Search Results (324)

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

The fault data of the aero-engine source domain are constrained by factors such as variable operating conditions, structural coupling, fault correlations, and information attenuation. Consequently, the obtained fault features often exhibit localities. This leads to significant discrepancies in fault feature distributions between the source and target domains, resulting in poor generalization capabilities and insufficient stability in aero-engine fault diagnosis. To address these issues, an aero-engine fault diagnosis method based on Domain-Adversarial Neural Network (DANN) and Feature Interaction (FI-DANN) is proposed. Firstly, a fault diagnosis network architecture is designed based on traditional DANN by incorporating a feature interaction module into its feature extractor. Secondly, the Kronecker product is employed to fully excavate nonlinear relationships between the features, thereby increasing the number of fault features to obtain higher-dimensional and more accurate fault features. Finally, based on information entropy theory, the number of interacted features is controlled through a weighted combination, ensuring that the retained features possess greater fault information content. This guarantees the strong generalization capability and high stability of the model. The experimental results show that the best fault diagnosis accuracies of Convolutional Neural Network (CNN), traditional DANN, and FI-DANN are 79.64%, 90.00%, and 99.03%, respectively, indicating that the proposed FI-DANN can effectively integrate multi-source fault information and enhance the accuracy, stability, and generalization capability of fault diagnosis models. Full article

While multi-view visualization systems are widely used for mining vehicle teleoperation, they often impose high cognitive load and restrict operator attention. To explore a more efficient alternative, this study evaluated a merged-view interface that integrates multiple camera perspectives into a single coherent display. In a controlled experiment, 35 participants navigated a teleoperated robot along a 50 m lab-scale path representative of an underground mine under both multi-view and merged-view conditions. Task performance and eye-tracking data—including completion time, path adherence, and speed-limit violations—were collected for comparison. The merged-view system enabled 6% faster completion times, 21% higher path adherence, and 28% fewer speed-limit violations. Eye-tracking metrics indicated more efficient and distributed attention: blink rate decreased by 29%, fixation duration shortened by 18%, saccade amplitude increased by 11%, and normalized gaze-transition entropy rose by 14%, reflecting broader and more adaptive scanning. NASA-TLX scores further showed a 27% reduction in perceived workload. Regression-based sensitivity analysis revealed that gaze entropy was the strongest predictor of efficiency in the multi-view condition, while fixation duration dominated under merged-view visualization. For path adherence, blink rate was most influential in the multi-view setup, whereas fixation duration became key in merged-view operation. Overall, the results indicated that merged-view visualization improved visual attention distribution and reduced cognitive tunneling indicators in a controlled laboratory teleoperation task, offering early-stage, interface-level insights motivated by mining-relevant teleoperation challenges. Full article

This work investigates the mechanisms of information transfer underlying causal relationships between brain regions during resting-state conditions in patients with schizophrenia (SCZ). A large fMRI dataset including healthy controls and SCZ patients was analyzed to estimate directed information flow using local Transfer Entropy (TE). Four functional interaction patterns—referred to as rules—were identified between brain regions: activation in the same state (ActS), activation in the opposite state (ActO), turn-off in the same state (TfS), and turn-off in the opposite state (TfO), indicating a dynamics toward converging (Acts/Tfs = S) and diverging (ActO/TfO = O) states of brain regions. These interactions were integrated within a multiplex network framework, in which each rule was represented as a directed network layer. Our results reveal widespread alterations in the functional architecture of SCZ brain networks, particularly affecting schizophrenia-related systems such as bottom-up sensory pathways and associative cortical dynamics. An imbalance between S and O rules was observed, leading to reduced network stability. This shift results in a more randomized functional network organization. These findings provide a mechanistic link between excitation/inhibition (E/I) imbalance and mesoscopic network dysconnectivity, in agreement with previous dynamic functional connectivity and Dynamic Causal Modeling (DCM) studies. Overall, our approach offers an integrated framework for characterizing directed brain communication patterns and psychiatric phenotypes. Future work will focus on systematic comparisons with DCM and other functional connectivity methods. Full article

