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The Dual Impact of Moral Injury: Links to PTSD Symptoms and Disinhibited Externalizing in U.S. Combat Veterans
No full textMoral injury is an experience of psychological distress that occurs when a person’s morals are violated by themselves or others, including institutions and organizations. Such violations of morality can be impairing and have high rates of comorbidity with internalizing disorders (posttraumatic stress, depression, anxiety, suicidality), which may indicate that moral injury is a transdiagnostic construct. This study had four aims, which were accomplished using from a nationally representative, probability-based sample of 1,353 US military veterans. In the first aim, we created structural models of moral injury using the Moral Injury Events Scale (for which a bifactor structure with a specific factor of perceived betrayals fit best) and posttraumatic stress using the PTSD checklist for DSM-5 (for which a seven correlated factors model fit best). The second aim created a bifactor structural model of disinhibited externalizing with symptoms of alcohol use, drug use, and gambling disorders as markers of the general factor alone, with a specific second-order normal-range inhibitory personality factor comprising traits of conscientiousness, cognitive impulsivity, and grit. In the third aim, we found that the general factor of moral injury was associated with the unique variance in the reexperiencing factor of PTSD along with disinhibited personality traits and drug and gambling use disorder symptoms.The perceived betrayals factor was associated with the unique variance in the anhedonia factor of PTSD. In the fourth aim, we verified that these relationships remained after adjusting for lifetime trauma and combat exposure. These results highlight the importance of examining these constructs at multiple levels to understand their relationships and formulate treatments targeted at the appropriate level of symptom presentation
Enhancing Reliability of Internet of Things Systems Through Machine Learning-Based and Data Fusion Techniques
No full textThe rapid proliferation of Internet of Things (IoT) devices has led to an exponential increase in connected devices across various sectors. Each IoT network typically operates independently, focusing on specific applications and utilizing data fusion techniques to combine sensor-generated data within its network to make a decision. Alongside the growth and development of IoT, vision of sustainable ubiquitous environments, such as smart cities, smart agriculture and health, smart societies etc. is also growing rapidly. Thus, as the number of IoT devices continues to grow and their applications and vision diversify, there arises a pressing need to create a cohesive and intelligent ecosystem of intelligent independent IoT networks. However, IoT systems face significant challenges, particularly in ensuring data reliability and continuity in scenarios of data loss or failure. Such failures, arising from device malfunctions, network disruptions, or environmental factors, result in incomplete or unreliable datasets, which can severely impact decision-making processes and system functionality. Therefore, this dissertation addresses these challenges by proposing an innovative approach interconnecting independent IoT architectures to share resources and advanced synthetic data generation techniques to mitigate the effects of data failures.This dissertation first proposes a novel interconnected or cross-network IoT architecture, facilitating communication between independent IoT systems. This architecture enables seamless data sharing and fusion across systems, creating a robust framework for addressing data failures. The proposed architecture emphasizes flexibility, modularity, and interoperability, incorporating multiple logical layers such as perception, network, data fusion, and security layers. By interconnecting standalone/independent IoT systems, the framework reduces the reliance on individual network components and enhances overall system resilience. The architecture is designed to harmonize heterogeneous IoT networks, leveraging shared data resources to address single-point failures and reduce the need for redundant sensor deployments. To complement this architecture, the research further extends its study by developing advanced synthetic data generation techniques based on K-Nearest Neighbors (KNN) combined with Iterative Principal Component Analysis (IPCA). These methods leverage the proposed cross-network data fusion framework to create reliable synthetic data for addressing missing or unreliable datasets. Two distinct data fusion methods are presented: (1) fusion of the same feature type across networks and (2) fusion of highly correlated feature types. These approaches enable the system to compensate for missing data by utilizing information from other IoT networks sensing similar or related features. The proposed methods eliminate the limitations of traditional techniques reliant on historical or redundant data from the same network, offering a more robust and adaptable solution for dynamic IoT environments. Comprehensive experimentation validates the effectiveness of the proposed approaches using real-world IoT datasets collected from diverse geographical locations and environmental conditions. Results demonstrate that the KNN+IPCA method significantly outperforms state-of-the-art machine learning, statistical, and probabilistic approaches, achieving lower Root Mean Square Error (RMSE) values across all tested scenarios. Furthermore, the integration of the proposed cross-network architecture with synthetic data generation techniques enhances data reliability and system adaptability, even in highly heterogeneous and failure-prone environments. Thus, this dissertation advances IoT systems by addressing data failure challenges, enabling operational integrity, resource optimization, and accurate decision-making. By enhancing system resilience, this dissertation paves the way for robust, scalable, and sustainable IoT ecosystems
