Search Results (8,098)

With the rapid development of edge computing and deep learning, the efficient deployment of deep neural networks (DNNs) on resource-constrained terminal devices faces multiple challenges (background), such as execution delay, high energy consumption, and resource allocation costs. This study proposes an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for PSO. Unlike the conventional PSO, our approach integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy to overcome its limitations in this application scenario. First, an end–edge–cloud collaborative computing framework is constructed. Within this framework, a multi-objective optimization model is established, aiming to minimize time delay, energy consumption, and cloud configuration cost. To solve this model, an optimization method is designed that integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy into the MOPSO algorithm. Experiments on six types of DNNs, including the Visual Geometry Group (VGG) series, have shown that this algorithm reduces execution time by an average of 58.6%, the average energy consumption by 61.8%, and optimizes cloud configuration costs by 36.1% compared to traditional offloading strategies. Its Global Search Capability Index (GSCI) reaches 92.3%, which is 42.6% higher than the standard PSO algorithm. This method provides an efficient, secure, and stable cooperative computing solution for multi-constraint task unloading in an edge computing environment. Full article

Deep-seated tumors are challenging pathologies to treat. Currently available approaches are limited, prompting innovative solutions. Hyperthermia treatment (HT) is a thermal oncological therapy that raises tumor temperature (40–44 °C for 60 min), enhancing radio- and chemotherapy. Biomaterials loaded with magnetic particles, called magnetic scaffolds (MagSs), are used as HT agents for cancer treatment using radiofrequency (RF) heating. MagSs can be manufactured via 3D printing using fused deposition modeling to create biomimetic architectures based on triply periodic minimal surfaces (TPMSs). TPMS-based MagSs have been tested in vitro for RF HT. However, there is a lack of understanding regarding the thermal properties of TPMS MagSs for RF hyperthermia. Significant discrepancies between simulated and measured temperatures have been reported, attributed to limited knowledge of the apparent thermal conductivity of MagSs. Since planning is crucial for HT, it is fundamental to determine the thermal properties of these heterogeneous and porous composite biomaterials. Magnetic polylactic acid (PLA) scaffolds, shaped in different TPMS geometries and variable porosities, were thermally investigated in this research study. A linear relationship was found between the apparent thermal conductivity of parallelepiped and cylindrical scaffolds, and the measured values were validated using a numerical model of the RF HT test. Full article

This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development. Full article

The rapid shift toward electric vehicles (EVs) has underscored the critical importance of battery pack crashworthiness, creating a demand for lightweight, energy-absorbing protective systems. This review systematically explores bio-inspired cellular structures as promising solutions for improving the impact resistance of EV battery packs. Inspired by natural geometries, these designs exhibit superior energy absorption, controlled deformation behavior, and high structural efficiency compared to conventional configurations. A comprehensive analysis of experimental, numerical, and theoretical studies published up to mid-2025 was conducted, with emphasis on design strategies, optimization techniques, and performance under diverse loading conditions. Findings show that auxetic, honeycomb, and hierarchical multi-cell architectures can markedly enhance specific energy absorption and deformation control, with improvements often exceeding 100% over traditional structures. Finite element analyses highlight their ability to achieve controlled deformation and efficient energy dissipation, while optimization strategies, including machine learning, genetic algorithms, and multi-objective approaches, enable effective trade-offs between energy absorption, weight reduction, and manufacturability. Persistent challenges remain in structural optimization, overreliance on numerical simulations with limited experimental validation, and narrow focus on a few bio-inspired geometries and thermo-electro-mechanical coupling, for which engineering solutions are proposed. The review concludes with future research directions focused on geometric optimization, multi-physics modeling, and industrial integration strategies. Collectively, this work provides a comprehensive framework for advancing next-generation crashworthy battery pack designs that integrate safety, performance, and sustainability in electric mobility. Full article

