Norfolk State University

We studied dispersion in Rhodamine laser dyes in the Kretschmann geometry and found (i) multi-branch “staircase” dispersion curves in singly doped and double doped PMMA polymer, (ii) emergence of the new dispersion “fork” branch, (iii) unparallel dispersion and coupling in the mixture of two different dyes, and (iv) effect of high dye concentration on strong coupling without metal.

This chapter illustrates developmental factors associated with people’s transition into late adulthood, including the opportunity to reflect on one’s life, deal with loss, and plan for retirement. It begins with an explanation of how ageist attitudes constrained the early development of mental health services for older adults. Legislative and organizational initiatives that social workers have and continue to participate in to ameliorate stereotyping and discrimination against older adults are also described. A practice example sheds light on how a father and daughter considered the meanings of past critical events. Family-centered narrative interviews clarify how a social worker facilitated the four phases of the resilience-enhancing stress model. The therapeutic goal was to help the family reconstruct its organizational and communication patterns so it could adapt to changing daily circumstances and maintain its resilient social functioning.

Soil–structure interaction (SSI) analysis evaluates the collective response of the structure, the deep foundation, and the soil underlying the foundation. Thus, it can alter the tall buildings behavior during earthquakes. This paper evaluated the seismic performance of tall buildings by estimating lateral displacements and inter-story drifts as engineering demand parameters (EDPs), as well as the peak story horizontal accelerations using SSI analysis. For this purpose, a 33-story RC frame-core structure was modeled in the MIDAS GTX NX platform with its pile-raft foundation. Then, several nonlinear dynamic analyses were conducted to capture the structure’s nonlinear response. Although the SSI model’s lateral displacement of the structure was smaller than that of the fixed-base model, the maximum inter-story drifts and peak story horizontal accelerations for the SSI model increased, particularly under 475-year earthquake records. These can significantly affect the seismic performance of the structure. Therefore, the key factors affecting the stability of tall structures with deep pile foundations in sandy soil include the properties of the soil, notable excitations, vibrational characteristics, size of soil particles, density, clay content, and groundwater level. This study’s results indicate that soil-structure interaction (SSI) must be considered in evaluating the seismic performance of high-rise buildings.

Background: Children exposed to intimate partner violence (IPV) experience disparities in health outcomes and healthcare access. Mechanisms explaining how IPV affects children’s health needs, particularly after parental separation, are poorly understood. Objective: The aim of this qualitative study is to examine maternal survivors’ experiences of IPV following separation from an abusive co-parent (“post-separation abuse”) and their children’s health needs. Participants and setting: The research team conducted individual semi-structured interviews with N =33 maternal post-separation abuse survivors from 18 states in the United States. Methods: Qualitative interviews were coded and analyzed using ATLAS.ti through an iterative thematic inquiry approach, with each interview coded by at least 2 study team members. Results: Most participants (85 %) reported difficulty accessing healthcare for their children. The analysis team identified five broad domains of post-separation abuse tactics contributing to children’s unmet health needs: (1) obstruction and manipulation of children’s healthcare, (2) stalking and intimidation, (3) legal abuse, (4) disregarding children’s well-being, and (5) economic abuse. Conclusions: This study provides foundational insights into specific behaviors by abusive co-parents and court-imposed barriers that impact children’s health needs. Improved understanding of post-separation abuse is essential to design interventions and policies to ensure children’s access to needed healthcare and to reduce health disparities.

The purpose of the study was to examine the parental experience of participating in a cultural physical activity (PA) intervention and to identify the aspects of this intervention that directly impact the changes in parents’ behaviors towards advocating the PA participation of children with Autism Spectrum disorder Autism (ASD). The study used a descriptive research design with the Theory of Planned Behavior (TPB) as the theoretical framework. Semi-structured interviews were used to collect data from eight South Korean immigrant parents of children with ASD. The study’s results reveal that while most South Korean immigrant parents expressed gratitude for attending their first PA intervention, which increased their motivation to support and advocate for the PA participation of children with ASD, they still demonstrated a lack of competence in advocating and teaching PA to their children due to numerous perceived control barriers, such as a lack of knowledge in PA, cultural and language differences, and the challenging behaviors of their child even after the PA intervention.

