Search Results (1,532)

Due to the perishable nature of products, high uncertainty, and conflicting objectives, food supply chain logistics management requires dynamic and adaptive decision-making frameworks. In this study, an integrated decision-making architecture is presented that integrates a multi-objective fuzzy optimization model into an adaptive digital twin along with an agentic AI-based dynamic goal reset mechanism. The main methodological innovation of this study is not in the separate development of each of these components but in their structured integration in the form of a self-regulating decision-making loop in which the priority of goals is dynamically adjusted based on the current state of the system. Computational results based on real and simulated data show that the proposed framework reduces the total logistics cost by about 4–5% and reduces product waste by about 13% while simultaneously improving the service level by about 4%. Resilience analysis shows faster performance recovery in the face of operational disruptions, and scalability results confirm the controlled growth of computational time with increasing problem size. These findings demonstrate the effectiveness of integrating adaptive digital twins and agentic AI in a multi-objective fuzzy optimization environment for intelligent and resilient food logistics management. Full article

Lithium is a critical mineral for traction batteries and a cornerstone of the sustainable transition toward low-carbon transportation. Understanding the supply–demand dynamics and resource-saving potential of lithium is essential for advancing circular economy goals and ensuring the long-term stability of the electric vehicle (EV) industry. This study develops an integrated lithium forecast framework by coupling a System Dynamics (SD) model with dynamic Material Flow Analysis (MFA) and multi-scenario pathways. To ensure robust conclusions, the model is validated against historical data, and a multi-level sensitivity analysis is conducted to address the inherent uncertainties of evolving socio-technical assumptions over a ten-year horizon. The simulation results reveal that under the baseline scenario, China’s EV stocks and annual lithium demand will grow by 8.3 and 4.7 times from 2024 to 2035, respectively. This rapid expansion poses a significant sustainability challenge, as cumulative demand will deplete 50–71% of China’s domestic lithium reserves by 2035. Despite a projected supply–demand gap of 110–120 kt/yr, the study identifies critical pathways for resource decoupling and circularity. Technology-driven interventions, such as enhancing energy density and extending battery lifespan, can reduce primary lithium demand by up to 18.9%. Furthermore, optimizing the closed-loop recycling system can contract the supply–demand gap by 31–39%, demonstrating the pivotal role of secondary resource recovery in building a resilient supply chain. Despite this reduction, a persistent reliance on international markets remains inevitable. These findings provide a quantified scientific foundation for policymakers, emphasizing that lithium security requires a synergistic transition from volume-based subsidies to resource efficiency mandates and standardized, formal closed-loop recycling systems. Full article

Promoting the commercialization of agricultural science and technology (S&T) achievements is a critical pathway toward achieving agricultural sustainability and a key governance challenge in advancing global food security and the Sustainable Development Goals (SDGs). However, China faces a structural paradox: despite sustained expansion of policy supply, the performance gains in technology commercialization remain limited. To uncover the underlying causes, this study integrates Latent Dirichlet Allocation (LDA) topic modeling with the Policy Modeling Consistency (PMC) index to conduct a systematic analysis of 82 central-level policy documents issued between 2015 and 2025. The findings reveal that policy attention is heavily concentrated on upstream R&D support, while insufficient emphasis is placed on downstream “last-mile” enablers—such as diffusion services, risk-sharing mechanisms, and intermediary capacity building. Moreover, many policies exhibit structural deficiencies in temporal specificity and multi-actor coordination, which hinder the formation of closed-loop implementation chains. The results suggest that policy structural inconsistency may be a key mechanism constraining policy effectiveness. By adopting a dual analytical lens of “attention allocation–structural design,” this study provides empirical evidence for optimizing policy formulation and enhancing institutional efficacy in agricultural S&T commercialization. Full article

Accurate time-domain photovoltaic (PV) models are needed to evaluate performance under outdoor variability beyond STC datasheet conditions. This paper presents a traceable modeling workflow based on the standard single-diode formulation, implemented in MATLAB/Simulink (R2023a) as a modular white-box architecture that explicitly resolves photocurrent generation and loss mechanisms (diode recombination, shunt leakage, and series resistance effects) with temperature-consistent propagation through V_T(T) and saturation-current terms. The method couples optical boundary conditions to the electrical model by embedding plane-of-array (POA) excitation via the incidence angle Θ(t) and roof albedo directly into the photocurrent source term, preserving the causal chain from mounting geometry to electrical response. Calibration is separated from prediction by initializing key parameters using the standard Simulink PV block and then freezing them for time-domain evaluation. The workflow is validated on a 395 W rooftop prototype using 1 min resolved POA irradiance (ISO 9060:2018 Class A radiometric chain) and module temperature (IEC 60751 Class A Pt100), synchronized with electrical measurements. Over a multi-week campaign, the model exhibits high fidelity, with a worst-case relative current error of ~1.1% and a consistently low bias and dispersion, quantified by ME, MAE, RMSE, σ_e, and thresholded MAPE. Full article