Reliable prediction of chemical–protein interactions (CPIs) remains a key challenge in drug discovery, especially under sparse or noisy biological data. We present MM-TCoCPIn, a Multi-Modal Topology-aware Chemical–Protein Interaction Network that integrates three causally grounded modalities—network topology, biomedical semantics, and a 3D protein structure—into an interpretable graph learning framework. The model processes topological features via a CTC (Comprehensive Topological Characteristics)-based encoder, literature-derived semantics via SciBERT (Scientific Bidirectional Encoder Representations from Transformers), and structural geometry via a GVP-GNN (Geometric Vector Perceptron Graph Neural Network) applied to AlphaFold2 contact graphs. Evaluation on datasets from STITCH, STRING, and PubMed shows that MM-TCoCPIn achieves state-of-the-art performance (AUC = 0.93, F1 = 0.92), outperforming uni-modal baselines. Importantly, ablation and counterfactual analyses confirm that each modality contributes distinct biological insight: topology ensures robustness, semantics enhance recall, and structure sharpens precision. This framework offers a scalable and causally interpretable solution for CPI modeling, bridging the gap between predictive accuracy and mechanistic understanding. Full article

The Integrated Choice and Latent Variable (ICLV) model has been widely applied in travel behavior studies, yet its use in understanding pedestrian route choice remains very limited. This paper seeks to address this gap by analyzing data from a series of controlled pedestrian route choice experiments. Four groups of experimental runs were designed, each involving two route options. The first three groups introduced specific controls: bottlenecks, distance constraints, and extra rewards, while the fourth group, without any imposed control, focused on the influence of route geometry (lengths and widths). For each group, we developed measurement and structural models, followed by three comparative models: a binary logit model using only measured variables (MV model), a model using only latent variables (LV model), and the ICLV model that integrates both. Across all the four scenarios, the adjusted R² values have been improved from 0.286/0.135/0.108/0.035 (MV model) to 0.329/0.161/0.111/0.056 (ICLV model), and the ICLV model can provide interpretable results. These findings highlight the value of incorporating latent constructs based on Structural Equation Modelling (SEM), which enhances the explanatory power of pedestrian route choice models. Moreover, the differences in significant latent variables across various experimental settings offers further insights into the distinct mechanisms underlying pedestrian decision-making under varying conditions. Full article

Active vibration control designs for journal bearings have improved rotordynamic stability and led to advancements in adjustable bearing types that enable precise control of bearing geometry. In this study, optimized machine learning (ML) algorithms were modeled and implemented to accurately predict the static performance envelope of a four-pad active journal bearing with features of controlling the radial and tilt positions of pads in real time. ML models developed for the adjustable bearing system help predict its behavior as a function of three key input parameters such as the eccentricity ratio and radial and tilt positions of pads. Four supervised regression models, such as Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a feedforward Artificial Neural Network (ANN), were chosen for their demonstrated ability to capture complex nonlinear patterns and their robustness against overfitting in such tribological applications. Hyperparameter tuning for each model was performed using the Optuna framework, which applies Bayesian optimization to efficiently determine the best parameter settings. The Optuna-optimized ensemble and neural network models were used to identify the optimal combinations of input variables that maximize the static performance envelope of the active bearing system with geometric adjustments. Full article

The article deals with the evaluation of dimensional deformations of a building element manufactured additively from a cement mixture. The study follows up on previous research within the 3DStar project and expands the methodology for monitoring deformations over time. The aim is to contribute to the development of more accurate simulation models for predicting the behaviour of printed structures, especially in the early stages after printing. For the analysis, an experimental ‘L’-shaped element was designed and printed, whose deformations were monitored using repeated 3D scanning and dimensional changes were evaluated for up to 93 days. The results show that the most significant deformations occur in the first hours after printing due to gravitational loading and mixture curing, while later changes are mainly due to shrinkage. The element’s geometry and the walls’ thickness also play a role. The analysis confirms the effectiveness of the ‘Caliper’ measurement method and outlines the potential for future use of photogrammetry as a method for online deformation monitoring. The data obtained will be used to optimise printing parameters and calibrate material parameters in the developed simulation software for non-linear numerical simulations in additive manufacturing using cement mixtures. Full article

