Search Results (4,351)

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and obtaining a golden reference chip; however, artificial intelligence can make everything possible. For the first time, this paper presents a systematic review of artificial-intelligence-based integrated circuit security approaches, focusing on the latest attack and defense strategies. First, the security threats of integrated circuits are analyzed. For one of several key threats to integrated circuits, hardware Trojans, existing attack models are divided into several categories and discussed in detail. Then, for summarizing and comparing the numerous existing artificial-intelligence-based defense strategies, traditional and advanced artificial-intelligence-based approaches are listed. Finally, open issues on artificial-intelligence-based integrated circuit security are discussed from three perspectives: in-depth exploration of hardware Trojans, combination of artificial intelligence, and security strategies involving the entire life cycle. Based on the rapid development of artificial intelligence and the initial successful combination with integrated circuit security, this paper offers a glimpse into their intriguing intersection, aiming to draw greater attention to these issues. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts. Full article

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

This study was conducted to investigate the effects of puffed jujube powder (PJP) supplementation in the diet on the slaughter characteristics, growth performance, meat quality, and serum antioxidant capacity of Hainan Black (HB) goats. Twenty-four healthy male HB goats, three months old with an initial body weight of 15.12 ± 3.67 kg, were randomly divided into three groups: the 10% PJP group (basal diet plus 10% PJP); the 20% PJP group (basal diet plus 20% PJP); and the control group (basal diet only). After a 10-day adaptation period, a feeding trial was carried out for 90 days in an ad libitum diet environment. The results show that the final body weight of the 20% PJP group was markedly higher (p < 0.05) than that of the control group (22.58 ± 0.94 kg vs. 20.45 ± 1.01 kg). The average daily gain of the 20% PJP group was 83.44 ± 1.78 g/d, which was substantially greater (p < 0.05) than the 59.22 ± 2.13 g/d of the control group. The feed intake of the 20% PJP group was 713.10 ± 4.54 g/d, notably higher (p < 0.05) than the 498.20 ± 4.33 g/d of the control group. In terms of slaughter characteristics, the carcass weight of the 20% PJP group was 13.99 ± 1.22 kg, considerably heavier (p < 0.05) than the 11.79 ± 1.38 kg of the control group. The muscle weight of the 20% PJP group was 11.43 ± 1.42 kg, distinctly greater (p < 0.05) than the 9.59 ± 1.99 kg of the control group. The slaughter rate of the 20% PJP group was 42.41%, showing a notable increase (p < 0.05) compared with the 37.42% of the control group, and the net meat rate of the 20% PJP group was 34.65%, with a significant rise (p < 0.05) compared with the 30.43% of the control group. Regarding serum antioxidant capacity and meat quality, the activities of serum antioxidant enzymes, superoxide dismutase (SOD) and catalase (CAT), were conspicuously increased (p < 0.05) in the 20% PJP group. The meat shear force of the 20% PJP group was decreased by 12.9%, and the cooking loss was improved by 8.9% in comparison with the control group. In conclusion, the supplementation of 20% PJP in the diet was demonstrated to enhance the growth performance, improve the meat quality, and boost the antioxidant status of HB goats, thus presenting a feasible strategy for optimizing tropical goat production systems. Full article

The complex system of high-entropy materials makes it challenging to reveal the specific function of each site for oxygen evolution reaction (OER). Here, with nickel foam (NF) as the substrate, FeCoNiCrMo/NF is designed to be prepared by metal–organic frameworks (MOF) as a precursor under an argon atmosphere. XRD analysis confirms that it retains a partial MOF crystal structure (characteristic peak at 2θ = 11.8°) with amorphous carbon (peaks at 22° and 48°). SEM-EDS mapping and XPS demonstrate uniform distribution of Fe, Co, Ni, Cr, and Mo with a molar ratio of 27:24:30:11:9. Electrochemical test results show that FeCoNiCrMo/NF has excellent OER characteristics compared with other reference prepared samples. FeCoNiCrMo/NF has an overpotential of 285 mV at 100 mA cm⁻² and performs continuously for 100 h without significant decline. The OER mechanism of FeCoNiCrMo/NF further reveal that Co and Ni are true active sites, and the dissolution of Cr and Mo promote the conversion of active sites into MOOH following the lattice oxygen mechanism (LOM). The precipitation–dissolution equilibrium of Fe also plays an important role in the OER process. The study of different reaction sites in complex systems points the way to designing efficient and robust catalysts. Full article

