Search Results (16,601)

Background: The optimal frequency of EEG biofeedback sessions for elite athletes remains unclear, despite growing adoption of neurofeedback in high-performance sport. Methods: This randomized, controlled study compared three EEG biofeedback protocols (daily, every-other-day, every-third-day) in 24 national-level male judo athletes stratified into three phenotypic groups. Each protocol comprised 15 standardized sessions. Pre- and post-intervention assessments included functional indices (strength, power) and neurophysiological measures (Frontal Alpha Index, EMG amplitude/RMS, corrected strength sum). Biosensor performance was validated via signal quality metrics. Results: Daily EEG biofeedback produced superior improvements in strength, FAI, and fatigue resistance. Although LRG showed the largest pre–post RMS increase (+17.44 μV vs. +16.54 μV in HRG), HRG maintained the highest post-intervention RMS values and best fatigue resistance (MF_drop = −2.15 Hz). Significant group × time interactions were observed for FAI (p = 0.027) and RMS (p = 0.019). Every-other-day protocols yielded moderate gains, while every-third-day protocols produced minimal or maladaptive EMG–load dynamics. A robust dose–response relationship was evident. Conclusions: Session frequency is critical for optimizing neurofeedback interventions in elite athletes. Daily EEG biofeedback confers superior adaptation compared to less frequent dosing. Full article

Background: The introduction of targeted therapy in oncology has led to several challenges. These medicines are relatively new in clinical practice and are not well known to specialists with regard to adverse drug reactions (ADRs) and potential drug–drug interactions (pDDIs). In addition, cancer affects multiple body systems, including weight loss, anemia, liver and kidney function, depression, and pain. Patients frequently have comorbidities, leading to polypharmacy and the use of special foods, nutritional supplements, and herbal products for self-medication. Identification of pDDIs is essential, as concomitant use of multiple medicinal products increases the risk of ADRs and may compromise treatment. Objective: This study aims to retrospectively review and analyze data on ADRs and pDDIs in the treatment of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) inhibitors and to evaluate the relationship between them. Method: EudraVigilance and UpToDate^® Lexidrug™ application were used to screen suspected ADRs and pDDIs, respectively. Descriptive statistical analysis was performed. Results: After reviewing Line Listing Reports (LLRs) from 2021 to 2023 in EudraVigilance, the number of suspected adverse drug reactions (ADRs) reported was higher when drug interactions classified as risk categories D and X were identified, compared with cases involving EGFR inhibitor monotherapy or other drug combinations. Of the 144 cases involving category D and/or X interactions, 63 demonstrated a possible association with the reported ADRs of EGFR inhibitors. The most common pDDIs detected were erlotinib–ranitidine (14 cases, category D) and osimertinib–amiodarone (13 cases, category D). Conclusions: Although EGFR inhibitors improve overall and progression-free survival in NSCLC, screening for pDDIs before treatment is essential to improve safety and quality of life. Full article

Background and Objectives: Population ageing is a major challenge of the 21st century and is associated with declining physical and mental abilities, increased disease burden, and higher mortality. Latvia has the lowest healthy life expectancy in the European Union. Social well-being is an important component of healthy and active ageing and may be associated with older adults’ quality of life (QoL). This study aimed to assess the relationship between social well-being, as a component of health, and QoL, including its components (control, autonomy, self-realisation and pleasure), among adults aged 50 and older in Latvia. Materials and Methods: Data from 1643 Latvian participants in wave 9 of the Survey of Health, Ageing, and Retirement in Europe (2022) were analysed using linear regression. QoL was measured using the 12-item Control, Autonomy, Self-Realisation, and Pleasure (CASP-12) scale. Social well-being factors included household composition, education, employment status, financial capacity, living area, social network (SN) characteristics, and received help, based on self-reported questionnaires. Results were considered statistically significant if the p-value was less than 0.05. Results: The factors positively associated with overall QoL were being employed, better financial capacity, greater satisfaction with SN, larger SN, participation in social activities, and higher educational attainment. Being employed and the ability to make ends meet easily were positively associated with all QoL components. Higher satisfaction with the SN and participation in social activities were positively related to the control, autonomy, pleasure, and self-realisation components. Conclusions: These findings underscore the importance of social and economic resources for QoL in later adulthood, suggesting that both the quality of social relationships and material security play a central role in shaping overall QoL and its components among older adults. Full article

