Search Results (185)

Background: Postural control in healthy young adults involves complex neuromuscular processes; however, the kinematic and kinetic consequences of small, forward leg perturbations in a defined population are not fully described. This study aimed to characterize the kinematic and kinetic consequences of forward leg perturbations during quiet standing. Methods: This investigation used a quasi-experimental repeated-measures design. Sixteen healthy young women (20.1 ± 0.7 years), all right-leg dominant, were tested using the Gait Real-Time Analysis Interactive Laboratory (GRAIL) system. Forward treadmill perturbations were applied to each limb during quiet standing, and joint angles, ground reaction forces, and torques were measured across baseline, perturbation, and response phases. As the data were non-normally distributed, paired comparisons were conducted using the Wilcoxon test, with significance set at p < 0.05 (Bonferroni corrected) and effect sizes (r) reported. Results: Joint angles remained symmetrical between limbs (no significant differences after correction). In contrast, kinetic measures showed clear asymmetries: at baseline, the dominant limb produced greater knee torque (p = 0.0003, r = 0.73), ankle torque (p = 0.0003, r = 0.76), and medio-lateral GRF (p = 0.0003, r = 0.87). During perturbation, it again generated higher knee (p = 0.0036, r = 0.43) and ankle torques (p = 0.0003, r = 0.53), with larger medio-lateral GRF (p = 0.0003, r = 0.87). In the response phase, the dominant limb showed greater hip torque (p = 0.0033, r = 0.43) and a small dorsiflexion shift at the ankle (p = 0.0066, r = 0.41). Anterior–posterior GRF changes were minor and non-significant after correction. Conclusions: Induced forward leg movements caused limb-specific kinetic adjustments while maintaining overall kinematic symmetry. The dominant leg contributed more actively to balance recovery, highlighting its role in stabilizing posture under small perturbations. These findings are specific to the studied demographic and should not be generalized to males, older adults, left-dominant individuals, or clinical populations without further research. Full article

This study aimed to evaluate the placement accuracy and reproducibility of Surface Guided Radiotherapy (SGRT) compared with the conventional tattoo/laser method in patients undergoing radiotherapy for abdominal malignancies. A retrospective analysis was conducted on 43 patients treated with either SGRT (Group A) or the tattoo/laser technique (Group B). Patients in both groups underwent CBCT to quantify the positioning shifts in the vertical (Svrt), lateral (Slat) and longitudinal (Slng) axes, as well as the total shift. Statistical indicators including median, interquartile range (IQR), and range were calculated, and Mann–Whitney U tests were performed due to non-normal data distribution. Median values in all axes were same between groups: Svrt = 0.4 cm, Slat = 0.2 cm, Slng = 0.4 cm. Group A showed a higher total median shift equal to 0.8 cm versus 0.7 cm of Group B. However, IQRs were smaller in the Group B for all directions and total shift, indicating greater method consistency. Statistically significant differences (p < 0.05) were observed in all axes, except the vertical. These findings suggest that, while SGRT achieves comparable median alignment, its use in a highly variable anatomical region such as the abdomen may be associated with greater setup variability. Full article

This paper addresses the challenges of fault diagnosis in gas turbines (GTs) utilized in oil and gas pipeline systems by proposing a novel multiparameter analysis framework that integrates dynamic, quantile-based Statistical Process Control (SPC) with prior domain knowledge. The proposed approach initially employs a dynamic quantile SPC model to establish adaptive control limits, effectively handling the non-stationarity and non-normality of gas turbine operational data. By analyzing parameter variations under typical operating conditions and incorporating expert insights, a multiparameter fault analysis matrix and corresponding weighting factors are constructed to facilitate fault diagnosis with prior knowledge. Furthermore, a fault probability model based on parameter change rates and weighting factors is developed to quantify the likelihood of different fault modes. An operating condition clustering and correction mechanism enables the dynamic adjustment of control limits, thereby preventing misdiagnoses caused by varying operational states. The validity of the proposed method is demonstrated using real data from a domestic pipeline gas turbine, validated by real domestic pipeline GT data, outperforming existing models, with a fault accuracy up to 10%. The approach efficiently estimates fault probabilities and accurately detects both sudden and gradual faults, significantly enhancing intelligent fault diagnosis capabilities for gas turbines. Full article

