Search Results (400)

Background/Objectives: University life is often accompanied by unhealthy lifestyle behaviors, reduced physical activity, lower fitness levels, and a high prevalence of mental health symptoms. Daily step count has emerged as a practical indicator of habitual physical activity; however, evidence on its association with cardiorespiratory fitness and symptoms of depression, anxiety, and stress in university students remains limited. Therefore, this study examined the association of daily step count with cardiorespiratory fitness and symptoms of depression, anxiety, and stress in university students. Methods: This cross-sectional association study included a convenience sample of 120 students aged 18 to 25 years from a single university. Daily step count was assessed over seven consecutive days using a Xiaomi Mi Band 9. Cardiorespiratory fitness was evaluated with the 20 m shuttle run test, and symptoms of depression, anxiety, and stress were measured using the Depression, Anxiety and Stress Scale-21 Items (DASS-21). Partial correlations, ANCOVA, MANCOVA, binary logistic regression, and restricted cubic spline models were performed after adjustment for sex, age, and socioeconomic status. Results: Higher daily step count was associated with greater cardiorespiratory fitness and with lower symptoms of depression, anxiety, and stress, although the associations with mental health symptoms were weak and not uniform across outcomes. Restricted cubic spline models showed inverse non-linear associations for mental health symptoms, with steeper inverse gradients at lower step-count levels and a tendency to level off at higher volumes, approximately around 9000 steps/day. For cardiorespiratory fitness, the association was positive across the step-count range. Step counts around 7500 steps/day were associated with lower odds of elevated symptoms of depression, anxiety, and stress. Conclusions: A higher daily step count was associated with more favorable mental health symptom profiles and greater cardiorespiratory fitness in this sample of university students. Full article

Large language model (LLM)-based document translation systems typically treat each segment independently, discarding terminology decisions, human corrections, and discourse cues after each generation step. This stateless approach causes terminology inconsistency across segments, failure to propagate approved post-edits downstream, and redundant prompt-token consumption. Existing solutions—document-level MT, retrieval-augmented generation, and computer-assisted translation (CAT) tools as a general category—address individual aspects but lack a unified, state-aware architecture with provenance, update rules, and rollback semantics. We propose Language Twin, a shared-state architecture that organizes translation projects into seven versioned layers (L0–L6), supporting selective context loading, scoped human-edit propagation, and reversible updates. A pilot study translated three curated English-to-Korean document bundles (17 segments) using GPT-4o with a temperature of 0.3. The Language Twin condition (P1) achieved numerically higher preferred-term accuracy than the strongest baseline (17/21 vs. 14/21; not statistically significant at this sample size) and showed no repeated downstream errors in the monitored set (0/5 vs. 5/5 against the propagation-disabled ablation; Fisher’s exact test: p = 0.008), while reducing prompt tokens by 39.2% relative to full-context loading (A4). In blinded human evaluation (quadratic-weighted κ = 0.71–0.78), P1 achieved the highest terminology rating (4.38/5 vs. 3.97/5) and lowest post-editing time (16.9 s vs. 19.1 s per segment). These pilot-scale results indicate that governed shared state can improve terminology consistency and editing efficiency. Full article

Images generated by artificial intelligence recombine visual fragments learned from datasets, producing representations based on criteria of semantic proximity and aesthetic familiarity. These images lie in an intermediate zone between verisimilitude and statistical construction, requiring new interpretative skills to understand their nature and limitations. This paper explores the need to develop visual literacy for generative AI, understood as the critical ability to analyse generation processes, recognise implicit biases, and verify the consistency of the representations produced. Through some case studies, prompting is analysed as a dialogical and reflective practice that highlights recurring patterns in datasets and diffusion models. The cases highlight how automatic composition tends to reproduce dominant cultural patterns related to gender, posture, and professional role. This paper introduces NEGOTIA, a seven-step framework designed to foster critical and operational visual literacy, applicable in educational and design contexts where synthetic images function as tools for representation, communication, and verification. NEGOTIA offers a replicable model for education and design practice. Full article

