Search Results (3,558)

Postural sway derived from center-of-pressure (CoP) trajectories is widely used to assess balance and fall risk in older adults, but conventional linear metrics mainly quantify sway magnitude and may overlook temporal organization. Guided by the loss-of-complexity hypothesis, we re-examined associations between fall history and linear and non-linear CoP metrics in an open-access dataset. Quiet-standing trials under eyes-open and eyes-closed conditions were analyzed in adults ≥60 years (fallers n = 19; non-fallers n = 57). To reduce confounding, propensity score matching was performed using age, sex, body mass index, activities of daily living level, illness status, number of medications, disability status, and orthosis/prosthesis use. Linear and non-linear indices, including recurrence quantification analysis, detrended fluctuation analysis, fractal dimension, multiscale entropy, stabilogram diffusion analysis, and sway density measures, were examined. After matching, no CoP metric differed significantly between groups. However, SHAP-based exploratory analysis suggested that non-linear features related to temporal structure and multiscale organization contributed more prominently to model output than conventional magnitude-based metrics. Given the limited sample size, these findings should be interpreted as exploratory and hypothesis-generating. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Background/Objectives: Wolfram syndrome type 1 (WS1) is a rare, progressive, multisystem neurodegenerative disorder characterized by diabetes mellitus, optic atrophy, diabetes insipidus, and sensorineural hearing loss. As survival has improved, an increasing number of affected women are reaching reproductive age. However, evidence on pregnancy and peripartum management in WS1 remains scarce, and practical guidance is limited. This case report describes the multidisciplinary management of pregnancy and delivery in a woman with genetically confirmed WS1 and highlights key considerations for peripartum care. Case Presentation: A woman with genetically confirmed WS1 and long-standing multisystem involvement, including diabetes mellitus, diabetes insipidus, neurogenic bladder requiring frequent self-catheterization, progressive neurologic manifestations, and severe sensory impairment, achieved pregnancy through assisted reproduction with oocyte donation and was closely monitored by a multidisciplinary team. Due to persistent breech presentation, a planned external cephalic version was performed at 37 + 5 weeks’ gestation with immediate availability for cesarean delivery. After unsuccessful attempts, cesarean delivery was performed under combined spinal–epidural anesthesia. Peripartum management focused on strict glycemic control, careful monitoring of fluid balance and urine output, neuraxial anesthesia with proactive hemodynamic management, precautions related to the cochlear implant, and tailored communication strategies. Postpartum recovery was favorable, although anemia on postoperative day 1 required transfusion of one unit of packed red blood cells and intravenous iron therapy. Discussion and Conclusions: Pregnancy in WS1 represents a high-risk clinical scenario because of the coexistence of endocrine, urologic, and neurologic comorbidities, while published evidence on peripartum management remains limited. This case supports an individualized, multidisciplinary approach to obstetric and anesthetic planning and the use of a practical framework to optimize peripartum management and enhance maternal–fetal safety in this rare condition. Full article

For the automation of Agrobacterium-mediated genetic transformation of tissue-cultured plantlets, accurate leaf vein segmentation is essential. The thin, low-contrast structure of leaf veins frequently leads to fragmented segmentation outputs, despite the proposal of various methodologies for vein segmentation. To address this issue, we propose Direction-Aware U-Net (DA-UNet), an improved U-Net architecture that incorporates a Direction-Aware Context Pooling (DACPool) module and Topology-aware Segmentation loss (TopoSeg loss). The DACPool module explicitly exploits vein orientation to aggregate directional contextual information, while the TopoSeg loss jointly optimizes pixel-level accuracy and topological continuity. DA-UNet achieves efficient leaf vein segmentation with improved continuity and structural integrity, according to evaluations on the self-constructed Tissue-Cultured Plantlet Vein Dataset 2025 (TCPVD2025). Comparative experiment results show that the improved model outperforms PSPNet, DeepLabV3+, U-Net, TransUNet, Swin-UNet, CCNet, and SegNeXt, as evidenced by Recall, Dice, and CONNECT scores of 71.35%, 69.08%, and −2.25, while maintaining competitive Precision of 66.98%. Ablation experiment results provide further evidence for the efficacy of the TopoSeg loss and the DACPool module. The results demonstrate the effectiveness of the proposed vein segmentation framework for generating outputs that are both accurate and structurally consistent, thus enabling reliable automated processes for plant genetic transformation. Full article

