Search Results (14,503)

Coastal shipping systems experience complex functional degradation and recovery under port congestion, extreme weather, channel restrictions, and environmental constraints. To support sustainable maritime governance and port management, this paper proposes an AIS–MRV consistency-enhanced dynamic network assessment framework. The framework converts vessel trajectories into a dynamic maritime traffic network composed of ports, anchorages, fairways, and traffic corridors, and it constructs normal-state baselines by region, time window, and vessel type. It jointly measures system functionality, resilience loss, recovery time, network efficiency, anchorage congestion, route deviation, and an AIS-derived green operational penalty. It also compares AIS-derived green activity proxies with MRV annual CO${}_2$ reports to assess external consistency. The short-term real-AIS experiment identifies 70 traffic nodes and produces comparable functionality curves and Resilience–Green Index values for five representative port regions. The DGX full-year AIS baseline experiment processes 365 daily AIS files and generates 22.76 million vessel-hour records. Under network-parameter perturbations, the Spearman correlations of the $Q^{*}$ time series range from 0.900 to 1.000, and the Spearman correlation of the five-region RGI ranking remains 1.000. The Los Angeles/Long Beach event window shows a standardized functionality difference of $-0.076$ relative to spatial controls, with a bootstrap 95% confidence interval of [$-0.090$, $-0.023$]. The five regions show an index range of 0.3168–0.8391, and Puget Sound remains the top-ranked region in 86.27% of 10,000 random weight perturbations. The MRV consistency test indicates a moderate positive correlation between the AIS vessel-size proxy and annual CO${}_2$ emissions, while the size-weighted AIS activity proxy is also positively correlated with reported emissions. The framework provides a reproducible basis for identifying vulnerable shipping segments, assessing traffic recovery, and supporting port management, congestion governance, and green shipping decisions. Full article

Background/Objectives: Acute respiratory infections represent a global health burden due to their high incidence, morbidity and mortality. Despite advances in prevention and treatment, strategies providing broad protection against respiratory pathogens remain limited. This study evaluated the antiviral activity of nasal spray forms based on an antigen complex from opportunistic bacteria, with or without a mucoadhesive copolymer, and their influence on cellular and humoral components of the immune response. Methods: Antiviral activity was assessed in experimental models of SARS-CoV-2 infection in Syrian hamsters and influenza A pneumonia in mice under prophylactic and therapeutic–prophylactic regimens. Parameters of cellular and humoral immune response were assessed using delayed-type hypersensitivity, antibody-forming cell assays, and leukocyte phagocytic activity. Results: Both forms showed comparable influence on cellular and humoral components of the immune response. In a mouse model of lethal influenza pneumonia induced by A/California/04/2009 (H1N1)pdm09 virus, intranasal administration at 100 µg/kg increased mean survival time and survival rate, and reduced body weight loss. In SARS-CoV-2-infected hamsters, both forms reduced clinical signs, body weight loss, weight lung index, lung pathology, and decreased viral titers. The copolymer-containing form showed the most pronounced effect under the therapeutic–prophylactic regimen, with greater protection against body weight loss and stronger suppression of viral replication. Conclusions: The findings support further investigation of these nasal spray forms as candidates for prophylaxis and adjunctive therapy of respiratory infections, particularly when pathogen variability may limit the effectiveness of pathogen-directed treatments. Further mechanistic studies are needed to clarify the pathways underlying the observed antiviral effect. Full article

