Search Results (12,819)

This paper investigates the rotational lift production of translating and rotating wings within a small insect’s Reynolds number range. Using the Reynolds number 1200 of a bumblebee, three wing section profiles were studied: a circular cylinder model as a reference for a blunt body for which the well-known Magnus effect will occur, a flat plate model as a reference for a sharp body for which the Kramer effect will occur, and finally, an elliptical cylinder model as a transition case. Direct force measurement and particle image velocimetry (PIV) experiments were performed to measure the lift produced and the surrounding flow velocity, and the Kutta–Joukowski theorem was applied to analyze the PIV results. The Kutta–Joukowski theorem gives the relationship between lift and circulation on a body moving at constant speed in a real fluid with some constant density. The experimental results were analyzed and verified by comparing them to the computational results. In general, there is reasonable agreement between the experimental and computational results, confirming that the Magnus effect is observed for the circular cylinder model and no Kramer effect is observed for the flat plate model. The elliptical cylinder model does not appear to be blunt enough for the Magnus effect to occur, and it is not sharp enough for the Kramer effect to occur. Full article

Cleated footwear in football increasingly incorporates sex-specific design features intended to address a clear gap in anthropometric and biomechanical differences in female athletes. However, experimental evidence evaluating how these designs may influence lower-extremity biomechanics during sport tasks in female athletes remains limited. The purpose of this exploratory pilot study was to examine the effects of sex-specific footwear on lower-extremity biomechanics, plantar pressure distribution, and perceived comfort in female football players during unanticipated side-step cutting. The study used a controlled laboratory-based repeated measures design. Twenty female football players performed unanticipated side-step cutting tasks in two randomized footwear conditions: a standard commercially available control cleat (CT) and a female-specific prototype cleat (PT). Ankle and knee biomechanics, in-shoe pressure distribution, and subjective comfort ratings were assessed. Compared with the CT, the PT cleat had reduced peak ankle inversion angle, inversion angular velocity, and inversion moment, indicating altered ankle biomechanics during cutting. No differences were observed in knee abduction between footwear conditions. However, participants subjectively rated greater comfort in CT compared to PT. Peak pressure was higher in the midfoot and central forefoot in the PT footwear compared to the CT. Given the pilot nature of the study, with multiple footwear alterations, the findings should be interpreted as hypothesis-generating and used to inform future targeted investigations. Full article

A kinetic and thermodynamic multi-phase model has been developed to describe the adsorption of gases on ice surfaces and their subsequent diffusional loss into the bulk ice phase. This model comprises a gas phase, a solid surface, a sub-surface layer, and the ice bulk. The processes represented include gas adsorption on the surface, solvation into the sub-surface layer, and diffusion in the ice bulk. It is assumed that the gases dissolve according to Henry’s law, while the surface concentration follows the Langmuir adsorption equilibrium. The flux of molecules from the sub-surface layer into the ice bulk is treated according to Fick’s second law. Kinetic and thermodynamic quantities as applicable to the uptake of small carbonyl compounds on ice surfaces at temperatures around 200 K have been used to perform model calculations and corresponding sensitivity tests. The primary application in this study is acetic acid. The model simulations are applied by fitting the experimental data obtained from coated-wall flow-systems (CWFT) measurements, with the best curve-fit solutions providing reliable estimations of kinetic parameters. Over the temperature range from 190 to 220 K, the estimated desorption coefficient, k_des, varies from 0.02 to 1.35, while adsorption rate coefficient, k_ads, ranges from 3.92 and 4.17, and the estimated diffusion coefficient, D, changes by more than two orders of magnitude, increasing from 0.03 to 13.0. Sensitivity analyses confirm that this parameter estimation approach is robust and consistent with underlying physicochemical processes. It is shown that for shorter exposure times the loss of molecules from the gas phase is caused exclusively by adsorption onto the surface and solvation into the sub-surface layer. Diffusional loss into the bulk, on the other hand, is only important at longer exposure times. The model is a useful tool for elucidating surface and bulk process kinetic parameters, such as adsorption and desorption rate constants, solution and segregation rates, and diffusion coefficients, as well as the estimation of thermodynamic quantities, such as Langmuir and Henry constants and the ice film thickness. Full article

