Search Results (179)

Lightning rods, while essential for protecting weather radars from direct lightning strikes, act as persistent non-meteorological scatterers that can interfere with signal transmission and reception and thereby degrade detection accuracy and product quality. Existing studies have mainly focused on X-band and C-band systems, and robust, measurement-based quantitative assessments for S-band dual-polarization radars remain scarce. In this study, a controllable tilting lightning rod, a high-precision Far-field Antenna Measurement System (FAMS), and an S-band dual-polarization weather radar (SAD radar) are jointly employed to systematically quantify lightning-rod impacts on antenna electromagnetic parameters under different rod elevation angles and azimuth configurations. Typical precipitation events were analyzed to evaluate the influence of the lightning rods on dual-polarization parameters. The results show that the lightning rod substantially elevates sidelobe levels, with a maximum enhancement of 4.55 dB, while producing only limited changes in the antenna main-beam azimuth and beamwidth. Differential reflectivity ($Z_{DR}$) is the most sensitive polarimetric parameter, exhibiting a persistent positive bias of about 0.24–0.25 dB in snowfall and mixed-phase precipitation, while no persistent azimuthal anomaly is evident during freezing rain; the co-polar correlation coefficient (${\rho }_{hv}$) is only marginally affected. Collectively, these results provide quantitative, far-field evidence of lightning-rod interference in S-band dual-polarization radars and provide practical guidance for more reasonable lightning-rod placement and configuration, as well as useful references for $Z_{DR}$-oriented polarimetric quality-control and correction strategies. Full article

Microalgae are sustainable sources of bioactive compounds with broad hepato-protective potential. This review synthesizes evidence for five major classes—carotenoids such as astaxanthin and fucoxanthin, polysaccharides such as paramylon and fucoidan, phycobiliproteins such as phycocyanin, omega-3 fatty acids, and phenolic extracts—linking their actions to key liver injury mechanisms. Preclinically, these compounds enhance antioxidant defenses, improve mitochondrial function, suppress inflammatory signaling, regulate lipid metabolism, modulate the gut–liver axis, and inhibit hepatic stellate cell activation, thereby attenuating fibrosis. Consistent benefits are observed in models of non-alcoholic and alcoholic fatty liver disease, drug-induced injury, ischemia–reperfusion, and fibrosis, with marked improvements in liver enzymes, oxidative stress, inflammation, steatosis, and collagen deposition. Emerging evidence also highlights their roles in regulating endoplasmic reticulum stress and ferroptosis. Despite their promise, translational challenges include compositional variability, a lack of standardized quality control, limited safety data, and few rigorous human trials. To address these challenges, we propose a framework integrating multi-omics and AI-assisted strain selection with specification-driven quality control and formulation-aware designs—such as lipid carriers for carotenoids or rational combinations like fucoxanthin with low-molecular-weight fucoidan. Future priorities include composition-defined randomized controlled trials in non-alcoholic fatty liver disease, alcoholic liver disease, and drug-induced liver injury; harmonized material specifications; and multi-constituent interventions that synergistically target oxidative, inflammatory, metabolic, and fibrotic pathways. Full article

This study develops a machine-learning-based framework for multiclass soil classification using Cone Penetration Test (CPT) data, aiming to overcome the limitations of traditional empirical Soil Behavior Type (SBT) charts and improve the automation, continuity, robustness, and reliability of stratigraphic interpretation. A dataset of 340 CPT soundings from 26 sites in Shanghai is compiled, and a sliding-window feature engineering strategy is introduced to transform point measurements into local pattern descriptors. An XGBoost-based multiclass classifier is then constructed using fifteen engineered features, integrating second-order optimization, regularized tree structures, and probability-based decision functions. Results demonstrate that the proposed method achieves strong classification performance across nine soil categories, with an overall classification accuracy of approximately 92.6%, an average F1-score exceeding 0.905, and a mean Average Precision (mAP) of 0.954. The confusion matrix, P–R curves, and prediction probabilities show that soil types with distinctive CPT signatures are classified with near-perfect confidence, whereas transitional clay–silt facies exhibit moderate but geologically consistent misclassification. To evaluate depth-wise prediction reliability, an Accuracy Coverage Rate (ACR) metric is proposed. Analysis of all CPTs reveals a mean ACR of 0.924, and the ACR follows a Weibull distribution. Feature importance analysis indicates that depth-dependent variables and smoothed p_s statistics are the dominant predictors governing soil behavior differentiation. The proposed XGBoost-based framework effectively captures nonlinear CPT–soil relationships, offering a practical and interpretable tool for high-resolution soil classification in subsurface investigations. Full article

