Search Results (641)

A great deal of effort has been made to investigate and develop approaches to address driver behavior, fatigue, and sleepiness for different road users worldwide. However, very little research has been conducted to explore these issues in the context of Bus Rapid Transit (BRT) drivers in a low-income countries such as Mexico. The present study fills this gap. The aim of this study is to identify the human factors contributing to aberrant driver behavior (ADB) among BRT professional drivers in Mexico City. A total of 152 drivers participated in a self-reported survey. Exploratory factor analysis was performed on the BRT-ADBQ to identify the behavioral factors, and the Checklist Individual Strength (CIS–Fatigue) subscale was employed to assess the fatigue of drivers. The key findings were the following: (a) the created BRT-ABDQ identified two ADBs (violations and errors); (b) violations factors, but not errors, contributed to accident involvement; (c) ADB, fatigue, poor sleep and age (30–39) were predictors to accidents and (d) a linear trend has been revealed indicating that as the hours of sleep decreased, the experience of fatigue increased proportionally. The conclusion of the study is that ADB, sleepiness, and fatigue are real and existent among BRT drivers and should be a matter of concern for the case of the BRT organization that participated in the study. More generally, organizations running these systems should intervene by implementing sleep and fatigue reduction strategies to mitigate the adverse impact of these and thereby contribute to sustainable traffic safety and urban mobility. Full article

Lung cancer remains one of the most formidable health challenges globally, with significant morbidity and mortality rates. Despite advancements in diagnostic and treatment technologies, the disease’s high prevalence, late-stage detection, and complex variations continue to hinder effective management. Early detection and accurate diagnosis play a pivotal role in improving survival rates. Crucially, the clinical and translational relevance of AI-based prediction lies in its potential to significantly reduce the incidence of late-stage diagnoses, thus increasing the chance of successful intervention. Lung cancer was first identified by medical professionals in the mid-19th century. Today, cancer remains a significant global health challenge, affecting an estimated 14 million individuals annually and causing 8.2 million fatalities worldwide. Lung cancer ranks among the leading causes of death associated with cancer. This research aims to bridge gaps in lung cancer diagnosis by exploring various learning methodologies. By focusing on studies from the last 10 years, this survey provides a contemporary understanding of the field, emphasizing the importance of automated diagnostic systems in reducing human error and improving efficiency. The selection of relevant research is based on a rigorous methodology, including specific inclusion and exclusion criteria, which are later discussed in detail with supporting figures and comparative data. Ultimately, this work underscores the critical need for innovative diagnostic solutions and comprehensive screening programs to combat lung cancer, save lives, and advance the field of medical research. Full article

This paper provides new field-based evidence on loss aversion and short-run learning using high-frequency performance data from professional golf. Leveraging over 100,000 putts recorded during the 2020 Korea Professional Golfers’ Association (KPGA) Tour, I examine how professional golfers adjust their putting behavior in response to reference-dependent incentives and immediate feedback. The structure of golf creates a natural empirical setting to test behavioral predictions: scoring rules establish salient reference points (e.g., par), while putting decisions are discrete, individually executed, and financially consequential. I find that players are significantly more likely to convert par-saving putts than birdie attempts from equivalent distances, consistent with loss aversion and reference-dependent preferences. Par putts are also executed more aggressively, but players regulate pace to avoid costly three-putt errors, indicating strategic self-regulation under loss-framed incentives. In addition, I document robust evidence of within-hole learning: second putts—taken shortly after the first under near-identical conditions—exhibit substantially higher success rates. These patterns are confirmed in logistic regression models with nonlinear distance controls and player fixed effects. This performance gap persists across scoring frames and aligns with models of reinforcement learning and dynamic belief updating. The findings illustrate how behavioral biases and adaptive learning interact in high-stakes, real-world decisions and highlight the value of professional sports data for testing core theories in behavioral economics. Full article

A growing body of literature highlights the need to improve mathematics teaching and learning, emphasising the role of teachers in deepening learners’ conceptual understanding. Scholars are increasingly advocating for teachers to explore innovative instructional approaches to enhance the teaching and learning of mathematics. This study contributes to the discourse by examining the perspectives of South African in-service and pre-service mathematics teachers on error analysis as a strategy to improve the teaching and learning of mathematics. Data was collected from five in-service mathematics teachers participating in a District professional development programme and seven fourth-year pre-service mathematics teachers enrolled in a Bachelor of Education programme. Using semi-structured interviews, focus group discussions, and classroom observations, the study found that participants viewed error analysis as a valuable tool for strengthening teacher content knowledge and facilitating learner engagement with mathematical errors. In-service teachers also reported that error analysis supported the provision of detailed feedback. Despite its perceived benefits, participants noted that error analysis was time-consuming and challenging, particularly in terms of tracing errors to underlying misconceptions. Full article