Conjugate heat transfer in non-Newtonian fluids is a fundamental phenomenon in thermal management systems. This study investigates the combined effects of magnetic field topology, heat absorption/generation, the thermal conductivity ratio, enclosure inclination, and power-law rheology using the lattice Boltzmann method. The parametric analysis shows that increasing the heat generation coefficient from −5 to +5 reduces the average Nusselt number by up to 97% for the pseudo-plastic fluids and up to 29% for the Newtonian fluids, while entropy generation increases by 44–86% depending on the thermal conductivity ratio. Increasing the inclination angle from 0° to 90° weakens convection and reduces heat transfer by nearly 77%. Magnetic field strengthening (Ha = 0–45) decreases the Nusselt number by 20–55% depending on the barrier temperature. Among all tested conditions, the highest thermal performance (maximum heat transfer and minimum entropy generation) occurs when using a pseudo-plastic fluid (n = 0.75), exhibiting high wall conductivity (TCR = 50) and heat absorption (HAPC = −5), a cold obstacle (${\mathsf{\theta }}_{\mathrm{b}}=0$), and zero inclination (λ = 0°), as well as in the absence of the magnetic field effects. These quantitative insights highlight the controllability of the conjugate heat transfer and irreversibility in the power-law fluids under coupled magnetothermal conditions. Full article

High-entropy ceramic (HEC) thin films generally refer to multi-component solid solutions composed of multiple metallic and non-metallic elements, existing in forms such as carbides, nitrides, and borides. Benefiting from the high-entropy effect, lattice distortion, sluggish diffusion, and cocktail effect of high-entropy systems, HEC thin films form simple amorphous or nanocrystalline structures while exhibiting high hardness/elastic modulus, excellent tribological properties, and thermal stability. Although the mixing entropy increases with the number of elements in the system, a higher number of elements does not guarantee improved performance. In addition to system configuration, the regulation of preparation methods and processes is also a key factor in enhancing performance. Arc ion plating (AIP) has emerged as one of the mainstream techniques for fabricating high-entropy ceramic (HEC) thin films, which is attributed to its high ionization efficiency, flexible multi-target configuration, precise control over process parameters, and high deposition rate. Through rational design of the compositional system and optimization of key process parameters—such as the substrate bias voltage, gas flow rates, and arc current—HEC thin films with high hardness/toughness, wear resistance, high-temperature oxidation resistance, and electrochemical performance can be fabricated, and several of these properties can even be simultaneously achieved. Against the backdrop of AIP deposition, this review focuses on discussions grounded in the thermodynamic principles of high-entropy systems. It systematically discusses how process parameters influence the microstructure and, consequently, the mechanical, tribological, electrochemical, and high-temperature oxidation behaviors of HEC thin films under various complex service conditions. Finally, the review outlines prospective research directions for advancing the AIP-based synthesis of high-entropy ceramic coatings. Full article

Mobile Global Positioning System (GPS) data offer a promising approach to inferring mental health status through behavioural analysis. Whilst previous research has explored location-based behavioural indicators including location clusters, entropy, and variance, persistent GPS measurement errors have compromised data reliability, limiting the practical deployment of smartphone-based digital phenotyping systems. This study develops and validates an algorithmic preprocessing method designed to mitigate inherent GPS measurement limitations in mobile health applications. We conducted comprehensive evaluation through controlled experimental protocols and naturalistic field assessments involving 38 participants over a seven-day period, capturing GPS data across diverse environmental contexts on both Android and iOS platforms. The proposed preprocessing algorithm demonstrated exceptional precision, consistently detecting major activity centres within an average 50-metre margin of error across both platforms. In naturalistic settings, the algorithm yielded robust location detection capabilities, producing spatial patterns that reflected plausible and behaviourally meaningful traits at the individual level. Cross-platform analysis revealed consistent performance regardless of operating system, with no significant differences in accuracy metrics between Android and iOS devices. These findings substantiate the potential of mobile GPS data as a reliable, objective source of behavioural information for mental health monitoring systems, contingent upon implementing sophisticated error-mitigation techniques. The validated algorithm addresses a critical technical barrier to the practical implementation of GPS-based digital phenotyping, enabling the more accurate assessment of mobility-related behavioural markers across diverse mental health conditions. This research contributes to the growing field of mobile health technology by providing a robust algorithmic framework for leveraging smartphone sensing capabilities in healthcare applications. Full article

