Search Results (31,047)

Sicyos angulatus L. is a rapidly spreading invasive alien vine that threatens natural and agricultural ecosystems globally. We collected occurrence data from 4886 sites and applied the maximum entropy (MaxEnt) model to assess current and future habitat suitability for S. angulatus. Future climate conditions were represented by low and high greenhouse gas concentrations under representative concentration pathways (i.e., RCP2.6 and RCP8.5, respectively). The MaxEnt model accurately predicted the distribution of S. angulatus, and the area under the receiver operating characteristic curve in the receiver operating characteristic test reached 0.921. Among the 19 climatic variables investigated, the best predictors for the distribution of S. angulatus were the precipitation in the driest month (with a contribution of 37.4%), annual precipitation (26.8%), average annual temperature (18.1%), and temperature seasonality (14.9%). Currently, the most suitable areas cover the central and eastern United States, parts of southern Europe, most Japanese islands, the majority of the Korean Peninsula, and eastern China, with a total area of 180.3 × 10⁴ km² (1.2% of the Earth’s land area). During the 2050s and 2090s under RCP2.6 and RCP8.5, the most suitable regions worldwide are projected to expand by factors of 1.0 and 2.2, respectively. In particular, suitable areas might expand to higher-latitude regions and encompass previously unsuitable areas, such as Liaoning Province in Northeast China. These findings may aid in the surveillance and management of S. angulatus’ invasion globally. Full article

County economies are essential drivers of national economic development, acting as critical engines for growth and regional equilibrium. This study uses the National Rural E-commerce Comprehensive Demonstration policy, initiated by the Ministry of Finance and the Ministry of Commerce, China to empirically investigate the impact of rural e-commerce on county economic development and inequality, based on economic and night light data from 2000 to 2021. By applying a staggered difference-in-differences (DID) model, we find that rural e-commerce significantly boosts county economic development. This result remains robust after a series of robustness tests. The impact is stronger on the primary sector compared to the secondary and tertiary sectors, and the effect is more pronounced in the central and western regions than in the eastern regions. Furthermore, rural e-commerce effectively reduces economic inequality, contributing to inclusive development. Mechanistically, e-commerce into rural demonstration policy fosters county economic development by enhancing human capital mobility, accelerating logistics development, and promoting the growth of local enterprises. Full article

This work presents an experimental study of the effect of blade count on the flow field and the radiated noise in a low-speed axial fan with a rotating shroud. A two-component Laser Doppler Velocimetry (LDV) system and Particle Image Velocimetry (PIV) instrumentation have been employed to investigate the flow in the gap region and in front of the rotor blades. Additionally, the fan has been installed in a hemi-anechoic chamber and far-field acoustic measurements have been taken with a microphone mounted on-axis upstream of the rotor to show changes in the spectral features of the radiated noise. The tested rotor is a variable-geometry one that has allowed for studying rotor configurations with different numbers of blades of the same chord and shape, i.e., of the same geometry but different solidity. Rotor pressure rise and flow rate are average quantities that have a relevant effect on the leakage flow. Keeping them fixed while varying solidity allows us to highlight the local effects of circumferential pressure non-uniformity caused by differing blade loading. The results show that, at low solidity, the flow leaving the gap is mainly directed radially outward and follows a longer path before being ingested by the rotor, thus losing strength due to mixing with the main flow. As solidity increases, the flow becomes less radial and is more rapidly ingested by the rotor. In all cases, the sound pressure level spectrum shows marked subharmonic humps and peaks originating from the interaction between the leakage flow and rotor. The departure of such peaks from the blade passing frequency increases with the solidity, while the associated energy increases up to seven blades and then decreases. Full article

Chronic kidney disease (CKD) is a progressive global health burden often diagnosed in late stages due to reliance on invasive and centralized blood and urine tests. Saliva, as a non-invasive diagnostic fluid, has emerged as a promising alternative for assessing renal function. This scoping review aims to evaluate the diagnostic accuracy of salivary biomarkers compared to traditional methods, and to explore the potential of emerging biosensing technologies for CKD detection and monitoring. Methods: A comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Cochrane Library up to 1 July 2025, following the PRISMA-ScR guidelines. Studies involving adult CKD patients and healthy controls that assessed the diagnostic performance of salivary biomarkers against validated reference standards (e.g., serum creatinine, eGFR) were included. A total of 29 eligible studies were selected after applying predefined inclusion and exclusion criteria. Results: Salivary creatinine and urea were the most frequently assessed biomarkers and demonstrated strong correlations with serum levels (AUCs up to 1.00; sensitivity and specificity frequently >85%). Several studies reported high diagnostic potential for novel salivary markers such as Trimethylamine N-oxide (TMAO), cystatin C, and amino acids. Technological innovations, including electrochemical biosensors and ATR-FTIR spectroscopy, showed promise for enhancing sensitivity and enabling point-of-care testing. However, heterogeneity in sampling protocols and limited data for early-stage CKD were notable limitations. Conclusions: Salivary diagnostics, supported by biosensor technologies, offer a feasible and non-invasive alternative for CKD screening and monitoring. Standardization, broader clinical validation, and integration into dental workflows are key to clinical implementation. Full article

