Search Results (1,053)

This paper developed a probabilistic framework for system level reliability and risk assessment that coupled hydraulic loading with structural response and explicitly modelled cascading interactions and statistical dependence between components. The contribution is a system-level reliability and risk modelling methodology that integrates dynamic cascading interactions, non-stationary design-life reliability accumulation, and system-level optimisation within a unified Monte Carlo architecture. Dynamic Monte Carlo simulation was used to evaluate individual, joint, conditional, and system-scale probabilities of failure across varying flood magnitudes and design lives. Model verification confirmed that discretisation and sampling errors were small relative to parameter-driven variability. Results showed that long-term system reliability arose from the combined influence of flood frequency, exposure duration, and the strength of interaction between interdependent structures. Frequent loading accelerates the accumulation of failure probability through repeated events, whereas rare events contribute more slowly but dominate extreme outcomes, indicating that cumulative reliability cannot be inferred by the linear extrapolation of annual probabilities. In an examined diversion–levee–basin configuration, strong structural coupling amplified vulnerability by contracting joint stability margins and increasing conditional failure probabilities. The system-level optimisation of structural parameters over the examined design life reduced cumulative system failure probability from 0.305 to 0.153, whereas single-component optimisation redistributed risk within the system without reducing total system risk. The framework advances beyond static risk analysis by integrating time-dependent reliability, cascading dependencies, and design-life optimisation for system-scale mitigation. Full article

Background and Objectives: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality, largely due to late-stage diagnosis and the absence of effective population-based screening. Intrapancreatic fat deposition (IPFD) has emerged as a potential risk phenotype. This narrative review critically appraises the clinical, metabolic, epidemiologic, and mechanistic evidence linking IPFD to PDAC and discusses its implications for risk stratification and prevention. Materials and Methods: A structured literature search was conducted in PubMed/MEDLINE and Scopus for studies published between 2007 and 2025 using predefined terms related to pancreatic steatosis and pancreatic cancer. After duplicate removal and screening according to predefined inclusion and exclusion criteria, 42 articles were included. Evidence was synthesized focusing on epidemiologic associations, mechanistic pathways, and imaging-based quantification methods. Results: A strong association between IPFD and PDAC was found. Although definitive causality remains unproven, some studies support temporal correlation between IPFD and PDAC, suggesting that IPFD precedes PDAC. A possible pathophysiological explanation to this correlation has been advanced in experimental models indicating IPFD as a pro-inflammatory factor cooperating with oncogenic KRAS to facilitate neoplastic progression. Finally, variability in IPFD definitions and heterogeneity in imaging assessment limit interpretability. Conclusions: Current evidence links IPFD to PDAC risk, suggesting a strong suspicion that pancreatic steatosis may represent an independent risk factor for PDAC. Still robust causal inference remains unproven. Well-designed prospective studies, standardized imaging protocols, and mechanistic investigations are required to clarify causality and determine whether pancreatic steatosis can be incorporated into risk-based screening and preventive strategies. Full article

Background: The cochlea is a specialized sensory organ essential for hearing. To elucidate its cellular and molecular architecture and prioritize candidate genes associated with hearing loss (HL), we constructed a cross-species single-cell transcriptomic atlas of human fetal and postnatal mouse cochleae. Methods: We integrated single-cell and single-nucleus RNA sequencing datasets from human fetal cochleae and postnatal mouse cochleae to build a comprehensive cross-species single-cell transcriptomic atlas. Cell-type annotation, transcriptional regulator analysis, intercellular communication, and disease phenotypes were performed to dissect the cochlear cellular landscape, regulatory programs, and potential HL gene candidates. Results: A total of 19 major cochlear cell types were identified in both species, with conserved cellular composition and transcriptional programs. Comparative analysis revealed strong transcriptional conservation between matched human and mouse cell types, particularly in supporting, schwann cells and hair cells. Cell–cell communication analysis revealed conserved signaling pathways, including the BDNF-NTRK2 axis, potentially involved in cochlear development and auditory function. Regulatory network inference uncovered conserved and previously undercharacterized transcription factors, such as SKOR1, RFX2, and PAX2, predicted to be associated with hair cell identity and function. We further defined a conserved gene module of 3138 hair cell-enriched genes, from which 24 candidate HL-associated genes (e.g., ATP8B1, BDNF, and SOD1) were prioritized through integration with human disease databases and mouse auditory phenotype annotations. Conclusions: This study provides a high-resolution cross-species cochlear atlas, revealing conserved molecular programs and candidate HL-associated genes, offering valuable insights into auditory biology and potential avenues for further investigation. Full article