Background: BI-RADS (Breast Imaging Reporting and Data System) mammogram classification is central to early breast cancer detection. Despite being an ordinal scale that reflects increasing levels of malignancy suspicion, most models treat BI-RADS as a nominal task using cross-entropy loss, thereby disregarding the inherent class order. This mismatch between the clinical severity of misclassification and the model’s optimization objective remains underexplored. Methods: We systematically evaluate whether incorporating ordinal-aware loss functions improves BI-RADS classification performance under controlled, architecture-fixed conditions and dataset imbalance. Using a unified training pipeline across multiple datasets, we compare ordinal losses to standard cross-entropy, analyzing the effect of dataset- and label-level balancing. Area under the receiver operating characteristic curve (AUROC) and macro-F1 scores are reported as averages over five seeds. Results: Balanced sampling across datasets during training led to statistically significant improvements. Ordinal loss functions, such as Earth Mover Distance (EMD), consistently achieved higher performance across multiple metrics compared to conventional cross-entropy approaches commonly reported in the literature. Improvements were particularly evident in reducing severe misclassifications, demonstrating that aligning the learning objective with the ordinal structure of BI-RADS enhances robustness and clinical relevance. Conclusions: Aligning the learning objective with the ordinal BI-RADS structure substantially improves classification accuracy without changing the underlying architecture. These findings emphasize the importance of loss design, regularization, and data-balancing strategies in medical AI, supporting more reliable breast cancer screening. Full article

The Yangtze River Economic Belt is a crucial driver of China’s economy, and its shoreline is a strategic, finite resource vital for ecological security, flood control, navigation, and socioeconomic development. However, intensive development has resulted in functional conflicts and ecological degradation, underscoring the need for accurate identification and suitability assessment of shoreline functions. Conventional methods, which predominantly rely on land use data and remote sensing imagery, are often limited in their ability to capture dynamic changes in large river systems. This study introduces an integrated framework combining macro-level “Three-Zone Space” (urban, agricultural, ecological) theory with micro-level Point of Interest (POI) data to rapidly identify shoreline functions along the Yichang section of the Yangtze River. We further developed a multi-criteria evaluation system incorporating ecological, production, developmental, and risk constraints, utilizing a combined AHP-Entropy weight method to assess suitability. The results reveal a clear upstream-downstream gradient: ecological functions dominate upstream, while agricultural and urban functions increase downstream. POI data enabled refined classification into five functional types, revealing that ecological conservation shorelines are extensively distributed upstream, port and urban development shorelines concentrate in downstream nodal zones, and agricultural production shorelines are widespread yet exhibit a spatial mismatch with suitability scores. The comprehensive evaluation identified high-suitability units, primarily in downstream urban cores with superior development conditions and lower risks, whereas low-suitability units are constrained by high geological hazards and poor infrastructure. These findings provide a scientific basis for differentiated shoreline management strategies. The proposed framework offers a transferable approach for the sustainable planning of major river corridors, offering insights applicable to similar contexts. Full article