Low work function (LWF) materials are essential for enabling efficient systems’ behavior in applications ranging from vacuum electronics to energy conversion devices and next-generation opto-electronic interfaces. Recent advances in theory, characterization, and materials engineering have dramatically expanded the candidates for LWF systems, including alkali-based compounds, perovskites, borides, nitrides, barium and scandium oxides, 2D materials, MXenes, functional polymers, carbon materials, and hybrid architectures. This review provides a comprehensive overview of the fundamental mechanisms governing the work function (WF) and discusses the state-of-the-art measurement techniques, as well as the most used computational approaches for predicting and validating WF values. The recent breakthroughs in engineering LWF surfaces through different methods are discussed. Special emphasis is placed on the relationship between predicted and experimentally measured WF values, highlighting the role of surface contamination, reconstruction, and environmental stability. Performance, advantages, and limitations of major LWF material families are fully analyzed, identifying emerging opportunities for next applications. Finally, current and fundamental challenges in achieving scalable, stable, and reproducible LWF surfaces are considered, presenting promising research directions such as high-throughput computational discovery and in situ surface engineering with protective coatings. This review aims to provide a unified framework for understanding, achieving, and advancing LWF materials toward practical and industrially relevant technologies. Full article

Measuring technology is used in various ways in the logistics industry for defect inspection and loading optimization. Recently, in the context of the fourth industrial revolution, research has focused on measurement automation combining AI, IoT technologies, and measuring equipment. The 3D scanner used for field logistics measurements offers high performance and can handle large volumes quickly; however, its high unit price limits adoption across all lines. Entry-level sensors are challenging to use due to measurement reliability issues: their performance varies with changes in object location, shape, and logistics environment. To bridge this gap, this study proposes a systematic framework for geometry measurement that enables reliable length and width estimation using only a single entry-level distance sensor. We design and build a conveyor-belt-based data acquisition setup that emulates realistic logistics transfer scenarios and systematically varies transfer conditions to capture representative measurement disturbances. Based on the collected data, we perform robust feature extraction tailored to noisy, condition-dependent signals and train an artificial neural network to map sensor observations to geometric dimensions. We then verified the model’s performance in measuring object length and width using test data. The experimental results show that the proposed method provides reliable measurement results even under varying transfer conditions. Full article

This paper proposes a low-cost measurement system that can be used to perform simultaneous blood pressure (BP) and breathing (BR) measurements. Regarding BP measurements, the main parameters that are accessed include systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure blood pressure (MAP), and heartbeat rate (HR). Concerning BR measurements, the main parameters that are accessed include the inspiration period and amplitude (IPA), the expiration period and amplitude (EPA), and the breathing rate (BR), as well as the statistical and standard deviation of all these parameters. The dual measurement capability of the proposed measurement system is very important since blood pressure and breathing parameters are not statistically independent and it is possible to obtain additional and valuable clinical information from the information provided by both biomedical variables when measured simultaneously. The analysis of the correlation between these variables is particularly important after performing intensive physical exercises, since it enables cardiac rehabilitation assessment, pre-surgical risk evaluation, detection of silent ischemia, and monitoring of chronic diseases recovery, among others. Regarding the performance evaluation of the proposed biomedical device, a prototype of the measurement system was developed, tested, and calibrated. Several experimental tests were carried out to evaluate the performance of the proposed measurement system and to obtain the correlation coefficients between different blood pressure and breathing parameters. The tests were based on a statistically significant number of measurements that were performed with a population that integrated twenty students in two groups with different habits of physical exercise practice but subjected to a set of common physical exercises, with graduated intensity levels. Full article

