International journal of statistics
Adaptive Deep Neural Network Approach for Automated Abnormal Event Recognition in Crowded Video Surveillance Systems
The rapid expansion of intelligent surveillance infrastructures has created a significant demand for automated systems capable of detecting abnormal activities in complex and densely populated environments. Traditional surveillance approaches relying on manual monitoring and handcrafted feature extraction often suffer from scalability limitations, delayed response, and reduced accuracy under challenging crowd conditions. This research presents an adaptive deep neural network approach for automated abnormal event recognition in crowded video surveillance systems. The proposed framework integrates spatial feature learning, temporal behavior modeling, adaptive representation refinement, and intelligent anomaly classification to improve recognition performance in dynamic crowd scenarios. The methodology is designed around a multi-stage deep learning architecture that extracts discriminative visual patterns, captures motion irregularities, and identifies deviations from normal behavioral patterns. The research analyzes existing deep learning-based anomaly detection approaches and develops a conceptual framework addressing limitations related to feature generalization, environmental variation, and computational efficiency. Previous studies have demonstrated the effectiveness of convolutional neural networks, autoencoder-based architectures, and spatial-temporal learning mechanisms for crowded scene analysis (Almazroey and Jarraya, 2020; Hu et al., 2020; Li et al., 2021). However, challenges remain in achieving adaptive recognition capabilities across diverse surveillance environments. This study contributes an adaptive learning model capable of dynamically updating feature representations for improved abnormal event identification. The framework emphasizes scalable deployment, real-time analysis, and robust decision-making for intelligent surveillance applications. The proposed approach provides theoretical and practical contributions by combining deep neural representation learning with adaptive anomaly recognition strategies. It establishes a foundation for future intelligent monitoring systems capable of supporting security operations in transportation hubs, public spaces, and urban surveillance networks.
Copyright (c) 2026 Muhammad Hamza Raza (Author) https://creativecommons.org/licenses/by/4.0
A Layered Feature Resolution Model with Intelligent Hashing for Scalable Cloud-Based Video Redundancy Elimination
The exponential growth of digital video content has intensified storage requirements in cloud environments, creating significant challenges related to redundancy, scalability, and resource optimization. Traditional deduplication mechanisms primarily rely on exact matching techniques, which often fail to identify semantically similar video segments due to variations in resolution, compression, encoding formats, and content representation. This research proposes a Layered Feature Resolution Model with Intelligent Hashing (LFRM-IH) for scalable cloud-based video redundancy elimination. The proposed approach integrates hierarchical feature extraction, adaptive resolution analysis, clustering-assisted similarity identification, and intelligent hashing mechanisms to improve redundancy detection efficiency while maintaining data integrity and storage security. The theoretical foundation of the model is derived from existing cloud deduplication studies focusing on hashing, clustering, content-defined chunking, and secure distributed storage mechanisms. The framework introduces multiple feature-resolution layers where coarse-level visual characteristics are initially analyzed before applying fine-grained hashing comparisons, reducing computational overhead during large-scale video processing. Existing research demonstrates the effectiveness of hashing and clustering strategies in cloud deduplication environments, while video-specific approaches highlight the importance of structural segmentation and adaptive processing. The proposed model addresses limitations in existing systems by combining scalable feature representation with intelligent hash decision-making. Furthermore, integration considerations with cloud-edge environments and secure deduplication mechanisms are analyzed to improve practical applicability. The research contributes a conceptual architecture for efficient video redundancy elimination capable of supporting modern cloud storage ecosystems with improved scalability, reduced storage consumption, and optimized computational performance.
Copyright (c) 2026 Dr. Chinedu Okafor (Author) https://creativecommons.org/licenses/by/4.0
Federated AI-Driven Intrusion Detection Framework for DDoS Mitigation in Cloud Networks
The rapid expansion of cloud computing infrastructures has intensified exposure to distributed denial-of-service (DDoS) attacks, creating significant challenges for service availability, security resilience, and operational continuity. Traditional centralized intrusion detection approaches often face limitations related to data privacy, communication overhead, scalability, and delayed threat response. This research presents a Federated AI-Driven Intrusion Detection Framework for DDoS Mitigation in Cloud Networks that integrates federated learning principles with intelligent anomaly detection mechanisms to enable decentralized and privacy-preserving security operations. The proposed framework distributes model training across cloud nodes while maintaining local data ownership, allowing collaborative threat intelligence generation without transferring sensitive network information. The research conceptually combines deep learning-based attack recognition, adaptive federated aggregation, and edge-enabled security intelligence for improving real-time DDoS detection capabilities. Existing studies demonstrate the effectiveness of artificial intelligence and federated learning in identifying network anomalies; however, challenges remain regarding communication efficiency, model adaptability, and cross-domain security coordination. The proposed framework addresses these limitations by introducing a collaborative security architecture capable of learning from heterogeneous cloud environments. The analysis highlights that federated AI-based intrusion detection can improve scalability, privacy preservation, and proactive defense against evolving DDoS attack patterns. The framework also aligns with emerging secure edge intelligence paradigms where distributed intelligence supports reliable next-generation communication systems (Varanasi et al., 2026). The findings indicate that federated AI security models provide a promising direction for developing resilient cloud infrastructures with enhanced autonomous threat mitigation capabilities.
Copyright (c) 2026 Nguyen Thanh Son (Author) https://creativecommons.org/licenses/by/4.0
Hybrid Deep Vision Framework for Diabetic Retinopathy Classification Using ResNet-ViT Feature Fusion
Diabetic retinopathy (DR) is a major vision-threatening complication of diabetes that requires accurate and early diagnosis to prevent irreversible retinal damage. Conventional image-based diagnostic systems often struggle with complex retinal variations, including microvascular abnormalities, lesion diversity, and differences in disease severity. Recent advancements in deep learning have demonstrated significant potential for automated DR analysis; however, existing approaches frequently face limitations in feature representation, generalization capability, and the integration of local and global retinal characteristics. This research proposes a Hybrid Deep Vision Framework for Diabetic Retinopathy Classification Using ResNet-ViT Feature Fusion, combining convolutional feature extraction capabilities of Residual Networks (ResNet) with the global contextual modeling strength of Vision Transformers (ViT). The proposed framework utilizes complementary feature learning, where ResNet captures fine-grained retinal structures while ViT enhances long-range dependency modeling across image regions. The study is positioned within modern intelligent medical imaging systems that require scalable, accurate, and computationally efficient classification mechanisms. Existing research on hybrid neural networks, segmentation-assisted analysis, transformer-based classification, and optimized transfer learning demonstrates the importance of integrating multiple learning paradigms for improved DR recognition. The proposed framework addresses these gaps by introducing a feature fusion strategy capable of improving representation diversity and classification robustness. The findings indicate that hybrid deep vision architectures provide a promising direction for automated retinal screening systems, although challenges related to computational complexity, dataset variability, and clinical deployment remain important considerations.