Urban pedestrianization has become a widely adopted strategy to promote sustainable mobility, improve urban livability, and enhance the quality of public space. Despite its potential benefits, pedestrianization interventions often generate heterogeneous perceptions among different user groups, which may influence their long-term acceptance. This study analyzes citizen perceptions of an urban pedestrianization intervention implemented in the city of Loja, Ecuador, considering residents, business owners or employees, and pedestrians or visitors. A structured survey was conducted, and the collected data were analyzed using exploratory analytical techniques, including rescaled single-item indices, user segmentation, and Spearman correlation analysis to identify patterns and relationships among variables. The results reveal significant associations between socio-demographic characteristics, user type, and acceptance of permanent pedestrianization, as well as differentiated patterns of urban experience. These findings provide empirical evidence to support decision-making in urban mobility policies and contribute to the academic discussion on pedestrianization in intermediate Latin American cities. Full article

Background/Objectives: Healthy food incentive programs (HFIP), such as fruit and vegetable voucher incentives, can supplement other nutrition assistance programs to support food and nutrition security. However, little is known about the prevalence of HFIP, particularly at the municipal level. This study examines the prevalence of HFIP in a nationally representative sample of U.S. municipalities and the association between the availability of HFIP and municipal characteristics. Methods: Using data from the CDC’s 2021 National Survey of Community-Based Policy and Environmental Supports for Healthy and Active Living (n = 1982 municipalities), a weighted bivariate analysis and multivariable logistic regression analysis were conducted to estimate the prevalence of HFIP overall and by municipal characteristics, and to assess the relationship between municipal characteristics and HFIP. Results: Only 7.8% of municipalities reported offering HFIP in 2021. The odds of having an HFIP were higher in municipalities with a food policy council (aOR 2.8; 95%CI: 1.9, 3.9) compared to those without. Larger communities (size ≥ 50,000 reported 24.6%) and those with a higher prevalence of residents living in poverty were also more likely to offer HFIP. Conclusions: Few municipalities reported offering HFIP. Results suggested that engaging institutions and individuals (e.g., via food access coalitions) may be strategies that could support municipalities initiating and implementing HFIP to improve diet quality and reduce chronic disease risk. Full article

Welding is a fundamental technique for joining materials in industrial applications and large-scale construction. Various methods are employed to ensure robust connections. Resistance spot welding is ideal for thin sheets due to its speed, low cost, short processing times, and easy integration into automation systems. Stainless steel is widely used in many food and beverage industries because of its durability and ability to withstand diverse conditions. However, despite the existence of modeling approaches, predictive models linking weld parameters to the simultaneous improvement of stiffness and tensile strength in different joint regions remain limited in published studies. Many studies treat the weld as a single homogeneous region or focus primarily on general indicators such as tensile strength or weld diameter. The spatial variation in properties between the weld region, the heat-affected region, and the base metal is often not modeled separately. This study examines the effect of welding current and welding time on the mechanical properties of weld beads. Scanning electron microscopy (SEM) was also used to characterize the weld microstructure. The combination of mechanical evaluation and microstructural analysis provides deeper insight into the relationship between welding parameters and weld quality. Among the conditions studied (6–8 kA, 60–120 ms), the optimal parameters (6 kA, 120 ms) produced the maximum hardness of 178.16 HV observed in the weld zone and a tensile strength of 12 kN. The experimental results demonstrated that welding parameters significantly influence weld bead quality, and the optimization study allowed us to identify the parameters that achieve the best possible mechanical properties and optimal operating conditions. The experimental results demonstrated that welding parameters significantly influence weld bead quality, and the optimization study using Response Surface Methodology (RSM) allowed us to identify the parameters that achieve the best possible mechanical properties and optimal operating conditions. Full article

