Search Results (1,550)

Regional reassessments of agricultural carbon emission efficiency are essential for improving the sustainability of food production systems under climate constraints. This study evaluates agricultural carbon emission efficiency (ACEE) across China’s major grain-producing zone (GPZ), major grain-consuming zone (GSZ), and grain production–consumption balanced zone (GBZ) during 2003–2022, excluding Hong Kong, Macao, Taiwan, and Tibet due to data limitations. A super-efficient EBM–GML model incorporating both desirable and undesirable outputs is employed to measure ACEE at the provincial level, with comparisons conducted within each functional zone and nationally unified efficiency values used as a benchmark. Spatial dependence is examined using Moran’s I, and a spatial Durbin model is applied to identify driving factors and spatial spillover effects. The results indicate that the average efficiency levels differ systematically across functional grain zones, following the order GBZ > GPZ > GSZ, while several provinces experience notable changes in their relative rankings. Carbon emissions increase in the earlier period and decline in later years, whereas efficiency exhibits an opposite temporal pattern, reflecting a gradual transition of grain production systems from extensive input-driven growth toward more sustainability-oriented practices. Substantial regional disparities in ACEE are also observed. Rational industrial organization and efficient allocation of production resources contribute to positive spillover effects on neighboring regions, whereas natural disasters and inefficient resource distribution tend to weaken such effects. These findings suggest that functional grain zones provide an effective framework for capturing intra-regional heterogeneity and should be adopted as the basic unit for efficiency assessment and the formulation of differentiated governance strategies. Full article

With the increasing constraints of resource and environmental factors and the prominent issues of regional development imbalance, how to scientifically measure the level of agricultural sustainable development and reveal its spatial-temporal differentiation patterns has become a key scientific question that urgently needs to be addressed in optimizing land use layout and promoting rural revitalization. This study takes the human-land spatial systems coupling theory as the core framework and constructs an evaluation index system for agricultural sustainable development covering five dimensions: economy, society, resources, ecology, and technology. Based on provincial panel data in China from 2001 to 2024, the entropy method is employed to measure agricultural sustainable development, while Dagum’s Gini coefficient, kernel density estimation, and convergence models are applied to analyze its spatial–temporal evolution. Furthermore, the fuzzy-set qualitative comparative analysis (fsQCA) method is introduced to identify multi-factor configurational driving pathways. The results indicate that the overall level of agricultural sustainable development in China shows a steady upward trend, exhibiting a regional gradient pattern characterized by “central region leading, eastern region steadily advancing, and western region gradually catching up”. The overall disparity presents a weak convergence trend, with inter-regional differences as the primary source, although their contribution is gradually declining. The development structure has evolved from regional fragmentation to a more complex spatial interaction pattern. The overall distribution shifts rightward with evident stage-based differentiation, accompanied by significant positive spatial dependence, with “high–high” and “low–low” clustering coexisting over the long term. Convergence analysis shows that σ-convergence exists at the national level. After accounting for spatial effects, significant absolute β-convergence is observed in the eastern and western regions, while the central region does not exhibit significant convergence. Conditional β-convergence further confirms the existence of regional convergence trends, although the convergence speeds vary. The fsQCA results indicate that agricultural sustainable development is not driven by a single factor but by multiple configurational pathways formed through the interaction of various conditions. These findings provide empirical evidence for optimizing agricultural spatial layout, strengthening land factor support, and promoting regionally coordinated agricultural sustainable development. Full article

Accurate prediction of surface rupture induced by seismogenic fault displacement is essential for the seismic safety assessment of major engineering projects. Most existing numerical simulations adopt quasi-static approaches, in which the effect of fault displacement is simplified as static loading. As a result, these methods cannot represent the dynamic characteristics of the fault rupture process, such as stress-wave propagation, soil inertial effects, and the influence of dynamic loading paths on rupture extension in soil layers. To address this issue, a full-process simulation method is established for simulating rupture of overlying soil subjected to dynamic fault displacement: Firstly, a non-uniform dynamic fault displacement loading is formulated for the two sides of the fault based on viscoelastic artificial boundaries, allowing the differential motion of the bedrock on both sides of the fault to be represented. Secondly, an improved dynamic skeleton curve constitutive model of soil is developed by introducing a minimum modulus constraint, providing an improved description of soil nonlinear dynamic behavior from small-strain hysteresis to large-strain shear failure. The reliability of the proposed method is verified through element-level tests and horizontal-site response simulation. As a benchmark, its ability to reproduce key rupture characteristics under quasi-static conditions is also assessed by comparison with classical quasi-static rupture studies. The method is then applied to simulate rupture extension and deformation response of overlying soil under strike-slip fault displacement. The results show that, compared to quasi-static analysis, dynamic fault displacement produces similar cumulative slip for surface rupture initiation and full connection, but induces transient amplification of peak surface displacement and a wider deformation zone with gentler displacement gradients. These findings demonstrate the necessity of considering dynamic fault dislocation of bedrock–overlying soil interaction in seismic assessments of engineering projects crossing active faults. Full article

