Search Results (19,048)

The rapidly changing dynamics of the digital age reshape the addiction relationship that high school students establish with technology. While smartphones remove boundaries in terms of communication and access to information, their usage triggers a source of anxiety and nomophobia. The increase in students’ anxiety levels because of their over-reliance on mobile phone use leads to significant behavioral changes in their mental health, academic performance, social interactions and financial dependency. This study examined the nomophobia levels of high school students according to selected socio-demographic indicators. Using the relational screening model, the multistage sampling technique was used to select a sample of 884 participants: 388 from Science High School and 496 from Anatolian High School (459 female, 425 male, Mage = 16.45 ± 1.14 year). Independent sample test and One-way ANOVA were applied. Depending on the homogeneity assumption of the data, Welch values were considered, and Tukey tests were applied as a second-level test from post hoc analyses. Comprehensive analyses of nomophobia levels revealed that young individuals’ attitudes towards digital technology differ significantly according to their demographic and behavioral characteristics. Variables such as gender, physical activity participation, grade level and duration of smartphone use are among the main factors affecting nomophobia levels. Female individuals and students who do not participate in physical activity exhibit higher nomophobia scores. Full article

Background: The waist–calf circumference ratio (WCR) is an index that combines waist and calf circumference measurements, offering a potentially effective method for evaluating the imbalance between abdominal fat and leg muscle mass in older adults. Objective: To assess the association between WCR and indicators of body composition, muscle strength, and physical performance in community-dwelling older women. Methods: This was a cross-sectional study involving 133 older women (≥65 years) from an urban-marginal community in Guayaquil, Ecuador. The WCR was categorized into quartiles (Q1: 2.07–2.57; Q2: 2.58–2.75; Q3: 2.76–3.05; Q4: 3.06–4.76). Body indicators included fat-free mass (FFM), skeletal muscle mass (SMM), appendicular muscle mass (ASM), appendicular muscle mass index (ASMI), visceral fat (VF), fat mass (FM), and fat mass index (FMI). Handgrip strength (HGS) and the Short Physical Performance Battery test (SPPB) score were used to assess muscle strength and function, respectively. Results: The median age of the participants was 75 [IQR: 65–82] years. The mean WCR was 2.92 ± 0.93. Statistically significant associations were found between WCR and VF (p < 0.001), WCR and SMM (p = 0.039), and WCR and ASM (p = 0.016). Regarding muscle function, WCR was associated with HGS (p = 0.025) and SPPB score (p = 0.029). Conclusions: A significant association was observed between WCR and body composition, and muscle strength and function in older women. 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

Early rehabilitation is essential for restoring functional recovery in patients with stroke, particularly during the early phase of post-acute care (PAC), or the subacute stage. We aimed to evaluate the effectiveness of a 7-week PAC rehabilitation program in improving muscle strength, physical performance, and functional recovery. A total of 219 inpatients with stroke in the subacute stage were initially recruited from the PAC ward of a regional teaching hospital in Northern Taiwan, with 79 eligible patients—within 1 month of an acute stroke—included in the analysis. The program was delivered 5 days per week, with 3–4 sessions daily (20–30 min each, up to 120 min daily), comprising physical, occupational, and speech–language therapies. Sociodemographic data, muscle strength, physical performance (Berg Balance Scale [BBS], gait speed, and 6-minute walk test [6MWT]), and functional recovery (modified Rankin Scale [mRS], Barthel Index [BI], Instrumental Activities of Daily Living [IADL], and Fugl–Meyer assessment: sensory and upper extremity) were collected at baseline, 3 weeks, and 7 weeks. Generalized estimating equations analyzed program effectiveness. Among the 56 patients (70.9%) who completed the program, significant improvements were observed in the muscle strength of both the affected upper (B = 0.93, p < 0.001) and lower limbs (B = 0.88, p < 0.001), as well as in their corresponding unaffected limbs; in physical performance, including balance (BBS score: B = 9.70, p = 0.003) and gait speed (B = 0.23, p = 0.024); and in functional recovery, including BI (B = 19.5, p < 0.001), IADL (B = 1.48, p < 0.001), and mRS (B = −0.13, p = 0.028). These findings highlight the 7-week PAC rehabilitation program as an effective strategy during the critical recovery phase for patients with stroke. Full article