This paper presents two broadband four-way power dividers based on a novel T-coil topology, operating in the 22–32 GHz band (covering the K/Ka bands). Type I adopts a cascaded power division structure, while Type II employs a direct-feed integrated architecture. The innovation lies in the introduction of isolating capacitors at the input and output ports, which significantly shortens the critical transmission line lengths in both topologies. This effectively reduces the equivalent inductance and raises the self-resonant frequency, achieving wideband response while maintaining structural simplicity, compact size, and ease of integration. Both circuits were fabricated using a standard 45 nm CMOS process. The measured core chip areas (excluding pads) are only 0.125 mm² for Type I and 0.066 mm² for Type II, demonstrating excellent integration density. Through even-mode and odd-mode theoretical analysis and full-wave electromagnetic simulation verification, both power dividers exhibit good impedance matching and port isolation across the target frequency band. Measurement results further confirm their performance: across the entire 22–32 GHz band, both power dividers achieve a return loss better than 11 dB and isolation exceeding 15 dB; the insertion loss is 1.1–1.4 dB for Type I and 0.8–1.3 dB for Type II; the amplitude imbalance is below ±0.3 dB and ±0.1 dB, respectively; and the phase imbalance is less than ±5° and ±3°, respectively. All measured data show good agreement with simulation results. In summary, Type I offers advantages in layout flexibility and isolation performance, while Type II excels in insertion loss and chip size. Both provide practical circuit solutions for broadband, high-performance, and compact power division systems. Full article

To meet the increasingly strict requirements of modern communication, radar detection and electronic measurement systems for wide-bandwidth, low-insertion-loss and high-isolation signal routing, this paper presents a 16 × 16 programmable switch matrix that simultaneously achieves wideband operation (DC-40 GHz), low insertion loss (≤0.9 dB maximum), high isolation (>50 dB typical), and systematic modular scalability, a combination not found in existing implementations. The matrix, constructed with high-quality coaxial switches and optimized RF circuitry and electromagnetic structures, provides flexible and stable single-pole multi-throw (SPMT) signal routing across an ultra-wide frequency range from DC to 40 GHz. The switch matrix features a modular architecture, integrating multiple RF switching units, drive control circuits, and communication interface modules. This architecture achieves minimal signal path depth while maintaining full connectivity between any input and output port, directly minimizing cumulative insertion loss. Through precise impedance matching design and isolation structure optimization, the system still exhibits outstanding transmission characteristics at the 40 GHz high-frequency end: typical insertion loss does not exceed 0.9 dB, and the isolation between channels is better than 50 dB, effectively ensuring the integrity of signals in complex multi-channel environments. To meet the requirements of automated testing and remote control, the equipment integrates dual communication interfaces (serial port/network port), supports the SCPI command set and TCP/IP protocol, and can be conveniently embedded in various test platforms to achieve instrument interconnection and test process automation. Experimental verification shows that this matrix exhibits excellent switching stability and signal consistency across the entire 40 GHz, with a switching action time of less than 10 ms. Furthermore, it is capable of real-time topology reconfiguration via a microcontroller or FPGA. These innovations collectively deliver a switch matrix that meets the demanding requirements of 5G communication, millimeter-wave radar, and aerospace defense systems—applications where bandwidth, signal integrity, and system flexibility are paramount. Full article

This study presents a novel neural parameter diffusion approach (FWA-PDiff) designed to enhance data quality. To address the limitations of conventional diffusion models—such as inefficient sampling and insufficient feature sensitivity, which may compromise output fidelity—this study introduces four key innovations. First, the proposed model introduces an adaptive recalibration of the sampling frequency in the Fourier domain to optimize feature extraction for image data. Second, a dual-channel autoencoder architecture is employed, featuring a multi-scale, fine-grained encoder (MFE) that enables the simultaneous capture of features at multiple resolutions. Third, a wavelet-attention mechanism (WA) is incorporated into the decoder to highlight subtle high-frequency details. Fourth, the proposed model introduces a hybrid loss function that combines Mean Squared Error (MSE) and Kullback–Leibler (KL) divergence to improve data reconstruction. Collectively, these improvements enable the generation of high-fidelity parameters, thereby contributing to enhanced data quality. Extensive experiments conducted on benchmark datasets—including MNIST, CIFAR-10, CIFAR-100, and STL-10—demonstrate the effectiveness of the proposed approach, which consistently achieves superior performance in improving data quality. Full article