Efficient navigation in crowded and dynamic environments is crucial for robot integration into human spaces. AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion) generates collision-free velocities using Velocity Obstacles and adaptation to the cooperation estimation among agents. However, it assumes holonomic motion and cannot handle non-holonomic constraints, such as those of differential-drive robots. We propose DD-AVOCADO, an extension of AVOCADO that incorporates differential-drive kinematics to compute feasible and safe velocities. The method combines AVOCADO-based planning with a non-holonomic controller and accounts for tracking errors to avoid collisions. Simulation results across diverse scenarios show a significant reduction in collisions and efficient navigation in scenarios with cooperative and non-cooperative agents, and hardware experiments demonstrate its applicability in robot platforms. The method has the potential to be applied to other dynamic models. Full article

This paper develops an operator-reduction framework for a class of coupled implicit Caputo-Hadamard fractional differential systems subject to nonlocal Hadamard integral constraints. The system, involving fractional derivatives in both state and auxiliary variables, is resolved through a pointwise contraction argument that eliminates the auxiliary components and reduces the problem to a two-dimensional fixed-point operator acting on a Banach space of continuous functions. This reduction overcomes the compactness obstruction that arises in direct multi-component formulations. Under explicit growth and smallness conditions, the existence of at least one solution is established via Mönch’s fixed-point theorem. By imposing strengthened Lipschitz hypotheses, the reduced operator becomes a strict contraction on an invariant ball, yielding uniqueness and Ulam-Hyers stability with explicit constant $C_{UH}=1/(1-\Lambda )$. A fully computed example demonstrates the verifiability of the theoretical assumptions and illustrates how the smallness condition $\Lambda <1$ governs both existence and stability. The results establish a systematic operator-based approach for implicit Caputo-Hadamard systems with nonlocal integral constraints. Full article

Patents are essential carriers of technological innovation, and their efficient transfer is critical for accelerating technological iteration in the lithium battery industry and supporting sustainability in the new energy sector. However, existing patent recommendation methods lack frameworks for handling heterogeneous enterprise demands, which limits the accuracy of supply–demand matching. This study proposes a knowledge graph-based differentiated patent recommendation framework for enterprise technological demands in the lithium battery domain. A five-element content framework—material, method, efficacy, product, and application—is constructed from both the supply and demand sides. Enterprise demands are classified into complete and incomplete types based on element coverage, and patent supply knowledge graphs are built for potentially relevant patents. Two differentiated recommendation methods are then developed. For complete demands, the Precision Recommendation Method for Complete Technological Demands integrates BERT-based semantic encoding, TransE-based structural modeling, and RAG-based constraint retrieval to achieve precise matching under full element coverage. For incomplete demands, the Fuzzy Recommendation Method for Incomplete Technological Demands incorporates multi-source enterprise data to enrich demand categories and constructs augmented query contexts to generate diversified candidate patent sets. Empirical validation based on 25 demand-driven patent transfer cases shows that the PR-CTD method exactly identifies the actual transferred patents in three cases. The FR-ITD method ranks 6 out of 14 actual transferred patents within the Top-5 results, while the remaining cases are all within the Top 13. These results demonstrate the effectiveness of the proposed framework in real-world patent transfer scenarios. This study provides a novel theoretical perspective for the structured modeling of heterogeneous technological demands and supply–demand semantic matching. It also offers practical value by improving the efficiency of patent retrieval and matching, thereby supporting patent technology transfer in the lithium battery industry. Full article

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers ($v\le 0.3$ m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking ($v\ge 1.0$ m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers. Full article

