Search Results (3,347)

Although cemented carbides have been manufactured by the powder metallurgy (P/M) technology for over a century now, systematic developmental efforts are still underway. In the present study, tool life improvements in metal grooving applications are the key objective. Four PVD-coated cemented carbides compositions, dedicated to groove steel, stainless steel, cast iron, and aluminium alloys, have been newly designed, along with their manufacturing conditions. Physical, mechanical and chemical characteristics—such as sintered density, modulus of elasticity, hardness, fracture toughness, WC grain size, and the chemical composition of the substrate material, as well as the chemical composition, microhardness, structure, and thickness of the coatings—have been studied. A series of grooving tests have also been conducted to assess whether modifications to the thus far marketed tool materials, tool geometries, and coatings can improve cutting performance. In order to compare the laboratory and application properties of the investigated materials with currently produced by reputable companies, commercial inserts have also been tested. The experimental results obtained indicate that the newly developed grooving inserts exhibit excellent microstructural characteristics, high hardness, fracture toughness, and wear resistance and that they show slightly longer tool life compared to the commercial ones. Full article

Vortex-induced vibrations (VIVs) pose significant risks to the structural integrity of subsea cables and pipelines under free-span conditions. It is extremely helpful to be able to screen for VIV and understand for a particular cable or pipeline what the minimum free-span threshold lengths are beyond which in-line and/or cross-flow VIV can be excited, causing fatigue problems. To date screening is a more complex and detailed task. This paper introduces a universal dimensionless velocity, V*, and one graph that can be used across all types of VIV free spans to quickly assess minimum free-span threshold lengths. Natural frequencies are not required to be calculated for screening each time, as they are implicit in the curve. The universal criteria are developed via non-dimensional analysis to establish the significant physical mechanisms, after which the relationships are populated, forming a single curve for in-line and for cross-flow VIV with a typical mass ratio and a conservative zero as-laid tension case. Full article

Background/Objective: Understanding how chronic conditions such as knee osteoarthritis (OA) and chronic obstructive pulmonary disease (COPD) influence long-term physical activity (PA) is essential for developing condition-specific rehabilitation strategies. This study aimed to examine whether baseline diagnoses of knee OA and COPD are independently associated with the trajectories of PA decline over five years in U.S. adults, informed by the disablement process model. Methods: We analyzed data from 855 adults aged ≥45 years enrolled in the Osteoarthritis Initiative (OAI). The participants were categorized into three baseline groups, control (n = 122), knee OA (n = 646), and COPD (n = 87), based on self-reports and prior clinical assessments. PA was measured annually for five years using the Physical Activity Scale for the Elderly (PASE). General linear mixed models assessed changes in PA over time, adjusting for demographic, behavioral, and clinical covariates. Results: Compared to the controls, participants with knee OA had a significant decline in PA over time (β = −6.62; 95% CI: −15.4 to −2.19; p = 0.014). Those with COPD experienced an even greater decline compared to the knee OA group (β = −11.2; 95% CI: −21.7 to −0.67; p = 0.037). These associations persisted after adjusting for age, sex, body mass index, comorbidities, and smoking. Conclusions: Baseline knee OA and COPD were independently associated with long-term reductions in PA. These findings underscore the importance of early, tailored rehabilitation strategies, particularly pulmonary rehabilitation, in preserving functional independence among older adults with chronic conditions. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. 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^® 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

This study presents a hybrid approach to hydrological forecasting by integrating the physically based Soil and Water Assessment Tool (SWAT) model with Prophet time-series modeling and machine learning–based multi-output regression. Applied to the Cahaba watershed, the objective is to predict key environmental variables (precipitation, evapotranspiration (ET), potential evapotranspiration (PET), and snowmelt) and their influence on hydrological responses (surface runoff, groundwater flow, soil water, sediment yield, and water yield) under present (2010–2022) and future (2030–2042) climate scenarios. Using SWAT outputs for calibration, the integrated SWAT-Prophet-ML model predicted ET and PET with RMSE values between 10 and 20 mm. Performance was lower for high-variability events such as precipitation (RMSE = 30–50 mm). Under current climate conditions, R² values of 0.75 (water yield) and 0.70 (surface runoff) were achieved. Groundwater and sediment yields were underpredicted, particularly during peak years. The model’s limitations relate to its dependence on historical trends and its limited representation of physical processes, which constrain its performance under future climate scenarios. Suggested improvements include scenario-based training and integration of physical constraints. The approach offers a scalable, data-driven method for enhancing monthly water balance prediction and supports applications in watershed planning. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

