Search Results (14,954)

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short‑Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end‑to‑end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean‑energy technologies. Full article

Over the years, macromycete fungi have been used as a source of food, part of religious rites and rituals, and as a medicinal remedy. Species with strong health-promoting potential include Hericium erinaceus, Cordyceps militaris, Ganoderma lucidum, Pleurotus ostreatus, Flammulina velutipes, and Inonotus obliquus. These species contain many bioactive compounds, including β-glucans, endo- and exogenous amino acids, polyphenols, terpenoids, sterols, B vitamins, minerals, and lovastatin. The level of some biologically active substances is species-specific, e.g., hericenones and erinacines, which have neuroprotective properties, and supporting the production of nerve growth factor in the brain for Hericium erinaceus. Due to their high health-promoting potential, mushrooms and substances isolated from them have found applications in livestock nutrition, improving their welfare and productivity. This phenomenon may be of particular importance in the nutrition of laying hens and broiler chickens, where an increase in pathogen resistance to antibiotics has been observed in recent years. Gallus gallus domesticus is a key farm animal for meat and egg production, so the search for new compounds to support bird health is important for food safety. Studies conducted to date indicate that feed supplementation with mushrooms has a beneficial effect on, among other things, bird weight gain; bone mineralisation; and meat and egg quality, including the lipid profile and protein content and shell thickness, and promotes the development of beneficial microbiota, thereby increasing immunity. Full article

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

The tolerance of the fall armyworm (Spodoptera frugiperda) to plant-derived secondary compounds gradually increases with instars. Therefore, even if plant-based additives are applied at early stages, such as the second or third instar, they may have a differential impact on the ecofriendly control of S. frugiperda. In this study, S. frugiperda larvae were exposed to vanillic acid or sinapic acid at the second and third instar, and physiological and growth parameters were measured. The results showed that the effects of vanillic acid treatment on S. frugiperda were similar at the different instars. They can significantly affect the larval carboxylesterase, glutathione S-transferase, and mixed-function oxidase activities. By reducing larval food intake, food conversion, and utilization efficiency while increasing the food consumption rate, it inhibits weight accumulation. This leads to a significant extension of the development of both the larval and pupal stages, and the adult longevity was reduced. Treatment with sinapic acid at the second instar extended the negative effects on the pupal duration of S. frugiperda when compared to treatment at the third instar, but did not affect adult longevity. Therefore, vanillic acid treatment at the second or third instar stage, can play an important role in the ecofriendly control process of S. frugiperda. The results of this study are of great significance for integrated pest management. Full article

Lotus corniculatus L. is a valuable fodder legume, recognized for its ecological adaptability and high potential for production and fodder quality. In this study, 18 genotypes collected from wild flora were analyzed to highlight genetic variability and facilitate the selection of genotypes with superior potential. The collection area was in the western part of Romania and featured a diverse topography, including parts of the Banat Plain, the Banat Hills, and the Southern and Western Carpathians. The genotypes selected from the wild flora were cultivated and evaluated for morpho-productive and forage quality traits, including pod weight, average number of seeds/pods, green mass production, and protein percentage. PCA highlighted the main components explaining the variability, and K-means clustering allowed for the identification of groups of genotypes with similar performances. ANOVA showed statistically significant differences (p < 0.001) for all traits analyzed. According to the results, genotypes LV-LC-3, LV-LC-4, LV-LC-6, and LV-LC-16 showed high productive potential and were highlighted as the most valuable for advancing in the breeding program. The moderate relationships between traits confirm the importance of integrated selection. The identified genetic variability and selected genotypes support the implementation of effective breeding strategies to obtain high-performance Lotus corniculatus L., adapted to local soil and climate conditions and with a superior forage yield. Full article

HIV infection and hypertensive disorders of pregnancy (HDP), particularly preeclampsia (PE) with severe features, are leading causes of maternal mortality worldwide. This study investigates the role of asymmetric dimethylarginine (ADMA) and prostacyclin (PGI2) concentrations in endothelial impairment in normotensive pregnant versus PE women within an HIV endemic setting in KwaZulu-Natal Province, South Africa. The study population (n = 84) was grouped according to pregnancy type, i.e., normotensive (n = 42) and PE (n = 42), and further stratified by HIV status. Clinical factors were maternal age, weight, blood pressure (both systolic and diastolic) levels, and gestational age. Plasma concentrations of ADMA and PGI2 were measured using the enzyme-linked immunoassay (ELISA). Differences in outcomes were analyzed using the Mann–Whitney U and Kruskal–Wallis test together with Dunn’s multiple-comparison post hoc test. The non-parametric data were presented as medians and interquartile ranges. Gravidity, gestational age, and systolic and diastolic blood pressures were significantly different across the study groups where p < 0.05 was deemed significant. Furthermore, the concentration of ADMA was significantly elevated in PE HIV-positive vs. PE HIV-negative (p = 0.0174) groups. PGI2 did not show a significant difference in PE compared to normotensive pregnancies (p = 0.8826) but was significantly different across all groups (p = 0.0212). An increase in plasma ADMA levels was observed in the preeclampsia HIV-negative group compared to the normotensive HIV-negative group. This is linked to the role played by ADMA in endothelial impairment, a characteristic of PE development. PGI2 levels were decreased in PE compared to the normotensive group regardless of HIV status. These findings draw attention to the importance of endothelial indicators in pathogenesis and possibly early prediction of PE development. Full article

