Search Results (1,442)

This study evaluates the accuracy of various Geographic Information System interpolation methods in predicting the stratified spatial distribution of organic pollutants (Benzene, Total Petroleum Hydrocarbons [TPH], and Methyl Tert-butyl Ether [MTBE]) in groundwater at a petrochemical-contaminated site. Given the limitations of traditional monitoring methods in predicting spatial distribution, this study focuses on the spatial computational prediction of volatile organic compound concentrations at a former petrochemical industrial site. Three interpolation methods—Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK)—were applied and evaluated. Prediction accuracy was assessed using leave-one-out cross-validation, with performance quantified through key metrics: Root Mean Square Error, Coefficient of Determination, and Spearman’s Rank Correlation Coefficient. Results demonstrate significant variations in optimal prediction methods depending on pollutant type and depth stratum. For pollutants predominantly enriched in shallow and middle layers (Benzene, TPH), OK yielded the highest accuracy and stability. Conversely, for predictions of pollutants primarily concentrated in deeper layers, RBF achieved superior performance. IDW consistently underperformed across all strata and pollutants. All interpolation methods generally exhibited systematic overestimation of pollutant concentrations (mean cross-validation error > 0). Through a hierarchical evaluation of the accuracy and interpolation effectiveness of these methods, this study develops a more accurate modeling framework to describe the composite groundwater contamination patterns at petrochemical sites. This study systematically evaluates the spatial prediction accuracy of various non-aqueous phase liquid species under differing groundwater-table depths, identifies the most robust interpolation method, and thereby provides a benchmark for enhancing predictive fidelity in subsurface contaminant mapping. Full article

This study utilizes QM/MM Born–Oppenheimer Molecular Dynamics in order to model the process of intermolecular binding between reduced graphene oxide (rGO) and butadiene–acrylonitrile copolymer (PBDAN) with a monomer ratio of 2:1. This research aims to elucidate the structural reasons behind the enhancing properties of the substrate, focusing on the polymer matrix. The behavior of each phase was examined and discussed. More importantly, the intermolecular interactions within the interphase zone of adsorption were investigated on an atomic scale. We found and characterized 58 such instances, grouped into hydrogen bonds and three types of stacking: π–π, σ–π, and σ–n. Each occurrence was analyzed through the use of radial distribution functions. Five spontaneous chemical processes within the rGO nanoparticle were modeled and characterized. Two of them were found to provide stabilization only within the substrate, while the rest are relevant for the overall constitution of the heteromaterial. Perhaps most intriguing is the process of self-repair as part of the vacancy defect. This occurs entirely within the carbon frame of the rGO layer. We believe our results to be of importance for a large set of ligand materials, mostly those which contain unsaturated bonds and electronegative atoms. Full article

This study examined cast AZ31 magnesium alloy and its variant containing micro-alloying elements of Y and Ca (AZXW alloy), evaluating their potential as anode materials in magnesium–air batteries. The AZXW alloy was fabricated via two manufacturing techniques: casting and extrusion. The synergistic influence of Y and Ca, in conjunction with the production procedure, on the microstructure, electrochemical characteristics, and anodic discharge behavior of the examined alloys was investigated. The addition of Y and Ca results in the formation of secondary phases that affect grain size, particle size, and distribution, as well as the electrochemical performance and discharge properties of the Mg–air battery constructed for this study, over 24 h or until fully discharged. This work demonstrates the potential to enhance discharge performance and electrochemical behavior by adjusting the aqueous electrolyte solution in the battery through the incorporation of Citric Acid (C.A) at varying concentrations. The incorporation of citric acid into the aqueous electrolyte improves battery stability and specific energy as long as citric acid is present in the solution. Magnesium hydroxide (Mg(OH)₂) begins to form on the anode surface as its concentration progressively decreases due to complexation with dissolved magnesium ions. This diminishes the effective anode area over time, ultimately resulting in the distinctive “knee-type” collapse characteristic of electrolytes containing citric acid. Full article