Spunbonding drafters play a decisive role in determining fiber attenuation, morphology, and final nonwoven quality; however, their internal airflow behavior remains poorly characterized due to limited physical accessibility and historically empirical design practices. This work employs high-fidelity computational fluid dynamics (CFD) to systematically resolve the airflow field inside a laboratory-scale drafter and to quantify the impact of geometry on fiber drawing conditions. The simulations reveal a previously unreported “braking effect,” where adverse flow structures reduce effective shear drag, limit drawability, and increase the likelihood of fiber breakage. Parametric virtual experimentation across seven geometric variables demonstrates that the drafter configuration strongly governs shear distribution, flow uniformity, and energy consumption. Using a performance-oriented optimization framework, three key processing objectives were targeted: (i) maximizing shear drag to promote stable fiber attenuation, (ii) improving axial drawing uniformity, and (iii) minimizing pressurized-air demand. CFD-guided design modifications—including controlled widening, tailored wall divergence and convergence, and an extensible lower section—were implemented and subsequently validated using a newly constructed prototype. Experimental measurements on polypropylene (PP) and high-density polyethylene (HDPE) fibers confirm substantial reductions in fiber breakage and improvements in drawing stability, thereby demonstrating the effectiveness of simulation-driven process optimization in spunbonding equipment design. Full article

Deep neural networks are vulnerable and susceptible to adversarial examples, which can induce erroneous predictions by injecting imperceptible perturbations. Transferability is a crucial property of adversarial examples, enabling effective attacks under black-box settings. Adversarial examples at flat maxima-those around which the loss peaks and grows slowly-have been demonstrated to exhibit higher transferability. Existing methods to achieve flat maxima rely on the gradient of the worst-case loss within the small neighborhood around the adversarial point. However, the neighborhood structure is typically defined as a Euclidean space, which neglects the input space’s information geometry, leading to suboptimal results. In this work, we build upon the idea of flat maxima but extend the neighborhood structure from Euclidean space to the manifold measured by the Fisher metric, which takes into account the information geometry of the data space. In the non-Euclidean case, we search for the worst-case point in the direction of the natural gradient with respect to adversarial examples. The natural gradient adjusts the original gradient using the Fisher information matrix, giving the steepest direction in the manifold. Furthermore, to reduce the computational cost of calculating the Fisher information matrix, we introduce a diagonal approximation of the matrix and propose an empirical Fisher method under the model ensemble setting. Experimental results demonstrate that our proposed manifold extensions significantly enhance attack success rates against both normally and adversarially trained models. In particular, compared to methods relying on the Euclidean metric, our approach demonstrates more efficient performance. Full article

As Battery Management Systems (BMSs) continue to expand in both scale and capacity, conventional state-of-charge (SOC) estimation methods—such as Coulomb counting and model-based observers—face increasing challenges in meeting the requirements for cell-level precision, scalability, and adaptability under aging and operating variability. To address these limitations, this study integrates a Deep Neural Network (DNN)–based estimation framework into a node-level BMS architecture, enabling edge-side computation at each individual battery cell. The proposed architecture adopts a decentralized node-level structure with distributed parameter synchronization, in which each BMS node independently performs SOC estimation using shared model parameters. Global battery characteristics are learned through offline training and subsequently synchronized to all nodes, ensuring estimation consistency across large battery arrays while avoiding centralized online computation. This design enhances system scalability and deployment flexibility, particularly in high-voltage battery strings with isolated measurement requirements. The proposed DNN framework consists of two identical functional modules: an offline training module and a real-time estimation module. The training module operates on high-performance computing platforms—such as in-vehicle microcontrollers during idle periods or charging-station servers—using historical charge–discharge data to extract and update battery characteristic parameters. These parameters are then transferred to the real-time estimation chip for adaptive SOC inference. The decentralized BMS node chip integrates preprocessing circuits, a momentum-based optimizer, a first-derivative sigmoid unit, and a weight update module. The design is implemented using the TSMC 40 nm CMOS process and verified on a Xilinx Virtex-5 FPGA. Experimental results using real BMW i3 battery data demonstrate a Root Mean Square Error (RMSE) of 1.853%, with an estimation error range of [4.324%, −4.346%]. Full article