This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture long memory, paired with both constant-volatility (CONST) and stochastic-volatility specifications via the Cox–Ingersoll–Ross (CIR) process. The novel family of models is based on Generalized Ornstein–Uhlenbeck processes with a fluctuating exponential trend (GOU-FE), which are modified to account for multiplicative fBm noise. Traditional Geometric Brownian Motion processes (GFBM) with either constant or stochastic volatilities are employed as benchmarks for comparative analysis, bringing the total number of evaluated models to four: GFBM-CONST, GFBM-CIR, GOUFE-CONST, and GOUFE-CIR models. Estimation by numerical optimization and evaluation through error metrics, information criteria (AIC, BIC, and EDC), and 95% Expected Shortfall (ES₉₅) indicated better fit for the stochastic-volatility models (GOUFE-CIR and GFBM-CIR) and the lowest tail-risk for GOUFE-CIR, although residual analysis revealed heteroscedasticity and non-normality. For intraday data, Exponential, Weibull, and Generalized Gamma Autoregressive Conditional Duration (ACD) models, with adjustments for intraday patterns, were applied to model the time between transactions. Results showed that the ACD models effectively capture duration clustering, with the Generalized Gamma version exhibiting superior fit according to the Cox–Snell residual-based analysis and other metrics (AIC, BIC, and mean-squared error). Overall, this work advances the modeling of Bitcoin prices by rigorously applying and comparing stochastic frameworks across temporal scales, highlighting the critical roles of long memory, stochastic volatility, and intraday dynamics in understanding the behavior of this digital asset. Full article

Objectives: Rapid maxillary expansion (RME) is widely used in orthodontics to correct transverse maxillary deficiencies. Beyond its skeletal and dental effects, RME may influence upper airway dimensions and respiratory function, particularly in growing individuals. This study aimed to evaluate the impact of RME on pulmonary function in adolescents using spirometric measurements. Materials and Methods: Fifteen adolescent patients (8 females, 7 males; mean age: 13.93 ± 2.89 years) diagnosed with maxillary transverse constriction underwent orthodontic treatment with acrylic-bonded RME appliances over a mean duration of 3.56 ± 0.67 months. Respiratory function was assessed via spirometry at baseline (T0) and one day after appliance removal (T1). Parameters recorded included peripheral oxygen saturation (SpO₂), forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), FEV₁/FVC ratio, and vital capacity (VC). Data were analyzed using the paired-samples t-test (for normally distributed variables) or the Wilcoxon signed-rank test (for non-normal distributions), with statistical significance set at p < 0.05. Results: Following RME treatment, all respiratory parameters showed a consistent upward trend but did not reach statistical significance. SpO₂ increased from 96.98 ± 0.96% to 97.01 ± 0.98% (p = 0.925). VC rose from 2.86 ± 1.07 L to 3.03 ± 0.80 L (p = 0.626). The FEV₁/FVC ratio improved from 90.88 ± 12.17% to 92.34 ± 7.37% (p = 0.742). Mean FEV₁ increased from 2.61 ± 0.72 L to 2.72 ± 0.68 L (p = 0.518), while FVC rose from 2.87 ± 0.75 L to 2.96 ± 0.69 L (p = 0.547). No adverse effects were reported during the treatment period. Conclusions: This study identified a non-significant but consistent trend toward improved pulmonary function following RME in adolescents. These preliminary findings should be considered hypothesis-generating rather than confirmatory evidence, as none of the outcomes reached statistical significance. While the observed upward trends in oxygen saturation, lung volumes, and expiratory performance suggest potential respiratory benefits, larger-scale, controlled, and long-term studies incorporating both spirometric and anatomical airway assessments are needed to validate these observations. Full article

This study analyzes students’ perceptions of cultural diversity in music education in the south of Spain and the northern region of the African continent, specifically in Andalusia, Ceuta, and Melilla—territories characterized by significant cultural and linguistic heterogeneity. Music, understood as a universal pedagogical tool, is approached as a strategic resource to promote educational inclusion and the development of intercultural competencies. The main objective was to examine the influence of gender, type of educational institution, and attendance at conservatories and/or music schools on students’ perceptions of intercultural inclusion in musical contexts. The sample consisted of 645 students aged between 11 and 54 years (M = 13.86; SD = 3.90), enrolled in primary schools, secondary schools, and professional and higher conservatories. Regarding gender, 55.2% identified as female, 43.6% as male, and 1.2% as non-binary. Data were analyzed using SPSS software (version 28). After verifying non-normality through the Kolmogorov–Smirnov test, non-parametric tests (Mann–Whitney U and Kruskal–Wallis H) were applied to the variables of gender, type of institution, and attendance at conservatories and/or music schools. The results show that female students tend to express more favorable perceptions regarding equality in musical ability and intercultural learning. Furthermore, students attending Conservatories and Primary Schools exhibit more positive perceptions than those in Secondary Schools. Attendance at conservatories enhances perceptions of equality in musical ability, though it does not necessarily improve intercultural relations or conflict resolution. In conclusion, the research confirms the potential of music as a vehicle for educational inclusion and the development of intercultural competencies, highlighting the need for inclusive and critical pedagogical approaches that respond to students’ cultural diversity. Full article