Background: Breast cancer remains a major public health challenge in Latin America, where access to personalized risk assessment tools is still limited. This study aimed to evaluate the implementation of a polygenic risk score (PRS)-based stratification model combined with remote genomic counseling in Colombian women with sporadic breast cancer and healthy women. Methods: In 2023, an embedded mixed-methods observational study was conducted in Medellín involving 1997 women aged 40–75 years who underwent clinical PRS testing. The intervention integrated PRS-based risk categorization with individualized risk factor assessment and lifestyle recommendations delivered through a remote counseling platform. Results: PRS analysis classified 9.7% of women as high risk and 46% as low risk. Healthier lifestyle patterns were significantly associated with lower PRS categories (p = 0.034). Physical activity showed a protective effect (OR = 0.60, 95% CI: 0.5–0.8), while prior smoking, elevated BMI, and sedentary behavior were associated with higher risk. The counseling model achieved high delivery (93%) and satisfaction (85%) rates. Qualitative insights revealed improved understanding of genomic risk and greater engagement in preventive behaviors. Only one new case of breast cancer was detected among intermediate-risk participants, with a diagnostic lead time of 12 months. Conclusions: These findings support the feasibility, acceptability, and potential impact of integrating PRS and genomic counseling in cancer prevention strategies in middle-income settings. Full article

The rising complexity of industrial systems following the Industry 4.0 era involves new challenges and the need for innovative solutions. In the context of arising digital technologies, Digital Twins represent a holistic solution to overcome heterogeneity and to achieve remote and dynamic control of cyber–physical systems. In common reference architectures, decision-making modules are usually integrated for system and process optimization. This work aims at introducing the adoption of a multi-purpose simulation module in a Digital Twin environment, with the objective of proving its versatility for different scopes. This is implemented in a relevant laboratory environment, strongly employed for the test and validation of mechatronic solutions. The paper starts from revising the common techniques adopted for decision-making modules in Digital Twin frameworks, proposing then a multi-purpose approach based on physics simulation. Performance profiling of the simulation environment demonstrates the potential of real-time-capable simulation while also revealing challenges related to computational load and communication latency. The outcome of this work is to provide the reader with an exemplary modular arrangement for the integration of such module in Digital Twin applications, highlighting challenges and limitations related to computational effort and communication. Full article

This paper proposes a novel suspended seismic structure system called Base-suspended Pendulum Isolation (BSPI) structure. The BSPI structure can isolate seismic action and reduce structural seismic response by hanging the structure with hanger rods set at the base. The viscous dampers are installed in the isolation layer to dissipate earthquake energy and control the displacement. Firstly, the configuration of suspension isolation layer and mechanical model of the BSPI structure are described. Then, an equivalent scaled BSPI structure physical model was tested on the shaking table. The test results demonstrate that the BSPI structure has a good isolation effect under earthquakes, and the viscous dampers had an obvious control effect on the structure’s displacement and acceleration response. Finally, numerical simulation of the tests was carried out. The accuracy of the numerical models are confirmed by the good agreement between the simulation and test results. The numerical models for the BSPI structure and conventional reinforced concrete (RC) frame structure are built and analyzed using the commercial software ABAQUS. Research results indicate that the lateral stiffness of the BSPI structure is reduced greatly by installing the suspension layer, and the acceleration response of BSPI structure is significantly reduced under rare earthquakes, which is only 1/2 of that of the RC frame. The inter-story displacement of the BSPI structure is less than 1/100, which meets the seismic fortification goal and is reduced to 50% of that of the BSPI structure without damper under rare earthquakes. Full article

High demand for sustainable solutions in the construction industry determines the significant relevance of developing new eco-friendly composites with a reduced carbon impact on the environment. The main aim of this study is to investigate the possibility and efficiency of using technical sulfur (TS) as a modifying additive for geopolymer composites and to select the optimal content of polypropylene fiber (PF). To assess the potential of TS, experimental samples of geopolymer solutions based on metakaolin and fly ash were prepared. The TS content varied from 0% to 9% by weight of binder in 3% increments. In the first stage, the density, compressive and flexural strength, capillary water absorption and microstructure of hardened geopolymer composites were tested. The TS additive in an amount of 3% was the most effective and provided an increase in compressive strength by 12.6%, flexural strength by 12.8% and a decrease in capillary water absorption by 18.2%. At the second stage, the optimal PF content was selected, which was 0.75%. The maximum increases in strength properties were recorded for the composition with 3% TS and 0.75% PF: 8% for compression and 32.6% for bending. Capillary water absorption decreased by 12.9%. The geopolymer composition developed in this work, modified with TP and PF, has sufficient mechanical and physical properties and can be considered for further study in order to determine its competitiveness with cement composites in real construction practice. Full article