Coaches in professional football need to estimate how many minutes a player can tolerate in a match before relevant fatigue occurs. This study aimed to develop a framework to translate monitoring information into individualised, minute-based fatigue thresholds. Over four seasons in an elite club, external load (total distance, high-speed running, mechanical work) and heart rate were collected in training. Machine-learning-derived fitness and fatigue indices were computed and combined with 7- and 28-day load variables in a Random Forest regression model predicting match minutes. The trained model was then used to simulate four fatigue conditions by fixing the match-day fatigue index (z-FA_match = 0, −1, −2, −3). In an independent test season, the model showed a mean absolute error of 22.5 min and R² = 0.17 for playing time prediction, with z-FA_match as the most influential predictor. Simulated fatigue thresholds occurred in an ordered way (0 = 57.1, −1 = 64.9, −2 = 84.8, −3 = 84.4) and differed across season period, playing position, overall seasonal minutes, and return-to-play status. Integrating external load with fitness and fatigue indices via machine learning can provide individualised estimates of when players are likely to reach fatigue states, supporting decisions on selection, substitutions, and return-to-play management. Full article

Hazard recognition on construction sites is crucial for ensuring worker safety. Traditional methods widely rely on expert assessments, on-site inspections, and checklists, which can be time-consuming and susceptible to human error. The integration of multimodal Large Language Models (LLMs), such as GPT-based systems, offers a promising opportunity to overcome these limitations. Therefore, this study evaluates the effectiveness of GPT-4o in recognizing workplace hazards from image inputs, with a specific focus on construction sites. The results indicate that the model can serve as a valuable decision-support tool for safety professionals by providing scalable and real-time insights. However, the study also highlights key limitations, including the model’s reliance on general visual features rather than domain-specific safety knowledge, and the continued need for human supervision. Additionally, ethical concerns, including bias in AI-generated hazard assessments, data privacy, and the risk of over-reliance on AI, must be carefully managed to ensure these tools contribute responsibly and effectively to proactive risk management strategies. Full article

Orthopedic surgery is undergoing a transformation driven by artificial intelligence (AI), which is reshaping clinico-surgical decision-making. While the operative strategy and professional responsibility traditionally relied on the surgeon’s intuition and manual skills, advanced algorithms now provide predictive, analytical, and procedural decision supports. This paradigm shift is redefining the concept of human error as well as the relationship between technological tools and human decision-makers. As a result, the foundational elements of the healthcare liability framework are being affected. This paper offers a narrative discussion on selected applications of artificial intelligence in orthopedic surgical practice, including patient risk stratification, surgical indication and prosthesis positioning, with a particular focus on the liability implications for healthcare professionals who rely on these systems in terms of therapeutic decision-making. The aim is then to provide a comprehensive medico-legal perspective within the highly regulated and high-risk field of biomedicine, acknowledging and critically assessing the roles and responsibilities of all stakeholders involved—patients, healthcare professionals, innovative technologies, healthcare organizations, and facility management—while balancing innovation, evidence-based practice, and accountability in healthcare delivery. Full article

Recent developments in computer vision have significantly enhanced the automation and objectivity of sports analytics. This paper proposes a novel deep learning-based framework for estimating football possession directly from broadcast video, eliminating the reliance on manual annotations or event-based data that are often labor-intensive, subjective, and temporally coarse. The framework incorporates two structurally improved object detection models: YOLOv8-P2S3A for football detection and YOLOv8-HWD3A for player detection. These models demonstrate superior accuracy compared to baseline detectors, achieving 79.4% and 71.1% validation average precision, respectively, while maintaining low computational latency. Team identification is accomplished through unsupervised DBSCAN clustering on jersey color features, enabling robust and label-free team assignment across diverse match scenarios. Object trajectories are maintained via the Norfair multi-object tracking algorithm, and a temporally aware refinement module ensures accurate estimation of ball possession durations. Extensive experiments were conducted on a dataset comprising 20 full-match Video clips. The proposed system achieved a root mean square error (RMSE) of 4.87 in possession estimation, outperforming all evaluated baselines, including YOLOv10n (RMSE: 5.12) and YOLOv11 (RMSE: 5.17), with a substantial improvement over YOLOv6n (RMSE: 12.73). These results substantiate the effectiveness of the proposed framework in enhancing the precision, efficiency, and automation of football analytics, offering practical value for coaches, analysts, and sports scientists in professional settings. Full article