Modeling the complex spatio-temporal dynamics of infectious diseases presents a significant computational challenge due to heterogeneous regional interactions, high-dimensional multimodal data streams, and the critical need for algorithmic fairness. This paper proposes a novel computational framework that unifies graph-based spatio-temporal forecasting, anomaly detection, and retrieval-augmented generation (RAG) into a single mathematical architecture. The predictive backbone employs a graph-augmented Temporal Fusion Transformer to capture non-linear temporal dependencies and spatial disease propagation. By formalizing regional topology and mobility flows as a weighted mathematical graph, the model systematically integrates structured epidemiological counts, continuous environmental covariates, and digital trace signals. To address algorithmic bias, we formulate a fairness-aware optimization problem by embedding a specific regularization term into the training objective, which mathematically penalizes disparities in true-positive rates across diverse socio-demographic strata. Furthermore, the numerical outputs and anomaly scores are processed by a large language model equipped with hybrid dense and sparse retrieval to generate interpretable, computationally grounded decision support. Extensive experiments on a longitudinal dataset comprising 62 administrative regions over 260 weeks validate the mathematical robustness of the proposed framework. The graph-augmented architecture improved forecasting accuracy by up to 24% and anomaly detection F1 scores by over 6% compared to state-of-the-art deep learning baselines, while the fairness-regularized loss function reduced the maximum subgroup recall gap by more than 50%. These findings demonstrate that predictive accuracy and algorithmic fairness can be jointly optimized, providing a rigorous computational methodology for spatio-temporal epidemic modeling and AI-driven surveillance. Full article

Modular multilevel converters (MMCs) for high-voltage direct current (HVDC) transmission require substantial submodule (SM) capacitance to limit capacitor voltage ripple, resulting in bulky and costly converter valves. The integrated gate-commutated thyristor (IGCT), with its higher voltage rating and lower conduction loss compared to the insulated-gate bipolar transistor (IGBT), enables a significant reduction in the number of SMs per arm, offering a pathway toward compact converter design. This paper investigates how the reduced SM count of IGCT-based MMCs affects the feasibility and benefit of operating with elevated capacitor voltage ripple to further decrease SM capacitance. An analytical framework is developed to evaluate the modulation boundary under increased ripple, explicitly accounting for the voltage ripple coupling (CVR) effect and circulating-current suppression. A ripple-tolerance coefficient κ is introduced, and its optimal value is determined by identifying the inflection point beyond which the achievable AC voltage output begins to decline. For a ±500 kV/2000 MW IGCT-MMC case study using 6.5 kV devices with 250 SMs per arm, the proposed method reduces the per-unit energy storage requirement by up to 39.4% compared with conventional-ripple operation. Simulation and prototype experimental results on a 400 V, 3 kW, 4-SM/arm test bench validate the analytical predictions and confirm the practical feasibility of the approach. Full article

Transfer learning has advanced significantly in domains like computer vision and natural language processing, yet its application to tabular data remains challenging, with traditional models like XGBoost often outperforming deep learning approaches due to issues like variance instability and slow convergence. This study investigates the impact of weight initialization techniques on transfer learning efficacy during partial fine-tuning, hypothesizing that optimized methods enhance (i) variance stability (consistent activation and gradient magnitudes), (ii) convergence speed (faster loss reduction), and (iii) generalization (improved out-of-distribution accuracy). Established techniques including Xavier/Glorot, He/Kaiming, Orthogonal, and fan-averaged initializations are evaluated on modern foundation models like TabuLa-8B, using benchmarks such as OpenML and CC-18 (72 datasets). Additionally, Feature-Adaptive Variance Initialization (FAVI) is proposed, which adapts variances based on per-feature statistics, which is mathematically proven to preserve unit output variance. Empirical results demonstrate 15–20% improvements in convergence speed and 1.5–2.5% in generalization. Research contributions include a theoretical formulation of FAVI and insights advancing tabular data modeling. Full article

Although halide solid electrolytes (HSEs) demonstrate a higher voltage window and superior interfacial stability toward Li-rich layered oxides (LLOs) compared to sulfide systems, HSE-based all-solid-state lithium batteries (HSE-ASSLBs) still face a fundamental trade-off between achieving high capacity and maintaining cycling stability. To resolve this issue, a rational adjustment of the depth-of-discharge (DOD) via discharge cut-off voltage control is proposed. Analysis of dQ/dV profiles and post-cycled electrodes indicates that excessive DOD (lower cut-off voltages) aggravates structural degradation and interfacial side reactions, whereas insufficient DOD (higher cut-off voltage) fails to fully utilize the compensatory capacity from low-voltage redox couples. Notably, an optimized cut-off voltage of 2.6 V activates a stable low-voltage redox reaction centered around 2.85 V, which effectively offsets high-voltage capacity loss while suppressing unfavorable interfacial evolution. As a result, the ASSLB configured with a Li_1.2Ni_0.13Mn_0.54Co_0.13O₂ cathode and a Li_2.75In_0.75Zr_0.25Cl₆ HSE delivers an initial discharge capacity of 281.6 mAh g⁻¹ at 1C and achieves significantly improved capacity retention from 71.8% to 86.1% over 300 cycles. This study confirms that DOD regulation offers a simple and effective electrochemical protocol for enabling durable high-capacity output in LLO-based ASSLBs. Full article