Newton’s law of cooling requires a reference temperature ($T_{ref}$) to define the heat-transfer coefficient ($h$). For external flows with multiple temperatures in the freestream, obtaining $T_{ref}$ is a challenge. One widely used method, referred to as the adiabatic-wall (AW) method, obtains $T_{ref}$ by requiring the surface of the solid exposed to convective heat transfer to be adiabatic. Another widely used method, referred to as the linear-extrapolation (LE) method, obtains $T_{ref}$ by measuring/computing the heat flux ($q_s^{\prime \prime }$) on the solid surface at two different surface temperatures ($T_s$) and then linearly extrapolating to $q_s^{\prime \prime }=0$. A third recently developed method, referred to as the state-space (SS) method, obtains $T_{ref}$ by probing the temperature space between the highest and lowest in the flow to account for the effects of $T_s$ or $q_s^{\prime \prime }$ on $T_{ref}$. This study examines the foundation and accuracy of these methods via a test problem involving film cooling of a flat plate where $q_s^{\prime \prime }$ switches signs on the plate’s surface. Results obtained show that only the SS method could guarantee a unique and physically meaningful $T_{ref}$ where $T_s=T_{ref}$ on a nonadiabatic surface $q_s^{\prime \prime }=0$. The AW and LE methods both assume $T_{ref}$ to be independent of $T_s$, which the SS method shows to be incorrect. Though this study also showed the adiabatic-wall temperature, $T_{AW}$, to be a good approximation of $T_{ref}$ (<10% relative error), huge errors can occur in $h$ about the solid surface where $|T_s-T_{AW}|$ is near zero because where $T_s=T_{AW}$, $q_s^{\prime \prime }\ne 0$. Full article

Background/Objectives: Obesity in adolescence is associated with a deterioration in cognitive functions, with significant implications for psychophysical well-being and academic performance. Recent studies highlight the importance of integrated interventions that combine nutrition education and physical activity to promote the overall health of students. The present study aims to evaluate the efficacy of an integrated intervention based on nutritional education and conscious body movement in improving cognitive functions, perceived well-being and nutritional knowledge in lower secondary school students with indicators of overweight and obesity. Methods: A quasi-experimental design with randomization at the class level was adopted, involving 60 students divided into an experimental group and control group. The intervention was divided into twelve weeks of activities, divided between nutritional education modules and physical activity courses. Standardized tests for the assessment of cognitive functions (Digit Span Forward, Digit Span Backward, Stroop Test, Trail Making Test B), motor tests (6-Minute Walk Test, Sit and Reach Test) and a food knowledge questionnaire were administered before and after the intervention. Results: The experimental group showed significant improvements compared to the control group in all cognitive, motor, and nutritional knowledge measures, indicating the effectiveness of the integrated intervention in promoting cognitive and physical well-being. Conclusions: The findings support the role of school as a generative environment of integrated well-being, suggesting the need to develop and implement curricular programs that integrate nutrition education and physical activity to counteract the negative effects of obesity on cognitive function in adolescents. Full article

Color is a primary determinant of the value of jadeite jade, but the petrological provenance of the chromogenic elements of jadeite jade remains uncertain. The characteristics of the associated chromite in Myanmar jadeite jade were systematically investigated through a series of tests, including polarized microscopy, microarea X-ray fluorescence spectroscopy (micro-XRF) mapping, electron probe microanalysis (EPMA), and backscattered electron (BSE) imaging. The results demonstrate that the chromite composition in Myanmar jadeite jade is characterized by a high concentration of Cr₂O₃ (46.18–67.11 wt.%), along with a notable abundance of MnO (1.68–9.13 wt.%) compared with the chromite from the adjacent Myitkyina peridotite. The diffusion of chromium (Cr) and manganese (Mn) in jadeite jade is accomplished by accompanying the metamorphic pathway of Mn-rich chromite → kosmochlor → chromian jadeite → jadeite. In the subsequent phase of jadeite jade formation, the chromium-rich omphacite veins generated by the fluid enriched in Ca and Mg along the fissures of kosmochlor and chromian jadeite play a role in the physical diffusion of Cr and Mn. The emergence of the lavender hue in jadeite is contingent upon the presence of a relatively high concentration of Mn (approximately 100–1000 ppmw) and the simultaneous absence of Cr, which would otherwise serve as a more effective chromophore (no Cr or up to a dozen ppmw). The distinctive Mn-rich chromite represents the primary origin of the chromogenic element Cr (green) and, perhaps more notably, an overlooked provider of Mn (lavender) in Myanmar jadeite jade. Full article

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling. Full article

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

Background/Objectives: Amorphous solid drugs exhibit physical instability and a propensity for crystallization, which leads to reduced solubility and bioavailability. Hence, this study optimized scale manufacturing parameters for producing a physically stable amorphous solid form of nilotinib using neutralization precipitation. Methods: A systematic evaluation of the effects of the solute concentration and filtration rate on amorphous physical stability was conducted using the pair distribution function (PDF), principal component analysis (PCA), and reduced crystallization temperature (R_c) values. Results: It showed concentration-dependent crystallization resistance, with optimal physical stability achieved at a solute concentration of 0.126 mol/L and a 124 mL/min filtration rate. Experiments carried out at a scale of 50 g confirmed the stability of the production process. Conclusions: These findings provide a validated framework for developing lab-scale amorphous drug products with improved shelf-life stability, assessed using indirect indicators (PDF, R_c) and confirmed through accelerated stability tests. Full article