Search Results (657)

This study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dimensionality and tested multiple agglomerative and divisive clustering techniques (e.g., Ward and DIANA) with different distance metrics (Euclidean, Manhattan, and Minkowski). Clustering quality was assessed using internal validation indices such as Silhouette, Dunn, Calinski–Harabasz, Davies–Bouldin, and Gap statistics. The results show that Ward.D and Ward.D2 methods using Euclidean distance generate well-balanced and clearly defined clusters. Two clusters offer the best overall quality, while four clusters allow for meaningful segmentation of game situations. The analysis revealed that teams that did not request timeouts often exhibited better scoring efficiency, particularly in the advanced game phases. These findings offer data-driven insights into timeout dynamics and contribute to strategic decision-making in professional basketball. Full article

Rapid vaccine availability is essential for effective epidemic and pandemic response. Building on the Coalition for Epidemic Preparedness Innovations (CEPI) 100 Days Mission, which aims to have new vaccines ready for initial authorization and manufacturing at scale within 100 days of recognition of a pandemic pathogen, the CEPI has developed a Chemistry, Manufacturing and Controls (CMC) Rapid Response Framework to define technical and logistical CMC requirements to enable rapid vaccine availability. Central to this framework is the availability of adaptable vaccine platforms that can be readily tailored to emerging pathogens. To support strategic decision-making and identify gaps in platform capabilities, CEPI has created the Platform Readiness Dashboard. This tool provides a structured, multi-dimensional initial assessment of platform maturity across six key categories: Adaptability, Compatibility, Suitability, Regulatory, Manufacturing, and Facility Readiness. Each category includes specific technical and operational considerations scored using a color-coded system to reflect outbreak response readiness level. This Dashboard aims to enable vaccine developers, manufacturers, funders, and outbreak response teams to evaluate platform strengths and limitations at any given time, informing funding, preparedness and response activities. By offering a dynamic view of essential platform readiness indicators, the dashboard can communicate progress supporting faster responses to future health emergencies. Full article

This study investigates how Greek professionals formulate upward requests and simultaneously manage rapport and workplace identity within hierarchical exchanges. The data comprise 400 written requests elicited through a discourse–completion task from 100 participants, supplemented by follow-up interviews. Integrating pragmatic perspectives on request mitigation with Spencer-Oatey’s Rapport-Management model and a social constructionist perspective on identity, the analysis reveals a distinctive “direct-yet-mitigated” style: syntactically direct head acts (typically want- or need-statements) various mitigating devices. This mitigation enables speakers to preserve superiors’ face, assert entitlement, and invoke shared corporate goals in a single move. Crucially, rapport work is intertwined with identity construction. Strategic oscillation between deference and entitlement projects four recurrent professional personae: the deferential subordinate, the competent and deserving employee, the cooperative team-player, and the rights-aware negotiator. Speakers shift among these personae to calibrate relational distance, demonstrating that rapport management functions not merely as a politeness calculus but as a resource for dynamic identity performance. This study thus bridges micro-pragmatic choices and macro social meanings, showing how linguistic mitigation safeguards interpersonal harmony while scripting desirable workplace selves. Full article

This paper explores the complex power dynamics of UK social work higher education through an autoethnographic account of a Black woman course leader’s experiences over a period of two years, focusing on issues related to race, internalized oppression, and class. Drawing on Critical Race Theory (CRT), narrative analysis, and lived experience, it examines how systemic inequities manifest through three interlinked themes: (a) academic contrapower harassment (ACPH), (b) internalized oppression and toxic team dynamics, and (c) the interplay of harassment, institutional failure, managerial inaction, and the marginalization of social work as a discipline. This study illustrates how the intersectionality of multiple identities—namely, race, gender, and professional identity—impacts career progression, well-being, and institutional inclusion. This study examines the tensions between social work’s ethical foundations and performance-driven academic environments, advocating for systemic and policy interventions to stimulate institutional reform and cultivate a more equitable culture that enhances educational outcomes and, ultimately, improves social work practice. Full article

This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision is grounded in provable theory. The approach defines clear phases, including mathematical modeling, virtual prototyping, parameter optimization, and theoretical validation. Each phase builds on the previous one to reduce unforeseen integration issues. Spanning from conceptualization to deployment, it offers a blueprint for developing mathematically valid and robust robotic solutions while streamlining the transition from design intent to functional prototype. By standardizing the design workflow, this framework reduces development time and cost, improves reproducibility across projects, and enhances collaboration among multidisciplinary teams. Such a generalized approach is essential in today’s fast-evolving robotics landscape where rapid innovation and cross-domain applicability demand flexible yet reliable methodologies. Moreover, it provides a common language and set of benchmarks that both novice and experienced engineers can use to evaluate performance, facilitate knowledge transfer, and future-proof systems against emerging application requirements. Full article

