Search Results (4,644)

Background: Minimally invasive surgery (MIS) is increasingly adopted in pediatric surgical practice, yet it demands specific technical skills that require structured training. Simulation-based education offers a safe and effective environment for skill acquisition, especially in complex procedures such as thoracoscopic repair of esophageal atresia with tracheoesophageal fistula (EA-TEF). Objective: This study aimed to evaluate the effectiveness of a 3D-printed simulator for training pediatric surgeons in thoracoscopic EA-TEF repair, assessing improvements in operative time and technical performance. Methods: A high-fidelity, 3D-printed simulator replicating neonatal thoracic anatomy was developed. Six pediatric surgeons at different training levels performed eight simulation sessions, including fistula ligation and esophageal anastomosis. Operative time and technical skill were assessed using the Stanford Microsurgery and Resident Training (SMaRT) Scale. Results: All participants showed significant improvements. The average operative time decreased from 115.6 ± 3.51 to 90 ± 6.55 min for junior trainees and from 100.5 ± 3.55 to 77.5 ± 4.94 min for senior trainees. The mean SMaRT score increased from 23.8 ± 3.18 to 38.3 ± 3.93. These results demonstrate a clear learning curve and enhanced technical performance after repeated sessions. Conclusions: Such 3D-printed simulation models represent an effective tool for pediatric MIS training. Even within a short time frame, repeated practice significantly improves surgical proficiency, supporting their integration into pediatric surgical curricula as an ethical, safe, and efficient educational strategy. Full article

Objective: This study aimed to assess the feasibility and acceptability of a Culinary Medicine and Wellness (CMW) class among undergraduate college students attending a U.S. Minority-Serving Institution (MSI), as well as their food security, mental health status, and eating behaviors. Methods: This pre- and post-intervention study was conducted at an MSI in a Southeastern U.S. University, where students enrolled in a 15-week, three-credit CMW class equivalent to 2.5 h per week and received instruction on cooking and preparing healthy meals on a budget. The primary outcomes were acceptability and feasibility of the CMW class. Participants’ food security status, mental health status, and fruit and vegetable intake were also assessed. Program evaluation utilized thematic analysis and descriptive statistics, and trend analyses of outcomes were performed. Results: Eleven participants completed both surveys. The average age was 24 years, with 73% identifying as Black/African American. All participants were female and experienced low or very low food insecurity, and most reported moderate stress levels. All participants reported they would recommend the CMW class to others, with 73% rating it as excellent. Additionally, 82% felt they had learned valuable cooking and budgeting skills. Conclusions: The acceptability and feasibility of a CMW class among college students at an MSI suggests a promising approach to improving cooking skills, enhancing nutrition knowledge, increasing fruit and vegetable intake, and reducing stress. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Background: Motor performance in childhood predicts physical fitness, cognitive capacity, socio-emotional development, and academic success. Parenting styles are especially important to such performance in the preschool period, as children’s gross motor abilities are shaped in part by their interactions with parents. Young children’s physical activity is also declining as they spend more time on screens. Methods: This quantitative survey-based study examined the relationships among 252 preschoolers’ gross motor skills, their tendency to become addicted to digital games, and their parents’ parenting styles. Results: The sampled preschoolers’ gross motor skill development and game addiction tendencies were both low, while the participating parents reported high levels of democratic and overprotective parenting attitudes, low levels of authoritarian ones, and moderate levels of permissive ones. Motor skills were not associated with children’s addiction tendency or parents’ democratic (also known as authoritative), authoritarian, or permissive styles. However, overprotective parenting was positively and significantly associated with gross motor skill scores. While no significant relationship was found between children’s digital game addiction tendencies and their parents’ adoption of a democratic parenting style, such tendencies were positively and statistically correlated with the authoritarian and permissive parenting styles. One dimension of such tendencies, constant gameplay, was also positively and significantly correlated with overprotective parenting. Conclusions: Although the participating children’s digital game addiction tendencies were low, the findings indicate that parents and carers should guide children to reduce their screen time and promote increased interaction with their surroundings and other people to mitigate screen time’s known negative effects on gross motor coordination. Full article