Copyright (c) 2026 Farhan Alamsyah (Author) https://creativecommons.org/licenses/by/4.0
Intelligent AI-Assisted Model for Optimizing Teacher Competence Through Knowledge Integration in Language Education
The integration of Artificial Intelligence (AI) into language education has transformed the mechanisms through which teachers acquire knowledge, develop instructional capabilities, and improve pedagogical decision-making. However, effective AI adoption requires more than technological availability; it demands structured knowledge integration models that combine explicit instructional knowledge with tacit professional experiences. This research proposes an Intelligent AI-Assisted Model for Optimizing Teacher Competence Through Knowledge Integration in Language Education. The study develops a conceptual framework that integrates AI-supported learning analytics, knowledge-sharing mechanisms, emotional intelligence factors, and adaptive pedagogical optimization. The proposed model is theoretically grounded in second language acquisition research, teacher cognition, and technology-enhanced learning perspectives. Existing literature highlights the importance of emotional, cognitive, and environmental factors in language learning, while recent technological approaches demonstrate the potential of intelligent systems for improving knowledge accessibility and decision support. The framework emphasizes AI as an augmentation mechanism that enables teachers to transform accumulated professional knowledge into adaptive instructional strategies. The research further examines system components, implementation mechanisms, expected outcomes, and limitations associated with AI-assisted teacher development. Findings indicate that AI-driven knowledge integration can enhance teacher self-efficacy, instructional personalization, reflective practice, and continuous professional development. The study contributes a structured approach for applying AI technologies to strengthen teacher competence and advance intelligent language education ecosystems.
Copyright (c) 2026 Yuki Harada (Author) https://creativecommons.org/licenses/by/4.0
Learning Analytics and Psychosocial Predictors of Academic Achievement: An Integrated Study of Personality, Motivation, and Self-Regulated Learning
The increasing integration of learning analytics into educational systems has transformed the understanding of academic achievement by enabling the identification of behavioral, psychological, and motivational patterns associated with student success. Traditional academic performance models focused primarily on cognitive ability and demographic variables; however, recent interdisciplinary studies indicate that psychosocial predictors such as personality traits, motivation, and self-regulated learning significantly influence educational outcomes. This technical paper investigates the combined influence of learning analytics and psychosocial constructs in predicting academic achievement within higher education environments. The study develops an integrated conceptual framework that combines behavioral analytics with psychological indicators to explain variations in academic engagement, persistence, and performance. The paper synthesizes concepts from machine learning, predictive analytics, behavioral modeling, and psychosocial theory to examine how student-centered variables interact with digital learning environments. Learning analytics techniques such as logistic regression, decision trees, neural networks, and classification-based prediction models are critically discussed in relation to educational data mining applications (Bradley, 1997; Benitez et al., 1997). Particular attention is given to the role of self-regulated learning, emotional adaptability, motivational persistence, and personality dimensions in shaping academic trajectories. The paper also highlights the importance of ethical data usage, interpretability of machine learning systems, and prevention of predictive bias in educational settings. Findings indicate that integrated models combining psychosocial indicators with learning analytics significantly improve prediction accuracy compared to conventional performance models. Students demonstrating strong self-regulation, intrinsic motivation, and adaptive behavioral traits consistently exhibit higher academic achievement and learning persistence. Conversely, poor emotional control, low engagement, and externalized motivational dependency contribute to academic underperformance. The study further emphasizes that educational institutions can utilize predictive analytics to identify at-risk students early and design personalized interventions that improve retention and learning outcomes. The paper contributes to the growing field of educational intelligence by proposing a multidimensional predictive framework suitable for modern digital education systems. The integration of psychosocial variables into learning analytics offers a more holistic understanding of academic performance while supporting evidence-based educational decision-making.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Exploring the Combined Influence of Personality, Motivation, and Self-Regulation on Academic Success in Higher Education
Academic success in higher education is influenced by multiple interconnected psychological, behavioral, and institutional factors. Traditional educational systems primarily evaluated academic performance through examination outcomes and cognitive capability, whereas contemporary higher education increasingly recognizes the importance of personality traits, motivation, and self-regulation in shaping student achievement. The expansion of learner-centered educational models, digital learning environments, and quality-oriented academic systems has intensified the need to understand how psychosocial dimensions affect academic persistence, engagement, and performance. This technical paper explores the combined influence of personality, motivation, and self-regulation on academic success within higher education institutions while connecting these variables with quality management and knowledge management practices. The paper adopts a conceptual and analytical approach based exclusively on the provided references related to quality management, organizational learning, educational improvement, and knowledge management systems. The study argues that academic achievement is not solely dependent on intellectual ability but emerges from interactions among learner personality characteristics, motivational persistence, self-regulatory behavior, and institutional support systems. Personality influences adaptability, communication, discipline, and learning engagement. Motivation drives persistence, effort allocation, and educational commitment, while self-regulation enables students to manage learning behavior, monitor progress, and adapt strategies according to academic demands. The paper further examines how Total Quality Management (TQM), knowledge management practices, leadership approaches, and institutional quality frameworks contribute to student success by creating supportive educational environments. Effective higher education institutions integrate quality teaching, collaborative learning, organizational knowledge sharing, and student-centered learning systems to enhance learner motivation and self-regulatory capacity. The analysis demonstrates that students exhibiting high motivational orientation, structured self-regulation, and adaptive personality characteristics consistently achieve stronger academic outcomes. Simultaneously, institutions implementing effective quality management and knowledge-sharing systems improve educational engagement and learning performance. The paper concludes that academic success in higher education should be understood through an integrated multidimensional framework combining psychological factors with institutional quality mechanisms. The study contributes to educational management research by highlighting the strategic relationship between psychosocial learner variables and institutional quality systems in promoting sustainable academic excellence.
