Kean University

Sport-related concussion (SRC) management has evolved substantially over the past 25 years. Early paradigms emphasized prolonged physical and cognitive rest; however, growing evidence has demonstrated that recovery following SRC is multidimensional and influenced by interacting neurological, vestibular, autonomic, cervical, cognitive, and psychological systems. Consequently, contemporary clinical practice has shifted toward active, multifaceted rehabilitation approaches. We aimed to synthesize and contextualize the evidence supporting a multifaceted approach to sport-related concussion management from 2000 through 2025, with emphasis on implications for athletic training practice. A structured literature search was conducted using PubMed, SPORTDiscus, CINAHL, and Web of Science to identify peer-reviewed publications related to SRC evaluation, management, and rehabilitation. Studies published between 1 January 2000, and 31 December 2025 involving human participants with sport-related concussion or sport-like mechanisms of mild traumatic brain injury were included. Evidence from randomized controlled trials, cohort studies, systematic and narrative reviews, and major consensus or position statements was considered. Relevant studies were reviewed and synthesized across key domains of SRC management, including aerobic exercise, vestibular and oculomotor rehabilitation, cervical spine management, multimodal and profile-based rehabilitation, return-to-learn strategies, psychological and behavioral health considerations, and implementation patterns within athletic training settings. A total of 182 publications contributed evidence to one or more components of multifaceted SRC management. Across domains, evidence supports early, symptom-limited aerobic exercise; targeted vestibular and cervical rehabilitation; structured return-to-learn planning; and the integration of psychological support. Multimodal rehabilitation and profile-based clinical categorization approaches were associated with shorter recovery timelines and improved functional outcomes compared with rest-only strategies. Despite strong evidence, implementation variability persists across athletic training settings. Evidence accumulated over the past 25 years supports a shift toward active, individualized, and multidisciplinary approaches to SRC management. Athletic trainers are uniquely positioned to coordinate multifaceted care addressing the diverse contributors to concussion recovery.

Since 2015, KUSA and WKU have jointly developed a robust student exchange program that has engaged more than 2,300 participants. Distinguished by its scale, participant diversity, and institutional composition, the program exemplifies an innovative model of cross-campus collaboration. Its success lies in the joint coordination between the Center for International Studies at both institutions, ensuring consistency in vision and implementation. Students from each campus pursue the exchange for varied academic and cultural motivations, while staff across continents operate cohesively as one team, managing the full cycle of sending and receiving students. Led by Dr. Haina Zhu, Dean of Students at WKU, and Jessica Goldsmith Barzilay, MSW, Assistant Vice President for Global Initiatives at KUSA, this chapter presents reflective insights from leaders who have shaped and sustained this transnational partnership.

ABSTRACT The self‐control literature emphasizes the importance of intervention programs to strengthen the self‐control skills of school‐age students. Accordingly, this study aimed to evaluate the efficacy of a self‐control intervention program (SCIP) on school‐age adolescents’ self‐control and grit traits. In this quasi‐experimental design study, an eight‐session SCIP grounded in the process model of self‐control was implemented in the experimental group with 24 students, whereas the control group consisted of 24 middle and high school students. Assignment to groups was nonrandom. The data were analyzed using a two‐way mixed ANOVA. The findings suggest that SCIP can be a promising program for improving the self‐control and grit of middle and high school students. School counselors and other school‐based mental health providers can use the SCIP to promote self‐control in their students, which is one of the goals of the ASCA National Model and the social‐emotional curriculum.