Background: Pre-season training is pivotal for optimizing athletic performance in collegiate basketball, yet the effectiveness of such programs in improving body composition (BC) under NCAA-mandated hourly restrictions remains underexplored. The aim of this study was to evaluate the impact of a four-week, NCAA Division II-compliant strength and conditioning (SC) program on BC in women’s basketball. Methods: Sixteen student athletes (20.6 ± 1.8 y; 173.9 ± 6.5 cm; 76.2 ± 20.2 kg) completed an eight-hour-per-week micro-cycle incorporating functional conditioning, Olympic-lift-centric resistance, and on-court skill development. Lean body mass (LBM) and body-fat percentage (BF%) were assessed using multi-frequency bioelectrical impedance on Day 1 and Day 28. Linear mixed-effects models were used to evaluate the fixed effect of Time (Pre, Post), including random intercepts for each athlete and covariate adjustment for age and height (α = 0.05). Results The LBM significantly increased by 1.49 kg (β = +1.49 ± 0.23 kg, t = 6.52, p < 0.001; 95% CI [1.02, 1.96]; R² semi-partial = 0.55), while BF% decreased by 1.27 percentage points (β = −1.27 ± 0.58%, t = −2.20, p = 0.044; 95% CI [−2.45, −0.08]; R² = 0.24). Height positively predicted LBM (β = +1.02 kg/cm, p < 0.001), whereas age showed no association (p > 0.64). Conclusions: A time-constrained, NCAA-compliant SC program meaningfully enhances lean mass and moderately reduces adiposity in collegiate women’s basketball athletes. These findings advocate for structured, high-intensity, mixed-modality training to maximize physiological readiness within existing regulatory frameworks. Future research should validate these results in larger cohorts and integrate performance metrics to further elucidate functional outcomes. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

School readiness during early childhood is crucial for future academic success. Existing guidelines recommend a comprehensive approach. This concurrent validation study developed a School Readiness Index (SRI) with five readiness domains: early learning skills, approach to learning, cognitive skills, socioemotional development, and physical health. Through a cross-sectional comparative design, the school readiness skills of 119 Puerto Rican children (63 males, 56 females) aged 54–65 months were assessed using standardized tests (e.g., Batería IV Woodcock-Muñoz and NIH Toolbox Cognition Battery), parental questionnaires (e.g., Ages and Stages Questionnaire-3), and physical health assessments. Each measure was scored and classified using a binary coding system (0 and 1) based on participant abilities (e.g., 1 for expected performance, 0 if below expectations). A composite SRI score was calculated using 25 indicators. Discriminant validity was assessed by comparing children’s registration status in the special education program (SEP). Sex, household income, and maternal education are key determinants of school readiness. Children registered in the SEP had significantly lower composite scores than those not registered, supporting the SRI’s discriminant validity. The SRI is a reliable tool for identifying Hispanic children from Puerto Rico who may benefit from additional support. Inclusive and multidisciplinary assessment strategies are essential. Full article

The increasing demand for data-driven decision making to maintain the innovations and competitiveness of organizations highlights the need for data science educations across academia and industry. At its core is a solid understanding of statistics, which is necessary for conducting a thorough analysis of data and deriving valuable insights. Unfortunately, conventional statistics learning often lacks practice in real-world applications using computer programs, causing a separation between conceptual knowledge of statistics equations and their hands-on skills. Integrating statistics learning into Python programming can convey an effective solution for this problem, where it has become essential in data science implementations, with extensive and versatile libraries. In this paper, we present a self-learning method for fundamental statistics through Python programming for data science studies. Unlike conventional approaches, our method integrates three types of interactive problems—element fill-in-blank problem (EFP), grammar-concept understanding problem (GUP), and value trace problem (VTP)—in the Programming Learning Assistant System (PLAS). This combination allows students to write code, understand concepts, and trace the output value while obtaining instant feedback so that they can improve retention, knowledge, and practical skills in learning statistics using Python programming. For evaluations, we generated 22 instances using source codes for fundamental statistics topics, and assigned them to 40 first-year undergraduate students at UPN Veteran Jawa Timur, Indonesia. Statistics analytical methods were utilized to analyze the student learning performances. The results show that a significant correlation ($\rho <0.05$) exists between the students who solved our proposal and those who did not. The results confirm that it can effectively assist students in learning fundamental statistics self-learning using Python programming for data science implementations. Full article