Predictive Modeling of Academic Achievement Through Personality, Motivation, and Self-Regulation: Insights from Learning Analytics
Academic achievement prediction has become an increasingly important area of interdisciplinary research integrating educational psychology, behavioral analytics, personality studies, and intelligent learning systems. Traditional academic prediction approaches primarily emphasized cognitive aptitude, prior performance, and demographic variables while neglecting the psychosocial dimensions that significantly influence learner outcomes. Contemporary educational environments, particularly technology-enhanced and data-driven systems, have generated new opportunities for incorporating personality traits, motivational orientation, and self-regulated learning behaviors into predictive academic frameworks. This study develops a multidimensional conceptual framework for predictive modeling of academic achievement through the integration of personality psychology, motivational theory, self-regulation mechanisms, and learning analytics. The paper adopts a conceptual analytical methodology grounded exclusively in the provided literature concerning personality theory, behavioral psychology, software engineering behavior research, motivational constructs, and learning performance studies. The framework conceptualizes academic achievement as a dynamic outcome emerging from interactions among personality characteristics, motivational persistence, behavioral adaptability, self-regulated learning strategies, and technology-mediated engagement patterns. Particular attention is given to the predictive role of conscientiousness, agreeableness, autonomy, reflective cognition, and behavioral consistency within educational environments supported by digital learning systems and learning analytics platforms. The study demonstrates that personality dimensions significantly influence learner engagement, persistence, strategic learning behavior, and motivational regulation. Motivation functions as a mediating mechanism connecting personality characteristics with academic performance, while self-regulated learning operationalizes these psychological tendencies into measurable educational behaviors. Learning analytics systems further enhance predictive accuracy by capturing interaction data associated with persistence, participation, collaborative engagement, and adaptive learning practices. The findings indicate that multidimensional psychosocial models provide stronger explanatory power than conventional achievement prediction systems based solely on cognitive indicators. Students exhibiting high levels of conscientiousness, intrinsic motivation, self-regulation, and collaborative adaptability demonstrate superior academic consistency and performance outcomes. Furthermore, learning analytics infrastructures enable educational institutions to identify at-risk learners through behavioral indicators linked to motivational decline and reduced engagement. The study contributes theoretically by integrating personality psychology and learning analytics into a unified academic prediction framework. Practically, the research supports the development of adaptive educational systems, personalized instructional strategies, predictive intervention models, and learner-centered analytics architectures. Limitations include contextual variability in psychosocial measurements and ethical concerns regarding predictive educational analytics. Future research should focus on empirical validation, longitudinal modeling, and hybrid artificial intelligence approaches for educational prediction systems.
A Multidimensional Psychosocial Framework for Academic Performance Prediction Using Personality, Motivation, and Self-Regulated Learning Indicators
Academic performance prediction has emerged as a critical domain within educational psychology, learning analytics, and technology-enhanced education due to increasing demands for adaptive instructional systems and evidence-based educational interventions. Traditional academic prediction models have primarily relied on cognitive variables such as prior grades, intelligence measures, and attendance patterns. However, contemporary educational research increasingly recognizes that psychosocial determinants—including personality traits, motivational orientation, and self-regulated learning behaviors—substantially influence learning outcomes and academic persistence. This paper proposes a multidimensional psychosocial framework for academic performance prediction by integrating personality dimensions, motivational constructs, and self-regulated learning indicators into a unified analytical structure. The study synthesizes foundational theories of self-regulation, metacognition, learner autonomy, and motivational psychology to construct a comprehensive predictive perspective suitable for both conventional and technology-enhanced learning environments. The research adopts a conceptual and analytical methodology grounded exclusively in established literature concerning self-regulated learning, reflective practice, learner engagement, online learning environments, and educational analytics. The framework conceptualizes academic performance as a multidimensional outcome influenced by dynamic interactions among motivational persistence, goal orientation, metacognitive monitoring, reflective learning practices, adaptive help-seeking behaviors, and technology-mediated engagement patterns. Particular emphasis is placed on the integration of learning analytics and educational data mining approaches for identifying psychosocial indicators associated with academic success, dropout risk, and learner persistence. The findings indicate that self-regulated learning functions as the central mediating construct connecting personality characteristics and motivational mechanisms with academic achievement. Students demonstrating higher levels of metacognitive awareness, strategic learning regulation, reflective thinking, and intrinsic motivation exhibit significantly stronger academic adaptability and performance consistency. Furthermore, technology-enhanced learning systems provide scalable mechanisms for monitoring psychosocial indicators through learner interaction data, enabling more accurate prediction models and personalized educational interventions. The study contributes to educational theory by establishing an integrated psychosocial framework that moves beyond purely cognitive prediction approaches. It also offers practical implications for adaptive learning systems, institutional policy design, personalized tutoring environments, and learning analytics infrastructures. Despite conceptual strengths, limitations include contextual variability in psychosocial measurements and challenges associated with operationalizing affective constructs across diverse educational settings. Future research should focus on longitudinal validation, hybrid artificial intelligence models, and cross-cultural educational analytics.
Integrating Personality Traits, Academic Motivation, and Self-Regulation for Predicting Student Performance: A Learning Analytics Approach
The rapid expansion of digital learning environments has generated unprecedented volumes of educational data, enabling researchers and institutions to investigate the factors influencing student performance through learning analytics frameworks. While traditional educational prediction models have primarily emphasized demographic variables and cognitive indicators, contemporary educational research increasingly highlights the importance of psychological constructs such as personality traits, academic motivation, and self-regulation in shaping academic outcomes. The present study explores the integration of these multidimensional variables within a learning analytics framework to predict student performance in higher education contexts. The study adopts an IMRaD-based empirical structure and examines how personality dimensions, motivational orientations, and self-regulatory behaviors collectively contribute to academic achievement prediction. A quantitative research design was employed using structured surveys, institutional academic records, and behavioral learning management system data collected from undergraduate students across multiple academic disciplines. The research incorporates validated psychometric instruments grounded in the Big Five Personality Theory, Self-Determination Theory, and self-regulated learning frameworks. Advanced analytical techniques, including regression modeling, correlation analysis, and machine learning-based prediction approaches, were utilized to evaluate the predictive relationships among variables. The findings demonstrate that conscientiousness, intrinsic motivation, goal orientation, time management, and metacognitive regulation significantly influence academic performance indicators. The integration of behavioral analytics with psychological constructs substantially improved predictive accuracy compared to conventional academic models. The study contributes to the growing body of literature on educational data mining and learning analytics by proposing a multidimensional predictive framework capable of supporting personalized learning interventions and early academic support systems. The findings also underscore the necessity of integrating psychological and behavioral variables into institutional analytics infrastructures. Implications for educators, policymakers, curriculum designers, and educational technology developers are discussed, along with limitations and future research opportunities involving adaptive learning environments and artificial intelligence-driven academic prediction systems.
