intelligent sustained-release properties, and environmental responsiveness, hydrogels offer innovative solutions to advance sustainable agricultural development. This review comprehensively outlines the fundamental types, crosslinking mechanisms, and key functional properties of hydrogels, with a focused discussion on their agricultural deployment as high-efficiency soil conditioners, fertilizer vectors, and pesticide carriers; it deciphers the microscopic water-holding mechanisms under the tristate water model, delineates the divergent water-uptake and retention behaviors between ionic and non-ionic hydrogels, and clarifies the cyclic water-holding and release mechanisms of hydrogels during soil amelioration. Thise paper further synthesizes hydrogel-enabled environmental remediation applications, in which heavy metals and pesticide residues in soils and aquatic systems are removed via functional-group coordination adsorption or photocatalytic degradation; concurrently, hydrogels have been shown to activate plant systemic immunity through calcium-signaling pathways, thereby inducing broad-spectrum antiviral defense responses. Moreover, hydrogels can be integrated into precision agriculture frameworks to enable real-time monitoring of crop physiological status and to support targeted irrigation and fertilization management. This work also evaluates the role of hydrogels in promoting seed germination, root system development, crop metabolic regulation, and stress resilience, while introducing tailored application strategies across distinct plant growth stages. Their documented economic advantages include water conservation, enhanced crop yields, reduced dependence on synthetic fertilizers, and lower labor costs. Nevertheless, the large-scale implementation of hydrogels continues to face multifaceted challenges—particularly poor degradability and latent ecological risks, as conventional polyacrylamide (PAM)-based gels resist soil mineralization and retain potentially neurotoxic monomers, leaving a critical gap in multi-annual field data concerning their non-target interference with native soil aggregate evolution, pore distribution, and rhizospheric carbon–nitrogen footprints. Mechanistically, many hydrogels with tensile strengths below 1 MPa are highly susceptible to three-dimensional network collapse under high-salinity osmotic shock and tillage mechanical stress, exhibiting a precipitous drop in water retention after more than three wet–dry cycles due to deficient long-term structural stability. Compounding these technical gaps are elevated production costs and low farmer adoption, driven by the absence of texture-specific performance thresholds—such as an available water increment ≥ 40% for sandy soils—and the lack of established life-cycle cost models and farmer incentive mechanisms for bio-based hydrogels. Moving forward, hydrogel technology should pivot toward materials innovation and cost-reduction engineering to broaden its applicability, employ ≥3-year, multi-habitat regional trials to delineate ecological benefit–risk boundaries, and ultimately position hydrogels as pivotal enablers of sustainable, green agricultural paradigms. Full article

With extremely low odor thresholds and potent flavor activity, sulfur-containing aroma compounds (SACs) constitute the molecular cornerstone of characteristic flavors in meat, coffee, and fermented foods. Research has advanced from early component identification to the elucidation of structure–activity relationships, olfactory receptor recognition mechanisms, and food-flavor improvement. This review first summarizes the detection and quantification methods for SACs, their distribution in foods, key odor contributions, and major formation pathways. It then highlights progress in understanding molecular structural parameters, olfactory receptor recognition, and computational simulations that decode flavor perception mechanisms. From a translational perspective, we further discuss flavor retention in real food matrices, off-flavor regulation, cross-modal perceptual enhancement, and functional applications. Current challenges include food matrix complexity, high compound reactivity, and nonlinear olfactory combinatorial coding. Future directions involve constructing a multiscale predictive framework integrating neuroscience, developing explainable artificial intelligence to decode olfactory coding, and advancing closed-loop green biomanufacturing for the precise design and sustainable production of SACs. Full article

Robotic joint motor drive systems must combine torque density and dynamic response with low mechanical impedance, safe interaction, and thermal robustness. This review treats gear ratio as a system-level design coordinate realized jointly by the motor, transmission, thermal path, sensing, and control. It synthesizes how ratio selection changes torque–speed capability, reflected inertia, losses, thermal duty, reducer nonidealities, backdrivability, and control bandwidth. The proposed spectrum uses nominal ratio as its primary coordinate while treating reducer topology, application domain, integration level, and compliance as distinct, overlapping descriptors. Mechanism-level conclusions are based on peer-reviewed studies; manufacturer specifications, open-source structures, and model-based engineering examples are identified and interpreted within narrower evidence boundaries. Representative robotic-joint cases connect these mechanisms to application demands, and an iterative framework translates the synthesis into checks on the task envelope, motor–reducer matching, thermal feasibility, transmission nonlinearity, sensing, and control. Relative to gearbox-centered reviews and task-specific motor–transmission optimization studies, this review provides a cross-domain decision map rather than a product ranking or universal predictive model. Full article