In this study, we propose a bidirectional chemical sensor platform based on a reconfigurable ion-sensitive field-effect transistor (R-ISFET) architecture. The device incorporates Ni-silicide Schottky barrier source/drain (S/D) contacts, enabling ambipolar conduction and bidirectional turn-on behavior for both p-type and n-type configurations. Channel polarity is dynamically controlled via the program gate (PG), while the control gate (CG) suppresses leakage current, enhancing operational stability and energy efficiency. A dual-control-gate (DCG) structure enhances capacitive coupling, enabling sensitivity beyond the Nernst limit without external amplification. The extended-gate (EG) architecture physically separates the transistor and sensing regions, improving durability and long-term reliability. Electrical characteristics were evaluated through transfer and output curves, and carrier transport mechanisms were analyzed using band diagrams. Sensor performance—including sensitivity, hysteresis, and drift—was assessed under various pH conditions and external noise up to 5 V_pp (i.e., peak-to-peak voltage). The n-type configuration exhibited high mobility and fast response, while the p-type configuration demonstrated excellent noise immunity and low drift. Both modes showed consistent sensitivity trends, confirming the feasibility of complementary sensing. These results indicate that the proposed R-ISFET sensor enables selective mode switching for high sensitivity and robust operation, offering strong potential for next-generation biosensing and chemical detection. Full article

This study aimed to optimize the inoculation dosage and fermentation duration to enhance the protein content and reduce soluble oligosaccharides in soybean meal using Aspergillus oryzae and assessed its performance in dog food extrusion. A 3 × 5 factorial design was used to determine the optimal fermentation conditions. These conditions were applied to ferment soybean meal in bulk for nutritional analysis. Finally, the impact of fermentation on extrusion processing was assessed by formulating and extruding four diets: SBM (30% soybean meal), AMF (30% soybean meal with 1% Amaferm^®—A. oryzae biomass), FSBM (30% fermented soybean meal), and SPI (18% soy protein isolate). Diets were extruded with a single-screw extruder, and physical characteristics of kibbles, particle size distribution, and viscosity of raw mixes were analyzed. The optimal fermentation conditions were 1 × 10⁴ spore/g substrate for 36 h, which increased the crude protein content by 4.63% DM, methionine and cysteine total content by 0.15% DM, and eliminated sucrose, while significantly reducing stachyose, raffinose, and verbascose (95.22, 87.37, and 41.82%, respectively). The extrusion results showed that FSBM had intermediate specific mechanical energy (SME), in-barrel moisture requirements, and sectional expansion index (198.7 kJ/kg, 28.2%, and 1.80, respectively) compared with SBM (83.7 kJ/kg, 34.5%, and 1.30, respectively) and SPI (305.3 kJ/kg, 33.5%, and 2.55, respectively). The FSBM also exhibited intermediate particle size distribution and the least raw mix viscosity. These findings demonstrate that A. oryzae fermentation enhances the nutrient profile of soybean meal while improving extrusion efficiency and kibble quality, supporting its potential use as a sustainable pet food ingredient. Full article

Microfluidic platforms offer a powerful approach for ultimately replicating vascularization in vitro, enabling precise microscale control and manipulation of physical parameters. Despite these advances, the real-time ability to monitor and quantify mechanical forces—particularly pressure—within microfluidic environments remains constrained by limitations in cost and compatibility across diverse device architectures. Our work presents an advanced experimental module for quantifying pressure within a vascularizing microfluidic platform. Equipped with an integrated Arduino microcontroller and image monitoring, the system facilitates real-time remote monitoring to access temporal pressure and flow dynamics within the device. This setup provides actionable insights into the hemodynamic parameters driving vascularization in vitro. In-line pressure sensors, interfaced through I2C communication, are employed to precisely record inlet and outlet pressures during critical stages of microvasculature tubulogenesis. Flow measurements are obtained by analyzing changes in reservoir volume over time (dV/dt), correlated with the change in pressure over time (dP/dt). This quantitative assessment of various pressure conditions in a microfluidic platform offers insights into their impact on microvasculature perfusion kinetics. Data acquisition can help inform and finetune functional vessel network formation and potentially enhance the durability, stability, and reproducibility of engineered in vitro platforms for organoid vascularization in regenerative medicine. Full article