To address the challenges commonly encountered in civil aircraft operating under multi-mode, strongly coupled closed-loop control—namely scarce fault samples, pronounced distribution shift, and root-cause explanations that are easily confounded by covariates—this paper proposes a root-cause analysis method that integrates ensemble learning with constraint-guided reasoning by large language models (LLMs). First, for Full Authority Digital Engine Control (FADEC) monitoring sequences, a feature system comprising environment-normalized ratios, mechanism-informed mixing indices, and multi-scale temporal statistics is constructed, thereby improving cross-mode comparability and enhancing engineering-semantic expressiveness. Second, in the anomaly detection stage, a cost-sensitive LightGBM model is adopted and a validation-set-based adaptive thresholding strategy is introduced to achieve robust identification under highly imbalanced fault conditions. Furthermore, for Root Cause Analysis (RCA), a “computation–reasoning decoupling” framework is developed: Shapley Additive exPlanations (SHAP) are used to generate segment-level contribution evidence, while causal chains, engineering prohibitions, and structured output templates are injected into prompts to constrain the LLM, enabling it to infer root-cause candidates and produce structured explanations under mechanism-consistency constraints. Experiments on real flight data demonstrate that our method yields an anomaly detection F1-score of 0.9577 and improves overall RCA accuracy to 97.1% (versus 62.3% for a pure SHAP baseline). Practically, by translating complex high-dimensional data into actionable natural language diagnostic reports, the proposed method provides reliable and interpretable decision support for rapid RCA. Full article

The kapok (Ceiba pentandra) fiber industry plays a vital role in Indonesia’s rural bioeconomy, particularly in regions with high production intensity such as Pasuruan Regency. Despite its economic potential and alignment with the green economy agenda, the industry faces increasing volatility due to seasonal harvest cycles, climate-induced disruptions, global demand fluctuations, and exchange rate instability. These conditions necessitate an adaptive and predictive approach to supply chain risk governance. We evaluated the performances of predictive analytics models, including linear regression, random forest, gradient boosting, XGBoost 3.2.0 libraries, K-nearest neighbors, and stacking regressor. Using multi-year monthly data on production volume, residual stock, and exchange rates, the stacking regressor was the most accurate model, achieving the lowest root mean square error and highest R² values. The results bridge the gap by applying predictive analytics to a resource-based, seasonal small industry sector. Practically, the results also enable leveraging AI in strengthening the long-term sustainability of agribusiness supply chains. Full article

Background/Objectives: Iron deficiency anemia (IDA) is a widespread nutritional disorder characterized by impaired iron absorption, inflammation-associated iron restriction, and disrupted iron homeostasis. Increasing evidence suggests that gut microbiota play an important role in iron metabolism; however, the mechanisms underlying probiotic-assisted iron supplementation remain unclear. Our research group previously conducted in vitro fermentation screening experiments and obtained a bacterial strain, B. lactis Ca360, which possesses iron absorption-enhancing activity. Methods: In this study, an IDA mouse model induced by a low-iron diet was used to investigate whether B. lactis Ca360 could synergistically improve iron metabolism when combined with iron supplementation. Mice were treated with FeSO₄ alone or FeSO₄ combined with B. lactis Ca360, and hematological parameters, organ indices, serum iron-related markers, histopathological changes, duodenal iron metabolism-related gene expression, hepatic inflammatory responses, gut microbiota composition, short-chain fatty acid (SCFA) levels, and correlation networks were analyzed. Results: Iron deficiency induced typical anemia phenotypes, multi-organ dysfunction, intestinal iron absorption dysregulation, hepatic inflammation, and gut microbiota dysbiosis. Compared with FeSO₄ alone, the combined intervention more effectively improved hematological parameters, reduced organ indices, restored liver and spleen histological integrity, normalized intestinal iron metabolism-related gene expression, and alleviated hepatic inflammation. In addition, B. lactis Ca360 markedly reshaped gut microbiota composition, enriching SCFA-producing anaerobic genera, including Ruminococcus, Roseburia, Acetatifactor, Intestinimonas, Eubacterium_coprostanoligenes_group_unclassified, and Oscillibacter, accompanied by increased acetate, propionate, and butyrate levels. Spearman correlation analysis further revealed close associations between gut microbiota remodeling, improved iron metabolism, reduced inflammatory status, and recovery of anemia-related phenotypes. Conclusions: Overall, these findings demonstrate that B. lactis Ca360 enhances the efficacy of iron supplementation by modulating SCFA-producing and anti-inflammatory gut microbiota, thereby coordinately regulating intestinal iron absorption, inflammation, and systemic iron homeostasis, supporting probiotic-assisted iron supplementation as a promising nutritional strategy for IDA management. Full article