Natural hydrogen is regarded as a potential resource for the global energy transition, and its accumulation is closely linked to water–rock reactions involving Fe²⁺ bearing minerals and effective sealing conditions. The Liaohe Basin, located on the northeastern margin of the North China Craton within a key metallogenic belt, is surrounded by sedimentary-metamorphic iron deposits and is a potential area for natural hydrogen accumulation. In this study, aeromagnetic and satellite gravity data were integrated to estimate basement depth through gravity interface inversion, followed by three-dimensional magnetic susceptibility and density inversion and structural–mineralization correlation analysis. The results reveal strong basement heterogeneity. Iron-rich anomalous bodies show clustered and belt-like to dome-like distributions, mainly along the transitional zone between deep depressions and basement uplifts. Combined density–magnetic zonation suggests that high-density, high-magnetic units may correspond to iron-rich bodies, whereas high-magnetic, low-density units likely indicate fractured and altered fluid pathways. Based on the measured results of surface hydrogen concentration, it is inferred that the high magnetic anomaly in the uplift transition zone at the edge of the depression might be the coupling area of iron-rich rock bodies and channel zones, which is the priority response area of natural hydrogen in the Liaohe Basin, China. Full article

Accurate demand forecasting and adaptive load balancing are critical for maintaining stability and efficiency in modern smart power grids. This study proposes a hybrid deep learning (DL) framework, termed Transformer-Generative Adversarial Network-Gated Recurrent Unit (Transformer-GAN-GRU), which integrates global attention-based temporal modeling, generative data augmentation, and sequential refinement into a unified architecture. The proposed framework captures both long- and short-term dependencies while improving representation of imbalanced demand patterns. The model is evaluated on three heterogeneous benchmark datasets, namely Pecan Street, the reliability test system-grid modernization laboratory consortium (RTS-GMLC), and the reference energy disaggregation dataset (REDD). Experimental results demonstrate that the proposed model consistently outperforms state-of-the-art baselines, achieving a maximum accuracy (Acc) of 99.49%, a recall of 99.67%, and an area under the curve (AUC) of 99.83%. In addition to high predictive performance, the framework exhibits strong stability, fast convergence, and low inference latency, confirming its suitability for real-time deployment in smart grid environments. Full article

Early detection of agricultural fires with Unmanned Aerial Vehicles (UAVs) is important for environmental safety, yet it remains difficult because ignition cues are extremely small, smoke patterns vary widely, and farmland scenes often contain strong background interference such as specular reflections. Model development is further constrained by the scarcity of data from the early ignition stage. To address these challenges, we propose a joint data and model optimization framework. We first build a hybrid dataset through an ROI-guided synthesis pipeline, in which latent diffusion models are used to insert high-fidelity, carefully screened fire samples into real farmland backgrounds. We then introduce EF-YOLO, a detector designed for high sensitivity to small targets. The network uses SPD-Conv to reduce feature loss during spatial downsampling and includes a high-resolution P2 head to improve the detection of minute objects. To reduce background clutter, a Dual-Path Frequency–Spatial Enhancement (DP-FSE) module serves as a lightweight statistical surrogate that extracts global contextual cues and local salient features in parallel, thereby suppressing high-frequency noise. Experimental results show that EF-YOLO achieves an AP_s of 40.2% on sub-pixel targets, exceeding the YOLOv8s baseline by 15.4 percentage points. With a recall of 88.7% and a real-time inference speed of 78 FPS, the proposed framework offers a strong balance between detection performance and efficiency, making it well suited for edge-deployed agricultural fire early-warning systems. Full article

