Search Results (526)

Coastal ecological regulation plays a crucial role in coordinating the human–environment system and promotes sustainable development, yet it often imposes constraints on the livelihoods of local farmers. Drawing on questionnaire survey data from Chinese coastal farmers, this study quantifies farmers’ opportunity loss through the expectation function and entropy method. Subsequently, a Multinomial Logit model and Generalized Structural Equation Modeling (GSEM) are employed to systematically investigate the mechanisms through which ecological regulation-induced opportunity loss influences coastal farmers’ livelihood transition between 2013 and 2023. The findings reveal that greater opportunity loss significantly inhibits the fishing households’ livelihood transition, exhibiting a ‘livelihood stickiness’ effect. This inhibitory effect is partially mediated by a narrowing of farmers’ psychological distance from environmental issues. Specifically, social distance, reflecting community attachment and identity, plays a dominant mediating role. Furthermore, regulation intensity significantly amplifies this inhibitory effect. Notably, in the absence of substantive compensation or alternative livelihood support, greater policy publicity further reinforces this inhibitory impact. These findings underscore the need for policy interventions that provide compensation and alternative livelihood support commensurate with farmers’ opportunity loss. Enhancing community participation is also crucial to better reconcile coastal conservation objectives with the sustainable livelihoods of local communities. Full article

As a non-invasive diagnostic technique, evolved gas analysis (EGA) holds significant value in assessing the insulation conditions of critical equipment such as power cables. Current analytical methods face two major challenges: insulation materials may undergo multiple aging mechanisms simultaneously, leading to interfering characteristic gases; and traditional approaches lack the multi-label recognition capability to address concurrent fault patterns when processing mixed-gas data. These limitations hinder the accuracy and comprehensiveness of insulation condition assessment, underscoring the urgent need for intelligent analytical methods. This study proposes a deep convolutional neural network (DCNN)-based multi-label classification framework to accurately identify the gas generation characteristics of five typical power cable insulation materials—ethylene propylene diene monomer (EPDM), ethylene-vinyl acetate copolymer (EVA), silicone rubber (SR), polyamide (PA), and cross-linked polyethylene (XLPE)—under fault conditions. The method leverages concentration data of six characteristic gases (CO₂, C₂H₄, C₂H₆, CH₄, CO, and H₂), integrating modern data analysis and deep learning techniques, including logarithmic transformation, Z-score normalization, multi-scale convolution, residual connections, channel attention mechanisms, and weighted binary cross-entropy loss functions, to enable simultaneous prediction of multiple degradation states or concurrent fault pattern combinations. By constructing a gas dataset covering diverse materials and operating conditions and conducting comparative experiments to validate the proposed DCNN model’s performance, the results demonstrate that the model can effectively learn material-specific gas generation patterns and accurately identify complex label co-occurrence scenarios. This approach provides technical support for improving the accuracy of insulation condition assessment in power cable equipment. Full article

With the growing deployment of Flying Ad hoc Networks (FANETs) in military and civilian applications, constructing a stable and efficient communication backbone has become a critical challenge. This paper tackles the Cluster Head (CH) optimization problem in large-scale and highly dynamic FANETs by formulating it as a Minimum Connected Dominating Set (MCDS) problem. However, since MCDS is NP-complete on general graphs, existing heuristic and exact algorithms suffer from limited coverage, poor connectivity, and high computational cost. To address these issues, we propose TGN-MCDS, a novel algorithm built upon the Temporal Graph Network (TGN) architecture, which leverages graph neural networks for cluster head selection and efficiently learns time-varying network topologies. The algorithm adopts a multi-objective loss function incorporating coverage, connectivity, size control, centrality, edge penalty, temporal smoothness, and information entropy to guide model training. Simulation results demonstrate that TGN-MCDS rapidly achieves near-optimal CH sets with full node coverage and strong connectivity. Compared with Greedy, Integer Linear Programming (ILP), and Branch-and-Bound (BnB) methods, TGN-MCDS produces fewer and more stable CHs, significantly improving cluster stability while maintaining high computational efficiency for real-time operations in large-scale FANETs. Full article