Personality, Motivation, and Self-Regulation as Integrated Predictors of Academic Performance: A Multidimensional Psychosocial and Learning Analytics Perspective
Academic performance has long been conceptualized as the outcome of a complex interplay between stable individual characteristics and dynamic motivational and regulatory processes. Across more than a century of psychological inquiry, scholars have attempted to identify the extent to which personality traits, beliefs about competence, motivational orientations, and learning strategies converge to explain why some learners consistently outperform others in formal educational settings. Building upon the contemporary frameworks of the Big Five personality model, self-determination theory, social–cognitive motivational theory, and learning analytics, the present research develops an integrative conceptual and methodological model that accounts for both the structural and functional dimensions of student learning behavior. Central to this investigation is the assumption that personality traits do not exert a direct and uniform effect on academic outcomes but instead operate through mediating and moderating psychological mechanisms such as self-efficacy, goal orientations, and intrinsic motivation, as demonstrated in seminal empirical and theoretical work (De Feyter et al., 2012; de Raad & Schouwenburg, 1996; Deci & Ryan, 2000; Diseth, 2011). Using advanced psychometric and analytic approaches grounded in exploratory structural equation modeling, bifactor modeling, and learning analytics theory, this study conceptualizes academic achievement as a multidimensional construct influenced by both internal dispositions and contextualized learning experiences. By synthesizing trait theory with motivational and affective frameworks, the research extends beyond traditional correlational models to offer a dynamic understanding of how learners engage with academic tasks, regulate their effort, and respond emotionally and cognitively to academic challenges. Previous research has demonstrated that conscientiousness, emotional stability, and openness to experience are consistently associated with higher academic performance, but these associations are often contingent upon students’ levels of self-efficacy and academic motivation (De Feyter et al., 2012; Duff et al., 2004; Dollinger et al., 2008). At the same time, motivational theories rooted in self-determination and expectancy–value models emphasize that learners’ beliefs about their competence and the value they assign to academic activities critically shape their persistence and engagement (Deci & Ryan, 2000; Eccles & Wigfield, 2002). The present article integrates these traditions into a coherent explanatory framework. Drawing on educational data mining and learning analytics perspectives, it further argues that the predictive validity of personality and motivational variables can be substantially enhanced when they are examined within digital learning environments that capture fine-grained indicators of student behavior, engagement, and persistence (Dekker et al., 2009; Drachsler & Greller, 2012). The methodological section outlines a mixed analytic design that combines psychometric modeling with classifier-based predictive techniques to illustrate how theoretically grounded psychological constructs can be operationalized in contemporary data-rich educational contexts. The results, interpreted through the lens of established motivational and personality theories, demonstrate that academic performance emerges from a layered system in which stable traits provide a dispositional baseline, motivational beliefs act as proximal drivers of behavior, and self-regulated learning strategies translate intention into measurable academic outcomes (DiBenedetto & Bembenutty, 2013; Dweck & Leggett, 1988). The discussion situates these findings within broader scholarly debates on the nature of academic competence, the limits of trait determinism, and the future of personalized education. By bridging psychometric theory, motivational psychology, and learning analytics, this research contributes to a more nuanced understanding of academic success that respects both individual differences and the malleability of learning processes. In doing so, it offers theoretical, methodological, and practical implications for educators, researchers, and policy makers seeking to design learning environments that support diverse learners in achieving their full academic potential.
Weighted Ensemble Learning Frameworks: A Comprehensive Analysis of Bagging, Boosting, Random Forests, and Efficiency-Driven Integration Methods
Ensemble learning has become a foundational paradigm in modern machine learning due to its strong association with improved predictive stability, generalization, and robustness in complex data environments. By combining multiple base learners, ensemble methods address fundamental limitations of single-model approaches, particularly in the presence of noise, high dimensionality, and model uncertainty. This study presents a comprehensive and integrative analysis of prominent ensemble learning strategies, including bagging, boosting, random forests, weighted voting mechanisms, stacked generalization, and efficiency-driven ensemble construction using data envelopment analysis (DEA). Drawing upon established theoretical frameworks and empirical findings, the article synthesizes classical ensemble methods with advanced weighting and optimization techniques to clarify their comparative strengths, limitations, and practical applicability. Emphasis is placed on how margin theory, variance reduction, classifier diversity, and efficiency optimization jointly contribute to ensemble effectiveness. Furthermore, the study examines the role of tunable parameters, hyperparameter optimization, and weighted aggregation in enhancing ensemble performance across classification and regression tasks. By systematically reviewing methodological developments and identifying unresolved challenges, this work provides a unified perspective that supports informed ensemble design in applied machine learning research. The analysis contributes to the literature by bridging statistical learning theory with efficiency-based optimization frameworks, highlighting opportunities for future research in adaptive, interpretable, and resource-aware ensemble systems.
A Socio-Technical Analysis of Continuous Integration and Continuous Delivery Practices in Large-Scale Software Systems
Continuous Integration and Continuous Delivery (CI/CD) have become foundational practices in contemporary software engineering, particularly within large-scale, distributed, and cloud-native systems. While early formulations of CI emphasized frequent code integration and automated testing, recent industrial practices demonstrate a broader socio-technical transformation encompassing organizational structure, repository strategies, developer autonomy, and platform governance. This article presents a comprehensive analytical review of CI/CD practices as they are conceptualized in classical literature and reinterpreted by modern technology organizations. Drawing upon established theoretical perspectives and recent industry-oriented analyses, the study synthesizes how CI/CD has evolved from a narrowly scoped engineering technique into a multi-layered operational paradigm. The manuscript adopts an IMRaD structure to examine conceptual foundations, methodological synthesis of literature and industry narratives, and interpretive results that highlight emerging patterns such as monorepo adoption, pipeline-as-code, and developer experience optimization. The discussion situates these findings within broader DevOps and platform engineering discourses, emphasizing tensions between standardization and autonomy, speed and reliability, and tooling and culture. Rather than asserting deterministic outcomes, the analysis identifies associations between CI/CD maturity and organizational learning, system scalability, and risk management. The article concludes by outlining theoretical implications for software engineering research and practical considerations for organizations seeking to align CI/CD practices with strategic objectives.
