Ohio Northern University

The purpose of the study was to examine concealed knife attacks from eight feet away of three different knife motions and how age and sex effected attack time. Seventy-four subjects completed three different knife attacks on a target using a rubber training knife. Mean attack times were: thrust (1.43 ± 0.26 s), horizontal (1.55 ± 0.33 s), and overhead (1.60 ± 0.32 s), with the fastest attack time of 1.04 s, a thrust, by a young male. Age groups did not differ in attack time, whereas males were significantly faster in each knife motion. Practicing defensive maneuvers and tactics in these timeframes may serve to determine optimal responses.

Background and Aims Stroke remains a leading cause of mortality and long‐term disability worldwide, presenting a significant global health challenge. Effective early prediction models are essential for reducing its impact. This study introduces a novel ensemble method for predicting stroke using two datasets: a primary dataset collected from a hospital, containing medical histories and clinical parameters, and a secondary dataset. Methods We applied several preprocessing techniques, including outlier detection, data normalization, k‐means clustering, and missing value detection, to refine the datasets. A novel ensemble classifier was developed, combining AdaBoost, Gradient Boosting Machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms to enhance predictive accuracy. Additionally, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME were integrated to elucidate key features influencing stroke prediction. Results The proposed ensemble classifier achieved an accuracy of 95% for the secondary dataset and 80.36% for the primary dataset. Comparative analysis with other machine learning models highlighted the superior performance of the ensemble approach. The integration of XAI further provided insights into the critical indicators influencing stroke classification, improving model interpretability and decision‐making. Conclusion Our study demonstrates that the novel ensemble classifier, supported by effective preprocessing and XAI techniques, is a powerful tool for stroke prediction. The high accuracy rates achieved validate its effectiveness and potential for practical clinical application. Future work will focus on incorporating deep learning techniques and medical imaging to further improve classification accuracy and model performance.

Double perovskite-based optoelectronic devices are gaining attention due to their unique characteristics, including a simple and stable crystal structure. This study employs density functional theory (DFT) with the full-potential linearized augmented plane-wave (FP-LAPW) method to investigate the structural, electronic, optical, mechanical, and thermodynamic properties of A2BIrCl6 (A = Cs, Rb; B = Na, K) double perovskite halides. The primary aim is to assess their potential applicability in optoelectronic devices and renewable energy technologies. The cubic stability of the predicted compounds was confirmed through the Goldsmith tolerance factor, octahedral factor, and a new tolerance factor. Additionally, to confirm their thermodynamic stability, we assessed the formation energy, binding energy, and phonon dispersion curves. We used the TB-mBJ potential to accurately predict the optoelectronic properties. The calculations of the electronic band structure indicated that the examined double perovskites exhibit a direct band gap semiconducting nature, with the following band gap values: 1.927 eV for Cs2NaIrCl6 1.991 eV for Cs2KIrCl6, 2.025 eV for Rb2NaIrCl6, and 2.102 eV for Rb2KIrCl6. The A2BIrCl6 (A = Cs, Rb; B = Na, K) compounds demonstrate impressive optical properties, including low reflectivity and high light absorption coefficients (10⁴ cm⁻¹) in the visible spectrum. Their spectral response extends from the visible to the UV range, making them ideal candidates for applications in solar cells and optoelectronic devices. The mechanical stability of the titled compounds was confirmed through the Born–Huang stability conditions based on their stiffness constants. The brittle nature of all the examined perovskites is confirmed by Pugh's ratio, Cauchy pressure, and Poisson's ratio. Finally, the Helmholtz free energy (F), internal energy (E), entropy (S), and specific heat capacity (Cv) are calculated based on the phonon density of states.

Currently, the Financial Accounting Standards Board (FASB) is conducting a post‐implementation review of the Accounting Standards Codification (ASC) 842 lease accounting standard, which underscores the need for academic research regarding the performance and impact of the lease standard in the capital market. This study provides evidence through the lens of tax and capital market consequences. Specifically, we examine the impact of ASC 842 on market uncertainty via the mediator of temporary book‐tax differences (BTDs) for the sample period of 2016–2021. Using difference‐in‐difference analyses, we find that relative to control firms, treatment firms report higher temporary BTDs and experience greater market uncertainty. Further analysis shows that the increase in market uncertainty among treatment firms is driven mainly by the increase in temporary BTDs. Our study contributes to the leasing accounting literature and considers the implications of the ASC 842 lease accounting standard from the tax perspective and market reaction.