Background: Health promotion programs based on physical activity have gained increasing attention due to their potential to enhance employees’ physical and mental well-being, particularly in university settings, where academic and administrative staff are vulnerable to chronic stress, sedentary lifestyles, and work-related disorders. This study evaluates the effectiveness of the “University on the Move” program, an exercise-based workplace health promotion intervention implemented at the University of L’Aquila, Italy. Methods: An intervention study was conducted on 29 university employees participating in a supervised physical exercise program. Their anthropometric parameters, biochemical markers, cardiovascular risk factors, blood pressure, and work ability index were assessed at baseline (t0), three months (t1), and six months (t2), with a one-sample pre–post-test quasi-experimental design. Results: Significant improvements were observed in the cardiovascular risk factors, particularly in the female subgroup, e.g., the total cholesterol decreased by 20.8 mg/dL (p < 0.01), the LDL cholesterol decreased by 16.1 mg/dL (p < 0.01), and the fasting glucose decreased by 7.4 mg/dL (p < 0.01). Although the body mass index values remained stable, these metabolic improvements indicate beneficial effects independent of weight changes. The work ability index remained stable. The participation rates varied, with lower adherence to the training sessions. Conclusions: The study has some limitations (small sample size, no controlled design), all related to the primary aim of the preventive project targeted at the University employees who freely engaged in the protocol. Despite the low adherence (only about 30%), the metabolic improvements still suggest that structured workplace physical activity programs can positively impact employees’ health, even without significant weight loss, thus supporting the effectiveness of workplace health promotion and primary prevention interventions for an improved quality of life. Further research should explore long-term adherence and the organisational factors influencing participation. Full article

The significant rise of electric vehicles in urban areas calls for research on smart charging to promote electric mobility. Existing research is fragmented, with inconsistent findings, focusing on single aspects of smart charging, such as challenges, charging technologies, and sustainability concerns. Thus, a bibliometric analysis was conducted to identify the key themes and propose future research agendas on smart charging for electric mobility in urban areas, to guide policy formulation and promote widespread uptake of electric vehicles. A total of 201 publications covering the period 2005 to 2025 were extracted from the Scopus database; the first was published in 2011 and numbers peaked in 2024, with 39 publications. The topic is young, with an average age per publication of 4.17 years, with China as the top-ranked country, with 97 publications. Research on smart charging for e-mobility in urban areas focuses on four key themes: smart charging technologies and optimisation strategies, grid integration and vehicle-to-grid systems, renewable energy and environmental sustainability, and urban mobility systems and infrastructure development. Despite their importance, real-world testing and smarter integration with cities and grids remain largely underexplored, especially in developing countries. Future research should focus on large-scale vehicle-to-grid integration, user behaviour analysis, and coordinated planning of smart charging with urban transport and policy frameworks. Full article

The common krait (Bungarus caeruleus) and the endemic Sri Lankan krait (B. ceylonicus) are two species of krait responsible for envenomings in Sri Lanka that result in progressive neuromuscular paralysis. We characterised the in vitro neurotoxicity of B. ceylonicus and B. caeruleus venoms and studied their neutralisation by two commercially available Indian polyvalent antivenoms (i.e., VINS and BHARAT), Thai banded krait antivenom and Australian polyvalent antivenom using the chick biventer cervicis nerve-muscle preparation. Both venoms displayed concentration-dependent neurotoxicity, showing equipotent pre-synaptic neurotoxicity at 0.03 μg/mL. At a higher concentration (1 μg/mL), both venoms showed post-synaptic neurotoxicity, with B. ceylonicus venom being more potent. VINS was unable to neutralise the neurotoxicity of B. ceylonicus venom, but neutralised both pre- and post-synaptic neurotoxicity of B. caeruleus venom. BHARAT neutralised in vitro pre- and post-synaptic activity of both B. ceylonicus and B. caeruleus venoms. Banded krait antivenom and Australian polyvalent antivenoms were unable to fully neutralise the neurotoxicity of either venom at tested concentrations. In conclusion, B. ceylonicus venom shows pre- and post-synaptic neurotoxicity similar to B. caeruleus venom. BHARAT effectively neutralises both pre- and post-synaptic neurotoxicity of B. ceylonicus venom. Both Indian polyvalent antivenoms effectively neutralise neurotoxicity induced by B. caeruleus venom. Full article