Background/Objectives: Point-of-care (PoC) manufactured fresh chimeric antigen receptor (CAR)-T cells are typically formulated in hypothermic preservation formulations (HPFs) and stored under hypothermic conditions (2–8 °C) until administered to the patient. However, in current HPFs the shelf life of fresh CAR-T cells is short (~24–36 h) due to limited CAR-T cell stability, which poses significant time constraints on manufacturing procedures and logistics. The objective of this study was to improve the stability and extend the shelf life of fresh clinical-grade CAR-T cell drug products (DPs). Methods: A novel HPF was developed by supplementing a base HPF with the novel excipient SUL-138, which stabilizes mitochondria during hypothermic storage and subsequent rewarming, alone or in combination with endogenous mitochondrial substrates. This panel of HPFs was first screened for their stability-improving characteristics in the model cell line Jurkat cells. Subsequently, HPFs were assessed for their stability-improving characteristics of clinical-grade CD19 CAR-T cell DPs. Critical quality attributes, including CAR-T cell viability, T-cell differentiation state, exhaustion markers, and functional potency were evaluated in a good manufacturing practice (GMP)-compliant stability study up to 72 h. Results: For Jurkat cells, HPFs supplemented with SUL-138 and a combination of glucose, glutamine, and succinate demonstrated the greatest stability improvement at 2–8 °C, improving cell viability from ~1% to >85% after 72 h. For CAR-T cells, supplementation of HPFs with SUL-138 alone demonstrated the greatest improvement, resulting in a CAR-T cell viability from ~40% to >85% after 72 h of storage at 2–8 °C, while no additional benefits from mitochondrial substrates were observed. The novel HPF did not significantly impact CAR-T cell potency test results, T cell subset distribution, or exhaustion markers compared to control. Conclusions: A novel clinical-grade HPF that significantly improved fresh CAR-T cell stability during hypothermic storage was developed. This novel HPF can aid in the establishment of GMP-compliant and PoC CAR-T cell manufacturing platforms. Full article

Brain stroke (BS) and heart disease (HD) are leading causes of global mortality and long-term disability, underscoring the critical need for early and accurate diagnostic tools. This research addresses the dual challenge of developing high-performance predictive models while ensuring the privacy of sensitive patient data. We propose a framework that integrates ensemble machine learning (ML) models with a formal differential privacy (DP) mechanism. Using a dataset of 5110 samples with clinical features, we evaluate Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CAT) for BS and HD prediction. To protect individual privacy, we apply the Gaussian mechanism of DP with two probabilities of failure (POF) parameters (10–5 and 10–6) and a privacy budget ranging from 0.5 to 5.0. A key novelty of this work is the application of Pareto frontier multi-objective optimization (PFMOO) to systematically identify the optimal trade-off between model accuracy and privacy constraints. Our approach successfully identifies optimal, privacy-preserving models: XGB achieves top performance for BS prediction (92.3% accuracy, 92.29% F1 score), with a POF of 10–6, while RF excels for HD detection (95.61% accuracy, 97.8% precision), with a POF of 10–5. Furthermore, we employ explainable AI (XAI) techniques, SHAP and LIME, to provide interpretability of the model decisions, enhancing clinical trust. This research delivers a robust, interpretable, and privacy-conscious framework for early disease detection, offering a significant advancement over existing methods by holistically balancing accuracy, data security, and transparency. Full article

Transparent and evidence-based representations of global crude oil refining systems remain limited in the public literature, constraining robust energy systems modeling and policy analysis. This study develops a comprehensive, configuration-based modeling framework for all operating crude oil refineries worldwide using plant-level process unit data. Forty unique refinery configurations are identified through an unsupervised decision tree-based clustering approach that accounts for process unit presence and relative conversion intensity. An extremely randomized trees (ETR) machine learning model is trained on approximately 11,000 refinery-year observations to predict refined product yields as a function of refinery configuration, capacity, and crude oil diet. The model achieves out-of-sample coefficients of determination exceeding 0.90 for all major products and outperforms multiple linear regression and other ensemble methods. The predictive model is integrated with a differential evolution optimization algorithm to enable refinery programming under operational and feedstock constraints. The application of this model to Gulf Cooperation Council (GCC) refineries shows that, under existing technologies, petrochemical feedstock yields are bounded at approximately 37%, significantly below announced long-term diversification targets of 70–85%. Yield improvements of up to 6 percentage points are feasible through operational optimization but are associated with capacity utilization adjustments and product trade-offs. The framework provides a scalable tool for refinery benchmarking, energy transition analysis, and strategic planning across facility, national, and global levels. Full article

