Search Results (583)

Introduction: Aquatic contaminants can alter fish behavior before overt toxicity becomes evident, making neurobehavioral endpoints relevant for ecosystem protection and for hazard prioritization within a One Health framework. We recently developed a high-throughput visual-acoustic zebrafish larval thigmotaxis assay in which edge preference is interpreted as an anxiety-like behavioral endpoint, thereby adding spatial phenotyping beyond conventional locomotion metrics. Objective: To evaluate assay performance in a multisubstance screening challenge and determine whether it can discriminate distinct behavioral fingerprints without prior knowledge of chemical identity. Methodology: Zebrafish larvae were exposed for 1 h at 120 hpf. For each substance, 24 larvae were tested per condition, with six concentrations per substance, plus positive and negative controls. Larvae were challenged using alternating light/dark and tapping/quiet paradigms. The primary endpoint was the percentage of time spent at the edge as a proxy for anxiety-like behavior, while total distance and mean total velocity when moving were used as contextual locomotor metrics; edge distance and edge velocity were used as supportive spatial metrics. Data from 37 substances were analyzed through a standardized automated workflow. Results: Controls performed as expected and supported assay stability across runs. The chemical screening revealed heterogeneous but reproducible behavioral fingerprints. Seven substances produced weak/minimal acute responses, ten showed predominantly suppressive profiles, three predominantly activating profiles, nine showed prominent thigmotaxis-specific anxiety-like signals not explained by locomotion alone, and eight displayed mixed or stimulus-dependent patterns, including non-monotonic responses. Several substances altered edge preference while distance and velocity changed less, differently, or in the opposite direction, indicating behavioral reorganization rather than simple hypo- or hyperactivity. The multi-stimulus design was critical because some effects were evident only under specific sensory contexts. Conclusions: The multisubstance challenge supports the discriminatory capacity, robustness, and added value of the assay for high-throughput neurobehavioral screening. By capturing anxiety-like behavior through thigmotaxis and complementing it with locomotor context, the method improves phenotypic resolution for aquatic pollution assessment and offers a sensitive fish-based NAM to prioritize chemicals of concern for both environmental and human health-oriented testing strategies. Full article

This study evaluated the combined effects of build orientation, layer thickness, and polishing protocols on surface roughness and bacterial adhesion of occlusal splints. Ten disc-shaped specimens (Ø16 × 3 mm) were fabricated for each group using a digital light processing (DLP)-based 3D printer. Specimens were printed at two orientations (0° and 90°) and two layer thicknesses (50 and 100 µm) using a splint resin. Surface roughness was measured with a contact profilometer, and bacterial adhesion was measured by optical density (OD) readouts for Streptococcus mutans using a spectrophotometer. Surface morphology was examined by field-emission scanning electron microscopy (SEM). Statistical analyses were performed using jamovi. Because normality and/or homogeneity assumptions were not met, robust analysis of variance was applied. Polishing protocol significantly affected surface roughness (Ra) values. Unpolished specimens showed the highest Ra values, whereas mechanical polishing combined with centrifugation produced the lowest values. No significant main effects of polishing protocol, layer thickness or orientation were observed for bacterial adhesion. SEM findings supported the roughness results. Surface roughness was primarily influenced by polishing protocols and their interactions, whereas bacterial adhesion remained relatively stable. The weak Ra–OD correlation indicated that surface roughness alone was not a reliable predictor of bacterial adhesion. Full article

Object detection in low-light scenes is difficult because weak illumination reduces local contrast, amplifies sensor noise, and makes small or occluded objects hard to localize. Existing enhancement-before-detection pipelines can improve visual brightness, but they are not always optimized for detection features, while transformer-style global reasoning is often too costly for lightweight detectors. To address this gap, we propose GE-Detection, a detector-side framework that integrates Global Sub-Sampled Attention (GSA), Efficient Multi-scale Attention (EMA), and dropout regularization into YOLO- and PicoDet-style architectures. GSA introduces lower-cost global context modeling through spatially reduced key-value tokens, EMA refines multi-scale fused features without aggressive channel compression, and dropout improves training-time regularization with no inference-time parameter overhead. Experiments on COCO, ExDark, BDD100K-Night, and NightOwls show that the method is most effective in low-light detection: on ExDark with YOLO11n, mAP50-95 improves from 34.39% to 36.74%, mAP50 from 56.24% to 59.27%, and Box (P) from 67.63% to 71.36%. The full YOLO11n variant uses 2.91M parameters and maintains 134.7 FPS on an RTX 2080 Ti under the tested setting. Cross-dataset and corruption experiments further indicate that the proposed modules improve localization under several nighttime domain shifts while retaining known limitations under severe noise and adverse weather. These results indicate that combining efficient global attention, multi-scale feature recalibration, and targeted regularization can improve low-light localization while keeping the detector practical for deployment. Full article

Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1$\sigma$ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. Full article

Underwater images exhibit severe colour distortion, low contrast, and blurred details due to light absorption and scattering, which limits their practical use in marine applications. Existing methods face poor generalisation, high computational costs and weak integration of physical priors. To address these issues, this paper proposes TIDE-Net, a triple-branch illumination and detail enhancement network for underwater images. It decomposed inputs into illumination, reflectance intensity, and chromaticity branches for parallel optimisation, enabling decoupled handling of brightness, texture, and colour degradation. A piecewise colour correction module mitigated complex colour casts without introducing artefacts; a lightweight U-Net branch enhanced fine details while suppressing noise; and a local gain compensation module improved brightness uniformity and reduced halo effects. Experiments on four datasets showed that TIDE-Net outperforms some state-of-the-art methods, achieving a PSNR of 29.44 dB, an SSIM of 0.94, and competitive UIQM/UCIQE scores with only 7.74 M parameters. The results confirmed that the proposed triple-branch strategy effectively balances physical interpretability, restoration quality, and computational efficiency. In conclusion, TIDE-Net provides a robust and lightweight solution suitable for deployment on resource-limited underwater platforms, offering practical value for real-world underwater vision tasks. Full article

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

Effective intrusion detection for Internet of Things (IoT) environments requires balancing predictive performance, resource efficiency, and interpretability—particularly in real-world deployments where traffic distributions and attack scenarios vary. While many studies report near-perfect detection on benchmark datasets, this often overlooks model stability under distribution shifts. This paper addresses this gap by introducing a stability-focused evaluation of lightweight, explainable intrusion detection models using compact IoT-23 scenarios and a constrained set of 14 connection-level features for interpretability. Four lightweight models—logistic regression, random forest, XGBoost, and LightGBM—are assessed within a unified pipeline. Beyond standard internal validation, we implement a strict cross-scenario evaluation framework featuring a fully unseen malware capture. Our proposed Internal–External Stability Gap (IESG) framework, enhanced with normalized and multi-metric measures, highlights the degradation in consistency between internal and external metrics. Surprisingly, even models with high internal F1 scores (up to 0.9994) may experience considerable drops in external macro-F1 and specificity, exposing weaknesses in conventional evaluation. Experimentally, LightGBM provides the best trade-off between performance and compactness (606 KB) and shows the smallest stability gap for malicious detection. Nevertheless, all models show reduced balanced performance under scenario shift, underscoring that deployment readiness hinges on stability under changing conditions. Feature ablation reveals that leveraging high-impact features, such as port information, can boost internal accuracy at the expense of generalization. In summary, we demonstrate that while lightweight models deliver strong detection, only those proven stable across scenarios are viable for real-world IoT intrusion detection. Our evaluation framework offers a practical, interpretable tool for assessing model robustness. Full article