This study proposes a novel four-pointed-star-shaped honeycomb structure having zero Poisson’s ratio, designed to overcome the stress concentration inherent in traditional point-to-point connected star-shaped honeycombs.By introducing a horizontal connecting wall at cell junctions, the new configuration achieves a more uniform stress distribution and enhanced structural stability. An analytical model for the in-plane equivalent elastic modulus was derived using homogenization theory and the energy method. The model, along with the structure’s zero Poisson’s ratio characteristic, was validated through finite element simulations and experimental compression tests. The simulations predicted an equivalent elastic modulus of 51.71 MPa (Y-direction) and 74.67 MPa (X-direction), which aligned closely with the experimental measurements of 56.61 MPa and 60.50 MPa, respectively. The experimental Poisson’s ratio was maintained near zero (v = 0.02). Parametric analysis further revealed that the in-plane equivalent elastic modulus decreases with increases in the wall angle, horizontal wall length, and wall thickness. This work demonstrates a successful structural optimization strategy that improves both mechanical performance and manufacturability for zero Poisson’s ratio honeycomb applications. Full article

Background: Ulcerative colitis (UC) is characterized by chronic mucosal inflammation, oxidative stress, and disruption of intestinal metabolic homeostasis. Immunomodulatory nutrients such as arginine, glutamine, and β-hydroxy β-methylbutyrate (HMB) have shown potential benefits; however, their combined molecular effects on UC remain insufficiently defined. Objective: To investigate the individual and combined effects of arginine, glutamine, and HMB on inflammatory and metabolic gene expression, oxidative stress markers, and histopathological outcomes in a dextran sulfate sodium (DSS)-induced colitis model. Methods: Female Sprague Dawley rats were assigned to six groups: control, DSS, DSS + arginine, DSS + glutamine, DSS + HMB, and DSS + mixture. Colitis was induced using 3% DSS. Colon tissues were examined histologically, serum MDA, MPO, and GSH levels were quantified, and mRNA expression of IL6, IL10, COX2, NOS2, ARG2, CCR1, and ALDH4A1 was measured by RT-qPCR. Pathway enrichment analyses were performed to interpret cytokine and metabolic network regulation. Results: DSS induced severe mucosal injury, elevated MDA and MPO, reduced GSH, and significantly increased IL6, COX2, NOS2, ARG2, and CCR1 expression. Glutamine demonstrated the strongest anti-inflammatory and antioxidant effects by decreasing IL6 and COX2 and restoring GSH. Arginine primarily modulated nitric oxide–related pathways, whereas HMB increased ALDH4A1 expression and metabolic adaptation. The combination treatment produced more balanced modulation across inflammatory, chemokine, and metabolic pathways, consistent with enrichment results highlighting cytokine signaling and amino acid metabolism. Histopathological improvement was greatest in the mixture group. Conclusions: Arginine, glutamine, and HMB ameliorate DSS-induced colitis through coordinated regulation of cytokine networks, oxidative stress responses, and metabolic pathways. Their combined use yields broader and more harmonized therapeutic effects than individual administration, supporting their potential as targeted immunonutritional strategies for UC. Rather than targeting a single inflammatory mediator, this study was designed to test whether combined immunonutrient supplementation could promote coordinated regulation of cytokine signaling, oxidative stress responses, and metabolic adaptation, thereby facilitating mucosal repair in experimental colitis. Full article