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across vision–language tasks, yet their safety alignment remains vulnerable to adversarial manipulation. Existing jailbreak attacks typically optimize adversarial perturbations using negative log-likelihood loss alone, which often leads to overfitting on target affirmative tokens and fails to elicit substantive harmful content. We propose Attention-Enhancement and Targeted Entropy Regularization for Adversarial Optimization (AERO), a novel jailbreak framework addressing these limitations through two complementary mechanisms. First, an attention enhancement loss strategically redirects cross-modal attention toward perturbed visual tokens, distracting safety-aligned features from scrutinizing malicious queries. Second, a targeted entropy regularization scheme maximizes output diversity over non-refusal tokens during initial generation, creating a permissive context that improves cross-query generalization and enables responses that genuinely address malicious requests. Extensive experiments on multiple state-of-the-art MLLMs demonstrate that AERO significantly outperforms existing methods, achieving Attack Success Rates (ASRs) of 65.8–70.7% on MM-SafetyBench and 71.0–84.5% on HarmBench. Our approach surpasses the strongest baselines by margins of up to 16.2% in success rate while consistently generating higher-quality harmful content. Full article

The paper presents a statistical model based on the two-weighted exponential distribution (TWED) to examine censored Human Immunodeficiency Virus (HIV) survival information. Identifying HIV as a disability, the study endorses an inclusive and sustainable health policy framework through some predictive findings. The TWED provides an accurate representation of the inherent hazard patterns and also improves the modelling of survival data. The parameter estimation is achieved in both a classical maximum likelihood estimation (MLE) and a Bayesian approach. Bayesian inference can be carried out under general entropy loss conditions and the symmetric squared error loss function through the Markov Chain Monte Carlo (MCMC) method. Based on the symmetric properties of the inverse of the Fisher information matrix, the asymptotic confidence intervals (ACLs) for the MLEs are constructed. Moreover, two-sided symmetric credible intervals (CRIs) of Bayesian estimates are also constructed based on the MCMC results that are based on symmetric normal proposals. The simulation studies are very important for indicating the correctness and probability of a statistical estimator. Implementing the model on actual HIV data illustrates its usefulness. Altogether, the paper supports the idea that statistics play an essential role in promoting disability-friendly and sustainable research in the field of public health in general. Full article

Against the backdrop of the carbon peaking and neutrality (“dual-carbon”) goals and evolving new-type power system dispatch, the share of pumped-storage hydropower (PSH) in power systems continues to increase, imposing stricter requirements on units for higher cycling frequency, greater operational flexibility, and rapid, stable startup and shutdown. Focusing on the entire hot-standby-to-generation transition of a PSH plant, a full-flow-path three-dimensional transient numerical model encompassing kilometer-scale headrace/tailrace systems, meter-scale runner and casing passages, and millimeter-scale inter-component clearances is developed. Three-dimensional unsteady computational fluid dynamics are determined, while the surge tank free surface and gaseous phase are captured using a volume-of-fluid (VOF) two-phase formula. Grid independence is demonstrated, and time-resolved validation is performed against the experimental model–test operating data. Internal instability structures are diagnosed via pressure fluctuation spectral analysis and characteristic mode identification, complemented by entropy production analysis to quantify dissipative losses. The results indicate that hydraulic instabilities concentrate in the acceleration phase at small guide vane openings, where misalignment between inflow incidence and blade setting induces separation and vortical structures. Concurrently, an intensified adverse pressure gradient in the draft tube generates an axial recirculation core and a vortex rope, driving upstream propagation of low-frequency pressure pulsations. These findings deepen our mechanistic understanding of hydraulic transients during the hot-standby-to-generation transition of PSH units and provide a theoretical basis for improving transitional stability and optimizing control strategies. Full article