Highway ramp merging requires autonomous vehicles to make safe and efficient decisions in dense mixed traffic, where strong vehicle interactions and rapidly changing acceptable gaps make the task particularly challenging. Existing reinforcement learning methods are often unimodal and overly conservative, while diffusion-based policies, despite their ability to generate multimodal actions, usually suffer from high inference latency and safety risks caused by unconstrained sampling. To address these issues, this paper proposes AM-DIMPO, an action-mask-guided safe diffusion-implicit policy optimization framework for ramp-merging tasks. The proposed method combines DDIM-based implicit sampling with a state-dependent continuous action mask to improve multimodal action generation efficiency while enhancing action feasibility. In addition, the mask correction signal is incorporated into policy learning to encourage the policy to generate actions closer to the safe feasible region. Experiments are conducted in a Gym-based ramp-merging simulator under both light-traffic and dense-traffic scenarios, where the proposed method is compared with classical reinforcement learning baselines, diffusion reinforcement learning baselines, and a safety-aware PPO baseline. The results show that, in dense traffic, AM-DIMPO achieves a merging success rate of 97.3%, an average speed of 16.27 m/s, and an inference latency of 68 ms; in light traffic, the success rate reaches 98.1%. Moreover, the proposed method maintains robust performance under the tested noisy-observation and reduced-visibility settings. Overall, AM-DIMPO achieves a favorable balance among empirical safety, traffic efficiency, robustness, and real-time inference performance in dense highway ramp-merging tasks. Full article

This paper presents a novel approach for the early detection of cattle diseases. We present a uniquely integrated image classification-based project for real-time cattle disease diagnosis that combines image classification models to identify diseases accurately; a seamless, user-friendly dashboard for real-time monitoring with data visualization and instant predictions; and a mobile application that acts as a data source. The mobile application enables real-time collection of farmer and cattle-related data, including age, number of cattle, vaccination cycles, cattle images, and location metadata. Our AI-based cattle health monitoring project enables the early, efficient, scalable, and timely detection of Lumpy Skin Disease (LSD) and Foot and Mouth Disease (FMD) in cattle with high accuracy. A dataset of approximately 1600 LSD/non-LSD images and 840 FMD images was used to train multiple classification networks such as EfficientNetB0, ResNet50, VGG16, EfficientNetV2B0, and EfficientNetV2S, along with a soft-voting ensemble at inference. The proposed framework achieved a maximum testing accuracy of 98.36% for LSD classification and 99.84% for FMD classification under internal validation. These results indicate strong disease recognition capability, with ensemble-based prediction improving robustness, particularly for FMD classification. The proposed system enables practical, early, efficient, and scalable applications of AI research to improve livestock health monitoring and support the early prevention of widespread disease outbreaks. Full article

This study examines whether international food price dynamics provide a reliable signal of undernourishment and human development outcomes relevant to the attainment of SDG 2 (Zero Hunger) by 2030. We apply wavelet coherence analysis to the FAO Food Price Index and the prevalence of undernourishment (SDG Indicator 2.1.1) over 2001–2023, testing statistical significance against an AR(1) red-noise null hypothesis. Hybrid ARIMA–Random Forest models generate probabilistic price forecasts through 2030. Despite strong raw coherence (R² ≈ 0.77), only 7.8% of time–frequency cells achieve statistical significance, indicating that apparent co-movement largely reflects autocorrelation rather than substantive dependence. Where significant coherence emerges, it concentrates at medium-run horizons (3–6 years), consistent with undernourishment as a habitual dietary adequacy measure linked to sustained affordability pressures affecting health, productivity, and human capital formation. Rolling correlation analysis reveals suggestive evidence of a regime change around 2012—from negative to positive correlation—coinciding with a slowdown in progress toward reducing hunger, although the 5-year rolling windows yield only 19 observations, limiting the power of formal structural break tests. Price forecasts exhibit rapidly widening confidence intervals (by ±131 index points by 2030), underscoring fundamental limits to predictability. The annual PoU series comprises only 23 observations, which constrains the estimation of long-run (8–12-year) wavelet cycles; results at those horizons should therefore be interpreted with caution. These findings caution against mechanistic inferences from global price indices to hunger and human development outcomes, redirecting policy emphasis toward domestic transmission channels and nutrition-sensitive safety nets. Full article