Peer interactions provide a crucial context for learning and development, especially during the preschool years (ages 3–5) when children experience rapid growth across all developmental domains. Guided by Vygotsky’s sociocultural theory, this qualitative, observation-based study explored prosocial and antisocial behaviors within the context of peer interactions. It included 13 preschool children from three age-specific classrooms (3-, 3.5-, and 4-year-olds) at a private childcare center in the United States. This configuration allowed for the examination of prosocial and antisocial behaviors across the 3- to 4-year-old continuum, with 3.5-year-olds included as an intermediate group. Eighteen 30-min observations were conducted to identify behavioral patterns, developmental trends, and contextual differences across five classroom activities. Thematic analysis revealed distinct behavioral patterns, with the three most frequently occurring types of prosocial behavior being (1) socially proactive, (2) socially responsive, and (3) sharing, while the two most commonly occurring types of antisocial behavior were (1) physically aggressive and (2) verbally aggressive. It also revealed developmental trends, with 4-year-olds exhibiting the highest percentage of overall prosocial behavior and the lowest percentage of overall antisocial behavior compared with their 3- and 3.5-year-old peers. Contextual differences were also evident: for overall prosocial behavior within each age group, unstructured playtime elicited the highest percentage among 3- and 3.5-year-olds, while circle time elicited the highest percentage for 4-year-olds. Unstructured playtime also accounted for the highest percentage of overall antisocial behavior across all age groups. All of these key findings were interpreted through the lens of self-regulation and theory of mind.

Force plate-derived metrics are increasingly used to assess performance and monitor musculoskeletal injury risk, yet the mechanisms linking jump-mechanics patterns to injury risk remain unclear, particularly when using proprietary, algorithmically derived risk scores. Clarifying these relationships is important for improving screening practices, program design, and load management in athletic populations. A total of 233 collegiate athletes completed countermovement vertical jump (CMVJ) testing on a commercial force plate, which produced 26 force-time variables and proprietary composite metrics. LASSO regression with bootstrapping identified important predictors of CMVJ height and algorithmically derived musculoskeletal injury (AMSKI risk), and Partial Least Squares (PLS) models characterized multivariate patterns across force-time variables. Sex-stratified analyses and post hoc modeling examined potential mechanisms. Greater AMSKI risk was associated with a coordinated pattern of greater concentric output, including greater power, velocity, and impulse, combined with reduced braking capacity. Braking rate of force development ("Load") showed an inverse association with AMSKI risk across sexes, and females in the elevated-risk category displayed significantly reduced braking values. Postural control measures contributed differently by sex. PLS models indicated that both CMVJ height and AMSKI risk reflected interactions among multiple variables, while proprietary composite scores showed inconsistent alignment with mechanistic predictors. Multivariate force-time profiling offers practical value for identifying athletes whose high-output movement strategies may elevate injury risk when braking control is insufficient. Because proprietary, algorithmically derived risk metrics show inconsistent associations with underlying mechanics, further independent validation is needed before such scores are used in clinical or training decisions.

Purpose: Due to an increased risk for developing reading and writing difficulties, research literature recommends integrating literacy targets into speech sound therapy for children with speech sound disorders. Translating guidance from research into daily practice warrants looking closely at applying evidence-based practices from more controlled environments into authentic settings because most speech-language pathologists practice in these real-world settings. This clinical focus article describes how embedding phonics and phonemic awareness activities into speech sound therapy in a real-world setting influenced speech sound production, letter naming, grapheme–phoneme correspondence, and phonemic segmentation in a preschool client with a speech sound disorder. Method: A female aged 4;5 (years;months) with a speech sound disorder was seen for 10 individual therapy sessions in a university speech clinic collaboratively with a graduate student clinician. The parent observed all sessions and was given specific feedback on how to implement strategies at home that were introduced in therapy. Data were collected for speech sound production, letter naming, grapheme–phoneme correspondence, and phonemic segmentation prior to therapy at the beginning, at midpoint, and at the end of the therapy sessions. Results: The client's speech sound errors decreased, phonemic repertoire expanded, phonological processes such as phoneme collapse decreased, and both letter naming and grapheme–phoneme correspondence increased; however, analyses indicated that the differences were not statistically significant. Phonemic segmentation remained unchanged. Conclusions: It is feasible to apply recommendations to integrate phonics and phonemic awareness activities into speech sound therapy. However, a clear understanding of literacy development is an important consideration when selecting targets. Results are discussed in the context of balancing clinical and statistical significance, and therapeutic decision making may benefit from integrating both.