Distributed denial-of-service (DDoS) attacks, a type of Denial-of-Service (DoS) attack in which the targeted server, service or network is overloaded with malicious traffic originating from various different sources with the aim of making such targets inaccessible for legitimate users, continue to pose a pertinent threat to the availability and integrity of organisational digital assets. While many studies have shown that machine learning models can provide high predictive accuracy in detecting such attacks, they often fail to evaluate the practicality of deploying such models to production. This study aims to address this gap by evaluating a considerable amount of pipelines based on five popular supervised classifiers for detecting DDoS attacks using the CICDDoS2019 dataset. The study employs a comprehensive methodology that combines both manual feature removal with automated encoding, scaling and feature selection integrated within pipelines. A total of 210 pipelines formed of five classifiers, three features selectors, two hyperparameter tuners and seven train–test splits were initially evaluated. Pipeline performance was assessed using both conventional and computational performance metrics. To identify the champion pipeline, a two-step approach was employed: composite scoring for shortlisting and statistical testing using Friedman and post hoc Nemenyi tests. The champion pipeline was shown to be Decision Tree coupled with Recursive Feature Elimination (with 20 features selected) and Grid Search hyperparameter tuning with a 90-10 train–test split. It achieved the most optimal balance of predictive capabilities and computational overheads, achieving an MCC of $0.993\pm 0.024$, training time of $0.194\pm 0.001$ s, inference time of $0.000998\pm 0.00008$ s, CPU time of $0.194\pm 0.008$ s and average memory usage of 15,167 ± 322 kilobytes across training and inference. The findings highlight the importance of a holistic and more nuanced approach when selecting a champion pipeline that is not only effective but also feasible for deployment in resource-constrained environments. Full article

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, $D=100$), 1.000 (CEC2020, $D=10$), and 2.208 (CEC2022, $D=10$); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant ($p<0.05$). Full article

Background/Objectives: Vision-language models (VLMs) such as GPT (Generative Pre-Trained Transformer) have shown potential for medical image interpretation; however, challenges remain in generating reliable radiological findings in clinical practice, as exemplified by dental pathologies. This study proposes a Self-correction Loop with Structured Output (SLSO) framework as an integrated processing methodology to enhance the accuracy and reliability of AI-generated findings for jaw cysts in dental panoramic radiographs. Methods: Dental panoramic radiographs with jaw cysts were used to implement a 10-step integrated processing framework incorporating image analysis, structured data generation, tooth number extraction, consistency checking, and iterative regeneration. The framework functioned as an external validation mechanism for GPT outputs. Performance was compared against the conventional Chain-of-Thought (CoT) method across seven evaluation items: transparency, internal structure, borders, root resorption, tooth displacement, relationships with other structures, and tooth number. Results: The SLSO framework improved output accuracy for multiple items compared to the CoT method, with the most notable improvements observed in tooth number identification, tooth displacement detection, and root resorption assessment. In successful cases, consistently structured outputs were achieved after up to five regenerations. The framework enforced explicit negative finding descriptions and suppressed hallucinations, although accurate identification of extensive lesions spanning multiple teeth remained limited. Conclusions: This investigation established the feasibility of the proposed integrated processing methodology and provided a foundation for future validation studies with larger, more diverse datasets. Full article