Efficient management for electrochemical reactions within Pt/C electrodes, specifically the oxygen reduction reaction (ORR) and methanol oxidation reactions (MOR), is critical to the performance and long-life stability of direct methanol fuel cells (DMFCs). Optimizing the hierarchical macro/mesoscale structures of Pt/C electrodes plays a decisive role in regulating the mass transport pathways and electrochemical reactions. In this work, bead-like Pt/C-ionomer hybrid porous nanofibrous networks are constructed via electrospinning. Ascribing to the hierarchical architecture consisting of continuous nanofibers and bead-like Pt/C-ionomer fibrous networks, the hybrid porous nanofibrous electrode exhibits a 55% increase in maximum mass power density in comparison to the conventional Pt/C electrode. Such enhancement is attributed to excellent ORR activity enabled by efficient triple-phase reaction regions, coupled with superior MOR tolerance resulting from restricted methanol transport from the hybrid porous nanofibrous electrode to triple-phase reaction regions. Full article

The integration of data-driven, intelligent components, particularly those based on Artificial Neural Network (ANN), is pivotal for enabling software systems to adapt to dynamic environments. However, the performance of these hybrid systems is critically dependent on the quality of their training data. Deficiencies in this data can propagate through the ANN, leading to violations of key system properties that are difficult to trace back to their root cause. To address this challenge, this paper introduces $D^{3}MV$, a novel verification-based methodology for tracing system-level property violations back to specific deficient training data. Our approach involves constructing a unified system model (Adaptive Petri Net) that integrates traditional components with ANNs, extracting interpretable fuzzy rules from the trained network to bridge the semantic gap, and employing model checking for formal verification. When a property is violated, a novel inverse mapping technique leverages the implicated fuzzy rules to pinpoint the responsible data samples in the training set. Experimental validation using a manufacturing case study demonstrates that the method $D^{3}MV$ successfully identified deficient data causing property violations. After replacing 200 problematic data samples and updating the model, the revised system met all specified performance metrics. This preliminary result suggests that by improving data quality, our approach helps ensure system reliability. Full article

Improving road safety is a fundamental goal of Intelligent Transportation Systems (ITS). However, the complex interplay of factors in accident-prone scenarios, such as highway–rail grade crossings, poses significant challenges for conventional analysis. This paper addresses this gap by proposing and validating a novel explainable artificial intelligence (XAI) framework, which integrates Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanations (SHAP), to enhance safety analysis within ITS. Applying this framework to a comprehensive dataset of highway–rail grade crossing collisions, our research moves beyond simple correlation to uncover the nonlinear relationships and interaction effects governing injury severity. The model identifies speed-related factors, driver age, and traffic exposure as primary predictors. More critically, the SHAP analysis quantitatively reveals significant synergistic risks, demonstrating that the combination of non-dry road surfaces and poor lighting conditions drastically amplifies injury severity. These findings offer granular insights for the “smart management” and development of “resilient infrastructures,” enabling targeted interventions like adaptive lighting systems and dynamic risk warnings. This study not only provides critical safety solutions for grade crossings but also showcases the power of XAI as a robust tool for “advanced analysis” across various complex transportation safety problems, ultimately contributing to the creation of safer and more reliable ITS. Full article

Millet yield per unit area in northern China’s drylands is constrained by climate, soil, and management factors, complicating forecasts under limited, nonlinear, heterogeneous data. In order to enhance the accuracy and stability of operational forecasting, this study utilised observational data from five locations in southwestern Heilongjiang Province spanning 2014 to 2023. Eight ground-based hydrothermal and meteorological factors were used as inputs to build an improved BP neural network optimised by IWOA, with enhancements to both algorithm and workflow. Adaptive inertia weight and EOBL were introduced to balance global exploration and local exploitation, enabling better hyperparameter solutions. Results show that IWOA-BP significantly outperforms baseline BP and WOA-BP on an annual scale. The RMSE was 2.74, the R² was 0.94, the MAPE was 5.9, and the RPD was 4.16. The implementation of additional seasonal rolling forecasts for the 2024 validation period entailed the construction of cumulative information flows from January to August. Cross-regional validation in Fangzheng County produced error magnitudes consistent with the primary study area, thereby demonstrating the model’s reliable generalization ability across both temporal and spatial dimensions. Full article