Background: Artificial intelligence (AI) is increasingly incorporated into higher education. However, most empirical studies focus on technological adoption or learner satisfaction rather than on how pedagogical design, perceived learning impact, and student experience interact within AI-mediated learning environments. Understanding these relationships is essential to determine whether AI supports higher-order reasoning processes rather than merely increasing technological engagement. Objective: This preliminary study aimed to develop and evaluate a theory-driven AI-mediated pedagogical framework and examine relationships between pedagogical design, perceived learning impact, and student satisfaction in a university learning context. Methods: An observational educational evaluation was conducted during implementation of an AI-mediated instructional framework in an undergraduate physiotherapy course. The full academic cohort (n = 22) completed a 24-item questionnaire assessing seven pedagogical domains on a 5-point Likert scale. Descriptive statistics, Pearson correlations, exploratory regression modeling, and factor analysis were used to examine relationships among domains. Academic performance indicators were summarized descriptively. Results: Students reported high evaluations across all domains (means > 4.5/5). The strongest association with satisfaction was perceived learning impact (r = 0.79, p < 0.001). Moderate correlations were found for usability (r = 0.66), AI content quality (r = 0.61), pedagogical coherence (r = 0.58), critical thinking (r = 0.52), and ethical integration (r = 0.47). Academic pass rates exceeded 90%. Conclusions: Perceived learning impact emerged as the central mechanism linking AI-mediated instructional design to student satisfaction, suggesting that the educational value of AI depends on alignment with cognitively demanding learning processes. Full article

Pinching-antenna systems (PASSs) offer a low-complexity and reconfigurable solution for near-field downlink communications by deploying multiple radiating elements along a single waveguide. Existing studies mainly assume simplified propagation conditions or focus on spectral efficiency, while the impact of environment-dependent interference patterns arising from user-specific blockage conditions on energy-efficient design remains unclear. An energy-efficient downlink design for single-waveguide PASS based on environment-division multiple access (EDMA) is investigated. Under a given propagation environment, EDMA exploits user-dependent blockage and visibility differences through proper pinching-antenna placement, thereby inducing different multi-user interference patterns without increasing radio-frequency hardware complexity. We examine how such blockage-dependent interference influences the relationship between spectral efficiency and energy efficiency, and develop an energy-aware EDMA framework that jointly considers pinching-antenna locations and transmit power allocation under quality-of-service constraints. The resulting coupled design problem is solved through an alternating optimization procedure. EDMA is compared with conventional time-division multiple access (TDMA) using a unified hardware and power-consumption model. Numerical results reveal clear energy-efficiency threshold behaviors with respect to blockage intensity, user population, and service requirements. The results further show that EDMA can significantly outperform TDMA in specific operating regimes. Full article

This study sought to identify key factors in university students’ physical and mental health by using a combination of methods, including classical statistical analysis and machine learning techniques. Physical and mental health test data were collected from undergraduates of the 2020 to 2023 cohorts at a university. A self-designed questionnaire on factors associated with physical and mental health was also sent to randomly selected undergraduate students from the same university. The study data were analyzed by one-way analysis of variance, Pearson correlation analysis, hierarchical regression analysis, and a machine learning model. The results revealed that participation in school sports clubs (β = −0.111, p < 0.001) and amount of exercise (β = 0.182, p < 0.001) were significant predictors of physical health status. Difficult family economic situation (β = 0.162, p < 0.001), major satisfaction (β = −0.092, p = 0.02), the quality of a romantic relationship (β = −0.121, p = 0.003), the quality of interpersonal relationships (β = −0.157, p < 0.001), and an overprotective family parenting style (β = 0.109, p = 0.011) were significant predictors of mental health status. The results of regression analysis and application of the machine learning model identified that the amount of exercise, quality of interpersonal relationships, and family parenting style had consistent effects on both the physical and mental health of university students. Full article

