Search Results (12,089)

Wood polymer composites (WPCs) represent a rapidly growing class of sustainable materials, formed by combining lignocellulosic fibers with thermoplastic or thermoset polymeric matrices. This review summarizes the state of the art in WPC development, emphasizing the use of recyclable (or recycled) and biodegradable polymers as matrix materials. The integration of waste wood particles into the production of WPCs addresses global environmental challenges, including plastic pollution and deforestation, by offering an alternative to conventional wood-based and petroleum-based products. Key topics covered in the review include raw material sources, fiber pre-treatments, compatibilizers, mechanical performance, water absorption behavior, thermal stability and end-use applications. Full article

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article

Emerging transportation challenges necessitate cutting-edge technologies for real-time infrastructure and traffic monitoring. To create a dynamic digital twin for intersection monitoring, data gathering, performance assessment, and calibration of microsimulation software, this study presents a state-of-the-art platform that combines high-resolution LiDAR sensor data with VISSIM simulation software. Intending to track traffic flow and evaluate important factors, including congestion, delays, and lane configurations, the platform gathers and analyzes real-time data. The technology allows proactive actions to improve safety and reduce interruptions by utilizing the comprehensive information that LiDAR provides, such as vehicle trajectories, speed profiles, and lane changes. The digital twin technique offers unparalleled precision in traffic and infrastructure state monitoring by fusing real data streams with simulation-based performance analysis. The results show how the platform can transform real-time monitoring and open the door to data-driven decision-making, safer intersections, and more intelligent traffic data collection methods. Using the proposed platform, this study calibrated a VISSIM simulation network to optimize the driving behavior parameters in the software. This study addresses current issues in urban traffic management with real-time solutions, demonstrating the revolutionary impact of emerging technology in intelligent infrastructure monitoring. Full article

This study proposes an integrated framework that combines nonlinear stochastic vibration analysis with reliability assessment to address the safety issues of cable systems under damage conditions. First of all, a mathematical model of the damaged cable is established by introducing damage parameters, and its static configuration is determined. Using the Pearl River Huangpu Bridge as a case study, the accuracy of the analytical solution for the cable’s sag displacement is validated through the finite difference method (FDM). Furthermore, a quantitative relationship between the damage parameters and structural response under stochastic excitation is developed, and the nonlinear stochastic dynamic equations governing the in-plane and out-of-plane motions of the damaged cable are derived. Subsequently, a Gaussian Radial Basis Function Neural Network (GRBFNN) method is employed to solve for the steady-state probability density function of the system response, enabling a detailed analysis of how various damage parameters affect structural behavior. Finally, the First-Order and Second-Order Reliability Method (FORM/SORM) are used to compute the reliability index and failure probability, which are further validated using Monte Carlo simulation (MCS). Results show that the severity parameter η shows the highest sensitivity in influencing the failure probability among the damage parameters. For the system of the Pearl River Huangpu bridge, an increase in the damage extent δ from 0.1 to 0.4 can reduce the reliability-based service life of by approximately 40% under fixed values of the damage severity and location, and failure risk is highest when the damage is located at the midspan of the cable. This study provides a theoretical framework from the point of stochastic vibration for evaluating the response and associated reliability of mechanical systems; the results can be applied in practice with guidance for the engineering design and avoid potential damages of suspended cables. Full article

Screw propulsors have attracted increasing attention for their potential applications in amphibious vehicles and benthic robots, owing to their ability to perform both terrestrial and underwater locomotion. To investigate their hydrodynamic characteristics, a two-stage numerical analysis was carried out. In the first stage, steady-state simulations under various advance coefficients were conducted to evaluate the influence of key geometric parameters on propulsion performance. Based on these results, a representative configuration was then selected for transient analysis to capture unsteady flow features. In the second stage, a Detached Eddy Simulation approach was employed to capture unsteady flow features under three rotational speeds. The flow field information was analyzed, and the mechanisms of vortex generation, instability, and dissipation were comprehensively studied. The results reveal that the propulsion process is dominated by the formation and evolution of tip vortices, root vortices, and cylindrical wake vortices. As rotation speed increases, vortex structures exhibit a transition from ordered spiral wakes to chaotic turbulence, primarily driven by centrifugal instability and nonlinear vortex interactions. Vortex breakdown and energy dissipation are intensified downstream, especially under high-speed conditions, where vortex integrity is rapidly lost due to strong shear and radial expansion. This hydrodynamic behavior highlights the fundamental difference from conventional propellers, and these findings provide theoretical insight into the flow mechanisms of screw propulsion. Full article