The tooth harmonics caused by stator slots are a major factor leading to low motor efficiency, high temperature rise and severe vibration. The application of a magnetic slot wedge (MSW) can effectively mitigate the adverse effects of the stator slot on the motor. However, it should also be noted that the MSW may be subject to the action of electromagnetic forces during motor operation and thus has a risk of falling off. In order to comprehensively analyze the impact of MSW on the electromagnetic and reliability performance of the motor, this paper selected three types of MSW with relative permeabilities of 5, 10 and 15 to be applied in a high-power line-start permanent magnet synchronous motor (LSPMSM). The effects of these three types of MSWs on the electromagnetic performance of the motor and the changes in the electromagnetic force acting on the MSW were studied. Finally, the research content of the paper was verified on a 630 kW, 6 kV, 4-pole LSPMSM, providing a reference for the selection and application of MSW in motors. Full article

An internal-cavity Tm-doped all-fiber laser at 1908 nm in-band-pumped by a 1570 nm Er/Yb co-doped fiber laser is proposed. An external-cavity fiber oscillator composed of a pair of high-reflectivity (HR) fiber Bragg gratings (FBGs) at 1570 nm pumped by 915 nm laser diodes (LDs) serves as the bidirectional pumping source for the 1908 nm internal-cavity fiber oscillator to achieve high-efficiency laser output. Firstly, a maximum output power of 10 W is realized at a 915 nm pump power of 36.8 W in the single 1570 nm Er/Yb fiber oscillator, with a corresponding slope efficiency and a signal-to-noise ratio (SNR) of 28.1% and 62 dB, respectively. The beam quality factor M² of the single 1570 nm Er/Yb fiber oscillator is about 1.2. In the 1908 nm internal-cavity Tm-doped all-fiber laser, the maximum output power is 482 mW when the pump power at 915 nm reaches 12.6 W, with a corresponding slope efficiency of 8.1%. Under the same 915 nm pump power, the slope efficiency of the 1908 nm Tm-doped fiber laser with an external-cavity pump is 5.3%. Full article

In security applications, visible-light pedestrian detectors are highly sensitive to changes in illumination and fail under low-light or nighttime conditions, while infrared sensors, though resilient to lighting, often produce blurred object boundaries that hinder precise localization. To address these complementary limitations, we propose a practical multimodal pipeline—Adaptive Energy–Gradient–Contrast (EGC) Fusion with AIFI-YOLOv12—that first fuses infrared and low-light visible images using per-pixel weights derived from local energy, gradient magnitude and contrast measures, then detects pedestrians with an improved YOLOv12 backbone. The detector integrates an AIFI attention module at high semantic levels, replaces selected modules with A2C2f blocks to enhance cross-channel feature aggregation, and preserves P3–P5 outputs to improve small-object localization. We evaluate the complete pipeline on the LLVIP dataset and report Precision, Recall, mAP@50, mAP@50–95, GFLOPs, FPS and detection time, comparing against YOLOv8, YOLOv10–YOLOv12 baselines (n and s scales). Quantitative and qualitative results show that the proposed fusion restores complementary thermal and visible details and that the AIFI-enhanced detector yields more robust nighttime pedestrian detection while maintaining a competitive computational profile suitable for real-world security deployments. Full article

Under intensive farming systems and the global ban on antibiotic growth promoters (AGPs), early-weaned piglets exhibit incomplete physiological development, increasing their susceptibility to stress-related liver dysfunction and growth performance impairments. This study first investigated the effects of dietary supplementation with 0.2% tributyrin on the growth performance of 21-day-old weaned piglets over a 28-day period. Subsequently, on the final day, we examined its influence on antioxidant capacity, immune responses, and liver macrophage polarization using a 2 × 2 factorial challenge model, with the factors being diet (basal or tributyrin-supplemented) and immunological challenge (saline or lipopolysaccharide). The experimental results indicated that tributyrin had a significant enhancement on the average daily gain (ADG) of weaned piglets within the 0–14-day period (p < 0.05). Under lipopolysaccharide (LPS) challenge, tributyrin significantly increased the levels of catalase (CAT) and interleukin-10 (IL-10) while reducing the levels of malondialdehyde (MDA) and interleukin-6 (IL-6) in both serum and liver. Additionally, it significantly increased glutathione peroxidase (GSH-pX) activity in the serum and reduced glutathione (GSH) levels in the liver, and also decreased the serum level of interleukin-1β (IL-1β). Tributyrin downregulated pro-inflammatory cytokine gene expression while upregulating anti-inflammatory cytokine expression (p < 0.05). Furthermore, tributyrin significantly inhibited the expression of M1 macrophage polarization markers while enhancing those of M2 polarization (p < 0.05). Additionally, tributyrin suppressed SIRT1/NF-κB signaling pathway activation and promoted JAK2/STAT6 signaling pathway activation (p < 0.05). These findings exhibit that tributyrin alters the polarization of liver macrophages by regulating the SIRT1/NF-κB and JAK2/STAT6 signaling pathways, enhances antioxidant and immune functions, reduces LPS-induced liver inflammatory damage, and improves the growth performance of weaned piglets. Full article