Hierarchical retail forecasting requires predictions that are both accurate and coherent across multiple planning levels, from total demand to individual product–store series. Because these forecasts guide inventory, replenishment, storage, and distribution decisions, improving their coherence and reliability can support more efficient resource use, reduce avoidable overstock and product waste, and limit the need for emergency logistics. This study investigates how global deep-learning architectures interact with post hoc reconciliation in point and probabilistic forecasting. Using an M5-derived hierarchical and grouped structure comprising 42,840 series, we compare three MLP-oriented models, MLP, N-BEATS, and N-HiTS, with five transformer-based models, Transformer, Temporal Fusion Transformer, Informer, PatchTST, and Autoformer. All models are evaluated under a common 28-day forecasting horizon, temporal partition, Optuna-based tuning protocol, and three complete seeded runs. Coherence is imposed using Bottom-Up reconciliation and four MinTrace variants, while probabilistic forecasts are generated through residual-block bootstrap reconciliation. Point and probabilistic performance are assessed level-wise and globally using MASE and scaled CRPS, respectively. The results show that the strongest transformer-based combination outperforms the strongest MLP-based combination at every hierarchy level. PatchTST combined with MinTrace-WLS-struct is particularly effective at aggregate and intermediate levels, achieving a Total-level MASE of 0.537 and sCRPS of 0.037. At the Product–Store level, Bottom-Up reconciliation becomes preferable, with the Transformer attaining the lowest MASE of 1.367 and sCRPS of 0.912. Because granular series dominate the hierarchy-wide average, the lowest overall MASE is obtained by TFT with Bottom-Up reconciliation (1.428), whereas the lowest overall sCRPS is shared by the Transformer and Informer with Bottom-Up reconciliation (0.813). These findings demonstrate that neither the forecasting architecture nor the reconciliation method should be selected independently of hierarchy depth and forecasting objective. Strategic and tactical levels benefit primarily from PatchTST with MinTrace reconciliation, whereas highly granular operational forecasting favors Bottom-Up reconciliation with transformer-based models. From a sustainability perspective, this level-aware framework provides a basis for aligning forecasting decisions with resource efficiency, waste reduction, service reliability, and greater resilience across the retail supply chain. Full article

Human milk (HM) represents the physiological reference for infant digestion, whereas infant formulas (IFs) differ in protein composition and lipid architecture. This study compared the gastric and intestinal digestion of HM, five commercial IFs, whole milk powder (WMP), and protein ingredients using a standardized infant in vitro model. Protein hydrolysis was assessed through digestibility and residual subunit patterns, and lipid digestion was evaluated through free fatty acid release and fatty acid profiling. Gastric digestion revealed pronounced differences driven by intrinsic structure and processing, whereas intestinal digestion reduced these disparities, although distinct subunit patterns persisted. Lipid analyses showed clear source-dependent separation before digestion and partial convergence during intestinal lipolysis. Several IFs containing structured lipid and vegetable-oil systems showed intestinal fatty acid or free fatty acid profiles closer to HM than WMP, whereas samples with stronger bovine-fat signatures remained more distinct. Overall, the results demonstrate that digestion-product patterns provide mechanistic insight into structure-driven digestion behavior in complex dairy-based matrices, beyond compositional comparison alone. Full article

Peru holds a prominent position in international fresh asparagus trade, with a strong export profile and a well-established presence in global markets. Nevertheless, sustaining this position requires diversification toward a more resilient and sustainable portfolio of destination markets. This study assessed the global competitive sustainability of Peru’s asparagus agro-export industry during 2010–2024. Official data from the World Bank, Veritrade, CEPII, and the Peruvian Ministry of Foreign Trade and Tourism (MINCETUR) were analyzed using a quantitative longitudinal single-case study design with descriptive and explanatory components. The results show that the Herfindahl–Hirschman Index (HHI) for Peruvian asparagus exporters remained low, indicating limited firm-level concentration and a relatively diversified supply structure. Thus, Peruvian asparagus exports did not depend on a single dominant firm but were distributed among multiple exporters. In contrast, the HHI for destination markets revealed high geographic concentration of demand. Regarding competitiveness, Peru maintained a strong net-export position, with trade competitiveness index values close to 1. The Revealed Symmetric Comparative Advantage (RSCA) index showed favorable performance in markets such as the United States, Spain, The Netherlands, and the United Kingdom, although this advantage had not yet reached full structural stability. The gravity model, estimated using EGLS with Panel-Corrected Standard Errors (PCSE), identified destination-country economic size as the most important determinant of Peruvian asparagus exports. Geographic distance constrained exports, whereas trade agreements did not show a consistent statistical effect. Potential expansion opportunities were identified in China, Australia, Norway, Ecuador, Sweden, Austria, Russia, Poland, New Zealand, and Romania. Consequently, the competitive sustainability of Peruvian asparagus depends not only on preserving export leadership but also on reducing destination-market concentration, improving logistical efficiency, expanding production capacity, and directing trade policy toward markets with favorable economic fundamentals. Full article