High-stakes logistics, defined as supply chains where delays, quality loss, or noncompliance have serious human, safety, financial, or geopolitical consequences, are a prominent case of a broader reality: global supply chains are safety-, cost-, and time-critical socio-technical systems where forecasting quality, vendor coordination, and operational decisions shape service levels and stakeholder welfare. At the same time, decarbonization pressures and the growing use of AI for planning and control introduce new risks and trade-offs across energy, computation, and physical logistics. We develop a multi-agent framework that models supply chain system-of-systems dynamics drawing on (1) supply chain decision functions (shipment planning, sourcing and vendor management), (2) national energy-transition conditions that determine grid carbon intensity, and (3) carbon-aware computation accounting for AI-enabled decision support. Methodologically, we combine predictive analytics, unsupervised segmentation, and a carbon-cost-of-intelligence layer in a scenario-based assessment of how national energy-transition profiles–from Norway to India–affect the intensity of AI compute carbon, meaning the carbon emissions generated by the hardware and data centers required to train and run AI models. We introduce the carbon-adjusted supply chain performance (CASP) metric that integrates physical transport carbon, cold-chain overhead where applicable, and AI compute carbon into a per-package-type performance measure. Our analysis yields three actionable outputs for systems engineering and environmental management: carbon, service, and cost trade-off frontiers; governance levers (sourcing portfolio rules, buffers, and compute policies); and system-level early-warning indicators for disruption amplification. This study implements a tool-augmented multi-agent system (orchestrator, risk, and sourcing agents) using AWS bedrock and strands agents, where LLM-based agents orchestrate deterministic analytical engines through structured tool interfaces with adaptive query generation. Theoretically, we extend previous systems-of-systems and sustainable supply chain findings by formalizing package-type-specific carbon–service frontiers and by embedding AI compute carbon into a socio-technical resilience framework. Practically, the CASP benchmark, governance lever analysis, and multi-agent implementation provide decision-makers with concrete tools to compare carriers, routes, and compute strategies across countries while making transparent the trade-offs between service reliability and total carbon. Full article

With the continuous integration of a large amount of renewable energy sources such as wind and solar power into the power system, the economic and secure scheduling of the power grid, as a crucial carrier for electricity transmission, becomes of paramount importance. However, issues such as voltage fluctuations at grid nodes, low renewable energy consumption rates, and increased active power losses, caused by the widespread integration of high proportions of renewable energy, urgently need to be addressed. To effectively solve these problems, this paper proposes a multi-objective coordinated optimization scheduling method for the economy and security of source–grid–load–storage based on an effective scenario-screening approach. Firstly, an iterative self-organizing data analysis algorithm based on density noise application spatial clustering is designed to efficiently generate typical output scenarios for renewable energy sources such as wind and solar power. Meanwhile, to achieve low-carbon scheduling objectives, green certificate and carbon trading mechanisms are introduced. A multi-objective coordinated scheduling and trading model for the economy and security of large power grids, sources, loads, and storage is constructed with the goal of enhancing renewable energy consumption, and it is solved using the weight assignment method and an improved particle swarm optimization algorithm. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated based on an improved IEEE standard node test system. Full article

Mastitis, a disease associated with milk production with multiple etiologies, causes significant economic losses among dairy farmers worldwide. This study aimed to detect mastitis using thermal images of the udder obtained during the milking phase from 500 Holstein dairy cows with the aid of an Artificial Neural Network (ANN). Mastitis levels were classified based on the California Mastitis Test (CMT) scores using somatic cell count (SCC) as the output variable. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. RGB (Red, Green, Blue) thermal images were used to construct the input matrices. The model achieved correlation coefficients (R) of 0.91, 0.97, and 0.97 for the training, validation, and test datasets, respectively. The close agreement between validation and test performances indicates the absence of overfitting and demonstrates strong generalization capability of the proposed model. These findings suggest that artificial neural networks combined with thermal imaging can provide high-quality and reliable results for mastitis detection. Full article