Pipelines that satisfy k-anonymity alone remain vulnerable to attribute disclosure under skewed sensitive attributes. We studied real-time anonymization of high-throughput data streams under strict delay budgets (β). We jointly enforced k-anonymity and l-diversity via a delay-aware Monitor–Trigger–Repair controller that selects swap vs. merge by minimizing a weighted objective λΔIL + (1 − λ)ΔRT while bounding overhead with a neighbor cap (c) and a growth cap (γ). On UCI Adult stream replay, we identified operating regions where stricter privacy does not necessarily increase distortion: with moderate-to-high $k$ and sufficiently large $\beta$, groups satisfy $l$ preemptively, reducing reconfigurations and avoiding aggressive generalization, thereby mitigating information loss relative to k-only baselines. Privacy metrics (l-satisfaction rate and entropy) also improved. We further report a focused sensitivity analysis on $\lambda$, $c$, and $\gamma$ and evaluate an entropy-driven adaptive $l_t$ controller, showing that these levers provide interpretable trade-offs between latency and distortion and can suppress excessive reconfiguration and tail latency. Full article

Rational allocation and coordinated operation of water resources in arid inland river basins are crucial for sustaining irrigated agriculture, maintaining ecological baseflow and ensuring reservoir safety. To address this need, this study develops and evaluates joint-operation schemes for the Jingou River-Hongshan Reservoir irrigation system in Xinjiang, northwestern China, to improve coordination among irrigation water supply, ecological baseflow maintenance and reservoir safety. A monthly reservoir-canal-irrigation operation model is formulated with irrigation demands, ecological flow constraints and key engineering limits. Using this model, operating schemes are generated to explore trade-offs among three objectives: shortages, reliability and non-beneficial reservoir releases. The non-dominated schemes obtained from multi-objective optimization are then ranked using an entropy-weighted TOPSIS framework, from which representative solutions are selected for further interpretation. The results indicate that the top-ranked schemes deliver comparable and relatively well-balanced performance across the objectives. Under the preferred compromise scheme, annual irrigation shortages amount to about 39% of total demand, the mean satisfaction level of irrigation and ecological requirements reaches roughly 57%, and the combined index of spill losses and end-of-year storage deviation remains low. Schemes that push shortage reduction or reliability enhancement to extremes tend to increase spill losses, compromise storage security or both, thereby degrading overall performance. The proposed optimization-ranking framework offers a transparent basis for identifying robust operating strategies that reflect local management priorities and is transferable to other reservoir-supported irrigation systems in arid regions. Full article

An early and accurate detection of retinal lesions is imperative to intercept the course of sight-threatening ailments, such as Diabetic Retinopathy (DR) or Age-related Macular Degeneration (AMD). Manual expert annotation of all such lesions would take a long time and would be subject to interobserver tendencies, especially in large screening projects. This work introduces an end-to-end deep learning pipeline for automated retinal lesion segmentation, tailored to datasets without available expert pixel-level reference annotations. The approach is specifically designed for our needs. A novel multi-stage automated ground truth mask generation method, based on colour space analysis, entropy filtering and morphological operations, and creating reliable pseudo-labels from raw retinal images. These pseudo-labels then serve as the training input for a U-Net architecture, a convolutional encoder–decoder architecture for biomedical image segmentation. To address the inherent class imbalance often encountered in medical imaging, we employ and thoroughly evaluate a novel hybrid loss function combining Focal Loss and Dice Loss. The proposed pipeline was rigorously evaluated on the ‘Eye Image Dataset’ from Kaggle, achieving a state-of-the-art segmentation performance with a Dice Similarity Coefficient of 0.932, Intersection over Union (IoU) of 0.865, Precision of 0.913, and Recall of 0.897. This work demonstrates the feasibility of achieving high-quality retinal lesion segmentation even in resource-constrained environments where extensive expert annotations are unavailable, thus paving the way for more accessible and scalable ophthalmological diagnostic tools. Full article

Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine learning promises adaptive detection, but deployment faces severe class imbalance: in the NeurIPS 2022 BAF Base benchmark used here, fraud prevalence is 1.10%. Standard metrics (accuracy, f1_weighted) can look strong while doing little for the minority class. We compare Logistic Regression, SVM (RBF), Random Forest, LightGBM, and a GRU model on N = 1,000,000 accounts under a unified preprocessing pipeline. All models are trained to minimize their loss function, while configurations are selected on a stratified development set using validation-weighted F1-score f1_weighted. For the four classical models, class weighting in the loss (class_weight $\in \{\mathrm{None},‘\mathrm{balanced}’\}$) is treated as a hyperparameter and tuned. Similarly, the GRU is trained with a fixed class-weighted CrossEntropy loss that up-weights fraud cases. This ensures that both model families leverage weighted training objectives, while their final hyperparameters are consistently selected by the f1_weighted metric. Despite similar AUCs and aligned feature importance across families, the classical models converge to high-precision, low-recall solutions (1–6% fraud recall), whereas the GRU recovers 78% recall at 5% precision (AUC $=0.8800$). Under extreme imbalance, objective choice and operating point matter at least as much as architecture. Full article

Traditional electrical resistivity tomography (ERT) technology confronts bottlenecks such as the volume effect in the detection of termite nests in levees, while the ERT based on deep learning has insufficient interpretation accuracy due to small sample data. This study proposes an intelligent ERT diagnosis framework that integrates generative adversarial networks (GANs) with semantic segmentation models. The GAN-enhanced networks (GFU-Net and GFL-Net) are developed, incorporating a Squeeze-and-Excitation (SE) attention mechanism to suppress false anomalies. Additionally, a comprehensive loss function combining binary cross-entropy (BCE) and the Focal loss function is used to address the issue of sample imbalance. Using forward modeling based on the finite difference method (FDM), a termite nest hidden danger ERT dataset, which includes seven types of high-resistance anomaly configurations, is generated. Numerical simulations demonstrate that GFL-Net achieves a mean intersection-over-union (mIoU) of 97.68% and a spatial positioning error of less than 0.04 m. In field validation on a red clay embankment in Jiangxi Province, this method significantly improves the positioning accuracy of hidden termite nests compared to traditional least squares (LS) inversion. Excavation verification results show that the maximum error in the horizontal center and top burial depth of the termite nest identified by GFL-Net is less than 7% and 16%, respectively. The research findings provide reliable technical support for the accurate identification of termite nest hidden dangers in embankments. Full article

Images, as carriers of rich information, are generated, stored, and transmitted in various forms across diverse scenarios. It has become an important issue in the field of information security today to encrypt images to ensure information security. To address this issue, this paper proposes a Pixel-Level and DNA-Level Image Encryption Method Based on a Five-Dimensional Hyperchaotic System, named PD5H. The proposed method combines a five-dimensional chaotic system, a novel pixel-block internal diffusion method, and a new flow diffusion method integrating Pixel-Level and DNA-Level encryption, hereinafter referred to as ‘joint diffusion’. The improved 5D chaotic system can generate highly complex and unpredictable chaotic sequences. The intra-block diffusion process utilizes the internal information of the image to perform preliminary diffusion and reduce pixel correlation. The joint diffusion process can effectively employ various encryption methods to encrypt images with different step sizes at the bit level. PD5H has a large key space, extremely low image correlation, a uniform ciphertext pixel distribution, an excellent ciphertext entropy value (>7.999), and strong resistance to differential attacks. It also demonstrates strong resistance to data loss. The security analysis confirms that PD5H demonstrates excellent performance in color image encryption and can effectively resist various common attacks. Full article