The Amazon Basin harbors a high diversity of phlebotomine sand flies, including several species that act as vectors of zoonotic pathogens such as Leishmania. Land-use changes, particularly forest conversion to agriculture, alter the sand fly diversity and community structure, with implications for the transmission of American Tegumentary Leishmaniasis (ATL). We evaluated the effects of forest-to-agriculture conversion on sand fly diversity and species composition in two rural areas, on opposite sides of the Amazonas River, in the Brazilian Amazon Region. Sand flies were collected using Center of Disease Control (CDC) light traps, installed in the forest and cropland environments, at the Presidente Figueiredo (North) and Urucurituba (South) municipalities, in Amazonas, Brazil. We collected a total of 1778 phlebotomine sand flies from 15 genera and 69 species. The most abundant species were Micropygomya rorotaensis (n = 436; 24.52%), Nyssomyia antunesi (n = 297; 16.70%), Sciopemyia sordellii (n = 101; 5.68%), Bichromomyia flaviscutellata (n = 84; 4.72%) and Evandromyia monstruosa (n = 72; 4.04%). In addition, four sand fly species were recorded for the first time in the Amazonas state: Brumptomyia mesai, Pressatia calcarata, Evandromyia aldafalcaoae and Lutzomyia carvalhoi. Sand fly richness, diversity, and community composition varied between riversides and environments, reflecting strong effects of anthropogenic disturbance. Although croplands supported reduced and more heterogeneous assemblages, several medically important vector species persisted across both environments. Species turnover was high, but patterns of species loss were weak, suggesting that community reorganization was driven by non-directional compositional change process. Our results indicate that land-use change reshapes sand fly communities without eliminating disease vectors, potentially increasing ATL transmission risk at the forest–anthropic interface. Full article

Understanding whether vegetation greening corresponds to changes in estimated forest carbon storage is important for evaluating ecological restoration under coupled climate change and human pressures. However, existing studies often rely on vegetation indices and have limited capacity to examine long-term forest carbon storage patterns or distinguish the roles of climatic and anthropogenic factors. This study integrates long-term remote sensing data with a two-way fixed effects model to examine forest ecosystem carbon storage in Shaanxi Province, China, from 1990 to 2023. Forest carbon storage was estimated by combining historical land-use data with static baseline carbon density coefficients derived from the 2012 field inventory, following an IPCC Tier 1-type approach. The carbon pools considered included aboveground biomass, belowground biomass, litter, and soil organic carbon. The results show that NDVI increased significantly, while estimated forest carbon storage increased by 4.27 × 10⁷ t (21.04%), with evident regional heterogeneity. A mismatch was observed between vegetation greenness and estimated forest carbon storage, and NDVI showed weak and unstable associations with carbon storage after controlling for fixed effects. Nighttime light exhibited a significant negative association with carbon storage, whereas climatic factors were generally insignificant. These findings suggest that vegetation indices alone may not reliably represent land-use-based carbon storage estimates. This study provides empirical evidence for understanding forest carbon storage patterns under ecological restoration and highlights the need for dynamic carbon density parameters in future assessments. Full article

Concrete crack segmentation in bridge health monitoring is crucial for ensuring the safety and longevity of infrastructure. However, this task is complicated by challenges such as weak contrast, background interference, and multi-scale crack structures, which hinder traditional methods’ accuracy. This study introduces a novel Boundary-Sensitive Hybrid Attention Network (BSA-Net) designed to address these issues by combining a hierarchical Transformer encoder (Hiera-A), a multi-scale context module (Light-ASPP), and a boundary-aware decoder (BAD). The hierarchical encoder effectively captures multi-scale features, while Light-ASPP enhances the network’s ability to aggregate contextual information with minimal computational cost, making it suitable for large-scale applications. The dual-branch decoder explicitly decouples the learning of semantic segmentation and boundary prediction, ensuring more accurate boundary detection and crack continuity. The extensive experiments on multiple benchmark datasets demonstrate that BSA-Net consistently outperforms existing crack detection models, particularly in complex, noisy environments. The model achieves competitive performance in terms of segmentation accuracy, boundary clarity, and recall rates, particularly for fine-scale and weak contrast cracks. The results indicate that BSA-Net not only enhances the performance of crack segmentation in real-world conditions but also provides a scalable and reliable solution for automated infrastructure monitoring and defect detection. Full article