Romanian agriculture is characterized by fragmented land structures, limited capitalization, and high labour dependence, conditions that may constrain the economic performance of family farms. This study integrates two complementary components: (i) a systematic literature review conducted within the PRISMA 2020 framework, synthesizing evidence from 34 empirical studies concerning Romanian and Central and Eastern European family farms; and (ii) an original sectoral analysis based on publicly available Eurostat data for 2016–2025, including national trends and NUTS-2 regional comparisons. The reviewed evidence indicates that market integration, production specialization, technological adoption, investment capacity, and human capital are frequently associated with stronger labour-productivity outcomes, whereas land fragmentation and surplus labour are more frequently associated with weaker performance, particularly among smaller farms. The Eurostat analysis identifies an overall upward, although fluctuating, trajectory in nominal agricultural output and gross value added per AWU. The polynomial models provide a descriptive fit to these temporal patterns but do not identify their causal determinants. Overall, the findings support a size-differentiated interpretation of labour-productivity patterns and underline the relevance of targeted policies addressing the different structural conditions of Romanian family farms. Full article

Agricultural and food-processing residues represent abundant renewable feedstocks for the sustainable production of functional oligosaccharides through biocatalytic conversion. Valorization of these underutilized biomass resources represents a promising strategy for producing high-value bioactive oligosaccharides while improving resource efficiency and supporting the circular bioeconomy. This review discusses current developments in the biocatalytic synthesis of functional oligosaccharides from agro-food residues, with emphasis on residue-specific biocatalytic techniques and carbohydrate-active enzymes (CAZymes). Along with new transglycosylation and glycosynthase-based methods for the synthesis of structurally defined oligosaccharides, the catalytic mechanisms and substrate specificity of hydrolytic enzymes involved in the synthesis of xylo-, cello-, pectic-, and chitooligosaccharides are critically discussed. Recent advances in biocatalytic platforms, such as multi-enzyme cascade systems, whole-cell biocatalysis, and immobilized enzyme technologies, are highlighted as promising strategies for improving catalytic efficiency, product selectivity, and operational performance, together with future opportunities in enzyme engineering and computationally assisted biocatalyst development. This review offers a biocatalyst-centered perspective for the sustainable production of functional oligosaccharides and provides insights into the development of effective biomass valorization strategies for future industrial biorefineries by linking residue-specific biomass resources with current biocatalytic approaches. Full article

The U.S. FSMA Intentional Adulteration (IA) Rule and GFSI-recognized certification schemes such as IFS Food and BRCGS Food include requirements for establishing and maintaining a written food defense plan, including vulnerability assessment. This study evaluates the suitability and policy relevance of two established food defense risk assessment methodologies, TACCP and CARVER+SHOCK, when applied to the processing of whole oranges with edible coatings. A structured case study was conducted at a medium-sized citrus processing facility certified under IFS Food and BRCGS Food standards. Food defense threats were systematically identified along the entire production line using the FDA Food Defense Self-Assessment Tool. The identified threats were subsequently assessed using TACCP, which is based on likelihood and impact, and CARVER+SHOCK, a multidimensional quantitative approach integrating operational, economic, and psychological impact factors. Twenty-four potential intentional contamination threats were identified. Both methodologies consistently identified critical vulnerabilities at key control points, particularly in washing systems, chemical handling areas, and stages with unrestricted access. TACCP proved to be rapid, intuitive, and easily integrated into existing food safety management systems. In contrast, CARVER+SHOCK provided a more differentiated ranking of the identified threats because of its multidimensional scoring structure, thereby supporting the development of a structured and economically rational mitigation plan and offering value for policymakers and certification bodies seeking to harmonize risk-assessment practices. Full article

Recent advances in digital technologies are transforming the production and visualization of 3D models for Augmented Reality (AR), Virtual Reality (VR), and cultural heritage reconstruction. Within this evolving context, ensuring scientifically grounded, transparent, and interpretable reconstruction processes remains essential, particularly for the hypothetical reconstruction of lost or unbuilt architecture. This article discusses the theoretical framework defined by the Critical Digital Model (CDM) and the Scientific Reference Model (SRM), both of which aim to define the 3D model as a scientific product generated through a transparent and falsifiable research process. The proposed approach integrates a structured reconstruction methodology based on source analysis, semantic segmentation, and iterative validation, distinguishing between Raw and Informative Models and applying these principles to selected case studies. Attention is devoted to visualization as an integral component of the reconstruction process. Rather than serving solely as a presentation tool, visualization is examined as a means of analysis, interpretation, and communication of uncertainty. Different visualization strategies—including photorealistic, non-photorealistic, uncertainty-driven, and diplomatic representations—are assessed together with XR visualization modalities, from static images and spherical panoramas to fully interactive VR environments. The results demonstrate how immersive and methodologically grounded visualization approaches can enhance both scholarly investigation and public dissemination, supporting informed choices according to specific research and communication objectives. Full article