This study investigates the behavior of glass fiber/polyester layered composites under low-velocity impact conditions, focusing on optimizing layer configurations and fiber types to enhance impact resistance. Composites were fabricated using a combination of E-glass mat and woven glass fibers with orthophthalic polyester resin, employing the hand lay-up method. Impact tests were conducted using a drop hammer device. Key variables included the number of layers and fiber types, with impacts performed using flathead and hemispherical impactors. The results showed that increasing the number of layers significantly enhanced energy absorption and peak contact force. Specifically, five-layer samples exhibited a 351% increase in energy absorption and a 212% increase in peak contact force compared to two-layer samples. Mat fibers outperformed woven fibers, with mat-only samples absorbing 21.8% more energy and showing 5.8% higher peak forces. Additionally, flathead impactors generated 101% higher peak forces and caused more severe damage than hemispherical impactors. Observed failure modes included fiber breakage, matrix cracking, and delamination, all influenced by impact parameters and material characteristics. These findings provide valuable guidance for designing composite materials with improved resistance to impact, with potential applications in the aerospace, marine, and automotive industries. Full article

A method to characterize the mechanical properties of cellular materials manufactured using 3D printing, specifically employing the fused deposition modeling (FDM) technique, is developed. Numerical simulations, virtual testing, and optimization based on genetic algorithms are combined in this approach to determine the anisotropic properties of the material, which are essential for biomedical applications such as tissue engineering. Homogenization using representative unit cells enabled the calculation of orthotropic properties, including elastic moduli ($E_1$, $E_2$, $E_3$), Poisson’s ratios (${\nu }_{12}$, ${\nu }_{13}$ and ${\nu }_{23}$), and shear moduli ($G_{12}$, $G_{13}$, $G_{23}$). These results validated the virtual tests using an L-shaped beam model, based on a known state of displacements and stresses. In the virtual test of the FDM model, a significant correlation with experimental results was observed, confirming the material’s anisotropy and its influence on deformations and stresses. Meanwhile, the effective medium test demonstrated over 95% agreement between simulated and experimental values, validating the accuracy of the proposed constitutive model. The optimization process, based on multi-objective genetic algorithms, allowed the determination of the material’s mechanical properties through controlled iterations, achieving a strong correlation with the results obtained from the homogenization model. These findings present a new approach for characterizing and optimizing 3D-printed materials using FDM techniques, providing an efficient and reliable method for applications in tissue engineering. Full article

The widespread expansion of Internet of Things devices has ushered in an era of unprecedented connectivity. However, it has simultaneously exposed these resource-constrained systems to novel and advanced cyber threats. Among the most impressive and complex attacks are those leveraging in-memory shellcode runners (malware), which perform malicious payloads directly in memory, circumventing conventional disk-based detection security mechanisms. This paper presents a comprehensive framework, both academic and technical, for detecting in-memory shellcode runners, particularly tailored to the unique characteristics of these networks. We analyze and review the limitations of existing security parameters in this area, highlight the different challenges posed by those constraints, and propose a multi-layered approach that combines entropy-based anomaly scoring, lightweight behavioral monitoring, and novel Graph Neural Network methods for System Call Semantic Graph Analysis. Our proposal focuses on runtime analysis of process memory, system call patterns (e.g., Syscall ID, Process ID, Hooking, Win32 application programming interface), and network behavior to identify the subtle indicators of compromise that portray in-memory attacks, even in the absence of conventional file-system artifacts. Through meticulous empirical evaluation against simulated and real-world Internet of Things attacks (red team engagements, penetration testing), we demonstrate the efficiency and a few challenges of our approach, providing a crucial step towards enhancing the security posture of these critical environments. Full article