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management. Full article

Real-world fault data for hydrogen-cooled turbogenerators are scarce and largely proprietary, which hinders data-driven health assessment aligned with severity standards. This paper proposes a standards-aligned, multi-source ordinal fusion framework and demonstrates it, as a proof of concept, on a reproducible four-domain surrogate collection. The collection combines public industrial datasets—SKAB (cooling loop), UCI-WWT (water chemistry), CARE Wind Farm A (electrical and thermal conditions)—and a physics-informed hydrogen-side stream derived from Henry’s law and a continuously stirred tank reactor (CSTR) mass balance, joined by paired sampling. The collection is a methodological benchmark, not a validated diagnostic for any specific machine. A dual-head classifier supervised by a hybrid CORN + EMD ordinal loss, a multi-stream fusion backbone, and a calibrated ensemble with per-model temperature scaling are aligned with the four-level GB/T 43188-2023 scheme (Normal/Attention/Abnormal/Serious). All methods are evaluated under a unified protocol (mean ± standard deviation over three seeds; the deterministic calibrated ensemble is reported as a single value). On 600 fused test samples, the ensemble reaches F1-macro 0.5349, Accuracy 0.6717, Cohen’s κ = 0.4713, and quadratic-weighted kappa (QWK) 0.5948, improving F1-macro by +22.4 pp over the strongest full-scale single-source baseline (InceptionTime on CARE, trained under the identical protocol), with larger rank-aware gains (+30.4 pp on κ, +36.2 pp on QWK). It further improves by +8.9 pp over the strongest cross-entropy fusion baseline retrained under the identical protocol. The results support the methodological claim that fusing four heterogeneous monitoring domains under rank-aware ordinal supervision yields coherent, standards-aligned severity grades, offering a reproducible benchmark and methodology whose transfer to real hydrogen-cooled turbogenerators remains to be validated on co-recorded plant data. Full article

Optimizing protein nutrition in ruminants is vital for enhancing growth performance, meat quality, and overall production efficiency. However, the precise protein requirement for ruminants remains unclear. Two experimental trials were conducted to investigate the impact of varying levels of rumen-undegradable protein (RUP) on in situ feed degradability, growth performance, meat quality, and rumen fermentation parameters in fattening Hu sheep. In EXP 1, six Hu sheep were fitted with rumen fistulas to measure rumen degradation characteristics of diets varying levels of crude protein (CP) (13%, 14%, 15%, and 16% X RUP: CP 40%, 45%, 50%). The results showed that the ruminal DM and GE degradation had no difference between the experimental diets (p > 0.05). However, the CP degradation rate gradually decreased as the dietary RUP proportion increased (p < 0.05). In EXP 2, a double factorial experiment was conducted using 720 male Hu sheep, divided into twelve dietary treatments with varying levels of crude protein (CP) (13%, 14%, 15%, and 16%) and RUP (40%, 45%, and 50%). Key performance indicators such as body weight gain (BWG), average daily gain (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR) were measured at the beginning and end of the trial. Post-slaughter, meat quality attributes, including drip loss and initial pH of the longissimus dorsi muscle, were assessed after 40 days of the trial. Rumen fluid samples were analyzed for volatile fatty acid (VFA) profiles to evaluate rumen fermentation efficiency. The results demonstrated that increasing RUP levels significantly improved BWG and ADG (p < 0.05) without affecting ADFI and FCR. Although dietary protein levels had minimal impact, higher RUP levels were associated with a trend toward increased final body weight (p = 0.09). Dietary protein levels and the RUP: CP ratio had no significant effect on meat quality (p > 0.05). Ruminal acetate, valerate, and iso-valerate concentrations were reduced as RUP: CP increased (p < 0.05), and propionate concentration showed a similar trend (p = 0.062). In conclusion, optimizing RUP levels in the diets of fattening Hu sheep significantly enhances growth performance without compromising feed efficiency or the overall rumen fermentation process. These findings provide a foundation for developing feeding strategies that maximize production efficiency in sheep farming. Full article