Assessing Thermal Comfort in a Landmark Urban Park: A Case Study and Methodological Framework of Alamo Square in San Francisco, USA
No full textUrban green spaces are increasingly challenged by climate stressors, yet strategies for climate-adaptive renewal are often difficult to evaluate in culturally and historically significant sites. This study exames Alamo Square in San Francisco, a globally recognized tourist landmark parkas a single-case analysis of how incremental reforestation and shading interventions influence human activity patterns and thermal comfort. This study integrates geotagged social media data (Flickr/Instagram), ENVI-met microclimate simulations, and spatial statistical analyses (Kernel Density Estimation, hot spot analysis). Focusing on Alamo Square’s reforestation initiativeaimed at doubling tree density with drought-tolerant species-the research reveals that pre-intervention activity clustered densely near the iconic “Painted Ladies,” driven by tourism. Post-reforestation, vegetation significantly reduced summer PET values, dispersing heat stress zones, but introduced winter cold zones and spatial mismatches between shaded areas and emergent activity hubs. Rest facilities (e.g., shaded seating) consistently outperformed entertainment amenities in attracting users, highlighting their role in thermal adaptation. This work innovatively combines dynamic crowdsourced data with human-centered thermal metrics to expose seasonal trade-offs, advocating for designs that align cooling interventions with activity hot spots while mitigating winter discomfort. The findings demonstrate that effective urban green space design requires balancing ecological goals (e.g., canopy restoration) with human-centered strategies, such as strategic shading of high-traffic paths. By bridging environmental modeling and behavioral analytics, this study offers transferable insights and practical ecological indicators for landmark or tourism-driven parks to evaluate thermal comfort and enhance preparedness for future climatic challenges
Use of Calcium Modification in Percutaneous Coronary Intervention: A Comprehensive Review
No full textCalcified coronary lesions remain a challenge in percutaneous coronary intervention (PCI) in both situations of acute myocardial infarction (MI) and stable coronary syndrome. It significantly increases the risk of procedural complications due to difficulty in equipment delivery, balloon expansion, and stent delivery. Furthermore, stent thrombosis, dissection, perforation, and future in-stent restenosis occur more frequently in calcified coronary lesions, impacting repeat target vessel revascularization and increasing the risk of future MI. With intracoronary imaging (intravascular ultrasound and optical coherence tomography), peri-procedural success for treating calcified lesions has increased significantly. Different modalities of calcium modification techniques have since been introduced. This review will discuss the pathophysiology and phenotypes of calcium deposition in the coronary vessels, including eccentric calcified plaques and calcified nodules. We will also focus on calcium modification techniques and their mechanisms: (1) Balloon escalation technique, (2) intravascular lithotripsy, (3) orbital atherectomy, and (4) rotational atherectomy. We will focus on the strengths and limitations of each technique, based on current recommendations and expert consensus from SCAI. We will also provide contemporary evidence of each modality for treating different phenotypes of calcified lesions. In summary, this article provides a comprehensive review of current guidelines for optimizing the treatment of calcified coronary lesions in PCI
Social Support and Its Associations With Mental Health and Parenting Among Mothers With Young Children in Western Kenya
No full textThis study examined associations between perceived social support, maternal mental health, and parenting practices among mothers with young children under aged 2 years in rural Western Kenya. Data were collected as part of the baseline evaluation of a cluster randomized controlled trial evaluating a parenting intervention. Maternal perceived social support was reported using the Multidimensional Scale of Perceived Social Support, and community connectedness with two study-developed items. Maternal depressive symptoms were measured using the CES-D, parenting distress with Parenting Stress Index–Short Form, and caregiver stimulation practices with Family Care Indicators. Multilevel linear regression models examined associations between social support and maternal mental health and parenting outcomes. The analytic sample included 539 mothers, of whom 49.4 % were aged 25–34 years, 36.4 % were at risk of depression, and 19.9 % reported high parenting stress. Higher perceived support was associated with fewer depressive symptoms (β = −0.14, p \u3c .001), lower parenting stress (β = −0.24, p \u3c .001), and greater stimulation practices (β = 0.08, p = .04). Family support was more strongly associated with mental health outcomes, while friend support was associated with stimulation. Community connectedness was associated with lower parenting stress but not with depressive symptoms. Findings highlight the importance of family support for maternal well-being and peer support for fostering stimulation practices among mothers with young children
Single Vs. Whole Muscle Group Assessments: Does The Specific Muscle Influence Its Association With Performance?