The efficiency of electric motors is highly dependent on their operating temperature, with lower temperatures contributing to enhanced performance, reliability, and extended service life. This study presents a comprehensive review of state-of-the-art cooling technologies and evaluates their impact on the thermal behavior of a commercial motor–generator system in high-demand applications. A baseline model of a permanent magnet synchronous motor (PMSM) was developed using MotorCAD 2023${}^{\circledR }$ software, which was supported by reverse engineering techniques to accurately replicate the motor’s physical and thermal characteristics. Subsequently, multiple cooling strategies were simulated under consistent operating conditions to assess their effectiveness. These strategies include conventional axial water jackets as well as advanced oil-based methods such as shaft cooling and direct oil spray to the windings. The integration of these systems in hybrid configurations was also explored to maximize thermal efficiency. Simulation results reveal that hybrid cooling significantly reduces the temperature of critical components such as stator windings and permanent magnets. This reduction in thermal stress improves current efficiency, power output, and torque capacity, enabling reliable motor operation across a broader range of speeds and under sustained high-load conditions. The findings highlight the effectiveness of hybrid cooling systems in optimizing both thermal management and operational performance of electric machines. Full article

The impact mechanism of long-term creep in gas-containing coal on coal and gas outbursts has not been fully elucidated and remains insufficiently understood for the purpose of disaster engineering control. This investigation conducted triaxial creep experiments on raw coal specimens under controlled confining pressures, axial stresses, and gas pressures. Through systematic analysis of coal’s physical responses across different loading conditions, we developed and validated a novel creep damage constitutive model for gas-saturated coal through laboratory data calibration. The key findings reveal three characteristic creep regimes: (1) a decelerating phase dominates under low stress conditions, (2) progressive transitions to combined decelerating–steady-state creep with increasing stress, and (3) triphasic decelerating–steady–accelerating behavior at critical stress levels. Comparative analysis shows that gas-free specimens exhibit lower cumulative strain than the 0.5 MPa gas-saturated counterparts, with gas presence accelerating creep progression and reducing the time to failure. Measured creep rates demonstrate stress-dependent behavior: primary creep progresses at 0.002–0.011%/min, decaying exponentially to secondary creep rates below 0.001%/min. Steady-state creep rates follow a power law relationship when subject to deviatoric stress (R² = 0.96). Through the integration of Burgers viscoelastic model with the effective stress principle for porous media, we propose an enhanced constitutive model, incorporating gas adsorption-induced dilatational stresses. This advancement provides a theoretical foundation for predicting time-dependent deformation in deep coal reservoirs and informs monitoring strategies concerning gas-bearing strata stability. This study contributes to the theoretical understanding and engineering monitoring of creep behavior in deep coal rocks. Full article

The growing demand for sustainable plant-based dairy alternatives has spurred interest in valorizing agro-industrial byproducts like hazelnut cake, a protein-rich byproduct of oil extraction. This study developed formulations for vegan ice cream using unfermented (HIC) and Aspergillus oryzae-fermented hazelnut cake (FHIC), comparing their physicochemical, functional, and sensory properties to conventional dairy ice cream (DIC). Solid-state fermentation (72 h, 30 °C) enhanced the cake’s bioactive properties, and ice creams were characterized for composition, texture, rheology, melting behavior, antioxidant activity, and enzyme inhibition pre- and post-in vitro digestion. The results indicate that FHIC had higher protein content (64.64% vs. 58.02% in HIC) and unique volatiles (e.g., benzaldehyde and 3-methyl-1-butanol). While DIC exhibited superior overrun (15.39% vs. 4.01–7.00% in vegan samples) and slower melting, FHIC demonstrated significantly higher post-digestion antioxidant activity (4.73 μmol TE/g DPPH vs. 1.44 in DIC) and angiotensin-converting enzyme (ACE) inhibition (4.85–7.42%). Sensory evaluation ranked DIC highest for overall acceptability, with FHIC perceived as polarizing due to pronounced flavors. Despite textural challenges, HIC and FHIC offered nutritional advantages, including 18–30% lower calories and enhanced bioactive compounds. This study highlights fermentation as a viable strategy to upcycle hazelnut byproducts into functional vegan ice creams, although the optimization of texture and flavor is needed for broader consumer acceptance. Full article