The growing implementation of Building Information Modelling (BIM) within the architecture, engineering, and construction (AEC) industries has placed increased pressure on higher education institutions to prepare graduates for interdisciplinary digital collaboration. In many emerging higher education environments, such as South Africa, structured pedagogical frameworks for BIM Level 3 collaboration are less well established. This paper addresses this gap by introducing and evaluating a seven-step BIM collaboration framework in an interdisciplinary final year undergraduate project. A comparative cohort case study design was adopted, analysing two cohorts: the 2022 cohort operating within a traditional siloed design model, and the 2023 cohort applying the proposed framework. Grounded in Habermas’s theory of communicative action, student design projects and self-reflection narratives from both the traditional siloed design process and the BIM-enabled framework were analysed deductively according to communication frequency, content, and quality as key categories. Communication quality was evaluated through intrinsic, contextual, representational, and accessibility information dimensions. Findings show that the BIM group had higher levels of established collaboration, better-quality contextually available information, more accessible structured data, and more effective communication. The findings indicate that structured BIM-based collaboration enhances a transformation from mere data exchange to constructive participation and comprehensive information development among students. Rather than functioning solely as a technical tool, BIM served as a structured communication environment that supported critical engagement and interdisciplinary workflows. This study offers a transferable pedagogical model for interdisciplinary BIM education and provides evidence supporting communication-oriented approaches to digital collaboration within built environment curricula. Full article

Background/Objectives: Youths with cerebral palsy (CP) have reduced levels of physical activity (PA) due to motor impairments and functional difficulties. Few studies have observed its link with various factors and none in young adults with CP. This study aimed to investigate the relationships between PA and various factors in young adults with CP, such as gait function, endurance, participation, and personal/environmental influences. Methods: Participants over 15 years old with CP who underwent Clinical Gait Analysis (CGA), the 6 min walk test, and wore an actimeter (ActiGraph GT3X+) for seven days were included. PA was assessed by daily step count (NbSteps/day). Explanatory factors included the Gait Profile Score (GPS), walking speed, subjective walking perception, joint pain, fatigue, 6 min walk distance, SF-36 questionnaire scores, and lifestyle habits. Correlations, univariate, and multivariate regression models were used for the analysis. Results: Forty-seven CP patients (28 males, 19 females, mean age 23.6 years) were included, with 82% classified as GMFCS I and 18% as GMFCS II. The average NbSteps/day was 5685. Significant correlations were found between NbSteps/day and subjective perception, pain, GMFCS level, and walking speed. Multivariate regression identified walking speed and physiotherapy (PT) sessions as significant predictors of PA. Conclusions: PA in young adults with CP is linked to walking speed, GMFCS level, joint pain, fatigue, and PT. No differences have been observed between patient unilateral or bilateral CP. However, individual behaviors vary and are not fully explained by linear regression analysis. Full article

The primary aim of this study is to develop a comprehensive theoretical model for creating digital content that enhances speech formation in toddlers aged 1–3, based on children’s folklore. This model seeks to integrate pedagogical, psychological, and cultural elements to offer a balanced and age-appropriate digital learning experience for young children. The study employed a systematic literature review using Creswell’s seven-step process, which involved identifying relevant research, reviewing and analyzing 22 peer-reviewed studies published between 2019 and 2023, and synthesizing their findings. VOSviewer version 1.6.18, a bibliometric visualization tool, was used to conduct a keyword co-occurrence analysis, identifying key concepts and trends in digital content creation for toddlers. The systematic review adhered to the PRISMA framework to ensure rigor in the selection and analysis of the included studies, which spanned fields such as education, psychology, and pediatric development. The study identified several key dimensions necessary for developing an effective theoretical model of digital content creation for toddlers: The content must be age-appropriate and consider the unique cognitive, linguistic, and developmental needs of toddlers. Children’s folklore plays a crucial role in language development, offering culturally rich and rhythmically engaging material for young learners. The model must address the balance between screen time and real-world interactions, ensuring that digital engagement does not replace essential real-life learning experiences. Ensuring the psychological and physiological safety of digital content is paramount, requiring the exclusion of inappropriate or harmful material and the inclusion of interactive, engaging content that supports speech development. The study concludes that a well-designed model for digital content creation, rooted in children’s folklore, can significantly enhance speech development in toddlers. Such a model must not only support language acquisition but also reflect cultural heritage, promote safe digital environments, and encourage a balance between digital and real-world interactions. By integrating the findings from various disciplines, this theoretical model provides a holistic framework that can guide the development of high-quality digital content aimed at supporting early childhood language development in the digital age. Full article