To compare the structure and properties of hydroxypropyl starch/carrageenan blends with different amylose/amylopectin contents, two types of hydroxypropyl starch—a high-amylose type (amylose content > 70%) and a high-amylopectin type (amylopectin content > 95%)—were used. These starches had similar molecular weights, degrees of hydroxypropyl substitution, and other properties, differing only in their amylose and amylopectin contents. Each starch was blended with carrageenan via a solution blending method, and the resulting blends were systematically characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis, rheological tests, texture analysis, mechanical property tests, contact angle analysis, and UV-Vis spectrophotometry. The results showed that, upon blending with carrageenan, the hydroxypropyl starch transformed from a weak viscoelastic solution into an elastic, strong gel. FTIR and XRD analyses confirmed that the hydroxypropyl starch and carrageenan formed a homogeneous, compact, three-dimensional network via hydrogen bonding. This significantly enhanced the mechanical strength and stability of the blends. The influence of starch molecular structure on the blend system’s properties exhibited a pronounced state dependence. In the gel state, hydroxypropyl amylopectin effectively filled the carrageenan network due to its high swelling capacity, thereby improving the thermal stability and textural properties of the blends. However, in the film state, hydroxypropyl amylose with higher crystallinity and denser molecular packing contributed to superior tensile strength, hydrophobicity and light transmittance. Furthermore, the optimal mass ratio of hydroxypropyl starch to carrageenan was found to be in the range of 2:1 to 4:1. With this ratio, excessive cross-linking and poor compatibility could be avoided, resulting in improved mechanical performance, hydrophobicity, and light transmittance. This study reveals the relationship between starch molecular structure, system state and macroscopic properties, providing a theoretical basis for the rational design and regulation of the properties of hydroxypropyl starch/carrageenan blends. Full article

Under complex lighting conditions, particularly in low-light environments, general object detectors often suffer from degraded detection performance due to insufficient brightness, severe noise, and loss of discriminative details. This issue is especially critical in underground mining scenarios, where weak illumination, complex backgrounds, dust interference, and frequent small or partially occluded targets make reliable visual perception highly challenging. To address this issue, we propose an Illumination-Aware Detection Network (IADNet) for object detection in low-light environments. Specifically, an Illumination Modeling Subnetwork (IMS) is designed to extract illumination-aware and degradation-aware auxiliary features from low-light images. Within the IMS, an Adaptive Weighted Downsampling (AWD) layer is introduced to reduce noise interference during feature downsampling and enhance illumination-aware representation learning. Furthermore, a Global Feature Enhancement Module (GFEM) is incorporated to strengthen global context modeling and improve feature representation capability in complex scenes. In addition, an extra contrastive loss is introduced to constrain the optimization of the IMS, and weighting factors are employed to balance the detection loss and the contrastive loss during training. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of the proposed method. On the public ExDark dataset, IADNet achieves an mAP@50 of 80.3%, outperforming the baseline YOLO11m by 3.4 percentage points. On the self-constructed mining low-light dataset Lowlight_Mine, the proposed method achieves 92.3% Precision, 82.0% Recall, 89.3% mAP@50, and 57.8% mAP@50:95, showing favorable performance in object detection tasks under mining-related low-light scenarios. On the DARK FACE dataset, IADNet achieves 54.6% mAP@50 and 31.2% mAP@50:95, further indicating its robustness under real low-light conditions. On the synthetic low-light Dark_VOC dataset, IADNet attains an mAP@50 of 91.6%, and on the normal-light VOC dataset, it achieves an mAP@50 of 93.0%, suggesting that the proposed method maintains stable detection performance under the evaluated illumination conditions. These results indicate that IADNet improves low-light object detection performance and provides a useful experimental reference for object detection tasks in mining-related low-light scenarios. Full article