Alfalfa cultivation is an effective way to achieve soil improvement while utilizing saline soils. Irrigation and drainage, as physical measures to leach salts, can effectively reduce the soil salt content, while application of organic fertilizer fermented with an effective microorganism (EM) may further enhance the improvement effect of saline–alkaline soil by improving soil fertility and microbial community structure. However, there is still a lack of systematic assessment on the effects of applying these three measures on the saline soil–plant system. In this study, we used alfalfa as the plant material and set three water depths of 8 mm (IR1), 16 mm (IR2), and 24 mm (IR3) under the condition of irrigating every 10 days with remote-controlled timed and quantitative irrigation, which is the most acceptable to farmers in the era of smart agriculture. EM organic fertilizer dosage was designed as 0 kg/ha (CK), 1500 kg/ha (OF1), 3000 kg/ha (OF2), 4500 kg/ha (OF3), and 6000 kg/ha (OF4). The multiple-crop alfalfa yield, quality (crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF)), and soil electrical conductivity (EC) were observed. The results showed that after the application of EM organic fertilizer, the soil’s EC value of fertilized treatments was higher than that of CK, but this difference became smaller with the prolongation of alfalfa’s growing period, implying that EM organic fertilizer could absorb more soil salts by promoting alfalfa’s growth; the water depth was obviously negatively correlated with the soil’s EC value, demonstrating that the increase in the water depth had a stronger ability to reduce the soil salts. By the end of the experiment, the soil’s EC values were reduced by 21.4–43.7% for the treatments. The alfalfa yield was significantly increased by EM organic fertilizer application, and the three alfalfa yields were increased by 63.3–69.1%, 65.4–83.6%, and 52.6–56.2%, respectively, when fertilizer application was elevated from CK to OF4. The highest alfalfa yields were all found at IR2OF4, reaching 1164.7, 2637.3 and 2519.7 t/ha, corresponding to the first, second, and third alfalfa crops, respectively. The analysis of alfalfa quality indexes revealed that higher CP values were found in the IR2 treatments, and increasing fertilizer application from OF1–OF4 resulted in an increase in CP values by 2.4–9.1%, 1.5–7.4%, and 0.8–6.7% for the three alfalfa crops. Relatively low NDF and ADF values were observed for alfalfa under IR2 conditions; however, the application of EM organic fertilizer reduced the NDF and ADF values within a certain range. According to the results of the entropy weight evaluation model, IR3OF4, IR3OF2, and IR3OF3 were the top three treatments with the best overall benefits, respectively, with relative closeness values of 0.71, 0.70, and 0.68, in that order, which suggests that the appropriate water depth is 24 mm, while the appropriate EM organic fertilizer dosage is in the range of 3000–6000 kg/ha. There was a pattern observed in our study, in which the treatments with better overall benefits were better distributed at high water depths, which emphasizes the critical role of the irrigation volume in ameliorating saline soils. The conclusions of the study are intended to provide a practical basis for the comprehensive utilization and sustainable development of saline soils. Full article

This study examines the evolution of Shannon entropy, a key measure of uncertainty and disorder, in a Laguerre-Gaussian correlated Schell-model (LGcSM) beam under ocean turbulence. We explore how the spatial coherence distribution of the LGcSM beam influences its Shannon entropy in both free space and ocean turbulent conditions. Our results show that tailoring the optical coherence distribution can significantly control spatial disorder, enabling the beam to restore order under turbulence. Furthermore, we analyze the impact of various ocean turbulence parameters on Shannon entropy evolution, offering a potential strategy to mitigate performance degradation in optical communication systems affected by turbulence. Full article

Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition that is typically identified through behavioral assessments and subjective clinical reports. However, electroencephalography (EEG) offers a cost-effective and non-invasive alternative for capturing neural activity patterns closely associated with this disorder. Despite this potential, EEG-based ADHD classification remains challenged by overfitting, dependence on extensive preprocessing, and limited interpretability. Here, we propose a novel neural architecture that integrates transformer-based temporal attention with Gaussian mixture functional connectivity modeling and a cross-entropy loss regularized through $\alpha$-Rényi mutual information, termed T-GARNet. The multi-scale Gaussian kernel functional connectivity leverages parallel Gaussian kernels to identify complex spatial dependencies, which are further stabilized and regularized by the $\alpha$-Rényi term. This design enables direct modeling of long-range temporal dependencies from raw EEG while enhancing spatial interpretability and reducing feature redundancy. We evaluate T-GARNet on a publicly available ADHD EEG dataset using both leave-one-subject-out (LOSO) and stratified group k-fold cross-validation (SGKF-CV), where groups correspond to control and ADHD, and compare its performance against classical and modern state-of-the-art methods. Results show that T-GARNet achieves competitive or superior performance (82.10% accuracy), particularly under the more challenging SGKF-CV setting, while producing interpretable spatial attention patterns consistent with ADHD-related neurophysiological findings. These results underscore T-GARNet’s potential as a robust and explainable framework for objective EEG-based ADHD detection. Full article