Accurate and efficient detection is pivotal for tiny objects in remote sensing. However, achieving a favorable accuracy-efficiency trade-off remains challenging due to the few informative pixels of small targets, frequent occlusions, cluttered backgrounds, and detail degradation introduced by downsampling and multi-scale fusion. To address these challenges, we propose WEYOLO, a wavelet-enhanced detector that explicitly models frequency components and adaptively strengthens high-frequency cues to improve tiny-object robustness while maintaining competitive efficiency in inference speed and model size for remote-sensing deployment. To preserve edges and textures when spatial resolution is reduced, we design a Frequency-Aware Lifting Haar (FaLH) backbone that decomposes features into directional sub-bands and retains them during downsampling, preventing the loss of high-frequency information. Next, to address the blurring and detail loss caused by conventional pooling during multi-scale fusion, we introduce a Frequency-Domain Pyramid-Pooling (FDPP) module that performs wavelet-based multi-resolution analysis for frequency-aware feature-pyramid fusion. Additionally, we propose a stable size-aware quality focal regression loss that unifies Focaler-CIoU and size-aware DFL into a single objective, improving robustness and overall accuracy for small objects. Comprehensive experiments show that WEYOLO improves precision and recall over the baseline by 3.2%/4.2% on VisDrone and 2.6%/9.7% on TT100K; on AI-TOD, it achieves 47.5% mAP@0.5 and 21.3% mAP@0.5:0.95. Meanwhile, it reduces the parameter count by 60%, achieving a strong accuracy-efficiency balance for practical aerial sensing deployment. Full article

Deep learning has accelerated progress in plant disease recognition, providing strong technical support for early diagnosis and precision management. However, models often lack robustness and generalization when confronted with novel crops absent from the training set, leading to a marked performance drop in cross-unseen-crop scenarios. Cross-crop generalization for plant disease recognition requires models to identify known disease categories in crop domains never observed during training. A central challenge is that disease symptoms are strongly coupled with crop-specific appearance cues, which severely degrades generalization. Here, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition. The proposed method employs a dual-branch visual encoder to separately capture disease semantic representations and crop-domain representations, and it leverages a frozen CLIP text encoder to use disease and crop prompts for text-guided semantic anchoring. A semantic-anchor-only contrastive disentanglement strategy is further formulated under a hybrid label space, where crop-branch features are incorporated as stop-gradient hard negatives to suppress semantic–domain information leakage and strengthen the intra-class aggregation of the same disease across crops. Residual domain-discriminative cues are mitigated via domain-adversarial learning. During inference, only the disease branch is retained for classification, improving generalization while reducing deployment overhead. Experiments demonstrate that under the PlantVillage cross-crop setting, the method achieves 98.04% and 74.29% Top-1 accuracy on seen and unseen crop domains, respectively. Moreover, it attains 81.99% on a real-world field dataset of strawberry powdery mildew and 76.31% on a low-illumination degradation set, validating robustness under realistic imaging distribution shifts. Full article