Pedestrian detection in nighttime security surveillance and other low-light visual sensing tasks is an important foundation for intelligent perception in complex environments. Under low-light conditions, visible-light images often suffer from missing texture details, intensified noise, and reduced contrast, which can easily lead to insufficient target representation, unstable cross-scale feature fusion, and an increased risk of missed detections. Although multimodal schemes, such as RGB–infrared approaches, can improve detection performance by exploiting modal complementarity, they involve relatively high hardware costs, cross-modal calibration complexity, and system integration overhead, which impose deployment limitations in lightweight or cost-sensitive scenarios. Therefore, developing an efficient pedestrian detection method for low-light monocular RGB scenarios is of clear practical value. This study focuses on low-light monocular RGB pedestrian detection and proposes an application-oriented structurally optimized model, termed GSC-YOLO, built upon YOLOv13. First, GhostNetV3 is introduced as the backbone to enhance multi-scale feature representation under weak-texture conditions. Second, a Semantic–Spatial Alignment (SSA) module is designed to improve information compensation and suppress noise during the feature fusion stage. Finally, C2f_Faster is incorporated into the high-level semantic branch to optimize information flow and reduce redundant computation. On the RGB subsets of the two public datasets, LLVIP and KAIST, GSC-YOLO achieves mAP@0.5:0.95 values of 57.70% and 66.61%, respectively, and Recall values of 89.93% and 90.49%, respectively, consistently outperforming the YOLOv13 baseline. The results demonstrate that, under the experimental settings adopted in this study, the proposed method effectively improves pedestrian perception performance in low-light RGB scenes while maintaining favorable real-time inference capability, and may provide a useful reference for front-end vision sensing research in low-altitude intelligent networks. Full article

Hyperspectral remote sensing has been successfully used to retrieve parameters such as chlorophyll and nitrogen content. However, effective models for inverting dynamic physiological processes like gas exchange are lacking, and the predictive accuracy and applicable limits of such inversions remain unclear. In a controlled water and nitrogen stress experiment, 350–1150 nm leaf reflectance spectra were obtained along with simultaneous measurements of 12 physiological parameters. PLSR, RF, XGBoost, and LightGBM models were constructed, and SHAP was utilized for model interpretation. The results showed that predictive accuracies could be categorized into high, medium, and low tiers (R² approximately 0.8, 0.5, and <0.3, respectively), corresponding to leaf water status and vapor pressure, E and Fm′, and g_sw and Fs. LightGBM performed best for six high-tier water-related parameters (R² = 0.75–0.81), while PLSR achieved the best performance for the medium-tier parameters E (R² = 0.49) and Fm′ (R² = 0.51). However, all models failed to predict g_sw, suggesting that the relevant signal in the 350–1150 nm range is either absent or too weak to detect in our dataset. This study outlines the practical limits of estimating dynamic photosynthetic processes using VNIR spectra, offering a reference for future sensor configuration and model development. Full article

Background: Currently marketed hepatitis B vaccines are primarily recombinant protein vaccines. However, their antigen immunogenicity is relatively weak, requiring combination with effective adjuvants to enhance the immune response. The development of novel, highly effective adjuvants is a key strategy for optimizing vaccine performance. Polyinosinic-polycytidylic acid (PolyI:C), a synthetic double-stranded RNA analog, activates TLR3/RLR pathways to enhance T-cell priming and cellular immunity. However, its utility as a sole adjuvant is limited by rapid nuclease degradation and poor cytosolic delivery. Lipid nanoparticles (LNPs), a mature delivery platform, enable high encapsulation efficiency, efficient cellular uptake, and endosomal escape. Objectives: This study aimed to evaluate the adjuvant effect of LNP-encapsulated PolyI:C (LNP-PolyI:C) on the immunogenicity of hepatitis B surface antigen (HBsAg) in vivo. Methods: The colloidal stability of LNP-PolyI:C stored at 2–8 °C for 9 months was monitored using dynamic light scattering (DLS) on a Zetasizer Lab instrument. Serum levels of HBsAg-specific IgG, IgG1, and IgG2a antibodies in immunized Kunming mice were measured by enzyme-linked immunosorbent assay (ELISA). The secretion of HBsAg-specific cytokines by splenocytes was analyzed using flow cytometry and enzyme-linked immunospot (ELISpot) assay. Results: The results demonstrated that the LNP-encapsulated PolyI:C adjuvant significantly increased the secretion of HBsAg-specific IFN-γ, IL-2, and TNF-α by splenocytes, indicating a Th1-biased and cytotoxic T lymphocyte (CTL)-mediated cellular immune response. In addition, this formulation markedly elevated serum titers of HBsAg-specific IgG, IgG1, and IgG2a. Conclusions: These findings underscore the advantages of the LNP-PolyI:C adjuvant in enhancing both humoral and cellular immunity, demonstrating its considerable potential as a novel adjuvant. Full article