The simultaneous diagnosis of diseases and evaluation of age quality grades in tea leaves are critical for precision agriculture and the economic valuation of tea products. Although deep learning has shown promise in agricultural vision tasks, current multi-task models often suffer from performance degradation due to feature conflicts: tea leaf disease recognition relies heavily on macro-structural lesions, whereas tea leaf-age quality grading depends on micro-textural features such as trichome density and color uniformity. To address this discrepancy, we propose a novel dual branch fusion network. Our architecture fundamentally decouples the feature extraction process by utilizing a dual branch mechanism. The first branch employs global average pooling to capture first-order spatial statistics; it can retain the global structural layout necessary for macro-lesion detection. The second branch introduces a dimensionality-reduced self-bilinear pooling module to compute second-order covariance matrices; it can effectively capture the fine-grained textural patterns essential for micro-grade classification. These decoupled features are subsequently fused and optimized through a weighted multi-task loss function. Experimental results on a comprehensive tea leaf dataset demonstrate that the proposed dual fusion framework significantly outperforms baseline models. The proposed network can rescue the disease classification accuracy drop observed in standard bilinear models while maintaining exceptional grading performance. Furthermore, the proposed network maintains a compact parameter footprint and low computational complexity. This balance renders it suitable for deployment on agricultural Internet of Things edge devices where inference speed is critical. Full article

This study aimed to evaluate the effects of dietary supplementation with guanidinoacetic acid (GAA) on growth performance, antioxidant status, nutrient digestibility, ruminal fermentation, and meat quality in lambs through a meta-analysis. The electronic databases Scopus, Web of Science, ScienceDirect, and Google Scholar were used to identify the articles required for the meta-analysis using the Preferred Reporting Elements for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The data used in this meta-analysis were obtained from 12 peer-reviewed English-language articles published in the last decade (January 2016 to April 2026). All data were analyzed using random-effects models. Dietary supplementation with GAA decreased the feed conversion ratio (p < 0.05) and increased (p < 0.05) dry matter intake, average daily gain, carcass weight and yield, and Longissimus dorsi muscle area. Dietary supplementation with GAA decreased (p < 0.001) serum malondialdehyde concentration and increased (p ≤ 0.05) serum concentrations of superoxide dismutase, catalase, glutathione peroxidase, and total antioxidant capacity. Dietary supplementation with GAA decreased (p < 0.001) ruminal pH and increased (p < 0.05) the digestibility of dry matter, organic matter, neutral detergent fiber, and acid detergent fiber, as well as the ruminal concentrations of total volatile fatty acids, acetate, and butyrate. Dietary supplementation with GAA decreased (p < 0.001) drip loss in meat and increased (p < 0.001) meat pH and meat protein content. In conclusion, guanidinoacetic acid can be used as a dietary additive to improve growth performance, antioxidant status, nutrient digestibility, ruminal fermentation, and meat quality in lambs. Full article

Sea cucumber cooking liquid contains water-soluble macromolecules, but its bioactive polysaccharide fractions remain insufficiently characterized. In this study, a polysaccharide-rich fraction, P0.7, was isolated from sea cucumber cooking liquid and evaluated for its antitumor activity against HCT-15 colorectal cancer. P0.7 was characterized as a relatively homogeneous sulfated acidic heteropolysaccharide-rich fraction containing 83.61 ± 3.65% total sugar, 13.67 ± 2.48% sulfate, 9.98 ± 1.22% uronic acid, and 5.11 ± 0.34% protein. It was mainly composed of galactose, mannose, and glucose, accounting for 34.12%, 26.93%, and 17.99%, respectively. Among the tested tumor cell lines, HCT-15 cells showed the highest sensitivity to P0.7, with inhibition rates of approximately 45% and 63% at 100 and 200 μg/mL, respectively. P0.7 promoted apoptosis, induced G2/M-phase accumulation, increased ROS production, disrupted mitochondrial membrane potential, regulated Bax, Bcl-2, and cleaved caspase-3 expression, and enhanced γ-H2AX-related DNA damage-response signaling in HCT-15 cells. In an HCT-15 xenograft mouse model, P0.7 reduced terminal tumor volume and tumor weight without causing obvious body weight loss. Histological and immunohistochemical analyses further showed reduced Ki67 staining, increased TUNEL-positive signals, and enhanced γ-H2AX staining in tumor tissues. These findings indicate that P0.7 suppresses HCT-15 colorectal cancer growth in vitro and in vivo, possibly through mechanisms associated with ROS accumulation, mitochondrial apoptosis, and γ-H2AX-related DNA damage response. These findings provide additional experimental evidence supporting the investigation of sea cucumber-derived polysaccharides for potential pharmaceutical applications. Full article