The study analyzes the heterogeneous impacts of the COVID-19 pandemic on financial performance across five strategic sectors of Slovakia’s economy. Using a longitudinal dataset of 500 companies (100 per sector) spanning 2015–2022, we examine changes in profitability (ROE) and liquidity (quick ratio). The examination is made by multivariate analysis and crisis matrix visualization. The research reveals four distinct sectoral response patterns: (1) the automotive industry maintained exceptional profitability (>65% ROE) but with critically low liquidity; (2) tourism and gastronomy experienced severe profitability decline but preserved stable liquidity; (3) healthcare demonstrated conservative liquidity strengthening with modest profitability impacts; (4) metallurgy and hazard sectors showed moderate volatility patterns. We introduce a crisis matrix framework combining profitability and liquidity indicators for sectoral resilience assessment. The results are validated through PERMANOVA analysis addressing non-normal data distributions that are common in crisis periods. The results demonstrate the need for differentiated crisis support policies, challenging uniform approaches to economic resilience. The study provides empirical evidence for sector-specific vulnerability patterns. It can inform strategies for future crisis preparedness. This research contributes to the crisis management literature by demonstrating how sectoral characteristics determine financial resilience pathways. The results offer insights that are applicable to similar transition economies in Central and Eastern Europe. Full article

Selection to a national team is a prestigious milestone, and the World Padel Championships showcase elite talent on a global stage. This study explored the relationship between national team quality—measured via individual FIP (Federacioón Internacional de Paádel, International Padel Federation) rankings and points—and final team placements at the 2024 World Padel Championships in Qatar. Data from 16 men’s and 16 women’s teams included final standings, average and median FIP rankings, and total and average FIP points. Pearson correlation and ANOVA were applied to average FIP rankings; due to non-normality, Spearman correlation and Kruskal–Wallis tests were used for total FIP points. Results indicated that top-ranked teams, such as Argentina, Spain, and Italy, had more players above the competition-wide ranking threshold, whereas teams like Uruguay and the USA had none. Balanced ranking distributions were observed in male teams such as Belgium and The Netherlands and in female teams such as Portugal and Argentina. A moderate positive correlation emerged between average team rankings and final placements for men, and a strong correlation for women. Total FIP points showed a very strong negative correlation with final rankings for both genders. In conclusion, individual player quality, as indicated by rankings and points, strongly correlates with team performance in the 2024 World Padel Championships. Full article

Water quality is crucial to the economy and ecology because a healthy aquatic eco-system supports human survival and biodiversity. We have developed the Neuro-Adapt Fuzzy Strategist (NAFS) to improve water quality index (WQI) forecasting accuracy. The objective of the developed model is to achieve a balance by improving prediction accuracy while preserving high interpretability and computational efficiency. Neural networks and fuzzy logic improve the NAFS model’s flexibility and prediction accuracy, while its optimized backward pass improves training convergence speed and parameter update effectiveness, contributing to better learning performance. The normalized and partial derivative computations are refined to improve the model. NAFS is compared with ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS), and current machine learning (ML) models such as LSTM, GRU, and Transformer based on performance evaluation metrics. NAFS outperforms ANFIS and ANN, with MSE of 1.678. NAFS predicts water quality better than ANFIS and ANN, with RMSE of 1.295. NAFS captures complicated water quality parameter interdependencies better than ANN and ANFIS using principal component analysis (PCA) and Pearson correlation. The performance comparison shows that NAFS outperforms all baseline models with the lowest MAE, MSE, RMSE and MAPE, and the highest R², confirming its superior accuracy. PCA is employed to reduce data dimensionality and identify the most influential water quality parameters. It reveals that two principal components account for 72% of the total variance, highlighting key contributors to WQI and supporting feature prioritization in the NAFS model. The Breusch–Pagan test reveals heteroscedasticity in residuals, justifying the use of non-linear models over linear methods. The Shapiro–Wilk test indicates non-normality in residuals. This shows that the NAFS model can handle complex, non-linear environmental variables better than previous water quality prediction research. NAFS not only can predict water quality index values but also enhance WQI estimation. Full article