Machine Learning–Driven Analysis of Imbalanced Mental Health Data for Understanding Treatment Acceptability and Preference
Mental health research increasingly relies on real-world clinical and survey data to understand patient preferences, treatment acceptability, and care outcomes. However, such data are frequently characterized by substantial class imbalance, reflecting unequal distributions across diagnostic categories, treatment modalities, and patient-reported preferences. This imbalance poses persistent methodological challenges for machine learning–based analysis, particularly when the objective is to model nuanced outcomes such as acceptability of psychotherapy, medication, or lifestyle-based interventions. The present study examines the role of contemporary data balancing techniques in improving the analytical reliability of machine learning models applied to mental health treatment preference data. Drawing on established theoretical frameworks for learning from imbalanced datasets and recent empirical studies in mental health informatics, this article synthesizes methodological insights with a focused application context relevant to low- and middle-income settings. The study outlines a structured analytical pipeline encompassing data preprocessing, sampling strategies, hybrid balancing approaches, and model evaluation under imbalance-sensitive metrics. Results indicate that appropriate balancing techniques are associated with improved minority-class representation and more stable predictive performance, particularly in settings involving patient acceptability and preference outcomes. The discussion situates these findings within broader debates on methodological rigor, ethical considerations, and interpretability in mental health machine learning research. The article contributes a comprehensive methodological perspective that supports more equitable and reliable computational analyses of mental health data, while acknowledging limitations related to generalizability, contextual variability, and evolving clinical practices.
Urinary Tract Infections Among Adolescent Girls: Epidemiology, Risk Factors, Preventive Strategies, and Public Health Implications
Urinary tract infections (UTIs) represent a significant yet often under-recognized health concern among adolescent girls, intersecting biological vulnerability, behavioral transitions, and sociocultural determinants of health. Adolescence is marked by rapid physical maturation, hormonal changes, evolving hygiene practices, and increasing autonomy, all of which are associated with altered susceptibility to genitourinary infections. While UTIs have been extensively studied in early childhood and adult women, focused academic attention on adolescents—particularly girls—remains limited. This review-based analytical manuscript synthesizes classical and contemporary literature to examine the epidemiology, clinical characteristics, risk factors, preventive strategies, and public health implications of UTIs among adolescent girls. Drawing upon historical developmental frameworks, global disease burden estimates, and region-specific public health data, the article contextualizes UTIs within adolescent health paradigms. The manuscript further evaluates modifiable behavioral determinants such as hydration, hygiene practices, and health education interventions, alongside non-modifiable biological and anatomical considerations. Emphasis is placed on prevention-oriented strategies, including structured teaching programs and school-based health initiatives, which are associated with improved awareness and reduced recurrence. The discussion highlights persistent knowledge gaps, methodological limitations in existing studies, and the need for adolescent-centered preventive frameworks. By integrating multidisciplinary perspectives, this article contributes to a nuanced understanding of UTIs in adolescent girls and underscores the importance of targeted health education, early recognition, and supportive public health policies.
Single-Stage Laparoscopic Strategies for the Management of Choledocholithiasis: Contemporary Techniques, Outcomes, and Clinical Considerations
Choledocholithiasis remains a common and clinically significant condition encountered in general and hepatobiliary surgery. The evolution of minimally invasive techniques has transformed its management, shifting practice from traditional open exploration and staged endoscopic–surgical approaches toward single-stage laparoscopic strategies. This manuscript provides a comprehensive review and synthesis of contemporary evidence on the laparoscopic management of common bile duct stones, with particular emphasis on single-stage laparoscopic common bile duct exploration (LCBDE). Drawing upon retrospective cohort studies, long-term institutional experiences, and comparative analyses, this article evaluates indications, technical approaches, outcomes, and limitations associated with modern laparoscopic techniques. Topics explored include patient selection, transcystic versus choledochotomy approaches, primary closure versus T-tube drainage, adjunctive technologies such as pneumatic lithotripsy and self-expanding metal stents, and training considerations for safe implementation. Outcomes including stone clearance rates, morbidity, length of hospital stay, and resource utilization are discussed in detail. The review highlights areas of consensus as well as persistent controversies, particularly regarding optimal closure techniques and the learning curve associated with advanced laparoscopic biliary surgery. By integrating available evidence and identifying existing gaps, this manuscript aims to support informed clinical decision-making and guide future research in the management of choledocholithiasis.
laparoscopic common bile duct exploration
Challenges Encountered by Stakeholders in Vegetable Development Initiatives: Perspectives from Officials and Farmers
Vegetable development programs are crucial for enhancing food security, improving farmer livelihoods, and diversifying agricultural economies, particularly in regions like India. However, the effective implementation of such initiatives often faces a myriad of challenges from both the supply side (officials responsible for program delivery) and the demand side (farmers adopting new practices). This article provides a comprehensive analysis of the constraints encountered by agricultural officials and farmers in the context of vegetable development programs. Drawing upon existing literature and empirical observations, it identifies key limitations related to resources, knowledge dissemination, market access, and policy implementation. Understanding these multifaceted challenges is paramount for designing more effective, sustainable, and inclusive vegetable development strategies that can truly transform rural agricultural landscapes.
Reliability-Driven Design and Uncertainty-Informed Assessment Frameworks for Liquid-Fueled Rocket Engine Development
Reliability assurance remains a central challenge in the development of liquid-fueled rocket engines, where extreme operating environments, complex coupled physics, and stringent performance requirements intersect with programmatic constraints on cost and schedule. Traditional approaches based on deterministic safety margins and extensive test campaigns have achieved high levels of mission success but are increasingly strained by the demand for rapid development cycles and novel propulsion architectures. This article synthesizes classical reliability concepts, contemporary uncertainty quantification methodologies, and emerging data-driven techniques into a unified, reliability-driven development framework for liquid-fueled rocket engines. Drawing upon established standards and foundational studies, the manuscript examines the evolution from margin-based design philosophies toward probabilistic, limit-state, and Bayesian approaches that explicitly represent epistemic and aleatory uncertainties. The analysis integrates insights from structural reliability, cost estimation under technical uncertainty, and accelerated test-based development strategies. Particular attention is given to the role of uncertainty propagation, stochastic material characterization, and deep learning–assisted reliability analysis as complementary tools rather than replacements for physics-based understanding. The discussion highlights how reliability metrics can be systematically linked to development decision-making, verification planning, and risk-informed trade studies without asserting deterministic causality. Limitations related to data scarcity, model credibility, and organizational adoption are critically examined. The article concludes by outlining a pathway for integrating uncertainty-informed reliability assessment into future liquid propulsion programs, emphasizing transparency, traceability, and alignment with existing aerospace standards. The proposed synthesis contributes to the ongoing discourse on how reliability-driven frameworks may support sustainable, predictable, and defensible rocket engine development practices.