Automatic detection of stress in social media text holds promise for supporting digital mental health, but most existing Transformer-based approaches are opaque and computationally demanding. This work presents DR-Transformer, a Dual-Regularized Transformer that combines two complementary mechanisms: (i) a group sparsity penalty ($L_{2,1}/L_2$ elastic net) applied to the query and key projection matrices of every attention head, which encourages whole-row sparsity, producing more concentrated and inspectable attention patterns; (ii) a supervised contrastive loss on the [CLS] projection, which organizes the latent space according to the stress label. The architecture is intentionally lightweight (six layers, eight heads, 256-dim embeddings; ∼9.5 M parameters) and runs entirely on consumer-grade hardware (NVIDIA GTX 1660, 6 GB). Experiments on the publicly available Dreaddit dataset (binary stress classification, 2838 train/715 test segments) compare DR-Transformer against Logistic Regression, BiLSTM, a Standard Transformer of identical architecture, and MentalBERT. Across five seeded runs, DR-Transformer (Full) reaches $F_1=0.876$ (bootstrap 95% CI $0.852$–$0.898$), outperforming the Standard Transformer ($F_1=0.842$; McNemar $p<0.001$ with Bonferroni correction) and performing comparably to the much larger MentalBERT ($F_1=0.879$; $p=0.421$). Sparse regularization increases the fraction of near-zero attention weights (below $0.01$) from $0.215$ to $0.682$, while the supervised contrastive loss improves the silhouette score of [CLS] embeddings from $0.312$ to $0.483$. Dual regularization thus combines accuracy, efficiency, and structurally induced attention concentration in a single model which can be trained without specialized infrastructure. We use the term “interpretable” throughout in this restricted, structural sense—to refer to concentrated and inspectable attention—rather than in the sense of established causal or mechanistic faithfulness; this is only partially and indirectly supported by our token deletion analysis. Full article

Self-Supervised Learning (SSL) has attracted increasing attention in remote sensing image understanding because it can learn transferable representations from unlabeled images. However, two issues remain insufficiently examined in collaborative SSL for remote sensing. First, when high-ratio masking removes entire small objects or structurally informative regions, the remaining visible patches may provide insufficient evidence for semantically coherent reconstruction. Second, heterogeneous self-supervised objectives may exhibit different loss scales, gradient magnitudes, and convergence behaviors such that fixed coefficients can produce uneven branch contributions during training. To address these issues, this paper proposes Context-Consistency Enhanced Collaborative Self-Supervised Learning (C²DSSL) for remote sensing image understanding. C²DSSL introduces a teacher–student context-consistency constraint, in which multi-scale reconstruction features from the complete teacher observation serve as contextual targets for the student network when reconstructing the corresponding masked observation. In addition, gradient-sensitive dynamic weighting uses temporally smoothed loss-gradient magnitudes as empirical signals to adjust the relative contributions of heterogeneous self-supervised objectives. Under the evaluated settings, adding the context-consistency constraint improves KNN representation evaluation, UCMerced classification, Potsdam semantic segmentation, and DOTA oriented object detection, while maintaining the Baseline performance on the Million-AID subset. Dynamic weighting shows task-dependent effects, including a performance gain on the Million-AID subset classification task when combined with CCL. Full article