Kicking is an important fundamental motor skill that is seldom studied. Young children who can kick with competence have opportunities to increase their physical activity and engage in social interactions. We examined the effects of developmentally aligned kicking instruction taught to five children (7–8 years old) with autism spectrum disorder (ASD). A multiple baseline design across participants was used to examine the effects of developmentally aligned kicking instruction. The primary dependent measure was percentage of correct kicking trials and this was supplemented by two outcome measures, (a) the TGMD‐2 kicking subtest score and (b) the kicking developmental sequence, which were measured at pretest, the end of baseline, post‐intervention, and 3 weeks following intervention. All five participants improved their kicking performance, demonstrating that the intervention was an effective strategy for learning kicking skills for children with ASD.

Purpose We evaluated healthcare providers’ current knowledge, practices, and perspectives on a novel clinical decision tool (beta-version) to facilitate individualized exercise prescriptions and discussions in clinical settings. Methods We recruited healthcare providers who had treated or provided care to breast cancer survivors aged ≥ 35-years in the past 12 months. The participants were presented with a tool to provide individualized exercise recommendations considering women’s individual, clinical, and contextual characteristics. Validated and reliable pre-existing instruments were used to survey providers’ current knowledge, practices regarding exercise discussions, and perspectives on the beta-version (paper-draft) of the novel tool. Results The sample consisted of complete survey responses from 177 healthcare providers including breast oncologists (27.7%), primary care physicians (10.7%), exercise specialists (19.8%), occupational/physical therapists (18.1%), advanced care providers, nurses, navigators, and social workers (23.7%). Median years of experience was 8-years (range: 5–13). Overall, 62.1% (n = 110) reported that they were knowledgeable about counseling survivors based on exercise guidelines. Among breast oncologists and primary care physicians (n = 68), only 39.7% reported that they were knowledgeable about identifying patients for exercise referals. The majority agreed that they would find the tool offering individualized information useful (n = 148, 83.6%), and would use it regularly to inform practice (82.5%). ‘Exercise Readiness’, ‘Exercise Resources at Home’, and ‘Quality-of-Life’ were the highest rated items for inclusion in the tool for exercise prescriptions. Provider perspectives were incorporated into the beta-version of the tool. Conclusion A clinical decision tool considering individual, clinical, and contextual characteristics may support exercise prescriptions and discussions in clinical settings. Implications for cancer survivors An evidence-based tool for exercise prescriptions may increase healthcare provider confidence to discuss, educate, encourage, and provide exercise referrals for breast cancer survivors.

Organic compounds containing luminous rare-earth ions are of interest for numerous nanophotonic and plasmonic applications, including nanoscale lasers, biosensors, and optical magnetism studies. Optical studies of Eu ³⁺ complexes revealed that ultra-thin LB monolayers are highly luminescent even when deposited directly on plasmonic metal, which makes these materials very promising for plasmonic applications and studies, including control and enhancement of magnetic dipole emission with a plasmonic environment. In this work, we synthesize amphiphilic complexes with various rare-earth ions Nd ³⁺ , Yb ³⁺ , and DPT ligands and show that they all are suitable for monolayer or multilayer deposition with the Langmuir–Blodgett (LB) technique. Graphical abstract

We present a novel approach to health monitoring through the development of a smart bandage network utilizing distributed machine learning and Bluetooth Low-Energy (BLE) communication technology. Each smart bandage is equipped with an ultra-low-power microcontroller that hosts machine-learning algorithms to analyze data from a network of attached sensor nodes in real-time, enabling the detection of anomalies and immediate feedback through a portable edge unit. The edge-enabled central unit facilitates communication with multiple smart bandages, employing BLE for efficient and reliable data transmission. Our system is designed for scalability, featuring a dynamic registration process that seamlessly integrates new bandages into the network, simplifying deployment and expanding coverage. By decentralizing data processing and implementing fault tolerant communication strategies, the system ensures robust and continuous monitoring. This research advances healthcare technology by providing a scalable, energy-efficient, and dependable solution for real-time remote health monitoring in diverse clinical and non-clinical settings.