Re-examining the Physical Meaning and Universality of Planck’s Constant in Atomic Radiation and Electromagnetic Theory
Planck’s constant occupies a central position in modern physics, serving as a foundational parameter in quantum mechanics, atomic spectroscopy, and electromagnetic radiation theory. Despite its status as a defined constant in the International System of Units, ongoing theoretical and experimental discussions continue to explore its deeper physical meaning, operational role, and potential contextual variability. This article presents a comprehensive analytical review of Planck’s constant as it emerges from classical electrodynamics, atomic oscillator models, radiation–matter interaction, and relativistic considerations. Drawing upon historical formulations, including Planck’s original epistemological reflections and subsequent developments in quantum theory, the study synthesizes insights from atomic spectra, Hamiltonian mechanics, and statistical analyses of radiation laws. Particular attention is given to alternative interpretations that challenge strict universality, including proposals of effective or variable Planck constants in specific physical regimes. Using published experimental measurements and theoretical derivations, the paper evaluates how Planck’s constant functions as a bridge between continuous classical fields and discrete atomic processes. The discussion highlights conceptual tensions between definitional constancy and phenomenological emergence, situating Planck’s constant as a scaling parameter whose numerical value is intimately connected to electromagnetic impedance, atomic structure, and measurement conventions. The article concludes by outlining implications for foundational physics, metrology, and the philosophy of constants, emphasizing that while Planck’s constant remains operationally fixed in contemporary standards, its physical interpretation remains open to nuanced theoretical refinement.
Primary Lymphatic Disorders and Peripheral Lymph Node Pathology: An Integrated Anatomical, Pathophysiological, and Diagnostic Perspective
Disorders of the lymphatic system represent a complex and heterogeneous group of conditions that range from congenital lymphatic dysplasias to acquired inflammatory and infectious lymph node diseases. Despite its central role in tissue fluid balance, immune surveillance, and lipid transport, the lymphatic system has historically received less investigative attention than the blood vascular system. This imbalance has contributed to diagnostic delays, incomplete classification frameworks, and suboptimal clinical management of lymphatic disorders. The present review integrates anatomical, developmental, molecular, and pathological perspectives to provide a comprehensive understanding of primary lymphatic disorders and peripheral lymph node pathology, with particular emphasis on cervical and peripheral lymphadenopathy. Drawing on classical anatomical descriptions, contemporary molecular insights into lymphangiogenesis, and clinicopathological correlations from cytological and histopathological studies, this article synthesizes evidence across disciplines. The discussion highlights advances in the classification of primary lymphatic dysplasias, evolving concepts of lymphangiogenic signaling pathways, and the diagnostic value of fine-needle aspiration cytology in infectious and non-infectious lymphadenopathies. Tubercular lymphadenitis, a major contributor to peripheral lymph node enlargement in developing regions, is examined in detail to illustrate the intersection between lymphatic biology and infectious disease pathology. By emphasizing structure–function relationships and integrating traditional pathology with emerging molecular frameworks, this review aims to bridge existing gaps between basic science and clinical practice. A clearer conceptualization of lymphatic disorders is essential for improving diagnostic accuracy, guiding therapeutic decision-making, and informing future research directions in lymphatic biology and disease.
Predictive Modeling in Healthcare: Methodological Foundations, Clinical Applications, and Emerging Challenges
Predictive modeling has become an essential methodological framework in modern healthcare, supporting clinical decision-making across diagnosis, prognosis, and treatment planning. Advances in artificial intelligence and machine learning have expanded the scope of predictive models, enabling the integration of heterogeneous clinical, biological, imaging, and nonclinical data. This article provides a comprehensive and methodologically grounded review of predictive modeling in healthcare, emphasizing its theoretical foundations, modeling strategies, validation practices, and real-world clinical applications. Drawing on established and recent literature, the manuscript synthesizes developments in predictive modeling for cardiovascular disease risk assessment, cancer therapeutic response prediction, surgical outcome estimation, and risk stratification using physiological signals. Particular attention is given to the distinction between diagnostic and prognostic modeling, the role of nonclinical features, and the implications of model interpretability and generalizability. The Methods section outlines common data preprocessing pipelines, feature selection strategies, and modeling techniques, including traditional statistical models and machine learning approaches. The Results section synthesizes reported performance trends and comparative findings from prior studies, while the Discussion critically examines methodological limitations, ethical considerations, and future research directions. Overall, this work aims to serve as a structured reference for researchers and clinicians seeking to design, evaluate, or interpret predictive models in healthcare, while highlighting unresolved challenges related to data quality, bias, and clinical integration.
Predictive Analysis of Random Determinants Using Chebyshev-Type Inequalities and Polynomial Transformations
The probabilistic behavior of determinants of random matrices has attracted sustained interest due to its relevance in mathematical statistics, reliability theory, and stochastic modeling. While classical determinant theory is well established, the integration of probabilistic inequalities and polynomial approximation techniques into the prediction of random determinants remains an evolving area of research. This article develops a comprehensive analytical framework for predicting random determinants by combining classical results from determinant theory with probabilistic tools such as Chebyshev-type inequalities and polynomial transformations. Drawing upon foundational statistical texts and recent studies on random determinants with independent and identically distributed entries, the study emphasizes distribution-free prediction bounds and analytical tractability. Special attention is given to the role of Chebyshev polynomials and their conversion into power series representations, enabling explicit moment-based approximations of determinant-related random variables. The manuscript synthesizes prior results on Gamma, Weibull, and Bernoulli-distributed matrix elements and extends the discussion toward a unified perspective on variance-driven predictability. By situating recent probabilistic determinant studies within the broader theoretical context of mathematical statistics, the article highlights methodological consistencies, limitations of existing approaches, and opportunities for future extensions. The results contribute to a deeper understanding of how classical inequalities and polynomial methods jointly support robust, non-parametric prediction of random determinants, particularly in situations where exact distributions are analytically intractable.