A promoter is an important non-coding DNA sequence, as it can regulate gene expression. Its abnormalities are closely associated with various diseases, such as coronary heart disease, diabetes, and tumors. Therefore, promoter identification is highly significant. Due to the insufficient nonlinear feature extraction and insufficient capture of sequence context relationships, existing single promoter identification models have a lower classification performance. To overcome these shortcomings, this paper proposed a new model called iPro2L-Kresidual. iPro2L-Kresidual integrated a residual structure with a KAN network to design a novel Kresidual module. The Kresidual module significantly enhanced the nonlinear expression capability of sequence features by using B-spline functions and residual networks. Additionally, to fully capture the sequence context relationship, iPro2L-Kresidual improved a Transformer encoder module by replacing the linear processing method with gated recurrent units, so then it can extract both local and global context features of a sequence. Furthermore, iPro2L-Kresidual designed a regularized label smoothing cross-entropy loss function to ensure training stability and prevent the model from becoming overly confident. Experimental results on 5-fold cross-validation showed that the accuracy of promoter identification and promoter strength identification, respectively, was 94.28% and 90.55%. Moreover, on an independent dataset, the prediction accuracy reached 93.13%, further demonstrating the model’s strong generalization ability. This provides a novel and effective predictive model for promoter site prediction. Full article

This study proposes an equipment-level framework for quantifying and grading tower-crane construction safety resilience that addresses three persistent gaps in construction safety research: subjective weighting, static scoring, and weak uncertainty treatment. The Entropy Weight Method (EWM) with Monte Carlo Simulation (MCS) is integrated to convert five objective indicators (fatalities, serious injuries, economic losses, accident-severity factor, and accident frequency) into (i) data-driven weights and (ii) interval-valued resilience estimates (mean and 95% CI). A quintile scheme yields an interpretable five-tier scale from Very Weak to Very Strong. On a multi-source dataset of 696 accidents, casualties and severity dominate the entropy weights and effectively separate resilience tiers. The MCS intervals are stable and decision-oriented. Using the obtained tiers as labels, a Random-Forest classifier achieves superior Accuracy and Macro-F1, demonstrating that the grading is predictable and thus operational for early warning. Two lightweight proxies were further introduced, the Management Behavior Index (MBI) and the Recovery Difficulty Index (RDI), to incorporate management/behavioral signals and recovery burden; both couple with the EWM-MCS score at small weights, smooth zero-event cases, and highlight priority risks. Sensitivity checks on binning rules, simulation budgets, perturbation magnitudes, and coupling coefficients confirm robustness. The proposed framework generates interconnected output metrics, including the mean value, confidence interval, risk tier, and result interpretability. Furthermore, it exhibits high portability and can be readily adapted to other types of critical construction equipment as well as online assessment workflows. Full article

Sitting posture recognition, defined as automatically localizing and categorizing seated human postures, has become essential for large-scale ergonomics assessment and longitudinal health-risk monitoring in classrooms and offices. However, in real-world multi-person scenes, pervasive occlusions and overlaps induce keypoint misalignment, causing global-attention backbones to fail to localize critical local structures. Moreover, annotation scarcity makes small-sample training commonplace, leaving models insufficiently robust to misalignment perturbations and thereby limiting cross-domain generalization. To address these challenges, we propose LAViTSPose, a lightweight cascaded framework for sitting posture recognition. Concretely, a YOLOR-based detector trained with a Range-aware IoU (RaIoU) loss yields tight person crops under partial visibility; ESBody suppresses cross-person leakage and estimates occlusion/head-orientation cues; a compact ViT head (MLiT) with Spatial Displacement Contact (SDC) and a learnable temperature (LT) mechanism performs skeleton-only classification with a local structural-consistency regularizer. From an information-theoretic perspective, our design enhances discriminative feature compactness and reduces structural entropy under occlusion and annotation scarcity. We conducted a systematic evaluation on the USSP dataset, and the results show that LAViTSPose outperforms existing methods on both sitting posture classification and face-orientation recognition while meeting real-time inference requirements. Full article