The adoption of information technology (IT) has become essential for improving operational efficiency and service quality in the restaurant industry. However, integrating IT into organizational structures and daily work processes often requires employees to acquire new competencies and adapt to revised workflows, which may also generate technology-related stress. To address this issue, this study examines the relationship between IT adoption and organizational performance while considering technostressors and computer self-efficacy as moderating variables. A questionnaire-based survey was administered to restaurant employees, and hierarchical regression analysis was employed to test the proposed hypotheses. The empirical results reveal that IT adoption has a significant positive effect on organizational performance. However, this relationship is contingent upon individual-level factors: technostressors weaken, whereas computer self-efficacy strengthens, the positive impact of IT adoption on organizational performance. These findings suggest that organizations seeking to enhance performance through digital transformation should not only invest in IT systems but also address employees’ psychological and competency-related factors. Full article

Background and Objectives: Adults diagnosed with type 2 diabetes mellitus (T2DM) frequently exhibit diminished sleep quality, which is affected by their glycemic control. Both poor sleep and inadequate glycemic management increase the risk of complications worldwide. However, the relationship between sleep quality, daytime dysfunction, and glycemic control in adults with T2DM is not well understood. To address this gap, this study aimed to examine the association between overall sleep quality, including daytime dysfunction, and glycemic control in adults with T2DM. Materials and Methods: A hospital-based cross-sectional study included 200 T2DM patients (103 females, 97 males) from January 2019 to January 2020. The Pittsburgh Sleep Quality Index (PSQI) was administered to each participant to assess sleep quality, including daytime dysfunction. Glycemic control was assessed by measuring hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) levels. The associations among sleep quality, daytime dysfunction, and glycemic metrics were examined utilizing both conventional statistical methods and Bayesian analytical approaches. Results: A total of 32% of patients had poor sleep quality (PSQI > 5), and 43.5% reported daytime dysfunction. Higher HbA1c and fasting blood glucose were each correlated with longer sleep-onset latency and greater daytime dysfunction. In multivariable analyses, higher HbA1c and longer sleep-onset latency were independently associated with poor sleep quality (generalized linear model, R² = 0.602), whereas higher fasting blood glucose and longer sleep-onset latency were associated with greater daytime dysfunction severity (R² = 0.378). Conclusions: Sleep quality and delay in falling asleep are interdependent with impaired glycemic control and daytime dysfunction in T2DM. Future randomized trials targeting sleep-onset latency are warranted to determine causal effects on glycemic outcomes. Full article

Background/Objectives: In recent years, the migrant population has been increasing. Migrants are at risk for malnutrition, mental disorders and related health problems. In this study, we aimed to examine the relationships among diet quality, eating behavior, posttraumatic stress disorders and sleep quality in Syrian migrants. Methods: This study included 78 female and 72 male Syrian adult migrants. The participants completed a questionnaire including demographic information, the Pittsburgh Sleep Quality Index, the Posttraumatic Stress Disorder Short Scale, and a three-factor eating questionnaire. For the diet quality calculation, a 24 h retrospective food consumption record was taken. Results: A total of 94.0% of the participants had mild to moderate risk of posttraumatic stress disorder. It was determined that 90.7% of Syrian migrants had low or moderate diet quality. There was a positive correlation between the PTSD score and age (r = 0.244) and the Pittsburgh sleep quality index score (r = 0.244) and between the Pittsburgh sleep quality index score and uncontrolled eating (r = 0.171) (p < 0.05). The probability of impaired sleep quality increased in individuals with PTSD scores in the T2 (11–19 points) (OR: 1.342; 95% CI: 1.073–1.678) and T3 (20–31 points) (OR: 1.485; 95% CI:1.157–1.905) groups, whereas the probability of improved diet quality increased in individuals in the T2 (11–19 points) (OR: 1.042; 95% CI: 1.000–1.086) group. Conclusions: Poor diet quality, risk of posttraumatic stress disorder and poor sleep quality are very common among Syrian migrants. In this respect, evaluating Syrian migrants and taking necessary precautions may help reduce the risk of chronic diseases related to nutrient deficiencies and mental problems. There is a need for policies and programs to manage PTSD among Syrian refugees. Full article