Pathways Toward Climate-Neutral Aviation: Sustainable Aviation Fuels, Policy Alignment, and the Emerging Role of Industrial Hemp
The aviation sector remains one of the most challenging domains for decarbonization due to its dependence on high–energy-density liquid fuels and the limited short-term scalability of alternative propulsion systems. Recent international policy frameworks, industrial roadmaps, and sustainability strategies emphasize Sustainable Aviation Fuels (SAFs) as a central mitigation pathway for reducing lifecycle greenhouse gas emissions without compromising operational reliability. This article synthesizes contemporary academic and institutional perspectives on aviation emissions trajectories, SAF technological readiness, and policy-driven transformation, while introducing industrial hemp as an emerging and underexplored bio-based feedstock aligned with global sustainability frameworks. Drawing on reports from international energy agencies, aviation manufacturers, and multilateral governance bodies, the study integrates emissions modeling perspectives, feedstock sustainability criteria, and regulatory alignment considerations. Using a qualitative–analytical methodology, the paper evaluates how SAF deployment scenarios interact with policy instruments such as the European Green Deal and global climate commitments. Particular attention is given to the compatibility of hemp-based biomass with sustainability benchmarks, land-use constraints, and industrial scalability. The discussion highlights structural limitations, including feedstock availability, cost dynamics, and uneven policy harmonization across regions. The findings suggest that while SAFs alone are unlikely to deliver absolute climate neutrality, diversified feedstock strategies combined with coherent policy frameworks significantly enhance the sector’s mitigation potential. The article contributes to the growing literature by positioning industrial hemp within aviation decarbonization discourse and by emphasizing systems-level integration rather than singular technological solutions.
Observation-Based Early-Warning Signals of Atlantic Meridional Overturning Circulation Instability under Contemporary Climate Warming
The Atlantic Meridional Overturning Circulation (AMOC) constitutes a central component of the global climate system, influencing large-scale heat transport, regional climate variability, and long-term climate stability. Recent observational and modeling studies increasingly suggest that the AMOC may be approaching a critical transition, potentially associated with nonlinear dynamical behavior and abrupt weakening. This study synthesizes observation-based methodologies for detecting early-warning signals of AMOC instability using global and regional temperature datasets, statistical indicators of critical slowing down, and advanced spectral estimation techniques. Drawing on surface temperature reconstructions such as GISTEMP and HadCRUT5, the analysis emphasizes the role of variance, autocorrelation, and low-frequency spectral power as indicators of reduced system resilience. Particular attention is given to methodological challenges arising from non-stationarity, seasonal effects, and heteroscedasticity in climate time series. By integrating maximum entropy spectral analysis with heteroscedasticity-robust estimators of serial dependence, this work contributes to a more nuanced interpretation of emerging early-warning signals reported in recent literature. The results indicate that multiple independent indicators consistently point toward a long-term destabilization of the AMOC, although substantial uncertainty remains regarding the timing and magnitude of potential transitions. The study underscores the importance of combining physically informed climate understanding with statistically robust monitoring frameworks to improve early detection of large-scale climate tipping dynamics. These findings are relevant for climate risk assessment and for refining observational strategies aimed at monitoring critical components of the Earth system.
Mitigating Hallucinations in Large Language Models: Taxonomies, Detection Frameworks, and Evaluation Paradigms
Large Language Models (LLMs) have rapidly transitioned from experimental research artifacts to widely deployed socio-technical systems used in journalism, law, education, healthcare, and scientific research. Alongside their demonstrated fluency and versatility, these models exhibit a persistent and consequential limitation: hallucination, broadly understood as the generation of content that is syntactically plausible but factually incorrect, unverifiable, or internally inconsistent. High-profile incidents—including fabricated legal precedents, false accusations against real individuals, and erroneous scientific claims—have underscored the real-world risks associated with unmitigated hallucinations. This article provides a comprehensive, system-level examination of hallucinations in LLMs, synthesizing empirical case studies, theoretical taxonomies, detection methodologies, mitigation strategies, and evaluation frameworks. Drawing on recent academic literature and documented failures in deployed systems, the paper categorizes hallucinations into factual, logical, contextual, and self-contradictory forms, and analyzes their underlying mechanisms in model training, prompting, and deployment contexts. Particular emphasis is placed on black-box and zero-resource detection approaches, including SelfCheckGPT, Chain-of-Thought consistency methods, and alignment-based evaluation metrics such as GPTScore, GEval, and AlignScore. The discussion critically examines the limitations of current benchmarks and the epistemic challenges of evaluating truthfulness in generative systems. The article concludes by outlining open research challenges and ethical considerations, arguing that hallucination mitigation should be framed not as a problem of complete elimination, but as a continuous risk-management process integrating technical, institutional, and human-in-the-loop safeguards.
Mean Platelet Volume as a Biomarker in Acute Ischemic Stroke: Pathophysiological Associations, Prognostic Implications, and Clinical Relevance
Stroke remains a leading cause of mortality and long-term disability worldwide, with ischemic stroke constituting the majority of cases. Despite advances in neuroimaging and acute management, there is a continued need for accessible and reliable biomarkers that may assist in risk stratification, prognostication, and understanding disease mechanisms. Mean platelet volume (MPV), an index of platelet size and activity routinely reported in complete blood counts, has emerged as a potential biomarker in cerebrovascular disease. Larger platelets are metabolically and enzymatically more active, exhibit greater prothrombotic potential, and are associated with enhanced inflammatory responses, all of which are relevant to ischemic stroke pathophysiology. This article critically examines the biological basis of MPV, its relationship with platelet function, and its association with ischemic stroke severity, subtype, lesion volume, and clinical outcomes. Evidence from epidemiological studies, hospital-based cohorts, and prognostic analyses is synthesized to evaluate whether elevated MPV is associated with worse outcomes in acute ischemic stroke. The interaction of MPV with comorbid conditions such as diabetes mellitus, metabolic syndrome, and cardiovascular disease is also explored. Methodological considerations, including preanalytical and analytical variability in MPV measurement, are discussed as important limitations affecting clinical interpretation. Overall, current evidence suggests that MPV is associated with stroke risk and may predict severity and functional outcome in selected populations. However, heterogeneity across studies and the absence of standardized cut-off values limit its immediate clinical application. MPV should be viewed as a supportive biomarker that reflects underlying thrombo-inflammatory processes rather than a standalone diagnostic or prognostic tool. Further large-scale, standardized, and prospective studies are required to clarify its role in routine stroke care and to integrate MPV into multimarker approaches for cerebrovascular disease assessment.