To address the issues of optimization instability and imbalance in the contributions of multiple governing equations in conventional Physics-Informed Neural Networks (PINNs) for hydraulic transient problems, a gradient-based equation adaptive weighting strategy is proposed in this study. This strategy is incorporated into the PINN framework, referred to as GEAW-PINNs (gradient-based equation adaptive weighting in Physics-Informed Neural Networks), for predicting pressure and flow velocity during water hammer events. In GEAW-PINNs, the loss terms associated with different governing equations in the partial differential equation (PDE) constraints are dynamically weighted, thereby enhancing training stability. In the model construction, the classical governing equations of water hammer are employed to establish the PDE constraints, in which the Brunone model is incorporated. Meanwhile, the corresponding model coefficient is treated as a trainable parameter, enabling simultaneous parameter inversion and prediction of pressure and flow velocity. High-accuracy numerical solutions are generated as reference data to validate the proposed framework. The results demonstrate that GEAW-PINN effectively improves the stability of PINNs for the prediction of pressure in water hammer phenomena, thereby enhancing overall optimization performance and prediction accuracy. For the reservoir–pipeline–valve system, the proposed method achieved relative errors of only 0.00742 for pressure and 0.0183 for velocity. And the proposed method can also provide accurate predictions in complex pipe network systems. For the pipeline network system, the absolute prediction errors were approximately 15 for pressure and 0.05 for velocity. Finally, the robustness of the proposed method was evaluated under different random seeds and 25 dB noise. The prediction error exhibited little variation across different random seeds, with a variance of only $1.1429\times {10}^{-7}$ and $6.87\times {10}^{-7}$. Under 25 dB noise, the prediction error increased only slightly to $9.88\times {10}^{-3}$ and $2.5\times {10}^{-2}$. This study provides a practical example for achieving stable PINN training in multi-physics coupled problems. Full article

Labeling images is expensive, but unlabeled data is abundant. Semi-supervised learning (SSL) addresses this gap, though the dominant pseudo-labeling methods can suffer from confirmation bias—reinforcing their own confident-but-wrong predictions—most severely when labels are scarcest. Under a controlled, reproducible compute-constrained protocol on CIFAR-10, SVHN, and CIFAR-100, we examine which ingredients of consistency-based SSL actually help when as few as four labels per class are available. We propose CCM (Confidence-Calibrated Consistency Matching)—a per-class curriculum threshold, a dual strong-view consistency loss, and a smooth confidence weighting that softly admits borderline pseudo-labels—together with a unified view in which FixMatch, FlexMatch, SoftMatch, and CCM instantiate a single generalized weighting function. On CIFAR-10 with 40 labels, CCM reaches 35.14%, a significant improvement over the FlexMatch design it directly extends (+3.30 percentage points (pp), paired t-test p = 0.001). SoftMatch, re-trained under the identical budget, performs better still at the two smallest budgets (37.04% at 40 labels, p = 0.049), while CCM leads numerically at 4000 labels: the two smooth-weighting designs top the extreme-scarcity board—convergent evidence that the smoothness of the weighting function, more than the placement of its threshold, is the decisive design axis. We also report a negative result: cross-view agreement helps neither as an admission gate nor as reliability reweighting, reducing accuracy by up to 4.37 pp; agreement is a positive correctness signal, but its absolute level (approximately 44% correct among agreeing pseudo-labels) is too low to filter on safely. Curriculum thresholding, by contrast, hurts on the easier SVHN dataset and fails outright on CIFAR-100 when its per-class statistics become too thin. CCM adds no inference-time cost. We do not claim universality; we characterize when each ingredient helps within a single, identical-budget protocol. Full article

Portfolio optimization is a fundamental problem in financial decision-making, and it is concerned with balancing expected return and investment risk. With the growing emphasis on sustainable investing, environmental, social, and governance (ESG) criteria have been incorporated into portfolio optimization. In practice, ESG-aware portfolio optimization faces parameter ambiguity from market fluctuations, delayed ESG disclosure, and rating disagreement, and the exposure to return uncertainty may depend on portfolio decisions rather than being fully exogenous. Existing studies, however, generally specify uncertainty sets independently of portfolio decisions. To address this limitation, an adjustable robust approach is proposed for ESG-aware portfolio optimization under decision-dependent return uncertainty. A joint polyhedral uncertainty set is constructed to capture the ambiguity in asset returns and ESG scores, where the return bounds depend on first-stage portfolio weights through ESG-related holdings, whereas ESG score uncertainty remains decision-independent. A two-stage robust framework with recourse rebalancing and proportional transaction costs is formulated, with financial loss and ESG performance balanced in the objective and tail risk controlled by a CVaR constraint embedded in a column-and-constraint generation scheme. The resulting minimax problem is solved by a column-and-constraint generation algorithm with a Rockafellar–Uryasev linearization of CVaR over iteratively generated scenarios. Numerical experiments using real stock data are designed to evaluate downside-risk control and portfolio ESG performance relative to deterministic and classical robust benchmarks. Full article