This paper investigates the asynchronous non-fragile $H_{\infty }$ control problem for a class of Markovian jump singularly perturbed systems (MJSPSs) with time-varying delays. By applying a multi-layer structure method, a non-fragile controller with time delay is designed for the MJSPSs to adapt to disturbances caused by nonstationary quantization and DoS attacks. To model the asynchronous dynamics between the system and the controller mode, an independent Markov chain is employed to capture the asynchronous quantization and control behavior. By constructing mode-dependent Lyapunov–Krasovskii functions, sufficient conditions are derived to ensure stochastic finite-time exponential stability and $H_{\infty }$ performance under conditions of delay, singular disturbances, and quantization uncertainty. The effectiveness of the method is validated using an inverted pendulum system controlled by a DC motor, demonstrating its ability to achieve robust stability and performance in bandwidth-constrained network environments. Full article

Aiming at the star centroid positioning error in dynamic star simulators, a super-resolution star map correction method is proposed based on a multi-layer perceptron (MLP). A complete technical chain of “system calibration–aberration field modeling–network correction” is constructed to establish a data-driven end-to-end framework for unified modeling and compensation of optical aberrations, assembly deviations, and device discreteness. Experimental results show that the proposed method achieves sub-pixel accuracy: the maximum star centroid and inter-star angular distance errors are reduced by 22.9% and 37.5% on average, respectively, which is significantly superior to traditional methods. This work provides a reliable technical approach for high-precision star map display and star sensor ground calibration, with clear engineering application value. Full article

Sliding contact wear at the pantograph–catenary interface directly impacts the current collection performance and power supply reliability of electrified railways. Addressing the challenges in multi-environmental wear studies—namely, fragmented modeling chains, inconsistent parameter calibrations, and prohibitive computational costs that hinder horizontal comparisons—this study develops an equivalent parameterized modeling framework tailored for engineering assessment. The framework encapsulates environmental effects as equivalent load increments and interface coefficient corrections, facilitating efficient multi-scenario parameter scanning within a 3D contact model. Findings reveal that environmental factors drive wear through a distinct “pressure-wear” nonlinear decoupling mechanism. In sandy environments, abrasive-mediated micro-cutting dominates, leading to a monotonic surge in wear depth as sand concentration increases, despite a buffered contact pressure response. In icing conditions, the synergy of low-temperature brittleness and geometric impact renders hotspot wear highly sensitive to temperature fluctuations. For salt spray conditions, the environmental impact is represented via equivalent corrections to the interfacial parameters; within this equivalent framework, the results suggest that salt spray intensity has a more pronounced effect on wear accumulation than humidity alone. This work reveals the divergence of dominant damage pathways across environments, offering a quantitative basis for the differentiated maintenance and remaining life estimation of pantograph–catenary systems in extreme climates. Full article

The digital transformation of the textile industry poses unique challenges due to its labor-intensive processes, complex global supply chains, and coexistence of traditional methods and emerging technologies. Despite the urgency of this transition, existing digital maturity models lack sector-specific frameworks and often fail to integrate multi-criteria decision-making (MCDM) methodologies for quantitative performance assessment. This study addresses these gaps by proposing a novel digital maturity model tailored specifically to the textile sector. The research employs an integrated decision-making framework using the Method Based on the Removal Effects of Criteria (MEREC) to determine objective criterion weights and the Operational Competitiveness Rating Analysis (OCRA) method to rank firm-level digital maturity performance. The findings indicate that Strategy is the most influential dimension, whereas Technology receives the lowest weight. At the sub-criterion level, Management Support, Market Analysis, and Vision and Strategic Awareness are the most critical factors, while Technology Usage Competency is less influential. The performance evaluation shows that Company A3 achieves the highest level of digital maturity, whereas Company A2 ranks lowest. The robustness of the proposed framework is comprehensively validated through a scenario-based sensitivity analysis and a comparative evaluation using the Additive Ratio Assessment System (ARAS) method. Overall, the results suggest that successful digital transformation in the textile sector depends primarily on strategic vision and managerial support rather than on technological infrastructure alone. Full article