The aim of this study was to develop an effective and fast tool to support the automatic segmentation of skin lesions, with particular emphasis on the precise differentiation between malignant and benign lesions. In response to the problem of high false positive rates in existing CAD systems, modern neural network architectures from the YOLO family (YOLOv8, YOLOv9, YOLOv11, YOLOv12, and YOLOv26) were used in this research. The models were trained and evaluated on a new, balanced dataset (7000 images) based on the ISIC archive, where the key innovation was the introduction of a dedicated background class representing healthy skin. Through a multi-stage, rigorous optimization process, it was demonstrated that the yolov11s-seg model is highly effective for this task. It achieved a strong balance between effectiveness and processing speed, obtaining an mAP@50 score of 0.840 and an overall precision of 0.852. From a clinical perspective, the model’s high sensitivity (85.9%) in detecting the most aggressive lesion, invasive melanoma (MI), is particularly noteworthy. Thanks to its extremely short inference time (only 4.8 ms), the proposed yolov11s-seg variant overcomes the limitations of heavy hybrid architecture, providing a stable and highly efficient solution showing significant potential for deployment in real-time medical mobile applications. Full article

Emergency evacuation in complex and dynamic building environments requires robust and adaptive routing strategies capable of responding to evolving hazards, blocked passages, and changing crowd behaviour. Most existing evacuation planners rely on static geometric representations and lack semantic awareness of the environment, limiting their ability to perform informed re-planning and backtracking when routes become unsafe. This paper proposes a neuro-symbolic evacuation planning framework that integrates Lifelong Planning A* (LPA*) with ontology-driven semantic reasoning and a Bidirectional Long Short-Term Memory (BiLSTM) prediction model. The building’s spatial and semantic knowledge is represented using the Web Ontology Language (OWL) and Resource Description Framework (RDF), enabling automated inference of implicit connections and enforcement of safety policies. The BiLSTM model learns temporal patterns from ontology-consistent evacuation trajectories and provides guidance for remaining-cost estimation and early prediction of routes likely to require backtracking, which is combined with a bounded semantic heuristic to preserve admissibility and optimality guarantees. Simulation results in a multi-floor academic building show that the proposed BiLSTM-guided semantic LPA* framework reduces average evacuation time by up to 9.6%, decreases node expansions by up to 32%, and increases evacuation success rates to 96.2% compared with a purely semantic baseline. The BiLSTM model also achieves strong predictive performance, with a test AUC of 0.92 for backtracking prediction and a next-state accuracy of 87.1%. The proposed framework is designed to support explainable, policy-compliant, and incrementally adaptable evacuation guidance under rapidly evolving emergency conditions. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

Background: Chronic obstructive pulmonary disease (COPD) is associated with systemic inflammation and frequent exacerbations, leading to disease progression and increased morbidity. Vitamin D deficiency has been suggested to contribute to COPD inflammation and exacerbations. Aim: This study investigated the association between serum 25-hydroxyvitamin D (25(OH)D) levels, systemic inflammation, and exacerbation frequency among patients with COPD GOLD group E. Methods: A cross-sectional study was conducted on 111 patients with stable COPD. Patients were divided into two groups based on their serum 25(OH)D levels (<50 nmol/L vs. ≥50 nmol/L). Data on exacerbation frequency for the past year, inflammatory markers, spirometric lung function parameters, and symptom burden were collected. Results: Patients with low serum 25(OH)D (<50 nmol/L) had a significantly higher CAT score and level of serum high-sensitivity (hs)-CRP and exhibited significantly more exacerbations compared to those with higher 25(OH)D levels (p < 0.001, p < 0.001, and p < 0.0001, respectively). Furthermore, lower vitamin D levels were associated with higher CAT scores (Pearson’s correlation coefficient, r = −0.30, p < 0.01) and higher serum hs-CRP levels (Spearman’s rank correlation coefficient, r = −0.25, p < 0.01), as well as a higher number of exacerbations (Pearson’s correlation coefficient, r = −0.74, p < 0.0001). Conclusions: Low vitamin D levels are significantly associated with greater symptom burden, elevated hs-CRP, and increased exacerbation frequency, indicating a strong relationship between vitamin D deficiency, systemic inflammation, and disease burden in patients with COPD belonging to GOLD group E. However, due to the cross-sectional design, no causal relationship can be inferred and prospective interventional studies are required to determine whether treating vitamin D deficiency improves clinical outcomes. Full article