Machine learning models and web-based tools have been developed for predicting key properties of imidazolium-based ionic liquids. Two high-quality datasets containing experimental density and viscosity values at 298 K were curated from the ILThermo database: one containing 434 systems for density and another with 293 systems for viscosity. Molecular structures were optimized using the GOAT procedure at the GFN-FF level to ensure chemically realistic geometries, and a diverse set of molecular descriptors, including electronic, topological, geometric, and thermodynamic properties, was calculated. Three support vector regression models were built: two for density (IonIL-IM-D1 and IonIL-IM-D2) and one for viscosity (IonIL-IM-V). IonIL-IM-D1 uses three simple descriptors, IonIL-IM-D2 improves accuracy with seven, and IonIL-IM-V employs nine descriptors, including DFT-based features. These models, designed to predict the mentioned properties at room temperature (298 K), are implemented as interactive applications on the atomistica.online platform, enabling property prediction without coding or retraining. The platform also includes a structure generator and searchable databases of optimized structures and descriptors. All tools and datasets are freely available for academic use via the official web site of the atomistica.online platform, supporting open science and data-driven research in molecular design. Full article

3D bioprinting has emerged as a key tool in tissue engineering by facilitating the creation of customized scaffolds with properties tailored to specific needs. Among the design parameters, porosity stands out as a determining factor, as it directly influences critical mechanical and biological properties such as nutrient diffusion, cell adhesion and structural integrity. This review comprehensively analyses the state of the art in scaffold design, emphasizing how porosity-related parameters such as pore size, geometry, distribution and interconnectivity affect cellular behavior and mechanical performance. It also addresses advances in manufacturing methods, such as additive manufacturing and computer-aided design (CAD), which allow the development of scaffolds with hierarchical structures and controlled porosity. In addition, the use of computational modelling, in particular finite element analysis (FEA), as an essential predictive tool to optimize the design of scaffolds under physiological conditions is highlighted. This narrative review analyzed 112 core articles retrieved primarily from Scopus (2014–2025) to provide a comprehensive and up-to-date synthesis. Despite recent progress, significant challenges persist, including the lack of standardized methodologies for characterizing and comparing porosity parameters across different studies. This review identifies these gaps and suggests future research directions, such as the development of unified characterization and classification systems and the enhancement of nanoscale resolution in bioprinting technologies. By integrating structural design with biological functionality, this review underscores the transformative potential of porosity research applied to 3D bioprinting, positioning it as a key strategy to meet current clinical needs in tissue engineering. Full article

In the process of heat contact sealing of thin, flexible polymer films, the choice of the film material, the layer structure, the sealing tools, and the process parameters influence the melt flow. A pronounced melt flow dynamic, which is to be expected in industrial applications due to the high temperatures and pressures, favors the formation of sealing edges that negatively affect the user-friendliness when opening the packaging. In this study, the influence of seal seam formation on the opening behavior was systematically investigated by numerical simulation. A detailed model was developed that simulates the seam opening process, accounting for the composite structure, the detailed geometry of the seal seam, and the separation process. The numerical results of the force-offset behavior showed good agreement with experimental data. Parameter studies revealed that the thicker and more pronounced seal seams lead to higher tear-off forces, while thinner and less pronounced seams result in lower forces. These findings provide valuable insights into the interactions between seam formation and the mechanical behavior of flexible films, enabling the optimization of sealing processes for improved package performance. Full article

Nowadays, there is an emphasis on reducing emissions due to industrial processes. In recent decades, filtration systems have become an integral part of the broadly understood heavy industry systems to reduce the emission of dust and other substances harmful to the environment and humans. Filters can also be found in heating, ventilation and air conditioning (HVAC) systems, in the transport industry, and their use in households is also increasing. The effective separation of micro- or nanometer contaminants is closely related to the development of new, sophisticated filter materials. Thanks to the use of modern tools for multiphase flow modeling, it becomes possible to model the flow inside the filter material. In this study, we propose a methodology to simulate the internal flow through porous structures with a fiber size of 5–30 µm. The geometry used to build the mathematical model is the actual geometry of the filter obtained using micro-Computed Tomography (CT) imaging method. The mathematical model has been validated against experimental data. In this article, we show the methodology to adapt a geometry scan for use in commercial Computational Fluid Dynamics (CFD) software (Ansys Fluent 2021 R1). Then we present the analysis of the influence of essential parameters of numerical model, namely the size of representative elementary volume (REV) of porous material, representation quality of porous matrix and numerical mesh density on the pressure drop in the filter. Based on the conducted research, the minimum size of the REV and the numerical mesh density were determined, allowing us to obtain a representative solution of the flow structure through the filtering material. The strong agreement between the model results and experimental data highlights the potential of using a multi-fluid mathematical model to understand filtration dynamics. Full article