In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications. Full article

Air traffic controllers (ATCOs) make a significant contribution to ensuring flight safety, making this profession a highly stressful job globally. Job demands–resources (JDR) theory proposes a health impairment process stemming from job demand (complexity) to mental workload, which causes job stress, resulting in compromised flight safety. This vicious cycle is evident among ATCOs and is recognized as an unsustainable management practice. To curb this process, we propose intra-functional flexibility as a conditional factor. Intra-functional flexibility refers to the flexibility in the reallocation and coordination of resources among team members to help in urgent times. This is a relatively new concept and is yet to be empirically tested in the ATCO context. ATCOs work in a dynamic environment filled with sudden surges of urgent jobs to be handled within short time limits. Intra-functional flexibility allows standby crews to be called to ease these tensions when needed. To ascertain the role of intra-functional flexibility in mitigating health impairment among ATCOs, a questionnaire was administered to 324 ATCOs distributed across Saudi Arabia. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis exhibited two critical findings: First, the study revealed the prevalence of a vicious cycle of health impairment among Saudi ATCOs, whereby job complexity leads to increased mental workload, resulting in elevated levels of job stress. Secondly, the presence of intra-functional flexibility weakened this vicious cycle by mitigating the influence exerted by mental workload on job stress. That is, the mediation-moderated model proposed in this study provides empirical evidence supporting the applicability of intra-functional flexibility in mitigating the dire suffering of ATCOs. This study discusses limitations and future research directions in the end. Full article

This perspective paper presents a first-person account of life with motor neurone disease (MND). Through the lens of lived experience, it explores the complex and often prolonged diagnostic journey, shaped in part by the protective grip of denial. This paper then delves into the emotional impact of MND on the individual and their close relationships, capturing the strain on identity and family dynamics. It also highlights the vital role of the multidisciplinary team in providing support throughout the journey. A central focus of the paper is the personal journey of voice banking. It reflects on the restorative experience of reclaiming a pre-disease voice through tools such as ElevenLabs^TM. This narrative underscores the critical importance of early intervention and timely access to voice banking, positioning voice not only as a tool for communication but also as a powerful anchor of identity, dignity, and agency. The paper concludes by highlighting key systemic gaps in MND care. It calls for earlier referral to speech pathology, earlier access to voice banking, access to psychological support from the time of diagnosis, and better integration between research and clinical care. Full article

An Application Programming Interface (API) is a formally defined interface that enables controlled interaction between software components, and is a key pillar of modern microservice-based architectures. However, asynchronous API changes often lead to breaking compatibility and introduce systemic instability across dependent services. Prior research has explored various strategies to manage such evolution, including contract-based testing, semantic versioning, and continuous deployment safeguards. Nevertheless, a comprehensive orchestration mechanism that formalizes dependency propagation and automates compatibility enforcement remains lacking. In this study, we propose a Compatibility-Driven Version Orchestrator, integrating semantic versioning, contract testing, and CI triggers into a unified framework. We empirically validate the approach on a Kubernetes-based environment, demonstrating the improved resilience of microservice systems to breaking changes. This contribution advances the theoretical modeling of cascading failures in microservices, while providing developers and DevOps teams with a practical toolset to improve service stability in dynamic, distributed environments. Full article

This paper presents a reinforcement learning (RL) approach for solving the team orienteering problem under both deterministic and dynamic travel time conditions. The proposed method builds on the transformer architecture and is trained to construct routes that adapt to real-time variations, such as traffic and environmental changes. A key contribution of this work is the model’s ability to generalize across problem instances with varying numbers of nodes and vehicles, eliminating the need for retraining when problem size changes. To assess performance, a comprehensive set of experiments involving 27,000 synthetic instances is conducted, comparing the RL model with a variable neighborhood search metaheuristic. The results indicate that the RL model achieves competitive solution quality while requiring significantly less computational time. Moreover, the RL approach consistently produces feasible solutions across all dynamic instances, demonstrating strong robustness in meeting time constraints. These findings suggest that learning-based methods can offer efficient, scalable, and adaptable solutions for routing problems in dynamic and uncertain environments. Full article