Unmanned Aerial Vehicle (UAV) swarms operating in contested environments face a critical “semantic gap” between raw, high-velocity network traffic and high-level mission security constraints, compounded by the risk of privacy leakage during collaborative learning. Existing deep learning (DL)-based Network Intrusion Detection Systems (NIDSs) suffer from opacity, prohibitive resource consumption, and vulnerability to gradient leakage attacks in federated settings, while traditional rule-based systems fail to handle encrypted payloads and evolving attack patterns. To bridge this gap, we present NeSySwarm-IDS (Neuro-Symbolic Swarm Intrusion Detection System), an end-to-end differentiable neuro-symbolic framework that simultaneously achieves high accuracy, strong privacy guarantees, and built-in interpretability under resource constraints. NeSySwarm-IDS integrates an extremely lightweight 1D convolutional neural network with a differentiable Łukasiewicz fuzzy logic reasoner incorporating attack-specific rules. By aggregating only low-dimensional logic rule weights with calibrated differential privacy noise, we drastically reduce communication overhead while providing $(ϵ,\delta )$-DP guarantees with negligible utility loss. Extensive experiments on the UAV-NIDD dataset and our self-collected dataset demonstrate that NeSySwarm-IDS achieves near-perfect detection accuracy, significantly outperforming traditional machine learning baselines despite using limited training data. A detailed case study on GPS spoofing confirms the interpretability of our approach, providing axiomatic explanations suitable for autonomous mission verification. These results establish that end-to-end neuro-symbolic learning can effectively bridge the semantic gap in UAV swarm security while ensuring privacy and interpretability, offering a practical pathway for deploying trustworthy AI in contested environments. Full article

Soybean mosaic virus (SMV) is a major constraint on global soybean (Glycine max (L.) Merr.) production, causing substantial economic losses worldwide. Despite these losses, the potential of resistance genes as a solution remains largely unexplored. In this study, the COPPER CHAPERONE FOR SUPEROXIDE DISMUTASE (GmCCS) was initially employed as a bait to screen the soybean cDNA library, leading to the identification of a protein homologous to Arabidopsis thaliana COP9 signalosome complex subunit 5B (AtCSN5B), designated as GmCSN5B. Quantitative real-time PCR (qRT-PCR) analysis revealed differential expression of GmCSN5B in the SMV-resistant (Qihuang No.1, QH) and susceptible (Nannong 1138-2, NN) variety following SMV-SC3 strain inoculation. Knockdown of GmCSN5B via Bean pod mottle virus (BPMV)-induced gene silencing (VIGS) significantly enhanced SMV resistance compared to control plants. This work further demonstrated that GmCSN5B can interact with the downstream GmVTC1 protein, which was potentially associated with ascorbic acid (AsA; Vitamin C) synthesis. Moreover, GmVTC1 also responded to SMV infection, and its knockdown led to a reduction in endogenous AsA levels within the host, thereby compromising the plant’s resistance to SMV. Together, these findings suggest that the GmCCS-GmCSN5B-GmVTC1 pathway in soybean modulates host resistance to SMV through the regulation of AsA synthesis. Full article

Eye-tracking is a key enabling technology for smart eyewear, supporting hands-free interaction, accessibility, and context-aware human–machine interfaces under strict constraints on size, power consumption, and computational complexity. While camera-based solutions provide high accuracy, their integration into lightweight and low-power wearable platforms remains challenging. This paper is a feasibility study for the design, simulation, and experimental evaluation of a photosensor oculography (PSOG) eye-tracking system that is fully integrated into an eyewear frame, based on near-infrared (NIR) emitters and photodiodes. The proposed approach combines simulation-driven optimization of the optical constellation, a multi-frequency modulation and demodulation scheme enabling parallel source discrimination and robust ambient-light rejection, and a resource-efficient signal acquisition pipeline suitable for embedded implementation. Eye rotations in azimuth and elevation are inferred from differential reflectance patterns of ocular regions (sclera, iris, and pupil) using lightweight regression techniques, including shallow neural networks and Gaussian process regression, selected to balance estimation accuracy with computational and power constraints. System performance is evaluated using a controllable artificial-eye platform under defined geometric and illumination conditions, enabling repeatable assessment of gaze-estimation accuracy and algorithmic behavior. Sub-degree errors are achieved in this controlled setting, demonstrating the feasibility and potential effectiveness of the proposed architecture. Practical considerations for translation to real-world smart eyewear, including human-subject validation, anatomical variability, calibration strategies, and embedded deployment, are discussed and identified as directions for future work. By detailing the optical design methodology, modulation strategy, and algorithmic trade-offs, this work clarifies the distinct contributions of the proposed PSOG system relative to existing frame-integrated and camera-free eye-tracking approaches, and provides a foundation for further development toward wearable and augmented-reality applications. Full article