V1413 Aql is an anomalous classical symbiotic star that has remained in an active state almost continuously. After failing to return to quiescence following the 2020 outburst, it underwent a new flare in 2025. We present an analysis of new photometric and spectroscopic observations, which provide estimates for the binary components at different stages of activity: $L_{\mathrm{cool}}=1700$${\mathrm{L}}_{\odot }$, $R_{\mathrm{cool}}=130$${\mathrm{R}}_{\odot }$; $L_{\mathrm{hot}}=800$${\mathrm{L}}_{\odot }$, $R_{\mathrm{hot}}=28$${\mathrm{R}}_{\odot }$ during minimum; $L_{\mathrm{hot}}=7000$${\mathrm{L}}_{\odot }$, $R_{\mathrm{hot}}=64$${\mathrm{R}}_{\odot }$ at the 2025 maximum. The significant increase in the luminosity of the hot component during the 2025 outburst deviates from the typical behavior observed in classical symbiotic stars, where outbursts are generally attributed to a redistribution of spectral energy rather than intrinsic changes in luminosity. Full article

Barium titanate (BaTiO₃) has long been recognized as a promising photocatalyst for solar-driven water splitting due to its unique ferroelectric, piezoelectric, and electronic properties. This review provides a comprehensive analysis of atomistic simulation studies of BaTiO₃, highlighting the role of density functional theory (DFT), ab initio molecular dynamics (MD), and classical all-atom MD in exploring its photocatalytic behavior, in line with various experimental findings. DFT studies have offered valuable insights into the electronic structure, density of state, optical properties, bandgap engineering, and other features of BaTiO₃, while MD simulations have enabled dynamic understanding of water-splitting mechanisms at finite temperatures. Experimental studies demonstrate photocatalytic water decomposition and certain modifications, often accompanied by schematic diagrams illustrating the principles. This review discusses the impact of doping, surface modifications, and defect engineering on enhancing charge separation and reaction kinetics. Key findings from recent computational works are summarized, offering a deeper understanding of BaTiO₃’s photocatalytic activity. This study underscores the significance of advanced multiscale simulation techniques for optimizing BaTiO₃ for solar water splitting and provides perspectives on future research in developing high-performance photocatalytic materials. Full article

Motor vehicle crashes are a persistent cause of unintentional deaths in the United States. Scholarship on how manmade interventions and natural phenomena interact to effectuate such calamitous outcomes is longstanding. One manmade intervention of interest in the literature is daylight saving time (DST). Unfortunately, results on how the natural phenomena attributable to DST interact with driver behavior are inconsistent. To advance knowledge on DST-safety interactions, this study adopts a multilevel model approach to fatal motor vehicle crash outcomes in the contiguous United States. Results from a national analysis contextualize results from zonal analyses to unmask within- and between-time zone differences in DST-safety interactions. In the national analysis, motor vehicle crash fatalities decrease somewhat during DST (−0.10%). In the zonal analyses, motor vehicle crash fatalities decrease more so in the Central and Eastern time zones (−2.00% and −2.00%, respectively), but increase somewhat in the Pacific and Mountain time zones (+0.30%) during DST. The spatiotemporal context of the national analysis highlights specific policy implications from the zonal analyses to decrease the lethality of motor vehicle crashes. Specifically, interdictions to target alcohol and/or drug involvement in the northern latitudes of the Pacific and Mountain time zones during DST, the Central time zone at dawn or dusk before or after DST, and the northern latitudes in the Eastern time zone before or after DST are important. Generally, national DST-safety benefits mask zonal DST-safety costs in the Pacific and Mountain time zones. Full article

Knowledge tracing (KT), a core educational data mining task, models students’ evolving knowledge states to predict future learning. In online education systems, the exercises are numerous, but they are typically associated with only a few concepts. However, existing models rarely integrate exercise information with high-order exercise–concept correlations, focusing solely on optimizing models’ final predictive performance. To address these limitations, we propose the Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism (HGKT), a novel framework that (1) captures correlations between exercises and concepts through a two-layer hypergraph convolution; (2) integrates hypergraph-driven exercise embedding and temporal features (answer time and interval time) to characterize learning behavioral dynamics; and (3) designs a learning layer and a forgetting layer, with the dual-gating mechanism dynamically balancing their impacts on the knowledge state. Experiments on three public datasets demonstrate that the proposed HGKT model achieves superior predictive performance compared to all baselines. On the longest interaction sequence dataset, ASSISChall, HGKT improves prediction AUC by least 1.8%. On the biggest interaction records dataset, EdNet-KT1, it maintains a state-of-the-art AUC of 0.78372. Visualization analyses confirm its interpretability in tracing knowledge state evolution. These results validate HGKT’s effectiveness in modeling high-order exercise–concept correlations while ensuring practical adaptability in real-world online education platforms. Full article