No full textTopics in Exercise Science and Kinesiology Volume 6: Issue 1, Article 11, 2025. Biometric Muscle size and echo intensity are thought to influence muscle function. Unfortunately, previous works often will examine a single muscle from a group of agonists as a metric of whole muscle performance. This approach may not always be the most appropriate to assess performance outcomes. The purpose of the study was to 1) quantify the association of muscle size and echo intensity with vertical jump performance (VJ), 2) Compare the associations between individual and whole muscle group size and echo intensity, 3) determine whether single or whole muscle size and echo intensity better predicted VJ performance. Nineteen adults (214 years) completed one visit to the laboratory for musculoskeletal imaging and vertical jump testing. B-mode ultrasound images of the vastus lateralis, vastus intermedius, vastus medialis, and rectus femoris muscles were acquired; CSA and echo intensity were quantified via offline analysis. Pearson’s product moment correlation coefficients (r) were calculated to determine association between individual and whole muscle size and echo intensity with VJ performance. Steiger’s z procedures were used to examine differences in correlations between individual muscles and total quadricep relationships to VJ. Follow-up stepwise multiple linear regressions were used to predict the individual and whole muscle association with VJ performance. The results suggested that muscle size but not echo intensity, may be associated with VJ. Steiger’s z procedures did not reveal differences in quadricep and individual muscle CSA with VJ performance. Furthermore, the CSA of the vasti musculature, but not the RF, had a meaningful impact on VJ
Concurrent Presentation of an Anomalous Right Coronary Artery and an Unusual Bovine Arch: Case Report
No full textAnomalous coronary arteries from the opposite sinus of Valsalva are rare but can increase the risk for life-threatening complications. We present the case of a 59-year-old man who was found to have an anomalous right coronary artery and a rare bovine arch variant with a single common brachiocephalic trunk, along with severe proximal left anterior descending artery stenosis on left heart catheterization. The patient underwent a minimally invasive coronary artery bypass graft and unroofing of the anomalous coronary artery. To our knowledge, this is the first reported case of these concurrent anomalies in an individual without tetralogy of Fallot
Knee Injury Prevention Program in an Early Pubertal Female Athlete During a Recreational Softball Season
No full textTopics in Exercise Science and Kinesiology Volume 6: Issue 1, Article 12, 2025. This case study was intended to analyze the impact of a knee injury prevention program (KIPP) for a young female athlete in a short recreational softball season. It was hypothesized that initiating a KIPP early within a recreational sports season would positively alter lower extremity biomechanics, subsequently reducing the risk of knee injuries during and after skeletal development. During a recreational softball season, a 13-year-old female athlete completed a preliminary biomechanical test, the drop vertical jump battery (DVJBpre), underwent a 7-week in-season KIPP, then was follow-up tested at season’s end (DVJBpost1) and again approximately one year later (DVJBpost2). Performance was recorded using a motion capture system and the biomechanical test battery consisted of three double-leg drop vertical jumps (DVJ) from two landing heights (30 and 50 cm). At testpost1 immediately following the 7-week recreational season, the athlete demonstrated greater maximal hip flexion and decreased maximal ankle dorsiflexion bilaterally from both heights. She also demonstrated greater ankle inversion bilaterally from both heights and greater maximum right knee valgus from 50 cm. The athlete then started high school and participated in varsity volleyball, basketball, and track and field while also participating in a regular general strength and conditioning program. At testpost2, the athlete demonstrated improved hip flexion, and right knee valgus improved to within normal limits with neutral knee positioning. This case demonstrated that a 7-week KIPP during a recreational softball season did influence positive hip and ankle mechanical changes during landing from both heights, but negatively influenced the right knee from the greater height during testing immediately following the season and program; however, this change was mitigated within the first year. These findings suggest that young athletes in the midst of significant growth and motor development changes may not respond predictably to training protocols commonly suggested for older athletes, however, negative responses may be transient and can be altered through a well-rounded sports and training experienc
Navigating Leadership: The Impact of Intersectional Identities on Female Leaders in Postsecondary Education
No full textThis study aimed to explore the lived experiences of female leaders in postsecondary education, focusing on how intersecting identities influence their leadership and decision-making processes. Using a hermeneutic phenomenological approach and the theoretical framework of intersectionality, the research explored the relationships between gender, leadership, career decision-making, and other social identities. Data were collected through semi-structured interviews and demographic surveys with 11 female leaders. The findings revealed seven key themes: Identity Influenced Experiences, Unintentional Navigation, Institutional Bias, Playing the Game, Institutional Champion, Supportive Mechanisms for Career Advancement, and Value Alignment. These themes illustrate how gender and intersecting identities shape career decisions and leadership experiences. The study highlights the impact of identity on career progression and offers insights into the strategies women employ to navigate leadership roles. It contributes to the intersectionality and leadership literature and provides practical implications for enhancing institutional support for female leaders in postsecondary education
The Startup Ecosystem in Southern Nevada: Growth, Energy, and Emerging Entrepreneurship in 2025
No full textThe startup ecosystem in Southern Nevada has undergone significant transformation during the past decade, evolving from a fragmented and nascent environment into a dynamic and increasingly coordinated landscape. This study examines the growth of the Southern Nevada startup ecosystem, analyzing key components such as funding sources, incubators, accelerators, and public-private collaborations. Utilizing both quantitative and qualitative data, including investment trends, economic impact reports, and interviews with over 100 stakeholders, this policy brief provides a comprehensive assessment of the strengths and gaps within the ecosystem. Key findings indicate that while the region has made substantial progress, particularly in fostering early-stage investments and building the infrastructure to support startups, challenges remain including the need to strengthen cross-sector communication, develop deep local venture capital, and broaden awareness of available resources. The study also benchmarks Las Vegas against other Mountain West metro areas, highlighting opportunities for further growth and includes policy recommendations aimed at strengthening the region’s ability to attract, develop, and retain high-growth startups. With targeted efforts in ecosystem coordination, investment expansion, and strategic alignment with emerging industries, Southern Nevada has the potential to become a more robust and sustainable hub for entrepreneurship and innovation