Exchange-traded funds (ETFs) provide low-cost, liquid access to broad equity and fixed-income exposures, including rapidly growing Asian and Asia-focused markets. Yet the academic evidence on Asian ETF portfolio construction remains fragmented, often limited to narrow country samples and centered on mean–variance trade-offs and standard performance statistics, with comparatively less emphasis on downside tail risk and on implementable long-only versus long–short designs under leverage constraints. This study examines the performance and risk characteristics of 29 Asian and Asia-focused ETFs over 2014–2025 and evaluates whether optimization using variance-based and tail-sensitive risk measures improves portfolio outcomes relative to a simple, implementable benchmark. We construct Markowitz mean–variance and conditional value-at-risk (CVaR) efficient frontiers and implement six optimized portfolios at the 95% and 99% tail levels under long-only and long–short configurations with leverage up to 30%. Performance is evaluated relative to an equally weighted Asian ETF benchmark using the Sharpe ratio and tail-sensitive measures, including the Rachev ratio and the stable tail adjusted return (STARR), complemented by fat-tail diagnostics based on the Hill tail-index estimator. The empirical results show that optimization improves efficiency relative to equal weighting in risk-adjusted terms and that moderate leverage can increase returns but typically amplifies volatility, dispersion, and drawdowns. Taken together, the evidence indicates that risk-measure choice materially affects portfolio composition and realized outcomes, with tail-based optimization generally producing more robust allocations than mean–variance approaches when downside risk is a primary concern.

Industry 5.0 promotes the transformation of manufacturing toward flexibility, personalization, and sustainability. As a critical component of closed-loop manufacturing systems, disassembly operations urgently require more flexible and efficient human–robot collaboration models. To this end, this work, for the first time, proposes a multihuman–robot collaborative circular disassembly line balancing problem. By allowing workers to move between robotic workstations, the proposed system enhances operational flexibility. Furthermore, a multiworker mechanism is introduced to improve fault tolerance and system stability, overcoming the limitations of fixed worker positions in existing collaborative disassembly research. To solve this problem, we formulate a discrete-time mixed-integer programming model based on product AND/OR graphs, aiming to maximize disassembly profit. The model’s correctness is verified using CPLEX. Additionally, we develop a heterogeneous graph neural network-enhanced proximal policy optimization (PPO) algorithm. By integrating product and workstation information into a heterogeneous graph, the algorithm performs two-stage feature extraction and node embedding via graph neural networks. Based on these embeddings, the agent dynamically selects multiple actions per decision step to simulate the behavior of multiple workers moving simultaneously. Experimental results show that the proposed method outperforms traditional reinforcement learning algorithms such as PPO and deep Q-network algorithm in terms of disassembly profit. Moreover, it demonstrates strong generalization capability in cross-task transfer and scalability experiments involving different task graph sizes. The improved performance is achieved with acceptable computational time.

Real-time fine-grained human activity recognition (HAR) remains a challenging problem due to rapid spatial–temporal variations, subtle motion differences, and dynamic environmental conditions. Addressing this difficulty, we propose NovAc-DL, a unified deep learning framework designed to accurately classify short human-like actions, specifically, “pour” and “stir” from sequential video data. The framework integrates adaptive time-distributed convolutional encoding with temporal reasoning modules to enable robust recognition under realistic robotic-interaction conditions. A balanced dataset of 2000 videos was curated and processed through a consistent spatiotemporal pipeline. Three architectures, LRCN, CNN-TD, and ConvLSTM, were systematically evaluated. CNN-TD achieved the best performance, reaching 98.68% accuracy with the lowest test loss (0.0236), outperforming the other models in convergence speed, generalization, and computational efficiency. Grad-CAM visualizations further confirm that NovAc-DL reliably attends to motion-salient regions relevant to pouring and stirring gestures. These results establish NovAc-DL as a high-precision real-time-capable solution for deployment in healthcare monitoring, industrial automation, and collaborative robotics.