The operation of heavy-haul railway trains with large loads results in significant cracking issues in reinforced concrete beams. Atmospheric carbon dioxide, oxygen, and moisture from the atmosphere penetrate into the beam interior through these cracks, accelerating the carbonation of the concrete and the corrosion of the steel bars. The rust-induced expansion of steel bars further exacerbates the cracking of the beam. The interaction between environmental factors and beam cracks leads to a rapid decline in the durability of the beam. To address this issue, a multi-physics field coupling durability assessment method was proposed, considering concrete beam cracking, concrete carbonation, and steel bar corrosion. The interaction among these three factors is achieved through sequential coupling, using crack width, carbonation passivation time, and steel bar corrosion rate as interaction parameters. Using this method, the deterioration morphology and stiffness degradation laws of 8 m reinforced concrete beams under different load conditions, including those of heavy and light trains in heavy-haul railways, are compared and assessed. The analysis reveals that within a 100-year service cycle, the maximum relative stiffness reduction for beams on the heavy train line is 20.0%, whereas for the light train line, it is only 7.4%. The degree of structural stiffness degradation is closely related to operational load levels, and beam cracking plays a critical role in this difference. Full article

Background/Objectives: Perceived physical literacy (PPL) appears to be a relevant strategy for combating the prevalent sedentary lifestyle among young people. Therefore, understanding their PPL levels will facilitate the implementation of appropriate strategies for this purpose. Therefore, this study aimed to analyze the differences in PPL considering the sex dimension (i.e., males and females) and the age-group (i.e., early compulsory secondary education, late compulsory secondary education and baccalaureate). Methods: Seven-hundred-and-four Spanish students (age = 14.3 ± 1.6 years old) belonging to three different Spanish secondary schools voluntarily participated in this study. To assess adolescents’ perceptions of their physical literacy, the Spanish Adolescents’ Perceived Physical Literacy Assessment (S-PPLI) was used. This instrument consists of nine items equally distributed across three categories: self-perception and self-confidence, self-expression and communication with others, and knowledge and understanding. Results: Males obtained higher scores in all the indicators of PPL, except for items 1, 8 and 9, compared to their female counterparts (p < 0.05), while the oldest age-group reported higher scores in the indicators of knowledge and understanding category compared to students in the early years of compulsory secondary education (p < 0.01). Conclusions: Programs based on increasing the PPL should be implemented specifically for females. Also, similar programs must be included into scholar curriculums from the beginning of secondary education, with the aim of promoting improvements in the health and physical condition of Spanish adolescents. Full article

Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article

The objective of this work is to evaluate how spent catalyst from fluid catalytic cracking (SCFCC) affects the physical, mechanical and durability properties of fly ash (FA) and blast furnace slag (BFS)-based alkali-activated materials (AAMs). Recycling of SCFCC by integrating it in a AAM matrix offers several advantages: valorization of the material, reducing its disposal in landfills and the landfill cost, and minimizing the environmental impact. Mineralogical, physical and mechanical characterization were carried out. The durability of the specimens was studied by performing acid attack and thermal stability tests. Mass variation, compressive strength and porosity parameters were determined to assess the durability. BFS- and FA-based AAMs have a different chemical composition, which contribute to variations in microstructure and physical and mechanical properties. Acid neutralization capacity was also determined to analyse the acid attack results. Porosity, including the pore size distribution, and the acid neutralization capacity are crucial in explaining the resistance of the AAMs to sulfuric acid attack and thermal degradation. Herein, a novel route was explored, the use of SCFCC to enhance the durability of AAMs under harsh operating conditions since results show that the compositions containing SCFCC showed lower strength decay due to the lower macroporosity proportions in these compositions. Full article

Background: Fibromyalgia (FM) is a chronic condition characterized by widespread pain, fatigue, cognitive difficulties, and psychological symptoms. Patients often present distinct personality traits and psychopathological patterns associated with symptom severity. Objective: To examine psychopathological profiles in FM patients based on functional, physical–somatic, and emotional impairment domains, as well as on cumulative disease severity. Materials and Methods: A cross-sectional study was conducted with 70 women clinically diagnosed with FM at a specialized Fibromyalgia Unit. Psychological functioning was assessed using the Personality Assessment Inventory, and disease impact was measured with the Fibromyalgia Impact Questionnaire. Hierarchical cluster analyses were used to classify participants into mild and severe clusters across FIQ domains, and psychological profiles were compared. Results: Patients with severe functional impairment had more affective dysregulation (76.43 vs. 70.20, p < 0.01) and somatic complaints (85.57 vs. 79.76, p < 0.05) than those with mild impairment. The severe–physical cluster showed greater mood instability, somatization, and suicidal ideation (60.94 vs. 53.61, p < 0.05). The severe–emotional cluster had higher rates of major depression (85.71% vs. 64.28%) and persistent depressive disorder (76.19% vs. 70.61%, p < 0.05). Severe showed more emotional instability and somatization, distinguishing it from mild. Greater cumulative severity intensified depressive and somatic disorders. Discussion: Findings support FM’s biopsychosocial profile, where emotional distress may relate to psychological and physical symptoms, reinforcing the need for personalized, multidisciplinary care and comprehensive assessment. Full article