To address the limitations of conventional single-track rail systems in challenging hilly and mountainous terrains, which are ill-suited for transporting heavy agricultural machinery, there is a critical need to develop a specialized the double-track rail transportation system optimized for orchard equipment. Recognizing this requirement, our research team designed and implemented a double-track rail transportation system. In this innovative system, the rail functions as the pivotal component, with its structural properties significantly impacting the machine’s overall stability and operational performance. In this study, resistance strain gauges were employed to analyze the stress–strain distribution of the track under a full load of 750 kg, a critical factor in the system’s design. To further investigate the structural performance of the double-track rail, the impact hammer method was utilized in conjunction with triaxial acceleration sensors to conduct experimental modal analysis (EMA) under actual support conditions. By integrating the Eigensystem Realization Algorithm (ERA), the first 20 natural modes and their corresponding parameters were successfully identified with high precision. A comparative analysis between finite element simulation results and experimental measurements was performed, revealing the double-track rail’s inherent vibration characteristics under constrained modal conditions versus actual boundary constraints. These valuable findings serve as a theoretical foundation for the dynamic optimization of rail structures and the mitigation of resonance issues. The advancement of hilly and mountainous rail transportation systems holds significant promise for enhancing productivity and transportation efficiency in agricultural operations. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

This study aimed to develop a Causal Loop Diagram (CLD) to visualise how urban environment factors impact dementia and cognitive decline, and potential causal mechanisms. In Group Model Building workshops with 12 researchers, a CLD was created to identify factors contributing to cognitive decline, and the dynamic interrelationships between these factors. The factors were classified in nine main themes: urban design, social environment, travel behaviours, urban design by-products, lifestyle, mental health conditions, disease/physiology, brain physiology, and cognitive decline outcomes. Five selected feedback loops illustrated some dynamics in the system. The workshops helped develop a shared language and understanding of different perspectives from an interdisciplinary team. The CLD creation was part of a comprehensive modelling approach based on experts’ knowledge which informed other research outputs such as an evidence gap map and an umbrella review, helped the identification of environmental variables for future studies and analyses, and helped to identify future possible systems-based interventions to prevent cognitive decline. The study highlights the utility of CLDs and Group Model Building workshops in interdisciplinary research projects investigating complex systems. Full article

Special police units like Austria’s EKO Cobra are uniquely trained to manage high-risk operations, including terrorism, amok situations, and hostage crises. This study explores how group dynamics contribute to operational safety and resilience, emphasising the interconnection between risk perception, training, and operational practices. Interviews with current and former EKO Cobra members reveal key risk factors, including overconfidence, insufficient training, inadequate equipment, and the challenges of high-stakes scenarios. Using a structured yet thematically flexible interview analysis approach, the study adopts group dynamics theory as its framework and applies a semi-inductive, semi-deductive qualitative methodology. It examines risk categorisation in ad hoc operations, as well as the interplay between risk perception and training, proposing actionable strategies to enhance safety and preparedness through tailored training programmes. The findings underscore the transformative impact of intensive scenario-based and high-stress training, which enhances situational awareness and reinforces team-based responses through cohesion and effective communication. Group dynamics, including cohesion and effective communication, play a pivotal role in mitigating risks and ensuring operational success. Importantly, this research advocates for continuous, adaptive, and specialised training to address evolving challenges. By linking theoretical frameworks with practical and actionable insights, this study proposes a holistic training approach that promotes both resilience and long-term sustainability in police operations. These findings offer valuable guidance to elite units like EKO Cobra for broader policy frameworks by providing insights that make police operations safer and more effective and resilient. Full article

This study examines how students perceive factors contributing to their sense of belonging (SoB) in an online project-based master’s course in social innovation, incorporating collaboration with external stakeholders and structured around real-world challenges from external partners. SoB has been shown to impact academic outcomes positively, but online teamwork and collaboration outside the institution walls present unique challenges. The study adopts a qualitative approach and an interpretivist standpoint to find meaningful aspects and generate an understanding of positions among the students. Data was gathered through interviews, allowing students to express their unique experiences and perspectives on SoB in the context of a master’s course, with participants from several countries. Thematic analysis of the interview data identified the following seven categories related to students’ perceived belonging: team formation and trust, personal introductions, group continuity, access to teachers, use of informal communication channels, shared tools, and digital fluency. Furthermore, working towards a common challenge or goal seems to promote SoB among team members. The results indicate that SoB was shaped by multiple interrelated factors, with team-based collaboration and structured group dynamics playing a central role. The study contributes to ongoing research on student belonging by identifying specific practices that may support SoB in digitally supported, team-oriented learning environments. Full article