Background: The incidence of anterior cruciate ligament ruptures in football has experienced a marked increase in recent years, affecting both professional and amateur players. This injury is characterised by being highly disabling, causing the player to withdraw from the field of play for prolonged periods and there is no clear consensus on how to carry out the different phases of rehabilitation, which poses a major challenge for health professionals. Case presentation: This study followed a semi-professional player who suffered an anterior cruciate ligament tear following two forced valgus actions without direct contact in the same match. Outcome and follow-up: The patient underwent surgery using an autologous hamstring graft. He followed a progressive rehabilitation programme consisting of one preoperative phase and six phases after the operation. After a 12-month follow-up, with exercises aimed at perfecting step-by-step basic and specific physical skills, the player showed a complete functional recovery, achieving the desired parameters. Conclusions: This case highlights the importance of structured rehabilitation adapted to the specific needs of the football player through an approach with coherent progressions, which considers the muscle chains that determine the movements performed on the football pitch. Full article

Background/Objective: This study aims to evaluate the predictive performance of three supervised machine learning algorithms—decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN) in forecasting key visual skills relevant to rhythmic gymnastics. Methods: A total of 383 rhythmic gymnasts aged 4 to 27 years were evaluated in various sports centers across Madrid, Spain. Visual assessments included clinical tests (near convergence point accommodative facility, reaction time, and hand–eye coordination) and eye-tracking tasks (fixation stability, saccades, smooth pursuits, and visual acuity) using the DIVE (Devices for an Integral Visual Examination) system. The dataset was split into training (70%) and testing (30%) subsets. Each algorithm was trained to classify visual performance, and predictive performance was assessed using accuracy and macro F1-score metrics. Results: The decision tree model demonstrated the highest performance, achieving an average accuracy of 92.79% and a macro F1-score of 0.9276. In comparison, the SVM and KNN models showed lower accuracies (71.17% and 78.38%, respectively) and greater difficulty in correctly classifying positive cases. Notably, the DT model outperformed the others in predicting fixation stability and accommodative facility, particularly in short-duration fixation tasks. Conclusion: The decision tree algorithm achieved the highest performance in predicting short-term fixation stability, but its effectiveness was limited in tasks involving accommodative facility, where other models such as SVM and KNN outperformed it in specific metrics. These findings support the integration of machine learning in sports vision screening and suggest that predictive modeling can inform individualized training and performance optimization in visually demanding sports such as rhythmic gymnastics. Full article

Robotic assembly, particularly peg-in-hole tasks, presents significant challenges in uncertain environments where pose deviations, varying peg shapes, and environmental noise can undermine performance. To address these issues, this paper proposes a novel approach combining visual–tactile fusion with reinforcement learning. By integrating multimodal data (RGB image, depth map, tactile force information, and robot body pose data) via a fusion network based on the autoencoder, we provide the robot with a more comprehensive perception of its environment. Furthermore, we enhance the robot’s assembly skill ability by using the Soft Actor–Critic (SAC) reinforcement learning algorithm, which allows the robot to adapt its actions to dynamic environments. We evaluate our method through experiments, which showed clear improvements in three key aspects: higher assembly success rates, reduced task completion times, and better generalization across diverse peg shapes and environmental conditions. The results suggest that the combination of visual and tactile feedback with SAC-based learning provides a viable and robust solution for robotic assembly in uncertain environments, paving the way for scalable and adaptable industrial robots. Full article