The growing demand for health-promoting and eco-friendly foods has driven interest in biofunctional dairy products. Goat milk yogurt, though nutritionally beneficial, faces sensory challenges, while antioxidant-rich spent coffee grounds (SCGs), a coffee by-product, offer sustainable enhancement potential. This study assessed the consumer acceptance of goat milk yogurt enriched with 2% and 3% SCG extract. A total of 137 untrained consumers evaluated six sensory attributes—appearance, aroma, taste, texture, coffee–yogurt balance, and aftertaste—on a five-point hedonic scale. Due to non-normal data, Wilcoxon rank-sum tests and Spearman correlations were applied. No significant differences emerged between formulations (p > 0.05). Taste, aftertaste, and aroma were strongly correlated (r > 0.65). All attributes significantly predicted purchase intent (p < 0.01), with taste as the strongest driver (OR = 2.24). Consumers aged 26–35, usually presenting health or environmental concerns, showed greater acceptance. The addition of SCG extract did not compromise sensory quality, supporting its viability as a sustainable functional ingredient. These findings present high acceptance of a newly developed eco-friendly and nutritionally beneficial product, responding to consumers’ current qualitative demands related to the food they consume. Full article

Water quality assessment plays a vital role in environmental monitoring and resource management. This study aims to enhance the predictive modeling of the Water Quality Index (WQI) using a combination of statistical diagnostics and machine learning techniques. Data collected from six river locations in Malaysia are analyzed. The methodology involves collecting water quality data from six river locations in Malaysia, followed by a series of statistical analyses including assumption testing (shapiro–wilk and breusch–pagan tests), diagnostic evaluations, feature importance analysis, and principal component analysis (PCA). Decision tree regression (DTR) and autoregressive integrated moving average (ARIMA) are employed for regression, while random forest is used for classification. Learning curve analysis is conducted to evaluate model performance and generalization. The results indicate that dissolved oxygen (DO) and ammoniacal nitrogen (AN) are the most influential parameters, with normalized importance scores of 1.000 and 0.565, respectively. The breusch–pagan test identifies significant heteroscedasticity (p-value = $\left(3.138e^{-115}\right)$), while the Shapiro–Wilk test confirms non-normality (p-value = 0.0). PCA effectively reduces dimensionality while preserving 95% of dataset variance, optimizing computational efficiency. Among the regression models, ARIMA demonstrates better predictive accuracy than DTR. Meanwhile, random forest achieves high classification performance and shows strong generalization capability with increasing training data. Learning curve analysis reveals overfitting in the regression model, suggesting the need for hyperparameter tuning, while the classification model demonstrates improved generalization with additional training data. Strong correlations among key parameters indicate potential multicollinearity, emphasizing the need for careful feature selection. These findings highlight the synergy between statistical pre-processing and machine learning, offering a more accurate and efficient approach to water quality prediction for informed environmental policy and real-time monitoring systems. Full article

Fibromyalgia is a complex disorder characterized by chronic widespread pain and a variety of related symptoms. Growing evidence suggests that the central and peripheral nervous systems are involved, with small fiber neuropathy playing a key role in its development. We retrospectively reviewed the medical records of 100 patients diagnosed with primary fibromyalgia. Those showing symptoms indicative of small fiber dysfunction who were treated with L-Acetyl Carnitine (LAC) and Palmitoylethanolamide (PEA) alongside standard care (SOC) were compared to matched controls who received only SOC. To ensure comparable groups, propensity score matching was used. Changes in Fibromyalgia Impact Questionnaire Revised (FIQR) scores over 12 weeks were analyzed using non-parametric tests due to the data’s non-normal distribution. After matching, 86 patients (43 in each group) were included. The group receiving LAC and PEA as add-on therapy experienced a significant median reduction in FIQR scores (−19.0 points, p < 0.001), while the SOC-only group showed no significant change. Comparisons between groups confirmed that the improvement was significantly greater in the LAC+PEA group (p < 0.001). These results suggest that adding LAC and PEA to standard care may provide meaningful symptom relief for fibromyalgia patients with suspected small fiber involvement. This supports the hypothesis that peripheral nervous system dysfunction contributes to the disease burden in this subgroup. However, further prospective controlled studies are needed to confirm these promising findings. Full article