This paper presents the design of a permanent-magnet-assisted synchronous reluctance motor (PMa-SynRM) for compressor applications using Sm-series injection-molded magnets that eliminate heavy rare-earth elements. The high shape flexibility of the injection-molded magnets enables the formation of a curved multi-layer flux-barrier rotor geometry based on the Joukowski airfoil potential, optimizing magnetic flux flow under typical compressor operating conditions. Furthermore, electromagnetic performance, irreversible demagnetization behavior, and rotor stress sensitivity were analyzed with respect to key design variables to derive a model that satisfies the target performance requirements. The validity of the proposed design was confirmed through finite element method (FEM) comparisons with a conventional IPMSM using sintered NdFeB magnets, demonstrating the feasibility of HRE-free PMa-SynRM for high-performance compressor drives. Full article

Accurate estimation of urban land surface temperature (ULST) is critical for studying urban heat islands, but complex three-dimensional (3D) structures and materials in urban areas introduce significant adjacency effects into remote sensing retrievals. To investigate the influence of different factors on the adjacency effects, this study employed the DART model to quantify brightness temperature differences (ΔTb) of urban pixels by comparing their simulated radiance in two scenarios: (1) an isolated state (no adjacent buildings) and (2) an adjacent state (with surrounding buildings). ΔTb, representing the adjacency effect, was systematically analyzed across spatial resolutions (1–120 m), building geometry (building height BH, roof area index ${\lambda }_p$, adjacent obstruction degree SVF_Obs.), and material reflectance (reflectance R = 0.05, 0.1, 0.15) to determine key influencing factors. The results demonstrate that (1) adjacency effects intensify significantly with higher spatial resolution (mean ΔTb ≈ 5 K at 1 m vs. ≈2 K at 30 m), with 60–90 m identified as the critical resolution range where the adjacency-induced error is attenuated to a level (ΔTb < 1 K) that is commensurate with the intrinsic uncertainty of current mainstream ULST algorithms; (2) increased building height, reduced density (${\lambda }_p$), and greater adjacent obstruction (SVF_Obs.) exacerbate adjacency effects; (3) material emissivity (ε = 1 − R) is the dominant factor, where low-ε materials (high R) exhibit markedly stronger adjacency effects than geometric influences (e.g., ΔTb at R = 0.15 is approximately three times higher than at R = 0.05); and (4) temperature differences among surface components exert minimal influence on adjacency effects (ΔTb < 0.5 K). This study clarifies key factors driving adjacency effects in high-resolution ULST retrieval and defines the critical spatial resolution for simplifying inversions, providing essential insights for accurate urban temperature estimation. Full article

In the presented work, the main challenge of small hydropower plants—converting low river flow velocities into high generator rotations—is investigated. It was established that applying the flow acceleration effect during interaction with surfaces makes it possible to increase the power output of a small hydropower plant by up to 25%, which corresponds to the level of an innovative solution. Stationary flow amplifiers and their influence on the dynamic interaction of blades were studied. It was revealed that the use of the amplification effect in paired configurations contributes to achieving a multiplicative effect. The potential of small hydropower plants was analytically evaluated, taking into account their dimensions and gear systems. The study was carried out using the method of computational fluid dynamics (CFD), which enables the modeling of complex hydrodynamic processes. Based on the developed three-dimensional model of the object and its discretization into a computational mesh, boundary conditions were set, and the finite volume method was applied to solve the Navier–Stokes equations. To account for turbulent flows, the k-epsilon turbulence model was employed. Full article