Background/Objectives: Sepsis is a high-mortality syndrome characterized by organ dysfunction resulting from a dysregulated host response to infection. This study aimed to evaluate the potential of growth differentiation factor 15 (GDF15), a stress-inducible cytokine, as a biomarker in patients diagnosed with urosepsis. Methods: A total of 13 patients diagnosed with urosepsis, based on an increase of ≥2 points in the Sequential Organ Failure Assessment (SOFA) score and positive urine culture, were included in the study. Daily blood samples were collected from patients for 10 days, and serum levels of GDF15, procalcitonin (PCT), and presepsin (P-SEP) were measured by ELISA. C-reactive protein (CRP), blood urea nitrogen (BUN), serum creatinine, estimated glomerular filtration rate (eGFR), hemoglobin, and neutrophil, lymphocyte, and platelet counts were determined using autoanalyzers. Temporal changes were analyzed using the Friedman test, and correlations were analyzed using Spearman’s test. Results: GDF15 levels began to decrease from Day 3, with a significant decline observed from Day 7 compared to Day 1 (p < 0.001). Similar decreasing trends were observed in CRP and PCT levels, whereas presepsin levels did not exhibit significant changes. Significant positive correlations were identified between GDF15 and CRP (r = 0.65, p = 0.015), BUN (r = 0.57, p = 0.041), and creatinine (r = 0.62, p = 0.024), and a significant negative correlation was observed with eGFR (r = −0.62, p = 0.024). No significant correlation was found between GDF15 and presepsin (p > 0.05). Conclusions: GDF15 is a biomarker sensitive to the resolution phase of inflammation and organ dysfunction in sepsis, demonstrating significant temporal changes. It holds potential as an indicator for monitoring clinical progression and assessing prognosis. Full article

In this article, the binding interactions of lysozyme with hexacyanoferrate(III)/(II), i.e., [Fe(CN)₆]³⁻ and [Fe(CN)₆]⁴⁻ ions, have been characterised using steady-state fluorescence spectroscopy (SF), isothermal titration calorimetry (ITC), circular dichroism spectroscopy (CD), cyclic voltammetry (CV), and molecular-dynamics-based computational approaches. Studies have shown that under experimental conditions (10 mM cacodylate buffer, pH 7, 298.15 K), complexes with a 1:1 stoichiometry are formed. Four distinct regions on the lysozyme surface patches with the potential to bind hexacyanoferrate(III)/(II) were identified and described. Thermodynamic parameters revealed that the interactions are predominantly governed by electrostatic and van der Waals forces. These interactions enhance the electron transfer kinetics of the [Fe(CN)₆]^3−/4− system. The secondary structure of the protein is not affected by these interactions. Enzyme activity studies demonstrated that the affinity of lysozyme for the substrate remained unchanged regardless of whether free lysozyme or the lysozyme-[Fe(CN)₆]^3−/4− complex was present in the test sample. Finally, biological tests performed on both Gram-positive (B. subtilis, S. aureus) and Gram-negative (E. coli, P. aeruginosa) bacteria confirmed the results of the biochemical analysis, indicating that [Fe(CN)₆]^3−/4− ions do not block the active site of the enzyme and do not interfere with its activity. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article

Accurate prediction of shear strength at the interface between new and old concrete is vital for the structural performance of repaired and composite systems. However, the underlying shear transfer mechanism is highly nonlinear and influenced by multiple interdependent factors, which limit the applicability of conventional empirical models. To address this challenge, an interpretable machine-learning (ML) framework is proposed. The latest database of 247 push-off specimens was compiled from the recent literature, incorporating diverse interface types and design parameters. The hyperparameters of the adopted ML models were optimized via a grid search to ensure the predictive performance on the updated database. Among the evaluated algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with R² = 0.933, RMSE = 0.663, MAE = 0.486, and MAPE = 12.937% on the testing set, outperforming Support Vector Regression (SVR), Random Forest (RF), and adaptive boosting (AdaBoost). Compared with the best empirical model (AASHTO, R² = 0.939), XGBoost achieved significantly lower prediction errors (e.g., RMSE was reduced by 67.8%), enhanced robustness (COV = 0.176 vs. 0.384), and a more balanced mean ratio (1.054 vs. 1.514). The SHapley Additive exPlanations (SHAP) method was employed to interpret the model predictions, identifying the shear reinforcement ratio as the most influential factor, followed by interface type, interface width, and concrete strength. These results confirm the superior accuracy, generalizability, and explainability of XGBoost in modeling the shear behaviors of new–old concrete interfaces. Full article