This study focuses on forecasting and optimizing the performance of a real-world object modelled by a multi-server queueing system that processes two types of users: primary (new) users and repeating users. The repeating users are those who succeeded in entering processing upon arrival and then decided to repeat it. These users have privilege and can enter processing when they wish once at least one device is idle. The primary user is admitted to the system only if the number of occupied devices is less than some threshold value and the quantity of repeating users residing in the system does not exceed certain thresholds. Repeating users are impatient and non-persistent. Arrivals of primary users are described by the Markovian arrival process. Processing times of primary and repeating users have distinct phase-type distributions. Utilizing the concept of the generalized phase–time distributions, the dynamics of this queueing system are formally characterized by the multidimensional Markov chain, which is examined in this paper. The ergodicity condition is derived. The relation of the key performance characteristics of the system and the thresholds defining the policy of the primary user’s admission is numerically highlighted. Optimal threshold selection is demonstrated numerically. Full article

To investigate the spatiotemporal evolution of ecosystem health in a typical rocky desertification control demonstration zone. This study utilized land use data and remote sensing imagery from 1992, 2003, 2009, 2015, and 2021. Landscape pattern analysis was employed to quantify landscape characteristics. A Pressure-State-Response (PSR) model framework was integrated to establish an ecosystem health assessment system comprising 14 indicator factors, enabling ecosystem health evaluation from the perspective of coupling landscape patterns and ecological processes. Key findings reveal: Significant cropland expansion occurred within the study area, accompanied by mutual transitions within ecological land types, yet the overall landscape structure remained relatively stable. The regional landscape underwent substantial transformations, characterized by grassland reduction alongside increases in cropland and shrubland. These changes led to decreased landscape heterogeneity and fragmentation, an increasingly dominant landscape matrix, significantly enhanced connectivity, and reduced diversity. Ecosystem health experienced an initial deterioration phase followed by gradual recovery. By 2021, a transition trend emerged where a suboptimal state prevailed, yet localized areas exhibited improved quality. Distinct variations in ecological response mechanisms were observed across different geomorphic types. Unhealthy ecosystems were predominantly distributed in areas of intensive human activity, specifically peak-cluster platforms (I), eroded platforms (III), and V-shaped valleys (V). These results underscore the necessity of considering differential ecological carrying capacities inherent to various geomorphic types during rocky desertification control. Implementing differentiated management strategies and adaptive governance is crucial for promoting the sustainable enhancement of regional ecosystem health. Full article

Valsartan (Val)—a lipophilic non-peptide angiotensin II type 1 receptor antagonist—is highly effective against hypertension and displaying limited solubility in water (3.08 μg/mL), thereby resulting in low oral bioavailability (23%). The limited water solubility of antihypertensive drugs can pose a challenge, particularly for rapid and precise administration. Herein, we synthesize and characterize valsartan-containing silver nanoparticles (Val-AgNPs) using Mangifera indica leaf extracts. The physicochemical, structural, thermal, and pharmacological properties of these nano-conjugates were established through various analytical and structural tools. The spectral shifts in both UV-visible and FTIR analyses indicate a successful interaction between the valsartan molecule and the silver nanoparticles. The resulting nano-conjugates are spherical and within the size range of 30–60 nm as revealed in scanning electron-EDS and atomic force micrographs. The log-normal distribution of valsartan-loaded nanoparticles, with a size range of 30 to 60 nm and a mode of 54 nm, indicates a narrow, monodisperse, and highly uniform particle size distribution. This is a favorable characteristic for drug delivery systems, as it leads to enhanced bioavailability and a consistent performance. Dynamic Light Scattering (DLS) analysis of the Val-AgNPs indicates a polydisperse sample with a tendency toward aggregation, resulting in larger effective sizes in the suspension compared to individual nanoparticles. The accompanying decrease in zeta potential (to −19.5 mV) and conductivity further supports the idea that the surface chemistry and stability of the nanoparticles changed after conjugation. Differential scanning calorimetry (DSC) demonstrated the melting onset of the valsartan component at 113.99 °C. The size-dependent densification of the silver nanoparticles at 286.24 °C correspond to a size range of 40–60 nm, showing a significant melting point depression compared to bulk silver due to nanoscale effects. The shift in Rf for pure valsartan to Val-AgNPs suggests that the interaction with the AgNPs alters the compound’s overall polarity and/or its interaction with the stationary phase, complimented in HPTLC and HPLC analysis. The stability and offloading behavior of Val-AgNPs was observed at pH 6–10 and in 40% and 80% MeOH. In addition, Val-AgNPs did not reveal hemolysis or significant alterations in blood cell indices, confirming the safety of the nano-conjugates for biological application. In conclusion, these findings provide a comprehensive characterization of Val-AgNPs, highlighting their potential for improved drug delivery applications. Full article