Purpose This study aims to investigate the complicated dynamics between Human Resource Management (HRM) practices and job satisfaction in the banking sector, with a particular focus on understanding how gender diversity mediates these relationships. By examining the roles of HRM practices particularly Compensation and Benefits, Workplace Environment, Recruitment and Selection, Safety and Security and Training and Development, the research seeks to disclose the differential impacts of these HRM practices on male and female job satisfaction in Bangladesh. Design/methodology/approach By employing a quantitative research design, this study utilizes a self-structured questionnaire administered to a random sample of banking sector employees in Bangladesh, covering both male and female respondents. The Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to determine the hypothesized relationship among the variables also incorporating tests for construct validity, reliability, discriminant validity and multicollinearity to determine and validate the mediating role of gender diversity and job satisfaction. Findings The findings disclose that certain HRM practices, particularly Compensation and Benefits are universally significant in enhancing job satisfaction, which is mediated by the gender diversity. However, the impact of Recruitment and Selection, Safety and Security and Training and Development, and Workplace Environment on job satisfaction shows and marked gender-specific variations. For males, equitable recruitment and selection practices and workplace environment play a crucial role, whereas, for females, safety and security, and training and development are more significant for ensuring job satisfaction through gender diversity. Research limitations/implications The study emphasizes the necessity for banking sector policymakers and Human Resources (HR) practitioners to incorporate gender diversity considerations into HRM strategies particularly. To adapt HRM practices to address through gender-specific needs and preferences can significantly increase job satisfaction. Besides, it will also help to improve organizational performance and employee success. The research is constrained to the banking sector in Bangladesh, which limits the generalizability of the findings across different industries and geographies. However, future studies could explore these dynamics in diverse sectors and cultural contexts to broaden the understanding of gender diversity’s role in HRM. Originality/value This research will significantly contribute to the HRM literature and diversity management by providing practical evidence on the mediating role of gender diversity in the relationship between HRM practices and job satisfaction within the banking sector. It also challenges the conventional HRM paradigms by demonstrating the different ways in which gender considerations influence the efficacy of HRM practices in promoting job satisfaction.

Purpose The design, implementation, and impact of a pharmacist-led employee wellness hypertension program that utilizes remote blood pressure monitoring are described. Summary Employees of a private university and health insurance beneficiaries with a diagnosis of hypertension or a documented high blood pressure reading at a previous screening encounter were eligible to participate in the program. Participants received a remote blood pressure monitoring device and followed up with a pharmacist in person or via telehealth throughout the program. The pharmacist provided education on lifestyle modifications to improve blood pressure control, and recommendations regarding changes to the participant’s medication therapy were made to the participant’s primary care provider. Participants completed an in-person appointment at month 3 of the program for blood pressure reassessment. Twenty-four participants were enrolled in the program. The mean baseline systolic and diastolic blood pressures were 134 mm Hg and 85 mm Hg, respectively. Of the total of 24 participants, 18 participants (75%) had a blood pressure above their goal at baseline. At month 3 of the program, 7 of these 18 participants (39%) had achieved their blood pressure goal, with average systolic and diastolic blood pressure decreases of 8.9 mm Hg and 7.8 mm Hg, respectively. Pharmacist recommendations to primary care providers regarding medication changes had an acceptance rate of 70%. Conclusion A pharmacist-led employee wellness hypertension monitoring program that utilized remote monitoring devices improved employee blood pressure control through education on lifestyle modifications and medication recommendations to the participants’ primary care providers.

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

In the current literature on compulsivity, it is unclear whether this construct is best conceptualized as an internalizing disorder, a fear disorder, a thought disorder, or some combination of the three. The Compulsivity (CMP) scale introduced with the MMPI-3 assesses compulsive behaviors. To address the question of compulsivity’s placement within a hierarchical psychopathology structure, the current study examined the degree to which CMP scores share variance with internalizing, fear, and thought dysfunction factors using confirmatory factor analyses. Results indicated that a model in which CMP scores cross-loaded onto latent fear and thought dysfunction factors exhibited preferential fit compared to a model in which CMP scores cross-loaded onto a higher-order internalizing factor and a thought dysfunction factor. Constraining equality in the cross-loading of CMP scores onto fear and thought dysfunction factors caused no significant decrement in fit. These findings indicate that the MMPI-3 CMP scale measures both fear and thought dysfunction. Implications and limitations of these findings and future research directions are discussed.

This study investigates how digital finance positively impacts the advancement of women's entrepreneurship, empowerment, and poverty reduction in Bangladesh. Covering the period from 2011 to 2023 and drawing from annual data, it sheds light on the transformative effects of digital finance on women's entrepreneurship, empowerment, and poverty reduction. Employing fully modified OLS and canonical co-integration regression models, the study explores the dynamic relationship among the variables. The findings reveal that mobile banking and agent banking have a significant influence on women's entrepreneurship at the 1 percent level. Additionally, women's employment, empowerment, and poverty reduction are shown to have a notable impact on the development of digital finance at the 5 percent level. Hence, policymakers are encouraged to formulate effective strategies to capitalize on the potential of the female population through digital finance. Introducing nationwide digital entrepreneurship schemes emerges as a promising strategy to maximize the benefits of digital finance.

In Drosophila melanogaster genetic screens are often used to identify genes associated with different biological processes. Here, we have utilized the Flp/FRT system to generate mitotic clones within the developing eye. These clones were screened for mutations that disrupt cell division, organ patterning, and cell growth. One such mutation from this screen, mutant M.3.2, resulted in an expansion of the cuticle within the area normally covered by ommatidium as well as an overall smaller eye size. Genetic and molecular mapping revealed this mutation to be in the gene, t out-velu ( ttv ).