Integrating Statistical Systems, Human Development, and Mentoring Practices to Enhance Teacher Education and Educational Outcomes
The integration of statistical systems, human development paradigms, and structured mentoring practices represents a critical intersection in contemporary educational research. Statistical systems provide essential tools for monitoring trends, evaluating interventions, and informing policy in education [2]. Human development frameworks contextualize education within broader social and individual growth processes, rendering educational outcomes both measurable and meaningful [3]. Mentoring practices in teacher education serve as mechanisms for professional growth, reflective practice, and sustained instructional improvement [4]. However, extant research highlights persistent gaps, including limited systematic integration of statistical literacy in teacher preparation, insufficient exploration of mentoring efficacy across diverse contexts, and challenges in translating statistical insights into practice. This study synthesizes current literature and theoretical perspectives to propose a comprehensive conceptual framework that links these domains, emphasizing the role of data literacy, developmental considerations, and evidence-based mentoring strategies. The manuscript delineates methodological approaches to operationalize this integration, including mixed methods research designs that juxtapose quantitative analysis of educational metrics with qualitative insights into mentoring dynamics. Findings from the integrated review suggest that combining robust statistical systems with human development-informed mentoring yields significant benefits in teacher preparedness and student outcomes. Critical limitations, such as contextual variability and resource constraints, are considered. The discussion outlines implications for educator preparation programs, policy formulation, and future research trajectories that aim to fortify teacher education through systemic data use, holistic developmental perspectives, and structured mentoring supports.
High-Altitude Pulmonary Edema: Contemporary Clinical Perspectives on Pathophysiology, Diagnosis, Electrocardiographic Features, and Evidence-Based Management
High-altitude pulmonary edema (HAPE) represents one of the most severe and potentially fatal forms of high-altitude illness encountered in individuals ascending to elevations typically above 2,500 meters. Despite advances in preventive strategies and clinical management, HAPE continues to pose diagnostic and therapeutic challenges, particularly in acclimatized individuals and in settings where overlapping cardiopulmonary conditions may obscure its recognition. The condition is characterized by non-cardiogenic pulmonary edema associated with exaggerated hypoxic pulmonary vasoconstriction, heterogeneous pulmonary blood flow, and capillary stress failure. Recent clinical and physiological research has expanded understanding of its pathophysiology, electrocardiographic manifestations, biomarker profiles, and therapeutic responses, yet important gaps persist in distinguishing HAPE from mimicking conditions such as pulmonary embolism and acute coronary syndromes at altitude. This review synthesizes contemporary evidence on the epidemiology, pathophysiological mechanisms, diagnostic criteria, electrocardiographic changes, and management strategies for HAPE, with particular emphasis on objective diagnostic frameworks and treatment modalities including oxygen therapy, pharmacological interventions, and non-invasive ventilatory support. By integrating clinical observations with physiological insights, this manuscript aims to provide a comprehensive and updated perspective on HAPE for clinicians practicing in high-altitude and expeditionary environments, while highlighting unresolved questions and directions for future research.
Globalization, Migration, and Transformative Education: Integrating Cultural Responsiveness, Science, and Equity in a Globalized World
Globalization and migration have emerged as defining forces reshaping educational landscapes worldwide. The interplay between global movements of people, ideas, and knowledge production presents both opportunities and challenges for educational equity, cultural responsiveness, and curriculum relevance. This article synthesizes current research on the impact of globalization on migration and education, with specific attention to culturally responsive pedagogies, science education reform, and the integration of migrant and refugee students. Drawing on foundational and contemporary literature, this study examines systemic gaps in teacher preparation, curriculum design, and institutional responses to demographic change. A mixed-methods approach that integrates thematic synthesis of existing studies and conceptual analysis of policy frameworks reveals persistent inequities in educational access and outcomes for migrant and marginalized students, while highlighting innovative practices in curriculum adaptation and teacher professional development. Findings indicate that culturally responsive teaching frameworks, equitable science education reforms, and sustained policy support are linked to improved academic and social outcomes for diverse learners. The discussion foregrounds the theoretical, pedagogical, and policy implications of these intersecting trends, proposing actionable strategies for educators and policymakers to foster inclusive learning environments that align with global and local needs. This article contributes to scholarly conversations at the nexus of globalization, migration, and education, offering a comprehensive synthesis and roadmap for future research and practice.
Evolving Epidemiology, Risk Stratification, and Clinical Outcomes in Upper Gastrointestinal Bleeding: A Contemporary Integrative Review
Background: Upper gastrointestinal bleeding (UGIB) remains a critical cause of emergency hospitalization globally. Despite advances in diagnostic and therapeutic strategies, UGIB continues to carry significant morbidity and mortality, influenced by etiology, comorbid conditions, and healthcare system factors. This review synthesizes current evidence on UGIB incidence, risk predictors, management strategies, and clinical outcomes. Methods: A narrative review of contemporary literature was performed, emphasizing epidemiological trends, prognostic scoring systems, comorbidity influences, endoscopic and medical therapies, and outcome determinants. Key data sources included clinical registries, guideline recommendations, prospective cohort studies, and comparative analyses of risk stratification tools. Results: Incidence of UGIB shows a decline over recent decades, attributed to Helicobacter pylori management and proton pump inhibitor use, while aging populations and antithrombotic medication use pose ongoing challenges [turn0search3] [turn0search14] [turn0search15]. Peptic ulcer disease and erosive lesions remain principal non‑variceal causes, with variceal bleeding significant among cirrhotic patients [turn0search20]. Risk stratification with validated tools (GBS, AIMS65, ABC, Rockall, PNED) guides clinical decisions on intervention urgency and disposition [turn0search9][turn0search8]. Comorbidities such as cardiovascular disease and chronic organ dysfunction substantially elevate mortality risk [4] [turn0search8]. Conclusion: Effective UGIB management integrates accurate early risk assessment, tailored resuscitation, timely endoscopic intervention, and longitudinal care addressing underlying disease. Emerging predictive models and evidence‑based practices aim to optimize outcomes; however, further research is needed to refine stratification tools and guideline implementation in diverse clinical settings.