Purpose of Review The cement industry, responsible for 7–8% of global greenhouse gas (GHG) emissions, faces growing pressure to mitigate its environmental impact while maintaining its critical role in global infrastructure and economic development. This report explores comprehensive strategies to decarbonize the sector, emphasizing the integration of innovative technologies, sustainable practices, and robust policy frameworks. Recent Findings Key technological solutions include carbon capture, utilization, and storage (CCUS); electrification of heat; adoption of alternative fuels; and the utilization of supplementary cementitious materials (SCMs) such as calcined clays and alternative materials. Additionally, emerging advancements like 3D printing, CO₂ mineralization, and biobased materials promise to revolutionize construction methods while reducing emissions. Policy interventions such as carbon pricing, cap-and-trade systems, research grants, tax incentives, and regulatory standards play a pivotal role in enabling this transition. Demand-side measures, including sustainable construction practices, recycling, and green procurement policies, further drive industry-wide adoption of low-carbon solutions. Summary Through a systems-thinking approach, this paper advocates for reducing material intensity across all stages of production and design, leveraging circular economy principles, and fostering resilient, low-carbon construction. Highlighting global initiatives, the study offers actionable insights for achieving net-zero targets in the cement industry by aligning stakeholders across the value chain to drive climate action while promoting equity, environmental justice, and economic sustainability.

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. Gradient boosting demonstrated superior predictive performance among all models. Nomograms were developed to visualize risk factors, with gender, age, comorbidities, and insurance type emerging as key predictors. The study revealed higher mental health disorder prevalence compared to national averages, suggesting a potentially greater mental health burden in this population. Despite the limitations of its retrospective design and regional focus, this research provides valuable insights into mental health disparities among African Americans in Southeastern Virginia, particularly regarding demographic and clinical risk factors.

Autonomous vehicles (AVs) are expected to eventually replace traditional vehicles that require human drivers. In recent years, several AV manufacturers have begun on-road testing to validate the safety of these vehicles. California is one of the few states to permit such testing, regulating it through a permit system. To ensure transparency and public awareness, the state mandates that any licensed AV manufacturer conducting on-road tests report crashes involving AVs. This must be conducted using a standardized format known as OL316, a requirement that has been in place since late 2014. While previous research has explored AV crash data, most studies have focused on specific timeframes without covering the entire period since 2014. Moreover, converting the data from PDFs to machine-readable formats has often been a manual process, and the description text field in reports has rarely been fully analyzed. This article presents a comprehensive, machine-readable dataset of AV crashes from 2014 to September 2024, along with publicly available parsing code to streamline future data analysis. Additionally, we provide an updated statistical analysis of AV crashes during this period. Furthermore, we leverage Generative AI (GenAI) to analyze the description text field of the OL316 reports. This analysis identifies common crash scenarios, contributing factors, and additional insights into moderate and major incidents. The final dataset comprises 728 crash entries. Notably, only 2% of the crashes were categorized as major, while 14% were classified as moderate. Furthermore, 43% of the crashes occurred while the AV was stationary, whereas 55% took place while the AV was in motion. Our GenAI analysis indicates that, in many instances, human drivers of non-autonomous vehicles were at fault. Common causes include rear-end collisions due to insufficient following distances, traffic violations such as running red lights or stop signs, and reckless behaviors like lane boundary violations or speeding.