Soil organic carbon (SOC) is a key indicator reflecting soil quality and management level. Understanding its spatiotemporal dynamics in cropland is necessary for sustainable land management. Revealing the relationship patterns between SOC (Sampling resolution: 1 km²; analysis resolution: 4 km²) and environmental factors in one period allows inferring SOC distribution in unsampled years, partly compensating for temporal data gaps. This study introduces a season-based crop rotation system (Winter wheat in the first season and summer corn in the next) as independent variables in a machine learning model innovatively, enriching variable selection in SOC inference and improving understanding of SOC accumulation. The Beijing–Tianjin–Hebei (BTH) region, characterized by a typical winter wheat–summer corn rotation system, was selected for analysis. The results show that in 2000, the average SOC was relatively low compared with global levels. Climatic variables were negatively correlated with SOC below the 0.8 quantile but positive above it, which corresponds to the upper 20% of the observed range of each climatic variable. Winter-wheat growth is more important on SOC distribution than summer-corn growth (two annual peaks of NDVI and EVI), showing a positive correlation with SOC, while corn showed a weak correlation and became negative above the 0.8 quantile. In the inferred results, the differences between observed and inferred mean values and their confidence intervals were approximately 0.1. This research provides a reference method for evaluating regional-scale SOC distribution patterns under data-limited conditions by integrating environmental factors and crop rotation characteristics. Full article

Quantitative structure–activity/property relationship (QSAR/QSPR) is a well-established methodology widely used to model molecular properties based on structure and is applied in fields such as drug design and environmental protection. The knowledge and procedures developed and used in QSPR modelling will be applied to the validation of protein folding rate models. Understanding the protein folding process is considered one of the most important scientific topics, and identifying the fundamental factors responsible for protein folding has been the subject of intensive research over the past 30 years. Among the structural descriptors determining the protein folding rate, the length of the protein sequence, the content of regular secondary structures, and the average contact row distance between amino acids in the 3D structure are the most important. Comparative studies of different methods for predicting protein folding rates are occasionally published, and we conducted one such study. We found that the experimental data in literature databases and the data available online are inconsistent and scattered. This is partly due to differences in experimental data and protein sequence lengths, but more so due to the questionable quality of the models themselves. We observed very large deviations in the predictions of ln(k_f) by some of the analysed models implemented as web servers. The root mean square errors (RMSEs) of some of the analysed models in predicting ln(k_f) for a new external set of proteins are much larger than the RMSEs obtained for the same models on the training sets. External validation demonstrates that protein folding rate models available on web servers have accuracy for external protein sets comparable to that of a simple model based solely on the logarithm of protein chain length. This finding, which highlights the importance of external model validation as recommended by the OECD guidelines for QSAR validation, is fundamental and offers a new perspective for improving protein folding rate models by applying the knowledge and procedures used in the QSPR methodology. Full article

Agroforestry, where trees and shrubs are planted in row-alley systems, can utilize the natural ability of plants to interact with pollutants and serve as a passive biotechnological method for improving air quality. A method for programming air phytoremediation processes is presented, using appropriately shaped plant structures, considering species characteristics and the spatial configuration of plants in row-alley plantings. The main objectives of this study were: to determine the relationship between pollution reduction and the characteristics of plant communities, considering the parameters of individual plants and group characteristics, to determine strategic parameters for the interaction between plants and pollutant flows, and to identify optimization paths for each stage. The optimization of the air phytoremediation process is presented using the example of changes in the fine particulate matter (PM_2.5) concentration pattern, analyzed through numerical experiments using micrometeorological computational fluid dynamics models (ENVI-met software). Ex-ante analysis of hypothetical scenarios showed that introducing appropriate configurations of variable vegetation structure could lead to pollution reductions of up to 19%. The effectiveness of the presented plant systems qualifies this method as a type of bioengineering technology, supporting the multifunctionality of agroforestry systems. Full article