We investigate serval models for two scalar fields in one space dimension with topologically stable solitons that are constructed from BPS equations. The asymptotic behavior of these solitons fully determines their classical energies. A particular feature of the considered models is that there are several translationally invariant ground states that we call primary and secondary vacua. The former are those that are asymptotically assumed by the solitons. Solitons that occupy a secondary vacuum in finite but eventually large portions of space are classically degenerate. Thus the quantum contributions to the energies are decisive for the energetically favored soliton. While some of these solitons were constructed previously, we, for the first time, compute the leading (one-loop) quantum contribution their energies. In all cases considered we find that this contribution is not bounded from below and that it is the more negative the larger the region is in which the soliton approaches a secondary vacuum. This corroborates the conjecture, earlier inferred from the Shifman-Voloshin soliton, that the availability of secondary vacua destabilizes these solitons on the quantum level. Full article

This study examines the energy behavior of a strengthening system consisting of a Fiber Reinforced Polymer (FRP) plate bonded to a rigid substrate and subjected to tensile loading, where the adhesive interface is governed by a bilinear bond–slip law with a vertical descending branch. The investigation focuses on the interaction between the elastic energy stored in the FRP and the adhesive interface, as well as the characteristic lengths that control the debonding process. Analytical expressions for the strain energy stored in both the FRP plate and the adhesive interface are derived, enabling the identification and evaluation of two critical characteristic lengths as the bond stress at the loaded end approaches its maximum value $l_c$, at which the elastic energies of the FRP and the adhesive interface converge, signaling energy saturation; and $l_{max}$, where the adhesive interface attains its peak energy absorption. Upon reaching the energy saturation state, the system undergoes failure through the sudden and complete debonding of the FRP from the substrate. The onset of unstable debonding is rigorously analyzed in terms of the first and second derivatives of the total potential energy with respect to the bond length. It is further demonstrated that abrupt debonding may also occur in cases where the length exceeds $l_c$ when the bond stress reaches its maximum, and the bond–slip law is characterized by a vertical branch. The findings provide significant insights into the energy balance and stability criteria governing the debonding failure mode in FRP-strengthened structures, highlighting the pivotal role of characteristic lengths in predicting both structural performance and failure mechanisms. Full article

Background: Chlamydia trachomatis is the most prevalent bacterial sexually transmitted infection (STI) globally, linked to severe complications such as pelvic inflammatory disease and infertility. In the Brazilian Amazon, socioeconomic vulnerability and the absence of screening policies exacerbate risks, particularly among female sex workers (FSWs). Objective: This study aimed to determine the seroprevalence of anti-C. trachomatis IgG antibodies among FSWs in five municipalities of Pará State, Brazilian Amazon, and identify epidemiological factors associated with infection. Methods: A retrospective cross-sectional study (2005–2007) included 348 FSWs recruited via convenience sampling. Sociodemographic and behavioral data were collected through questionnaires, and blood samples were analyzed by ELISA for anti-C. trachomatis IgG. Statistical analyses included Fisher’s exact tests, odds ratios (ORs), and 95% confidence intervals (CIs), using SPSS 21.0. Results: Overall seroprevalence was 93.9% (327/348; 95% CI: 83.1–90%). Significant associations included a household income of 1–3 minimum wages (98.4%; p = 0.0002), sexual partners from the same region (98.8%; p = 0.0421), and age >42 years (96.3%). Most reported inconsistent condom use (43.7%), multiple monthly partners (54.6%), and illicit drug use (53.4%). Discussion: The extremely high seroprevalence reflects chronic C. trachomatis exposure, driven by socioeconomic deprivation and limited healthcare access. Comparisons with global data underscore the urgent need for screening policies, absent in Brazil for FSWs, and highlight the vulnerability of this population. Conclusions: The findings reveal an alarming burden of C. trachomatis exposure among Amazonian FSWs. Integrated strategies, including routine screening, sexual health education, and inclusion of FSWs in priority health programs, are critical to reduce transmission and associated complications. Full article