Background/Objectives: The rapid emergence of antibiotic-resistant Escherichia coli in food and clinical environments necessitates new, clean-label antimicrobials. This study assessed eight Greek native essential oils—oregano, thyme, dittany, rosemary, peppermint, lavender, cistus and helichrysum—for activity against six genetically and phenotypically diverse E. coli strains (reference, pNorm, mecA, mcr-1, blaOXA and O157:H7). We aimed to identify oils with broad-spectrum efficacy and clarify the chemical constituents responsible. Methods: Disk-diffusion assays measured inhibition zones at dilutions from 50% to 1.56% (v/v). MIC and MBC values were determined by broth microdilution. GC–MS profiling identified dominant components, and Spearman rank-order correlations (ρ) linked composition to activity. Shapiro–Wilk tests (W = 0.706–0.913, p ≤ 0.002) indicated non-normal data, so strain comparisons used Kruskal–Wallis one-way ANOVA with Dunn’s post hoc and Bonferroni correction. Results: Oregano, thyme and dittany oils—rich in carvacrol and thymol—exhibited the strongest activity, with MIC/MBC ≤ 0.0625% (v/v) against all strains and inhibition zones > 25 mm at 50%. No strain-specific differences were detected (H = 0.30–3.85; p = 0.998–0.571; p_adj = 1.000). Spearman correlations confirmed that carvacrol and thymol content strongly predicted efficacy (ρ = 0.527–0.881, p < 0.001). Oils dominated by non-phenolic terpenes (rosemary, peppermint, lavender, cistus, helichrysum) showed minimal or no activity. Conclusions: Phenolic-rich EOs maintain potent, strain-independent antimicrobial effects—including against multidrug-resistant and O157:H7 strains—via a multi-target mode that overcomes classical resistance. Their low-dose efficacy and GRAS status support their use as clean-label food preservatives or adjuncts to antibiotics or bacteriophages to combat antimicrobial resistance. Full article

Formative research is an essential component of higher education, aimed at developing research competencies in students, with an emphasis on critical thinking, academic autonomy, and analytical capacity. Its purpose is not the production of original knowledge but the systematic preparation for research activity. Within this framework, the objective of this study is to analyze how conceptual appropriation, which encompasses theoretical, methodological, procedural, and normative knowledge, is related to students’ perceived research skills. This study is grounded in the imperative of fostering higher education that cultivates critical, autonomous, and ethically responsible researchers. For this purpose, a quantitative methodology was used, with a non-experimental and correlational design, applying a census sampling to 10,536 students from a higher education institution. Data were collected through a structured survey on conceptual appropriation and perceived research skills. After the removal of inconsistent records, the data were processed statistically using non-parametric tests, particularly Spearman’s correlation, due to the non-normal distribution of the variables. The results reveal strong and significant correlations between conceptual appropriation and key research skills such as hypothesis formulation, critical thinking, and motivation for research, demonstrating that greater conceptual mastery promotes a more solid and engaged research training. These findings reinforce the need to systematically and progressively integrate research content into the university curriculum, fostering an authentic, reflective, and contextualized education. Full article

Researchers conduct experiments to discover factors influencing the experimental subjects, so the experimental design is essential. The response surface methodology (RSM) is a special experimental design used to evaluate factors significantly affecting a process and determine the optimal conditions for different factors. The relationship between response values and influencing factors is mainly established using regression analysis techniques. These equations are then used to generate contour and surface response plots to provide researchers with further insights. The impact of regression techniques on response surface methodology (RSM) model building has not been studied in detail. This study uses complete regression techniques to analyze sixteen datasets from the literature on semiconductor manufacturing, steel materials, and nanomaterials. Whether each variable significantly affected the response value was assessed using backward elimination and a t-test. The complete regression techniques used in this study included considering the significant influencing variables of the model, testing for normality and constant variance, using predictive performance criteria, and examining influential data points. The results of this study revealed some problems with model building in RSM studies in the literature from three engineering fields, including the direct use of complete equations without statistical testing, deletion of variables with p-values above a preset value without further examination, existence of non-normality and non-constant variance conditions of the dataset without testing, and presence of some influential data points without examination. Researchers should strengthen training in regression techniques to enhance the RSM model-building process. Full article