Emerging research indicates that neuronal activity is maintained by an architectural system of protons in a multi-scale fashion. Proton architecture is formed when organelles (such as mitochondria, endoplasmic reticulum, lysosomes, synaptic vesicles, etc.) are coupled together to produce dynamic energy domains. Techniques have been developed to visualize protons in neurons; recent advances include near-atomic structural imaging of organelle interfaces using cryo-tomography and nanoscale resolution imaging of organelle interfaces and proton tracking using ultra-fast spectroscopy. Results of these studies indicate that protons in neurons do not diffuse randomly throughout the neuron but instead exist in organized geometric configurations. The cristae of mitochondrial cells create oscillating proton micro-domains that are influenced by the curvature of the cristae, hydrogen bonding between molecules, and localized changes in dielectric properties that result in time-patterned proton signals that can be used to determine the metabolic load of the cell and the redox state of its mitochondria. These proton patterns also communicate to the rest of the cell via hydrated aligned proton-conductive pathways at the mitochon-dria-endoplasmic reticulum junctions, through acidic lipid regions, and through nano-tethered contact sites between mitochondria and other organelles, which are typically spaced approximately 10–25 nm apart. Other proton architectures exist in lysosomes, endosomes, and synaptic vesicles. In each of these organelles, the V-ATPase generates steep concentration gradients across their membranes, controlling the rate of cargo removal from the lumen of the organelle, recycling receptors from the surface of the membrane, and loading neurotransmitters into the vesicles. Recent super-resolution pH mapping has indicated that populations of synaptic vesicles contain significant heterogeneity in the amount of protons they contain, thereby influencing the amount of neurotransmitter released per vesicle, the probability of vesicle release, and the degree of post-synaptic receptor protonation. Additionally, proton gradients in each organelle interact with the cytoskeleton: the protonation status of actin and microtubules influences filament stiffness, protein–protein interactions, and organelle movement, resulting in the formation of localized spatial structures that may possess some type of computational significance. At multiple scales, it appears that neurons integrate the proton micro-domains with mechanical tension fields, dielectric nanodomains, and phase-state transitions to form distributed computing elements whose behavior is determined by the integration of energy flow, organelle geometry, and the organization of soft materials. Alterations to the proton landscape in neurons (e.g., due to alterations in cristae structure, drift in luminal pH, disruption in the hydration-structure of the cell, or imbalance in the protonation of cytoskeletal components) could disrupt the intracellular signaling network well before the onset of measurable electrical or biochemical pathologies. This article will summarize evidence indicating that proton–organelle interaction provides a previously unknown source of energetic substrate for neural computation. Using an integrated approach combining nanoscale proton energy, organelle interface geometry, cytoskeletal mechanics, and AI-based multiscale models, this article outlines current principles and unresolved questions related to the subject area as well as possible new approaches to early detection and precise intervention of pathological conditions related to altered intracellular energy flow. Full article

The Eurasian Griffon Vulture (Gyps fulvus) is the most abundant obligate scavenger in Europe. It depends on wild and domestic carcasses whose availability and location are relatively unpredictable in terms of space and time, but also on predictable sources of anthropogenic origin. In this study, satellite and accelerometer data from 10 adult individuals captured in the Basque Country (N Spain) were analysed with the aims of identifying feeding sites and determining the types of resources used. The annual cycle of the species was subdivided into three phases: pre-laying and incubation (December–March), rearing (April–July) and post-rearing (August–November). Our results showed that 64% of trophic resources were consumed in mountain pastures and on extensive or semi-extensive livestock farms, highlighting the importance of these farming systems for the species in the study area. However, 36% of the resources were exploited in more predictable anthropic environments, such as landfills and supplementary feeding stations and, to a much lesser extent, intensive farms. Individual variability was detected in terms of trophic behaviour. On semi-extensive farms, the most consumed carcasses were sheep (48%) and horses (37%), while on intensive farms, it was pigs (81%). During the pre-laying and incubation phase, feeding events detected in landfills were reduced, with vultures focusing on resources close to the colony. We observed that the population studied differed from other Spanish populations in its greater use of trophic resources from extensive and semi-extensive livestock farms, as expected from their spatial-temporal distribution and local availability. Full article