The overlying rock in the weathering and oxidation zone has well-developed micro-fissures, making roadway roof control highly challenging. Ordinary cement slurry is hard to inject, failing to achieve effective reinforcement. By introducing admixtures like ultrafine fly ash and polyvinyl alcohol (PVA) to modify ultrafine cement, this paper developed a PVA-modified ultrafine cement-based grouting material (PVAM-UFCG). It systematically investigated the influences of various factors on the slurry’s setting time, fluidity, water separation rate, viscosity, and 28-day uniaxial compressive strength, determining the optimal mix ratio through comprehensive analysis. The results show that the water–cement ratio is the dominant factor affecting slurry viscosity, strength, and setting time; the polycarboxylate superplasticizer concentration has the most significant influence on fluidity and water separation rate; a 20% ultrafine fly ash replacement rate can optimize particle gradation and enhance long-term strength; and a 1.0% polyvinyl alcohol concentration can effectively control the water separation rate (≤5%) and improve slurry cohesiveness. Through range analysis and multi-indicator comprehensive evaluation based on the entropy weight method, the performance-balanced optimal mix ratio meeting the grouting requirements for the Weathering and Oxidation Zone was determined: a water–cement ratio of 0.6, an ultrafine fly ash replacement rate of 20%, a polyvinyl alcohol concentration of 1.0%, and a polycarboxylate superplasticizer concentration of 0.4%. This mix ratio material exhibits good permeability, stability, and appropriate reinforcement strength. The research results can provide a new material choice and theoretical basis for controlling the surrounding rock of roadways under similar geological conditions. Full article

Roof water inrush is a major hazard threatening coal mine safety. This paper addresses the risk of roof water inrush during mining in the shallow-buried Jurassic coalfield of Northern Shaanxi, taking the Guojiawan Coal Mine as a case study. A systematic framework of “identification of main controlling factors–coupling of subjective and objective weighting–GIS-based spatial evaluation” is proposed. An integrated weighting system combining the Fuzzy Analytic Hierarchy Process (FAHP) and the Coefficient of Variation (CV) method is innovatively adopted. Four weight optimization models, including Linear Weighted Method, Multiplicative Synthesis Normalization Method, Minimum Information Entropy Method, and Game Theory Method, are introduced to evaluate 10 main controlling factors, including the fault strength index and sand–mud ratio. The results indicate that the GIS-based vulnerability evaluation model using the Multiplicative Synthesis Normalization Method achieves the highest accuracy, with a Spearman correlation coefficient of 0.9961. This model effectively enables five-level risk zoning and accurately identifies high-risk areas. The evaluation system and zoning results developed in this paper can provide a direct scientific basis for the design of water prevention engineering and precise countermeasures in the Guojiawan Coal Mine and other mining areas with similar geological conditions. Full article

The Bactrocera minax (Enderlein) (Diptera: Tephritidae) and Bactrocera tsuneonis (Miyake) (Diptera: Tephritidae) are the only members of the subgenus of the Tetradacus of Bactrocera. They share nearly identical morphological characteristics and occupy highly overlapping ecological niches, specifically harming citrus crops and causing substantial damage to citrus production in China. To determine the suitable habitat of the two pests and how the citrus coverage affects this distribution. This study employed the Maximum Entropy model (MaxEnt) to predict the potential geographic distributions (PGDs) of B. minax and B. tsuneonis under current and future climate scenarios, using species occurrence data and key environmental variables. The result indicate that the MaxEnt model performed well, with an area under the curve value (AUC) of 0.969. The citrus distribution index, precipitation of driest month (BIO 14), min temperature of coldest month (BIO 6), and elevation were identified as the primary environmental factors affecting their PGDs. The PGDs for these pests are mainly concentrated in southern China, where citrus is extensively cultivated. Guizhou and Hunan identified as the most significant high-suitability habitat. The projected distribution of B. minax and B. tsuneonis show minimal changes under the future climate conditions estimated by the MaxENT model. However, under global warming scenarios, their PGDs are projected to gradually shrink, although eastern Sichuan remains at high risk of invasion by B. tsuneonis. Prevention, quarantine, and control measures for B. tsuneonis require continued attention. The findings of this study offer a more robust theoretical basis for the targeted monitoring and control of B. minax and B. tsuneonis in China. Full article