The year 2020 marked the eve of the explosive growth in China’s BEV market, which may lead to substantial carbon emission implications. This study quantifies the full life-cycle carbon emissions of battery electric vehicles (BEVs) across China’s 31 provinces using a multi-regional input-output-based life-cycle assessment (MRIO-based LCA) model, covering four phases: manufacturing, driving, battery replacement, and scrapping. Moreover, the coupling coordination degree (CCD) model was employed to evaluate the coordination degree between provincial BEV deployment and a green electric system. Results show that the total carbon emissions amount to 48.95 million tons, with manufacturing contributing 58.4% and driving for 33.4%. Hebei (5.72 million tons) and Shandong (4.16 million tons) account for the largest shares, driven by embodied emissions from heavy industry and coal-intensive power systems. Interprovincial embodied carbon flows reveal a dominant north-to-south transfer pattern. Furthermore, coupling coordination between BEV deployment and a green electric system is generally medium (0.5 < CCD ≤ 0.7), with Guangdong (CCD = 0.73) standing out as an exemplary case, demonstrating an effective equilibrium between BEV industry expansion and the integration of renewable energy. These findings highlight that in provinces with rapidly growing BEV industries, such as Guangdong, policies promoting low-carbon supply chains and accelerating green electricity infrastructure development are crucial to reducing emissions. Full article

Fracture healing is a multistage regenerative process requiring the coordinated regulation of inflammation, osteogenesis, and bone remodeling, yet pharmacological agents that effectively modulate these processes remain limited. Adenophorae Radix (AR), a traditional medicinal herb used for tissue repair, has not been mechanistically investigated in skeletal regeneration. In this study, a mouse femoral fracture model was employed to evaluate the effects of short-term (7 days) and long-term (5 weeks) oral administration of AR. Bone regeneration was assessed using micro-computed tomography, histological staining, and quantitative real-time polymerase chain reaction. Network pharmacology and molecular docking were applied to predict bioactive AR constituents and their target pathways, followed by in vivo validation. Short-term AR treatment significantly upregulated osteogenic markers, including RUNX2 and osteocalcin, in the bone marrow, indicating early activation of osteoblast differentiation. Long-term administration enhanced bone mineral density, trabecular organization, and callus maturation. Network pharmacology analysis identified cycloartenol acetate, β-sitosterol, and mandenol as major active compounds targeting osteogenesis- and osteoclast-related pathways, converging on HIF1A, PTGS2, and PPARG. Molecular docking demonstrated strong binding affinities between these compounds and their predicted targets, which was supported by increased expression of HIF1A, PTGS2, and PPARG in AR-treated femora. Collectively, these findings suggest that AR promotes fracture healing by regulating osteogenic differentiation and bone remodeling through multi-target transcriptional networks. Full article

This paper proposes a Stackelberg game-based collaborative optimization strategy for Park-Level Integrated Energy Systems (PIESs) operating in carbon and green certificate markets. The strategy addresses interest conflicts and low-carbon transition challenges in multi-agent optimization by integrating a carbon capture, power-to-gas, and liquid carbon dioxide energy storage technology chain. Innovatively integrates LCES into the CCS-P2G-LCES chain, achieving internal carbon cycling and energy storage. First, a market environment for PIESs integrating carbon trading and green certificate trading is constructed, and a deeply coupled low-carbon technology chain model of CCS-P2G-LCES is established to realize internal carbon resource cycling and energy time shifting. Second, a one-leader, multiple-follower Stackelberg game framework is developed with the Integrated Energy Service Provider (IESP) as the leader and the User Load Aggregator (ULA) and Electric Vehicle Aggregator (EVA) as followers. The IESP guides demand response on the user and electric vehicle sides by formulating differentiated energy prices. On this basis, a collaborative optimization dispatch model is constructed with the objective of maximizing the comprehensive revenue of the IESP. Finally, case study analysis verifies that the proposed method not only enhances operational revenue and reduces user energy costs, but also significantly reduces system carbon emissions and improves renewable energy consumption rates. The results demonstrate the feasibility and superiority of integrating market mechanisms, low-carbon technologies, and multi-agent game-based collaborative optimization. Full article

Traditional Agent-based Models (ABMs) often struggle to capture the nuance of adaptive human decision-making during complex crises due to their reliance on static, predefined rules. Large Language Models (LLMs) offer a transformative solution by acting as cognitive engines that empower agents with human-like common-sense reasoning. In this paper, we introduce an LLM-driven Multi-Agent Simulation framework to investigate coupled epidemic–economic dynamics, incorporating a Perception-Deliberation-Action (PDA) loop. Agents, acting as heterogeneous cognitive entities, utilize Chain-of-Thought processes to autonomously balance health risks against economic necessities. This approach endogenously generates adaptive behaviors without explicit scripting. Extensive experiment results across diverse LLM backends confirm the framework’s robustness, revealing divergent socio-economic trajectories under distinct macroscopic conditions and effectively quantifying the trade-offs between public health and economic stability. This approach establishes a high-fidelity computational laboratory for investigating complex scenarios under distinct macroscopic conditions, effectively bridging the gap between micro-level cognition and macro-level societal outcomes. Full article