Steel surface defect detection remains challenging due to difficulties in multi-scale feature extraction, limited effectiveness of heterogeneous feature fusion, and loss of spatial detail information. To address these issues, this paper proposes CCP-YOLO, an improved steel surface defect detection algorithm based on YOLOv11n. The proposed method introduces four targeted improvements: (1) a Multi-Scale Dilated Reparameterization module (C3k2_MSD) for enhanced multi-scale feature extraction via a three-branch parallel reparameterization architecture; (2) an Interactive Adaptive Feature Fusion Module (IAFM) for effective integration of heterogeneous features; (3) a Progressive Shared-Weight Context Aggregation (PSWCA) module replacing the original SPPF structure to preserve spatial detail; and (4) a Wise-Inner-MPDIoU fusion loss function for improved bounding box regression accuracy and stability. Experimental results on the NEU-DET dataset demonstrate that CCP-YOLO achieves an mAP50 of 80.2%, representing a 4.1 percentage-point improvement over the YOLOv11n baseline, with a recall of 0.754, 2.7 M parameters, 6.6 GFLOPs, and an inference speed of 133.14 FPS. Further validation on the GC10-DET dataset confirms a 3.7 percentage-point improvement in mAP50. These results indicate that CCP-YOLO effectively enhances detection accuracy while maintaining computational efficiency, demonstrating strong potential for real-world industrial deployment. Full article

Starch-based bioplastics are renewable and biodegradable, but their wider use is constrained by moisture sorption, humidity-dependent aging, insufficient tensile performance, and weak water- and oxygen barrier stability. This review critically synthesizes the peer-reviewed literature from 2020 to 2026 on enzymatically engineered starch for film, packaging, and thermoplastic applications using an architecture–property framework that links enzyme specificity, chain-length distribution, crystallinity, processing route, and material response. Controlled α-1,4 hydrolysis mainly improves processability by lowering molecular weight, viscosity, and gelatinization resistance. However, excessive hydrolysis can increase water uptake, solubility, and loss of cohesive strength. Debranching by pullulanase or isoamylase increases amylose-like linear chains and can promote B-type crystallinity or V-type starch–lipid complexes, with reported gains in tensile strength, contact angle, and water vapor barrier when the chain lengths and recrystallization conditions are controlled. Branching enzymes and transglycosylases increase branch density or redistribute glucan chains, suppressing retrogradation and improving flexibility, water retention, and aging resistance, but often with trade-offs in strength, crystallinity, and barrier performance. Lipase- and laccase-catalyzed functionalization expands starch functionality by increasing hydrophobicity, compatibility with hydrophobic phases, antioxidant activity, and active-packaging potential. The evidence indicates that enzymatic modification should not be generalized as uniformly improving starch bioplastics; performance gains are conditional on the starch source, amylose content, enzyme dosage, reaction severity, plasticizer composition, processing method, film conditioning, and storage humidity. Industrial implementation remains limited by enzyme cost and reuse, high-solids mass transfer, reaction time, enzyme stability under heat and shear, and reproducibility across botanical sources. Overall, enzymatic molecular editing is most promising when mechanistic architecture control is coupled with standardized structure–property reporting and scalable processing, such as immobilized-enzyme reactors, high-solids systems, and reactive extrusion. Full article