Background: High-mix manufacturing environments are characterized by increasing product variety, dynamic material flows, and growing operational complexity, creating significant challenges for intralogistics layout redesign. Existing research addresses manufacturing complexity, facility layout planning, simulation, and multi-criteria decision-making; however, these approaches are typically applied independently and provide limited guidance for systematically translating manufacturing complexity into layout redesign decisions. Methods: This study proposes the Complexity-Driven Intralogistics Layout Redesign Framework (CDILRF), which integrates manufacturing complexity assessment, Complexity Translation, layout design, digital layout evaluation, and structured decision support into a unified redesign methodology. The framework was demonstrated through an industrial case study in a high-mix automotive manufacturing environment. Results: The framework transformed manufacturing complexity into structured layout redesign requirements, supported the evaluation of five layout alternatives, and identified the preferred redesign solution digital material-flow analysis and multi-criteria assessment. The selected layout reduced logistics effort by approximately 37% while increasing available warehouse space. Conclusions: CDILRF establishes an explicit link between manufacturing complexity assessment and intralogistics layout redesign through a structured, transparent decision-support process. The framework contributes to manufacturing logistics research and provides a practical methodology for data-driven intralogistics layout redesign in high-mix manufacturing environments. Full article

Increasing variability in electricity production and demand contributes to greater electricity price volatility across different markets. The effective implementation of business processes related to broadly defined electricity trading therefore requires reliable electricity price forecasts. Consequently, electricity price forecasting has grown in both importance and research interest. This article presents an in-depth literature review of short-term point electricity price forecasting at the native temporal resolutions of the reviewed markets, primarily hourly and 30 min intervals, with selected studies using 15 min and 5 min intervals. The review concerns forecasts of individual market-interval prices and does not address forecasts of daily aggregated prices. The analysis covers the input data used in forecasting models, the main forecasting approaches, modelling techniques, and error measures. Forecast quality is examined with respect to market characteristics, forecasting methods, and explanatory variables. General trends in the reported results are identified, together with descriptive relationships among selected error measures. Particular attention is given to the RMSE-to-MAE ratio, referred to in this review as the Error Dispersion Factor (EDF), which is treated solely as a descriptive summary of the relative inequality of absolute forecast-error magnitudes in a given sample. The article concludes with findings and recommendations concerning best practices in electricity price forecasting. The review differs from broader conceptual and market-specific surveys by focusing narrowly on short-term point forecasts for individual market delivery intervals and by providing a structured quantitative synthesis of studies published between January 2021 and May 2026. Full article

Generative artificial intelligence (GenAI) is being absorbed into education as a new infrastructural layer, promising individualized tutoring, instant feedback, translation, and accessibility support at marginal cost. Sustainable Development Goal 4 (SDG 4) calls for inclusive and equitable quality education for all, yet the evidence on whether GenAI advances or undermines that goal points firmly in both directions at once. At the task level, GenAI and intelligent tutoring systems repeatedly compress performance distributions, with the largest gains accruing to lower-skilled and lower-baseline participants. At the system level, a parallel literature on access, AI literacy, language, disability, teacher capacity, and over-reliance finds that benefits are conditioned by resources that track prior advantage. These two literatures are usually read as being in tension, and the tension is usually resolved by privileging one of them. This article argues that both are correct and that the appearance of contradiction is an artifact of conflating distinct stages of a single pathway. We develop the divide cascade: a four-stage filter model—access, effective use, benefit realization, and durable learning—in which each stage has a pass rate that correlates with prior advantage. Because pass rates compound multiplicatively across stages while compression acts additively within a stage, a technology can compress outcomes among those who clear every filter and still stratify outcomes across the population as a whole. We formalize this structure, derive the condition under which the stratifying force dominates the equalizing one, and state three predictions that distinguish the cascade from an access-centred account: that access-only interventions should attenuate rather than close benefit gaps, that a single intervention can narrow one gap while widening another simultaneously, and that measured equity gains should decay as the evaluation horizon lengthens. We also specify what would falsify the model. The framework is then used to identify the conditions that set the sign of GenAI’s distributional effect, to map those conditions onto SDG 4 targets, and to derive a testable research and policy agenda. A recurring corollary is methodological: the strongest evidence for compression comes from workplace-productivity studies that measure produced artifacts rather than durable learning, so its transfer to education is an open question that the cascade locates precisely rather than assumes. GenAI, we conclude, is neither inherently an equalizer nor an amplifier; it is a multiplier whose sign is set by how completely the cascade is engineered for the learners who start behind. Full article