Background/Objectives: Healthcare workplace violence has evolved into a global crisis, significantly impacting high-risk specialties. While the “Second Victim Phenomenon” (SVP) is well-established for trauma following medical errors, the specific psychological trauma resulting from intentional external aggression remains conceptually under-defined. This study aims to introduce and define the “Violence Victim Phenomenon” (VVP) by exploring the lived experiences of anesthesiology and intensive care specialists, providing a theoretical framework for this distinct clinical state. Methods: A qualitative study was conducted with ten anesthesiology and intensive care specialists using a semi-structured focus group discussion. The session was subjected to thematic analysis using MAXQDA software. The analysis focused on the nature of violence encountered, psychological and professional impacts, and the role of institutional support systems. Results: The thematic analysis identified six core dimensions of VVP: forms and trajectories of violence, vulnerability amplifiers, psychological and occupational sequelae, coping and containment strategies, expectations of institutional support, and pandemic-specific intensifiers. Participants described a trauma profile comparable to SVP in severity but distinct in its etiology, rooted in intentional harm and “institutional abandonment.” VVP is characterized by a profound sense of vulnerability, loss of professional dignity, and a perceived lack of legal and administrative protection. Conclusions: VVP represents a critical gap in current academic literature. Defining VVP allows for a more nuanced understanding of the trauma healthcare workers face due to intentional aggression. To mitigate VVP, healthcare institutions must move beyond basic security measures toward a “just culture” that provides structured psychological, legal, and managerial support, recognizing clinicians as victims of systemic failure. Full article

Urban trees provide important benefits but can also pose safety risks when stability is reduced. Visual Tree Assessment (VTA) is typically the first step in risk analysis and is sometimes complemented by instrumental methods such as dynamic and static tests. Static pulling tests provide quantitative information on anchorage, but their cost and logistics limit use to site-specific applications. This study evaluates a low-cost Micro-Electro-Mechanical Systems (MEMS) inclinometer for quasi-static inclination measurements during a static pulling test, combining a laboratory calibration against a geometric reference with field comparisons against a professional high-precision inclinometer commonly used in static pulling tests. In the laboratory, using a calibrated tilting beam and a 120 s averaging window, the MEMS sensor yielded absolute errors on the order of a few hundredths of a degree (up to ≈0.015°) compared to the geometric expectation. In the field, comparisons were performed in the relative domain (baseline on the first stable plateau) along the longitudinal component, showing high concordance with the reference high-precision inclinometer commonly used in arboricultural pulling tests (e.g., $r\ge 0.99$, RMSE $\approx 0.04$–$0.07°$, Deming slope $\approx 1.02$–$1.05$). These results support the feasibility of low-cost MEMS for static tilt assessment. Given battery-powered wireless operation and simple processing, they indicate a potential for wider deployments in repeated or scheduled quasi-static assessments (e.g., during controlled pulling tests), complementing professional instrumentation. Full article

Most existing reactive agility assessments rely on screen-based or light-based stimuli that are spatially separated from the movement execution plane, thereby limiting ecological validity. The purpose of this study was to develop and validate a novel projection-based, ground level reactive agility test (RAT) designed to better reflect the perceptual motor demands of soccer. A total of 57 male soccer players (24 professional and 33 amateur) participated in the study. The system projects sport-specific visual stimuli onto the ground and uses a three-dimensional depth camera to track foot–stimulus interactions in real time. Two reactive agility protocols—a randomized simple reaction test and a randomized selective reaction test—were implemented. Construct validity was examined by comparing reactive agility and planned change-of-direction (PCOD) performance between professional and amateur players, as well as by analyzing relationships between PCOD and RAT outcomes. Professional players demonstrated significantly faster performance than amateurs across all tests (p < 0.01), with larger between-group differences observed in reactive agility compared with PCOD measures. Correlations between PCOD and reactive agility outcomes were low to moderate (r = 0.34–0.61), indicating that reactive agility captures performance components beyond planned movement ability. The reactive agility protocols showed excellent test–retest reliability (ICC = 0.92–0.99) with low measurement error (CV = 0.96–3.47%). In conclusion, the proposed projection-based, ground-level RAT provides a valid and reliable assessment of reactive agility in soccer. By integrating sport-specific stimuli and movement execution within the same spatial plane, the system enhances ecological validity and offers a scalable framework for both performance assessment and perceptual cognitive training in open-skill sports. Full article

Near misses—clinical events that could have resulted in patient harm but did not—are increasingly recognized as critical yet underutilized sources of insight in surgical quality improvement. In thoracic surgery, where procedures are physiologically demanding and care pathways are highly interdependent, near misses frequently precede major complications and expose latent system vulnerabilities rather than isolated technical errors. A structured narrative review methodology was employed, including a targeted literature search of major biomedical databases and thematic synthesis of relevant studies. This narrative review synthesizes evidence from patient safety science, surgical quality literature, and thoracic surgery—specific outcomes research to examine how near misses can be systematically leveraged to improve care. We discuss the transition from individual-centered explanations of adverse events to system-based models that emphasize human factors, communication, escalation pathways, and organizational culture. Particular attention is given to contemporary quality frameworks such as failure to rescue and textbook outcome, which highlight the importance of early recognition, coordinated response, and recovery from complications rather than complication avoidance alone. We further explore the central role of psychological safety and leadership behaviors in enabling meaningful learning from near misses. By reframing near misses as actionable data rather than anecdotal “close calls,” quality improvement emerges as a core professional responsibility in thoracic surgery. We conclude that excellence in thoracic surgery should be defined not by the absence of complications, but by the capacity of surgical systems to learn, adapt, and prevent future harm. Full article