Wavelength modulation spectroscopy (WMS) is a representative implementation of tunable diode laser absorption spectroscopy (TDLAS), enabling reliable gas component analysis with concentration-related information derived from harmonic component extraction, while offering enhanced noise immunity for trace gas sensing in open environments. However, due to the strong coupling between laser wavelength and intensity, wavelength modulation inevitably introduces residual amplitude modulation (RAM), which significantly degrades measurement accuracy. To address this issue, this study introduces a RAM suppression algorithm based on multiple harmonic component decoupling (MHCD), using the second-harmonic lateral peak inclination angle (LPIA) as a characteristic indicator. Unit harmonic operators for the first, second, and third harmonics are designed, and an original harmonic reconstruction model is established via linear superposition of harmonic components. The optimal harmonic component ratio is determined at the composite operator with the maximum cross-correlation coefficient, and RAM noise is eliminated through a multi-harmonic decoupling matrix. Repetitive measurements on 22 mm pharmaceutical vials with 4% oxygen concentration demonstrate that MHCD reduces the second-harmonic LPIA from 18.07° to 8.56°. Concentration discrimination experiments conducted on seven groups of 22 mm vials with 2% concentration steps (0–12%) show that MHCD increases the true positive rate by 6–11% and decreases the false positive rate by 4–9%, confirming its effectiveness for pharmaceutical online inspection applications. Full article

Energy dissipation in stepped weirs depends on the complex interaction between geometry, flow regime, and surface aeration. The research proposes a dimensionless empirical model (RE3T) to predict the overall energy dissipation in three-section stepped weirs with variable slopes. The formulation integrates dimensional analysis based on the Vaschy–Buckingham theorem, controlled physical experimentation, and three-dimensional numerical simulations using CFD employing the RANS–SST turbulence model implemented in ANSYS CFX. Eighteen numerical simulations were performed covering seven geometric configurations and four hydraulic inlet conditions, covering slug, transitional, and skimming flow regimes. The CFD model was previously validated by comparison with a physical scale model, obtaining a discrepancy of only 0.38% in relative energy dissipation. The validated dataset was then used to calibrate an empirical multiplicative correlation composed of eight dimensionless groups associated with sectional slopes, number of steps, overall geometric ratio, and upstream Froude number. The proposed model achieved a coefficient of determination R² = 0.81, with relative errors generally less than 1% and a maximum deviation of 2.34%. The statistical indicators (RMSE, MAE, and bias) confirm the absence of significant systematic trends within the defined domain of validity. The results show that the Froude number and the slopes of the sections are the variables with the greatest influence on overall dissipation. The RE3T formulation is a physically consistent and computationally efficient predictive tool for the design and analysis of stepped weirs with variable slopes, extending the scope of traditional correlations developed for uniform slopes. Full article

The rise of carbon emissions from fossil fuel-based power generation has intensified the need for efficient and low-carbon energy systems. The global CO₂ concentration has risen from 285 ppm in the pre-industrial era to nearly 420 ppm today, and this contributes to a 1°C increase in average temperature. Therefore, in this article, a hybrid photovoltaic–thermoelectric generator (PV–TEG) system integrated with a reduced-switch multilevel inverter (MLI) is proposed. This enhances renewable energy utilization and power quality. The proposed PV–TEG model recovers waste heat from PV modules, which yields an overall efficiency improvement of approximately 2–8% compared to standalone PV systems. Further, the proposed MLI operates in symmetric (seven-level) and asymmetric (11-level) modes using eight switches. The system develops high-quality stepped output voltages with a minimum component count. Simulation work is performed, and the results show a peak output voltage of ±220 V with Total Harmonic Distortion (THD) of 7.2% under R-load and reduced THD below 5% under RL and variable load conditions. The integrated system demonstrates improved efficiency, reliability, and suitability for sustainable power generation and rural electrification. Full article