The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. Full article

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li₂O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li₂O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide. Full article

Aquatic macrophytes are a vital component of lake ecosystems, profoundly influencing ecosystem structure and function. Under future scenarios of more frequent extreme floods and intensified lake eutrophication, aquatic macrophytes will face increasing challenges. Therefore, understanding aquatic macrophyte responses to flood disturbances and nutrient enrichment is crucial for predicting future vegetation dynamics in lake ecosystems. This study focuses on Huangmaotan Lake, a Yangtze River floodplain lake, where we reconstructed 200-year successional trajectories of macrophyte communities and their driving mechanisms. With a multiproxy approach we analyzed a well-dated sediment core incorporating plant macrofossils, grain size, nutrient elements, heavy metals, and historical flood records from the watershed. The results demonstrate a significant shift in the macrophyte community, from species that existed before 1914 to species that existed by 2020. Unlike the widespread macrophyte degradation seen in most regional lakes, this lake has maintained clear-water plant dominance and experienced continuous vegetation expansion over the past 50 years. We attribute this to the interrelated effects of floods and the enrichment of ecosystems with nutrients. Specifically, our findings suggest that nutrient enrichment can mitigate the stress effects of floods on aquatic macrophytes, while flood disturbances help reduce excess nutrient concentrations in the water column. These findings offer applicable insights for aquatic vegetation restoration in the Yangtze River floodplain and other comparable lake systems worldwide. Full article

Effective building operation requires a careful balance between energy conservation and indoor environmental comfort. Although numerous methods have been developed to reduce energy consumption during the operational phase, their objectives and applications vary widely. However, the complexity of building energy management makes it challenging to identify the most suitable methods that simultaneously achieve both comfort and efficiency goals. Existing studies often lack a systematic framework that supports integrated decision-making under comfort constraints. This research aims to address this gap by proposing a decision-making tree for selecting energy conservation methods during building operation with an explicit consideration of indoor environmental comfort. A comprehensive literature review is conducted to identify four main energy-consuming components during building operation: the building envelope, HVAC systems, lighting systems, and plug loads and appliances. Three key comfort indicators—thermal comfort, lighting comfort, and air quality comfort—are defined, and energy conservation methods are categorized into three strategic groups: passive strategies, control optimization strategies, and behavioural intervention strategies. Each method is assessed using a defined set of evaluation criteria. Subsequently, a questionnaire survey is administered for the calibration of the decision tree, incorporating stakeholder preferences and expert judgement. The findings contribute to the advancement of understanding regarding the co-optimization of energy conservation and occupant comfort in building operations. Full article

High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method. Full article

Biochar serves as an effective adsorbent for the heavy metal cadmium, with its performance significantly influenced by its physicochemical properties and various environmental features. Traditional machine learning models, though adept at managing complex multi-feature relationships, rely heavily on expertise in feature engineering and hyperparameter optimization. To address these issues, this study employs an automated machine learning (AutoML) approach, automating feature selection and model optimization, coupled with an intuitive online graphical user interface, enhancing accessibility and generalizability. Comparative analysis of four AutoML frameworks (TPOT, FLAML, AutoGluon, H₂O AutoML) demonstrated that H₂O AutoML achieved the highest prediction accuracy (R² = 0.918). Key features influencing adsorption performance were identified as initial cadmium concentration (23%), stirring rate (14.7%), and the biochar H/C ratio (9.7%). Additionally, the maximum adsorption capacity of the biochar was determined to be 105 mg/g. Optimal production conditions for biochar were determined to be a pyrolysis temperature of 570–800 °C, a residence time of ≥2 h, and a heating rate of 3–10 °C/min to achieve an H/C ratio of <0.2. An online graphical user interface was developed to facilitate user interaction with the model. This study not only provides practical guidelines for optimizing biochar but also introduces a novel approach to modeling using AutoML. Full article