Background/Objectives: Neuromuscular fatigue (NMF) can impair manual dexterity and strength in healthcare professionals. Due to their high physical and cognitive workloads, physiotherapists (PTs) are particularly susceptible to NMF. This study investigated whether NMF, expressed as changes in manual dexterity and grip strength, occurs over a workday and across a workweek in PTs, and explored its relationship with stress and sleep quality. Methods: A total of 43 full-time PTs (25 female, mean age 37.72 ± 11.94 years) were recruited. Manual dexterity was assessed using the Functional Dexterity Test (FDT), while maximal grip strength (MGS) was measured by a hand dynamometer. Reliability was evaluated on a subgroup using Intraclass Correlation Coefficients (ICC_3,1) and Standard Error of Measurement (SEM). Evaluations were conducted at the beginning and at the end of the work shift, on Monday and Friday. Subjective fatigue, perceived stress, and sleep quality were also recorded. Results: The FDT showed excellent intra-rater reliability (ICC > 0.93; SEM < 0.94 s). FDT performance was significantly slower on Friday evening compared to all other time points (p < 0.01), exceeding the minimal detectable change thresholds. No significant changes were observed in MGS across the week. Perceived stress was strongly correlated with fatigue levels on Monday (ρ = 0.731) and Friday (ρ = 0.612) evenings. Sleep quality and professional experience did not correlate with performance changes. Conclusions: PTs experience a significant decline in manual dexterity by the end of the workweek, suggesting an accumulation of NMF. While MGS remains stable, fine motor control is more sensitive to fatigue. Psychosocial stress appears to be a major driver of perceived fatigue in this population. Full article

Literature reviews are a cornerstone of doctoral research in general, and of economic and business research, in particular. However, the exponential growth of scientific publications has made comprehensive and transparent reviews increasingly difficult. Conventional approaches, largely based on manual searches across a small number of databases, tend to be slow, error-prone, and incomplete. As a result, they constrain the scope of inquiry and, consequently, the robustness of theory development and empirical validation. This paper proposes and analyses an information system architecture driven by research questions and keyword taxonomies to automate core tasks of the literature search phase across multiple academic databases. Focusing on the domain of corporate and municipal financial distress, the authors employ a two-stage research design. First, the theoretical analysis integrates the literature on systematic reviews, automation, and financial distress prediction to derive a set of functional and non-functional requirements. Second, the experimental analysis documents a prototype front-end application designed to accelerate the literature review. The prototype is conceptualised as a socio-technical artefact that enhances IT competences and scientific resilience by enabling more efficient, reproducible, and extensible reviews. The authors conclude by discussing the scientific, technical, professional, and societal implications of the prototype, including opportunities for intellectual-property protection and avenues for future research. Full article

Handheld personal laser scanning (PLS) systems are increasingly being tested in forest inventory as an efficient alternative to labor-intensive, time-consuming field-based methods. However, comparative evaluations across different PLS instrument classes and the influence of operator experience on estimation accuracy remain insufficiently explored. This study presents a controlled comparison of three handheld PLS instruments representing different performance and cost classes, namely professional-grade (high-end) and lower-grade (entry-level and open-source) systems, and evaluates the influence of operator experience on the accuracy of diameter at breast height (DBH) and tree height estimation. Data were collected in even-aged European beech stands using consistent acquisition and processing workflows. Tree attributes were independently estimated by operators with high, medium, and low experience and validated against reference measurements obtained from diameter tape and multi-scan terrestrial laser scanning. Accuracy was assessed using mean difference (bias) and root mean square error, and the effects of instrument type and operator experience were analyzed using one-way and two-factor repeated-measures ANOVA. Results show that instrument type is the dominant factor determining estimation accuracy. The high-end system produced the most accurate DBH and tree height estimates across all operator experience levels, whereas the entry-level and open-source systems yielded acceptable DBH accuracy but consistently underestimated tree height, particularly for taller trees. Operator experience had a secondary effect, improving DBH estimates when lower-grade instruments were used, but had little influence on tree height accuracy. Significant interaction effects indicate that operator influence depends on instrument class. These findings demonstrate that PLS can support operational forest inventory when instrument capabilities align with inventory objectives. High-end systems are currently optimal when reliable tree height estimation is required, whereas lower-grade systems may provide cost-effective solutions for inventories focused primarily on DBH. Full article