Objective: There is growing interest in analyzing whether ethical climates influence the emotional states of organizational members. For this reason, the main objective of this study is to evaluate the relationship between a benevolent ethical climate, emotional exhaustion, and depersonalization, taking into account the mediating effect of job autonomy. Methodology: To evaluate the research hypotheses, data were collected from 448 people belonging to six organizations in the Colombian electricity sector. Statistical analysis was performed using two structural equation models (SEMs). Results: The results show that a benevolent climate and its three dimensions (friendship, group interest, and corporate social responsibility) mitigate the negative effect of emotional exhaustion and depersonalization. A work environment focused on people and society triggers positive moods that prevent the loss of valuable psychological resources. On the other hand, job autonomy is a mechanism that has a direct impact on the emotional well-being of employees. Therefore, being able to intentionally direct one’s own sources of energy and motivation prevents an imbalance between resources and demands that blocks the potential effect of emotional exhaustion and depersonalization. Practical implications: This study has important practical implications. First, an ethical climate that seeks to build a caring environment needs to strengthen emotional communication among employees through a high perception of support. Second, organizations need to grow and achieve strategic objectives from a perspective of solidarity. Third, a benevolent ethical climate needs to be nurtured by professionals with a clear vocation for service and a preference for interacting with people. Finally, job autonomy must be accompanied by the necessary time management skills. Social implications: This study highlights the importance to society of an ethical climate based on friendship, group interest, and corporate social responsibility. In a society with a marked tendency to disengage from collective problems, it is essential to make decisions that take into account the well-being of others. Originality/value: This research responds to recent calls for more studies to identify organizational contexts capable of mitigating the negative effects of emotional exhaustion and depersonalization. Full article

Indonesia’s commitment to digital transformation is exemplified by the Gerakan 100 Smart City program, aiming to enhance public sector performance through technology integration. This study examines how information technology capability and 21st century digital skills influence public sector performance, mediated by managerial digital readiness and moderated by digital leadership. Grounded in Dynamic Capability Theory and Upper Echelon Theory, data from 1380 civil servants were analyzed using PLS-SEM via SmartPLS 4.1.0.9. Results show that both IT capability and digital skills significantly improve managerial digital readiness, which in turn positively impacts public sector performance. Managerial readiness mediates the effect of both predictors on performance, while digital leadership strengthens these relationships. Theoretically, this study frames managerial digital readiness as a dynamic capability shaped by leadership cognition. Practically, it highlights the importance of aligning infrastructure, skills, and leadership development to advance digital governance. Future research should consider longitudinal, multilevel, and qualitative designs to deepen insights. Full article

Objectives: The primary objective of this study was to determine whether motor disorders are significantly more prevalent in children with Autism Spectrum Disorder (ASD) without co-occurring genetic or neurological conditions compared to neurotypical children. Another aim was to explore the applicability of the MABC-2 test for assessing motor skills in a Polish cohort of children with ASD. Additionally, this study sought to develop a basic framework for motor skill assessment in children with autism. Methods: This study included 166 Caucasian children, both sexes, aged 5–12 years, without intellectual disability (IQ ≥ 70), without concomitant genetic or neurological disorders, particularly epilepsy or cerebral palsy. The study group consisted of children with ASD (n = 71), and the control group consisted of neurotypical children (n = 95). The participants were assessed with the Movement Assessment Battery for Children–second edition (MABC-2), MABC-2 checklist and the Developmental Coordination Disorder Questionnaire (DCDQ), used as a reference point. Results: The children with ASD obtained significantly lower MABC-2 test results in all subtests in comparison with the control group. The children with suspected or diagnosed coordination disorders were characterized by a significantly greater number of co-occurring non-motor factors than the other participants of this study. MABC-2 test showed greater consistency with DCDQ than with the MABC-2 questionnaire. Conclusions: Children with ASD present a lower level of manual dexterity and balance and greater difficulties in performing tasks, including throwing and catching, in comparison with neurotypical children. The MABC-2 test with the MABC-2 checklist and DCDQ questionnaire constitute a complementary diagnostic tool. Full article

Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption. Full article