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid interactions and often lack labelled examples. To address this, the present work introduces a unique, frequency-centric framework for unsupervised detection and root cause analysis of grid anomalies using high-resolution micro-Phasor Measurement Unit ($\mu$PMU) data. Unlike previous studies that focus primarily on voltage phasors or rely on predefined event labels, this work employs SpectralNet, a deep spectral clustering approach, integrated with autoencoder-based feature learning to model the nonlinear interactions between frequency, ROCOF, voltage, and current. These methods are particularly effective for unexpected frequency variations because they learn intrinsic, hidden structures directly from the data and can group abnormal frequency behavior without prior knowledge of event types. The proposed model autonomously identifies distinct root causes such as unbalanced loads, phase-specific faults, and phase imbalances behind hazardous frequency deviations. Experimental validation on a real solar-integrated distribution feeder in the UK demonstrates that the framework achieves superior cluster compactness and interpretability compared to traditional methods like K-Means, GMM, and Fuzzy C-Means. The findings highlight SpectralNet’s capability to uncover subtle, nonlinear patterns in $\mu$PMU data, offering an adaptive, data-driven tool for enhancing grid stability and situational awareness in renewable-rich power systems. Full article

Background. This study aimed to quantify differences in the internal training load (ITL) of an elite rink hockey (RH) team across days within and between three types of microcycles: pre-season, in-season regular, and in-season congested, to provide insights to optimise microcycle scheduling. Methods. One international-level male RH team comprising seven outfielders (29.6 ± 4.7 years; height, 178.9 ± 2.3 cm; body mass, 77.8 ± 5.7 kg) and one goalkeeper (32 years; height, 180.4 cm; body mass, 83.6 kg) was monitored for 21 microcycles. The ITL was assessed using the session rate of perceived exertion (sRPE) and quantified as time based on a triphasic classification commonly utilised in team sports: low-intensity training (LIT, <80% heart rate maximum (HRmax)), medium-intensity training (MIT, 80–90% HRmax), and high-intensity training (HIT, >90% HRmax). Generalized estimating equations were used to examine differences across within-microcycle training days and between seasonal phases, with linear mixed models applied as sensitivity analyses. Results. Across all phases, significant day-to-day variations in ITL were observed within microcycles (all p < 0.001), with both subjective (sRPE) and objective (LIT–HIT) ITLs progressively decreasing as match days (MDs) approached, showing moderate-to-large population-averaged effects with 95% confidence intervals consistently not crossing zero. The pre-season exhibited the highest overall ITL (p < 0.001), characterised by a substantially greater sRPE and increased time spent across all intensity zones, with the largest magnitudes observed for LIT and MIT compared with the in-season phases. Conclusions. Findings suggest that an international-level RH team progressively reduced the ITL as MDs approached with the highest loads scheduled earlier within microcycles. Moreover, the pre-season had the highest ITLs. This ITL distribution may provide useful guidance for RH coaches and support staff in optimising microcycle planning. Full article

This study investigates the presence and distribution of second phases in continuous rheo-extrusion (CRE)-processed Al-La-Ti-B grain refiners and their effect on refining A356 Al-Si alloy. Thermodynamic calculations and microstructural characterization revealed that the main second phases include α-Al, TiAl₃, TiB₂, Al₁₁La₃, LaB₆, and Ti₂AL₂₀La, with their types evolving with varying Ti/La ratios. The CRE process effectively refined and homogenized these phases. Among the tested refiners, the addition of 0.2 wt.% Al-2.5La-1Ti-1B showed the most effective refinement for A356 alloy, achieving the smallest average α-Al grain size of 221 μm and secondary dendrite arm spacing (SDAS) of 24.62 μm. This optimal refinement corresponded to superior mechanical properties: a tensile strength of 164.52 MPa and elongation of 9.0% in the as-cast state, which were further improved to be 261.13 MPa after T6 heat treatment with elongation of 5.5%. The enhancement is attributed to La’s dual role in modifying the morphology and distribution of TiB₂ and TiAl₃ phases and acting as a surface-active element to reduce nucleation work, thereby promoting heterogeneous nucleation. This work demonstrates that the CRE process is an effective route to fabricate high-performance La-bearing refiners with engineered microstructures and reveals that optimizing the Ti/La ratio is critical for maximizing grain refinement and mechanical performance in Al-Si alloys. Full article