Excessive use of social media (SM) platforms and digital technology (DT), often driven by habitual scrolling due to adaptive feed experiences, has been linked to anxiety, sleep disturbances, and obsessive-compulsive behaviors while also exacerbating mental health concerns. Yet, the role of "digital detox", defined as a voluntary reduction or temporary cessation of device use, remains only partially understood as both a clinical and lifestyle intervention. This comprehensive scoping review was conducted to consolidate existing research on digital detox interventions and evaluate contextual factors that may influence their effectiveness for mental health and well-being. A targeted keyword search for "digital detox" was conducted in the PubMed database on December 12, 2024, yielding 34 initial results. This review followed the approach recommended by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) to identify, screen, and extract evidence from relevant studies as per pre-specified inclusion criteria. A total of 14 studies were found eligible, and data from these studies and their relevant references (totaling 640 citations) were extracted and synthesized. Our findings suggest that digital detox interventions may alleviate depression and problematic internet use, and individuals with higher baseline symptom severity appear to derive higher benefits. However, the impact on broader outcomes such as life satisfaction and overall well-being remains variable. Divergent intervention approaches, ranging from short-term SM abstinence to sustained, moderate device restrictions and individual differences in baseline severity of symptoms, coping styles, environmental pressures, and support systems, may contribute to different outcomes across various studies and systematic reviews. Overall, age, gender, baseline mental health, and range and duration of DT usage prior to detox are the key variables that may determine the effectiveness of digital detox interventions. Tailored DT usage in moderation, aligned with each individual's age, developmental stage, and academic needs, has greater benefits among younger populations, particularly adolescents and young adults, while mindful and regulated SM use is especially advantageous for female populations. However, other populations could also benefit, provided interventions address self-regulation challenges specific to adult lifestyles. Given the growing global prevalence of problematic smartphone use (PSU) and its documented comorbidity with psychiatric disorders, digital detox strategies have the potential to be integrated into clinical recommendations and policy initiatives. However, a framework for assessing intervention quality and long-term outcomes is essential.

Definition Poverty is an important social determinant of health disparities across the lifespan. Poverty also influences other life challenges such as pecuniary instability, food insecurity, housing instability, educational inequality, and limited career mobility. According to the World Bank, more than 700 million people worldwide live in global poverty, surviving on less than USD 2.15 a day. Poverty may also be viewed as a state of deprivation that limits access to resources that address basic needs (i.e., food, water, shelter, clothing, health), limiting an individual’s opportunity to participate optimally in society. A large body of research has identified a positive relationship between poverty and chronic health concerns such as heart disease, diabetes, high cholesterol, kidney problems, liver problems, cancer, and hypertension. This entry examines health disparities associated with economic status, discrimination, racism, stress, age, race/ethnicity, gender, gender identity, and nationality from a social justice perspective.

Remnant explosive devices are a deadly nuisance to both military personnel and civilians. Traditional mine detection and clearing is dangerous, time-consuming, and expensive. And routine production and testing of explosives can create groundwater contamination issues. Remote detection methods could be rapidly deployed in vegetated areas containing explosives as they are known to cause stress in vegetation that is detectable with hyperspectral sensors. Hyperspectral imagery was employed in a mesocosm study comparing stress from a natural source (drought) to that of plants exposed to two different concentrations of Royal Demolition Explosive (RDX; 250 mg kg⁻¹, 500 mg kg⁻¹). Classification was accomplished with the machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), and Least Discriminant Analysis (LDA). Leaf-level plant data assisted in validating plant stress induced by the presence of explosives and was detectable. Vegetation indices (VIs) have historically been used for dimension reduction due to computational limitations; however, we measured improvements in model precision, recall, and accuracy when using the complete range of available wavelengths. In fact, almost all models applied to spectral data outperformed their index counterparts. While challenges exist in scaling research efforts from the greenhouse to the field (i.e., weather, solar lighting conditions, altitude when imaging from a UAV, runoff containment, etc.), this experiment is promising for subsequent research efforts at greater scale and complexity aimed at detecting emerging contaminants.