This study investigates the tribological behavior of selective laser-sintered (SLS) sliding bearings under dry-sliding operating conditions. These polyamide-12 bearings reinforced with glass beads (PA 3200 GF) were tested against a stainless-steel sleeve in three different pressure–velocity (PV) regimes that represent real operating conditions. The coefficient of friction (COF) and contact temperatures were monitored throughout the experiment, while the specific wear rate was quantified based on mass loss measurements. The evolution of surface topography was analyzed using roughness parameters of the Abbott-Firestone family. Scanning electron microscopy (SEM) analysis was performed to identify the dominant wear mechanism. The results show a pronounced running-in phase, after which a stable thermomechanical equilibrium occurs in all regimes. Heavy-loaded regimes increase temperature but accelerate surface adaptation and lower stable coefficients of friction. Lower load regimes have the lowest thermal load but higher friction due to lower real contact. The medium PV regime has a low COF and moderate temperature rise, while peak and core roughness metrics increase more significantly. These results provide an experimentally based insight into the influence of the load regime on the tribological behavior and topography of the SLS-made polymer sliding bearings, thus contributing to a deeper understanding of their operation in real dry-sliding conditions. Full article

Welding of high-carbon rail steels is widely applied in railway maintenance to restore worn rail surfaces and extend service life. However, the weldability of these steels is limited by their high carbon content and susceptibility to brittle microstructures in the heat-affected zone. This paper evaluates the quality of weld deposits applied to different grades of railway rails (UIC 1100, UIC 900A, and UIC HSH) using submerged arc welding (SAW) and flux-cored arc welding (FCAW) technologies with various filler materials. Weld quality was assessed through macrostructural examination, HV30 hardness measurements, and microstructural analysis. The results show that inappropriate combinations of filler materials and welding parameters lead to excessive hardness and martensitic structures, which are undesirable for in-service performance. In contrast, selected multi-layer welding procedures produced bainitic or tempered microstructures with favourable hardness distributions. Based on the experimental results, optimal welding procedures and filler material combinations for rail renovation are proposed. Full article

This work presents an analytical homogenization model developed to predict the tensile and bending behavior of double-wall corrugated cardboard. The proposed approach replaces the complex three-dimensional geometry, composed of five paper layers, with an equivalent two-dimensional homogenized plate. Based on lamination theory and enhanced by sandwich structure theory, the model accurately captures the orthotropic behavior of the material. To achieve this objective, three configurations of double-wall corrugated cardboard were investigated: KRAFT LINER (KL), DUOSAICA (DS), and AUSTRO LINER (AL). A comprehensive experimental characterization campaign was conducted, including physical analyses (density measurement, SEM imaging, and XRD analysis) and mechanical testing (tensile tests), to determine the input parameters required for the homogenization process. The proposed model significantly reduces geometric complexity and computational cost while maintaining excellent predictive accuracy. Validation was performed by comparing the results of a 3D finite element model (ANSYS-19.2) with those obtained from the homogenized H-2D model. The differences between both approaches remained systematically below 2%, confirming the ability of the H-2D model to accurately reproduce the axial and flexural stiffnesses of double-wall corrugated cardboard. The methodology provides a reliable and efficient framework specifically dedicated to the mechanical analysis and optimization of corrugated cardboard structures used in packaging applications. Full article

Understanding wake interactions between multiple tidal stream turbines is essential for optimizing the performance and layout of tidal energy farms. This study investigates the hydrodynamic behavior of two horizontal-axis tidal turbines arranged in tandem under simplified inflow conditions, where the incoming flow was dominated by the streamwise velocity component without imposed external disturbances. Wake measurements were conducted in a large circulating water tunnel using a mobile, submerged particle image velocimetry (PIV) system capable of long-range, high-resolution measurements. Performance tests showed that the downstream turbine experienced a decrease of approximately 9% in maximum power coefficient compared to the upstream turbine due to reduced inflow velocity and increased turbulence generated by the upstream wake. Phase-averaged PIV results revealed the detailed evolution of velocity deficit, turbulence intensity, turbulent kinetic energy, and tip vortex structures. The tip vortices shed from the upstream turbine persisted over a long downstream distance, remaining coherent up to 10D and merging with those generated by the downstream turbine. These merged vortex structures produced elevated turbulence and complex flow patterns that significantly influenced the downstream turbine’s operating conditions. The results provide experimentally validated insight into turbine-to-turbine wake interactions and highlight the need for high-fidelity wake data to support array optimization and numerical model development for tidal stream turbine array. Full article