Physical activity (PA) is an important factor for maintaining health and well-being, especially in older adults. This study aims to apply machine learning methods to predict PA patterns and identify key factors influencing these behaviors among community-dwelling older adults. Linear and Logistic Regression, Elastic Net, and Light Gradient Boosting Machine (LightGBM) models were used to analyze cross-sectional data. While longitudinal data collected over 14 days were analyzed using LightGBM, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). The most important predictors identified in the cross-sectional analysis were the Exercise Self-efficacy Scale (ESES) for PA levels and the Geriatric Depression Scale (GDS) for the International Physical Activity Questionnaire (IPAQ) as a continuous measurement. In the longitudinal analysis, using a seven-day sequence of step count data provided the best performance for forecasting physical activity for the entire next day. Overall, the findings indicate that combining wearable sensor data with machine learning and deep learning methods can provide valuable insights into physical activity behaviors among older adults. In the cross-sectional analysis, psychological and motivational factors such as self-efficacy were identified as important factors for activity levels, while in the longitudinal analysis, using a week of past step count data provided the most reliable predictions of future-day physical activity. Full article

The circular economy (CE) has emerged as a compelling alternative to the dominant linear “take–make–waste” model, which has contributed to environmental degradation, resource scarcity, and social inequalities across global value chains. By emphasizing the reduction in waste, the circulation of products and materials at their highest value, and the regeneration of natural systems, CE offers a pathway toward more sustainable and resilient forms of production and consumption. Despite its growing prominence, organizational implementation of CE remains uneven and challenging. This paper synthesizes current developments on CE implementation in business, with particular attention to environmental, economic, and social dimensions. Building on this synthesis, the paper identifies key internal and external challenges and proposes a practical framework outlining seven transition steps for organizations, ranging from strategic commitment and governance to monitoring and continuous improvement. Two case vignettes from the consumer goods and fashion industries illustrate how firms implement circular principles through business model innovation, supply-chain collaboration, and consumer engagement, while also highlighting ongoing limitations and trade-offs. Overall, the paper demonstrates that while the transition to a circular economy is complex, it is achievable through coordinated organizational change, stakeholder involvement, and systemic innovation, offering benefits for businesses, society, and the environment. Full article

Introduction: Maintaining adherence and access to antiretroviral treatment is basic for good management of people with HIV (PWH), while enhancing patient satisfaction. With the aim of shifting from drug-centered into patient-centered care and integrating care interventions into community settings, here we share an outpatient hospital pharmaceutical care implementation model for PWH. This model involves the delivery of medication through a community center, BCN-Checkpoint, using a proprietary app and coordinated with automated locker systems. Methods: During the pre-implementation phase the circuit was defined and seven steps were considered critical for successful implementation: (1) assignation of teams and roles; (2) adaptation of the self-developed app; (3) development of a patient journey map; (4) locker installation and system integration with data from the electronic records; (5) staff training; (6) review of data protection regulations; (7) simulation tests. A two-phase simulation—with fictitious users and with real ones—validated the system. The implementation phase included an initial pilot study, in which 46 patients were included in the project. Results: System uptake was high, with strong adherence to the dispensing pathway; only five discontinuations due to personal preferences or availability of alternative dispensing pathways. Several barriers to implementation emerged, primarily categorized into technical issues, process and operational challenges, coordination, and user-related difficulties. However, a communitarian setting, flexible attention times and protocols, and the strong intersectoral collaboration between specialists are believed to increase patient retention and overall satisfaction. Conclusions: The implementation of an outpatient dispensing hospital medication model using an app and automated locker systems is feasible, considering detail to procedures, timely adaptations, and staff training. Full article