Background: The consumption of coffee has been widely debated regarding its effects on health. This study aims to analyze the correlations between daily coffee intake and sleep, blood pressure, anthropometric measurements, and biochemical markers in individuals with type 2 diabetes (T2D) and hypertension over a 12-month period. Methods: An observational study was conducted with 40 participants with T2D and hypertension, comprising 20 females and 20 males. Participants were monitored for their daily coffee consumption over a 12-month period, being assessed every 3 months. Linear regression was utilized to assess interactions and relationships between variables, providing insights into potential predictive associations. Additionally, correlation analysis was performed using Pearson’s and Spearman’s tests to evaluate the strength and direction of linear and non-linear relationships. Statistical significance was set at p < 0.05. Results: Significant changes were observed in fasting blood glucose (FBG), glycated hemoglobin (HbA1c), body weight, body mass index, sleep duration, nocturnal awakenings, and waist-to-hip ratio (p < 0.05) over the 12-month study in both sexes. No significant differences were noted in the remaining parameters (p > 0.05). The coffee consumed by the participants was of the “traditional type” and contained sugar (2g per cup) for 100% of the participants. An intake of 4.17 ± 0.360 cups per day was found at baseline and 5.41 ± 0.316 cups at 12 months (p > 0.05). Regarding correlation analysis, a higher coffee intake was significantly associated with shorter sleep duration in women (r = −0.731; p = 0.037). Conversely, greater coffee consumption correlated with lower LDL cholesterol (LDL-C) levels in women (r = −0.820; p = 0.044). Additionally, a longer sleep duration was linked to lower FBG (r = -0.841; p = 0.031), HbA1c (r = -0.831; p = 0.037), and LDL-C levels in women (r = -0.713; p = 0.050). No significant correlations were observed for the other parameters in both sexes (p > 0.05). Conclusions: In women, coffee consumption may negatively affect sleep duration while potentially offering beneficial effects on LDL-C levels, even when sweetened with sugar. Additionally, a longer sleep duration in women appears to be associated with improvements in FBG, HbA1c, and LDL-C. These correlations emphasize the importance of a balanced approach to coffee consumption, weighing both its potential health benefits and drawbacks in postmenopausal women. However, since this study does not establish causality, further randomized clinical trials are warranted to investigate the underlying mechanisms and long-term implications—particularly in the context of T2D and hypertension. Full article

E-commerce growth has reshaped consumer behavior and retail services, driving parcel demand and challenging last-mile logistics. Existing research predominantly relies on survey data and global regression models that overlook intra-urban spatial heterogeneity in shopping behaviors. This study bridges this gap by analyzing e-commerce demand’s spatial distribution from a retail service perspective, identifying key drivers, and evaluating implications for omnichannel strategies and logistics. Utilizing waybill big data, spatial analysis, and multiscale geographically weighted regression, we reveal: (1) High-density e-commerce demand areas are predominantly located in central districts, whereas peripheral regions exhibit statistically lower volumes. The spatial distribution pattern of e-commerce demand aligns with the urban development spatial structure. (2) Factors such as population density and education levels significantly influence e-commerce demand. (3) Convenience stores play a dual role as retail service providers and parcel collection points, reinforcing their importance in shaping consumer accessibility and service efficiency, particularly in underserved urban areas. (4) Supermarkets exert a substitution effect on online shopping by offering immediate product availability, highlighting their role in shaping consumer purchasing preferences and retail service strategies. These findings contribute to retail and consumer services research by demonstrating how spatial e-commerce demand patterns reflect consumer shopping preferences, the role of omnichannel retail strategies, and the competitive dynamics between e-commerce and physical retail formats. Full article