The thalamus is an important sensory relay station. It integrates all somatic sensory pathways (excluding olfaction) and transmits information through thalamic relay neurons before projecting to the cerebral cortex via thalamocortical axons (TCAs). Emerging evidence has shown that FGF3, a member of the morphogen family, is an axon guidance molecule that repels TCAs away from the hypothalamus and into the internal capsule so that they subsequently reach different regions of the cortex. However, current studies on FGF-mediated axon guidance predominantly focus on phenomenological observations, with limited exploration of the underlying molecular mechanisms. To address this gap, we investigated both direct and indirect downstream signaling pathways mediating FGF3-dependent chemorepulsion of TCAs at later developmental stages. Firstly, we used pharmacological inhibitors to identify the signaling cascade(s) responsible for FGF3-triggered direct chemorepulsion of TCAs, in vitro and in vivo. Our results demonstrate that the PC-PLC pathway is required for FGF3 to directly stimulate the asymmetrical repellent growth of developing TCAs. Then, we found the FGF3-mediated repulsion can be indirectly induced by Slit1 because the addition of FGF3 in the culture media induced an increase in Slit1 expression in the diencephalon. Furthermore, by using downstream inhibitors, we found that the indirect repulsive effect of FGF3 is mediated through the PI3K downstream pathway of FGFR1. Full article

Acute hepatopancreatic necrosis disease (AHPND), caused by the bacterium Vibrio parahaemolyticus, is a major threat to global shrimp aquaculture. In this study, we evaluated the therapeutic effects of phage therapy in Litopenaeus vannamei challenged with AHPND-causing Vibrio parahaemolyticus. Phage application at various concentrations significantly improved shrimp survival, with the 1 ppm group demonstrating the highest survival rate. Enzymatic assays revealed that phage-treated shrimp exhibited enhanced immune enzyme activities, including acid phosphatase (ACP), alkaline phosphatase (AKP), and lysozyme (LZM). In addition, antioxidant defenses such as superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-PX), and total antioxidant capacity (T-AOC) significantly improved, accompanied by reduced malondialdehyde (MDA) levels. Serum biochemical analyses demonstrated marked improvements in lipid metabolism, particularly reductions in triglyceride (TG), total cholesterol (TC), and low-density lipoprotein (LDL), alongside higher levels of beneficial high-density lipoprotein (HDL). Transcriptomic analysis identified 2274 differentially expressed genes (DEGs), notably enriched in pathways involving fatty acid metabolism, peroxisome functions, lysosomes, and Toll-like receptor (TLR) signaling. Specifically, phage treatment upregulated immune and metabolic regulatory genes, including Toll-like receptor 4 (TLR4), myeloid differentiation primary response protein 88 (MYD88), interleukin-1β (IL-1β), nuclear factor erythroid 2-related factor 2 (Nrf2), and peroxisome proliferator-activated receptor (PPAR), indicating activation of innate immunity and antioxidant defense pathways. These findings suggest that phage therapy induces protective immunometabolic adaptations beyond its direct antibacterial effects, thereby providing an ecologically sustainable alternative to antibiotics for managing bacterial diseases in shrimp aquaculture. Full article

Cold-water immersion (CWI), as a common recovery method, has been widely used in the field of post-exercise fatigue recovery. However, there is still a lack of comprehensive and systematic scientific evaluation of the combined effects of cold-water immersion combined with other therapies (CWI + Other). The aim of this study was to compare the effects of CWI and CWI + Other in post-exercise fatigue recovery and to explore the potential benefits of CWI + Other. We systematically searched PubMed, Embase, Web of Science, Cochrane Library and EBSCO databases to include 24 studies (475 subjects in total) and performed a meta-analysis using standardized mean difference (SMD) and 95% confidence intervals (CIs). The results showed that both CWI + Other (SMD = −0.68, 95% CI: −1.03 to −0.33) and CWI (SMD = −0.37, 95% CI: −0.65 to −0.10) were effective in reducing delayed-onset muscle soreness (DOMS). In subgroup analyses of athletes, both CWI + Other (SMD = −1.13, 95% CI: −1.76 to −0.49) and CWI (SMD = −0.47, 95% CI: −0.87 to −0.08) also demonstrated significant effects. In addition, CWI + Other significantly reduced post-exercise C-reactive protein (CRP) levels (SMD = −0.62, 95% CI: −1.12 to −0.13), and CWI with water temperatures higher than 10 °C also showed a CRP-lowering effect (MD = −0.18, 95% CI: −0.30 to −0.07), suggesting a potential benefit in anti-inflammation. There were no significant differences between the two interventions in the metrics of creatine kinase (CK; CWI: SMD = −0.01, 95% CI: −0.27 to 0.24; CWI + Other: SMD = 0.26, 95% CI: −0.51 to 1.03) or countermovement jump (CMJ; CWI: SMD = 0.22, 95% CI: −0.13 to 0.57; CWI + Other: SMD = 0.07, 95% CI: −0.70 to 0.85). Full article