Auxetic materials and structures exhibit negative values of Poisson’s ratio, which is the source of their unusual deformation pattern. Auxetic materials can be utilized in the development of multiphase materials with increased Young’s modulus by properly distributing the different phases in the volume of composite material and utilizing the auxetic effect. This work presents the results of an optimization of multiphase materials with an auxetic phase, with the aim of obtaining increased stiffness and near-zero lateral strain. Geometries of auxetic unit cells and conventional unit cells were subjected to optimization to obtain the desired values of effective material properties via multiscale modelling. Values of material properties of all considered phases were obtained via multiscale modelling of representative volume elements of their respective auxetic and conventional unit cells. Four types of unit cells and three types of inclusion patterns in the hybrid material sample were considered. The simulation results demonstrate that the application of an auxetic phase region in the multiphase material allows it to obtain effective Young’s modulus greater than that of component phases, as well as near-zero lateral strain during uniaxial tension of the sample. Increase of effective Young’s modulus and significant reduction of effective Poisson’s ratio of the sample were obtained in all considered optimization cases. Full article

This paper focuses on exploring the existence and uniqueness of solutions for a specific type of impulsive fractional-order complex-valued stochastic neural network within the complex domain, a topic hitherto undocumented. The combination of fractional order, stochastic nature, complex values, and impulses allows the model to seize memory-related, noise-resilient, phase-sensitive, and discontinuous dynamics. These dynamics are crucial for applications in neuroscience, signal processing, engineering control, and time-series prediction. In contrast to more simplistic models, this framework provides greater fidelity when simulating real-world systems and wider applicability without the need for redundant component splitting, thus justifying the requirement for such a comprehensive model. Leveraging the contraction mapping principle and contradiction, sufficient conditions are deduced to guarantee the existence and uniform stability (in the distribution sense) of solutions for the impulsive fractional-order complex-valued stochastic neural networks under study. Finally, a numerical example is presented to illustrate the feasibility and precision of our findings. Full article

Proton exchange membrane fuel cells (PEMFCs) represent a new generation of clean and efficient energy conversion devices, demonstrating broad application prospects in transportation, distributed power generation, and other fields. The geometric configuration of the cathode gas channel (GC) and the surface microstructure of the gas diffusion layer (GDL) are core factors influencing the efficiency of reactant gas transport and water management performance. However, conventional rectangular flow channels suffer from insufficient convective enhancement and restricted oxygen supply beneath the fins. Furthermore, homogeneous GDLs exhibit limited diffusion and drainage capabilities, often leading to oxygen depletion and flooding downstream of the cathode, significantly limiting overall cell performance. To address these challenges, this study designs a novel centrally positioned fin-type barrier block. A three-dimensional multiphysics numerical model integrating GDL surface microporosity with the internal barrier block flow channels is constructed to systematically investigate the synergistic mechanisms of microporous topology and flow channel structure on two-phase flow distribution, oxygen mass transfer, and electrochemical performance. The results demonstrate that this model accurately captures the dynamic evolution of flow fields within the GDL. Compared to conventional structures, significant coupling effects exist between the GDL microporous structure and the novel barrier block. Their synergistic interaction forms multi-scale mass transfer enhancement and dewatering pathways, providing quantifiable optimization pathways and structural parameter references for high-power-density PEMFC cathode design. Full article

Hydrothermal liquefaction (HTL) is a thermochemical process by which biomass feedstocks are converted into bio-oil and multiple by-products, including aqueous co-product (ACP), gaseous co-product (GCP), and biochar. Bio-oil produced from food waste feedstocks represents a potential candidate for use in commercial waste-to-energy conversions. The objective of this study is to further develop this technology by investigating the product distribution and quality from the HTL of food waste feedstocks. Four food waste feedstocks were selected for analysis: brewery grains, pear lees, coffee grounds, and honeydew skins. Solids analysis was conducted on each as-received feedstock, with the results determining dilution ratios for optimizing water content for HTL (≥80%). HTL conversions were conducted at 300 °C with a retention time of 30 min. Biochar was measured after product filtration, while ACP and bio-oil were measured via liquid–liquid phase separation. Coffee grounds produced the highest percentage of bio-oil (0.460%) and biochar (9.96%), while pear lees produced the highest percentage of ACP (89.5%). After quantification, ACP was characterized for nutrient concentrations. The quality of the ACP differed significantly from values in the literature, highlighting the influence of feedstock type and reaction conditions on HTL product characteristics (in addition to distribution) and underscoring the need for further research to optimize co-product utilization and process efficiency. Full article