The contamination of drinking water by per- and polyfluoroalkyl substances (PFASs) presents a global concern due to their extreme persistence, driven by strong C–F bonds. This study investigated the potential of graphene as a filtration material for PFAS removal, focusing on six key compounds regulated by the U.S. EPA: PFOA, PFNA, GenX, PFBS, PFOS, and PFHxS. Using molecular simulations, adsorption energy, diffusion coefficients, and PFAS-to-graphene distances were analyzed. The results showed that adsorption strength increased with molecular weight; PFOS (500 g/mol) exhibited the strongest adsorption (−171 kcal/mol). Compounds with sulfonic acid head groups (e.g., PFOS) had stronger interactions than those with carboxylate groups (e.g., PFNA), highlighting the importance of head group chemistry. Shorter graphene-to-PFAS distances also aligned with higher adsorption energies. PFOS, for example, had the shortest distance at 8.23 Å (head) and 6.15 Å (tail) from graphene. Diffusion coefficients decreased with increasing molecular weight and carbon chain length, with lower molecules like PFBS (four carbon atoms) diffusing more rapidly than heavier ones like PFOS and PFNA. Interestingly, graphene enhanced PFAS mobility in water, likely by disrupting the water structure and lowering intermolecular resistance. These results highlight graphene’s promise as a high-performance material for PFAS removal and future water purification technologies. Full article

Seafood contamination by heavy metals is a growing public health concern, particularly in regions like Tunisia where seafood is a major dietary component. This study assessed concentrations of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) in the muscle tissue of the red shrimp Parapenaeus longirostris, collected in 2023 from four coastal regions: Bizerte, Monastir, Kerkennah, and Gabes. Metal analysis was conducted using flame atomic absorption spectroscopy. This species was chosen due to its ecological and economic importance. The study sites were chosen based on their differing levels of industrial, urban, and agricultural influence, providing a representative overview of regional contamination patterns. Mean concentrations were 1.04 µg/g for Zn, 0.59 µg/g for Cu, 1.56 µg/g for Pb, and 0.21 µg/g for Cd (dry weight). Pb was the most prevalent metal across sites. Statistically significant variation was observed only for Cu (p = 0.0334). All metal concentrations were below international safety limits set by FAO/WHO and the European Union. Compared to similar studies, the levels reported were similar or slightly lower. Human health risk was evaluated using target hazard quotient (THQ), hazard index (HI), and cancer risk (CR) values. For adults, THQ ranged from 5.44 × 10⁻⁶ to 8.43 × 10⁻⁴, while for children it ranged from 2.40 × 10⁻⁵ to 3.72 × 10⁻³. HI values were also well below 1, indicating negligible non-carcinogenic risk. CR values for Cd and Pb in both adults and children fell within the acceptable risk range (10⁻⁶ to <10⁻⁴), suggesting no significant carcinogenic concern. This study provides the first field-based dataset on metal contamination in P. longirostris from Tunisia, contributing valuable insights for seafood safety monitoring and public health protection. Full article

Background: Nutrition is of paramount importance during early development, since suboptimal growth in this period of life is linked to adverse long- and mid-term outcomes. This is particularly relevant for preterm infants, who fail to thrive during the first weeks of life and develop extrauterine growth restriction (EUGR). This group of premature babies represents an interesting population to investigate using a metabolomic approach to optimize nutritional intake. Aims: To analyse and compare the urinary metabolomic pattern at birth of preterm infants with and without growth restriction at 36 weeks of postmenstrual age or at discharge, searching for putative markers of growth failure. Methods: We enrolled preterm infants between 23 and 32 weeks of gestational age (GA) and/or with a birth weight <1500 g, admitted to the Neonatal Intensive Care Unit (NICU) at the Department of Women’s and Children’s Health of Padova University Hospital. We collected urinary samples within 48 h of life and performed untargeted metabolomic analysis using mass spectrometry. Results: Sixteen EUGR infants were matched with sixteen non-EUGR controls. The EUGR group showed lower levels of L-cystathionine, kynurenic acid, L-carnosine, N-acetylglutamine, xanthurenic acid, aspartylglucosamine, DL5-hydroxylysine-hydrocloride, homocitrulline, and L-aminoadipic acid, suggesting a lower anti-inflammatory and antioxidant status with respect to the non-EUGR group. Conclusions: Metabolomic analysis suggests a basal predisposition to growth restriction, the identification of which could be useful for tailoring nutritional approaches. Full article

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

This study investigated the association between temperament and retrospective growth rates in 84 Nili Ravi buffalo heifers aged 18 to 24 months. Temperament was assessed using chute score and exit velocity, measured twice at a seven-day interval, and classified as calm (≤3) or nervous (>3). Retrospective average daily weight gain data were retrieved from farm records, and blood samples were collected to measure cortisol levels. ANOVA was used to analyze data, considering temperament, age group, season, and year of birth as fixed effects, with birth weight as a covariate. Results showed that 48 heifers were calm and 36 were nervous. Calm heifers exhibited significantly higher average daily gains than nervous heifers during the post-weaning period, with an increase of 240 g/day from 4 to 6 months and 190 g/day from 6 to 12 months (p < 0.001). However, this difference was not significant at 18–24 months (p = 0.144). Calm heifers have numerically lower cortisol levels (0.96 vs. 1.27 μg/dL; p = 0.11). These findings suggest that calmer heifers grow faster in early life, emphasizing the importance of temperament in breeding programs aimed at improving growth performance and welfare. Full article