The efficient resource utilization of industrial solid wastes, such as ground granulated blast-furnace slag (GGBS) and coal gangue (CG), is essential for sustainable development. However, their activation commonly depends on expensive and corrosive chemical alkalis. This study proposes a solution by developing a fully waste-based cementitious material using calcium carbide slag (CS), another industrial residue, as an eco-friendly alkaline activator for the GGBS-CG system. The influence of CS dosage (0–20 wt%) on hydration evolution and mechanical properties was examined using uniaxial compression testing, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The results indicated that a CS dosage of 10 wt% yielded the highest compressive strength, reaching 10.13 MPa—a 16.5% improvement compared to the 20 wt% group. This enhancement is ascribed to the formation of hydrotalcite (HT) and calcium silicate hydrate (C-(A)-S-H) gel, which densify the microstructure. In contrast, higher CS contents led to a passivation effect that restrained further reaction. This work offers a practical and theoretical basis for the development of low-carbon, multi-waste cementitious materials and presents a promising strategy for large-scale valorization of industrial solid wastes. Full article

This study characterized the spatial distribution and assessed the ecological risks of carbon, nitrogen, and phosphorus in sediments of the Dongzhai Harbor mangrove wetland, Hainan, China. Analysis of key environmental indicators (grain size, pH, TOC, TN, TP) across twenty-seven sediment cores (0–100 cm depth) revealed distinct decreasing land–sea gradients and vertical stratification of nutrient concentrations. Mangrove plant debris was identified as the primary source of sedimentary organic matter. Elemental ratio analysis indicated terrestrial inputs as the dominant phosphorus source. Significant positive correlations between TOC, TN, and TP in surface sediments suggested coupled nutrient dynamics. Vertical distribution of C/N to C/P ratios increased with depth, which may be related to increased nitrogen and phosphorus inputs due to regional human activities. Pollution assessment showed significantly higher ecological risks in surface sediments (0–50 cm), particularly near inland areas and dense mangroves, indicating co-regulation by vegetation processes and human impacts. These findings highlight significant spatial heterogeneity in ecological risks, underscoring the need for enhanced monitoring and targeted management strategies in critical land–sea transition zones. Full article

The early flowering and less lateral branches in watermelon hold significant agricultural value. The synergistic effects of these traits provide an ideal template for watermelon plant architecture improvement. However, the molecular regulatory networks underlying the development of lateral organs (including branches and flowers) in watermelon remain unclear. In this study, we found ClTFL1 knockout lines significantly promote flowering time and inhibit lateral branching and tendril formation, while also leading to a mild apical flower phenotype. These findings indicate that the function of ClTFL1 in watermelon is more extensive than that of its homologous genes in Arabidopsis, rice, and tomato. Through yeast two-hybrid screening, we identified the interacting proteins of ClTFL1, including members of the 14-3-3 family ClGRF8, ClGRF9, and ClGRF12. Bimolecular fluorescence complementation (BiFC) assays further demonstrated ClTFL1 could directly interact with the ClGRF8, ClGRF9, and ClGRF12 protein. The knockout of ClGRF8 and ClGRF12 leads to reduced lateral branches and early flowering. These phenotypes are highly consistent with those of ClTFL1 knockout mutants. Our findings demonstrate the important role of the ClTFL1-ClGRFs module in regulating lateral organ development and flowering time in watermelon, offering important targets for watermelon plant architectural modification and molecular breeding. Full article

Synaptic activity is fundamental to memory and learning in the nervous system. However, most artificial synaptic devices are limited to mimicking static plasticity, and tunable plasticity has not been achieved at the device level. Here, we introduce a dynamic all-optical synapse based on photonic crystal nanobeam cavities with a ferroelectric carrier injection valve. By leveraging the nonlinear and ferroelectric electrostatic doping effects in silicon, integrated with Hf_0.5Zr_0.5O₂ (HZO) film as the ferroelectric layer and indium tin oxide (ITO) as the top electrode, we enhance linearity and reduce power consumption. Increasing the bias voltage further improves linearity while decreasing power consumption. This innovation offers a promising pathway for developing energy-efficient nanophotonic devices in neuromorphic computing. Full article