University of Maryland Global Campus

Background: The present study aimed to assess the psychometric properties and the factor structure of the Ukrainian version of the Edinburgh Postnatal Depression Scale (EPDS) in the prenatal and postpartum women. Method: In a cross-sectional study, 625 participants, including 207 (33.2%) pregnant and 408 (66%) postpartum women, completed online questionnaires: Edinburgh Postnatal Depression Scale (EPDS), the City Birth Trauma Scale (City BiTS), Generalized Anxiety Disorder (GAD-7) and a sociodemographic questionnaire. Results: A 3-factor model (anhedonia, anxiety, and depression) is the best fit to the data, both for antepartum and postpartum samples. EPDS exhibits excellent internal consistency for the overall scale and good internal consistency for its subscales. The total sample’s Cronbach’s alpha has a value of .87 for antepartum women and .86 for postpartum women, similar to those of the original scale. Conclusions: The Ukrainian version of EPDS is reliable and can be used to screen in the perinatal period, during pregnancy, and in the postpartum period.

This study examines the convergence of rapid Central Bank Digital Currency adoption and the growing threat that quantum computing poses to existing cryptographic systems. As digital sovereign currencies expand globally, with many economies actively exploring or piloting CBDCs, the vulnerability of classical public key cryptography to quantum attacks creates an urgent need for quantum resistant solutions. The research responds to this challenge by developing an evaluation framework focused on secure and interoperable cross border CBDC systems in a post quantum environment. Using desk based computational analysis and simulation methods suitable for remote researchers, the study evaluates lattice based algorithms standardized by the National Institute of Standards and Technology, specifically ML KEM for key encapsulation and ML DSA for digital signatures. The analysis confirms strong quantum resistance against known quantum algorithms, effectively removing harvest now decrypt later risks for future transactions. Interoperability assessments show that ISO 20022 with cryptographic abstraction is well suited for retail CBDC systems, while bridge based architectures such as BIS mBridge are more effective for wholesale settlements. Performance testing on thousands of simulated transactions indicates that post quantum protocols are feasible for wholesale use but introduce significant computational and bandwidth overheads that limit immediate retail scalability without hardware acceleration. Overall, the findings support a phased migration strategy that combines classical and post quantum cryptography in the near term, guided by cryptographic agility. The study provides practical insights for central banks seeking to balance security, efficiency, and cross border compatibility in an evolving quantum threat landscape.

ObjectiveAntimicrobial resistance and mental health are critical global health concerns, yet the intersection of these fields remains underexplored. This study presents a bibliometric analysis of research trends at the nexus of antimicrobial resistance and mental health.Study designBibliometric study design.MethodsPublications related to antimicrobial resistance and mental health from January 1, 2004 to March 13, 2025 were extracted from the Web of Science and Scopus databases. Bibliometric analysis and visualisation were performed using the R-Bibliometrix package (biblioshiny). We analysed publication patterns, citation metrics, and key research themes to identify leading topics, countries, institutions, authors, and journals contributing to this emerging area.ResultsThe analysis included 3449 documents from 1397 sources, authored by 22,900 researchers, with an annual growth rate of 2.35 % and an average of 41.7 citations per paper. Output increased steadily after 2017, reaching its peak in 2020–2021. PLOS One was the most featured journal (78 articles), and the University of California, San Francisco, led institutional output (173 articles). The United States showed the highest collaboration and publication volume. Thematic and keyword analyses revealed dominant attention to depression, schizophrenia, bipolar disorder, and anxiety, alongside a smaller but growing focus on antimicrobial resistance, particularly its links to the gut–brain axis, where terms such as antibiotic resistance appeared less frequently but formed emerging clusters in recent years.ConclusionThis study identifies research hotspots and gaps in the relationship between antimicrobial resistance and mental health, highlighting the need for public health policies that integrate mental health into antimicrobial resistance strategies. Prioritising this integration and enhancing global surveillance and collaboration are essential to addressing these interconnected challenges effectively.

This study developed and assessed an AI-driven adversarial defense framework using Generative Adversarial Networks (GANs) to secure healthcare IoT ecosystems against rising cybersecurity threats in medical settings. The research drew on datasets (CICIoMT2024, WUSTL-EHMS-2020, BoT-IoT, and Kaggle) and peer-reviewed studies to achieve three objectives: building a detailed threat model, designing a GAN-based defense optimized for healthcare IoT, and rigorously testing its effectiveness. The threat model revealed 127 vulnerability vectors, with adversarial attacks (32%) most prevalent, and a mean risk score of 7.82, highest for critical care devices (9.34). The GAN framework, featuring a multi-layer generator–discriminator pair and 128-dimensional encoder, achieved a mean accuracy of 95.8% against major adversarial attacks (FGSM 97.1%, PGD 94.8%, C&W 95.9%, UAP 96.4%), outperforming traditional defenses by 39.4%. With an MTTD of 82 ms, the system enables real-time deployment, allowing healthcare providers to integrate it directly into hospital IoT networks for proactive protection. Limitations include reliance on secondary data and high computational cost. Recommendations include hybrid datasets, explainable AI integration, real-world pilots, standardized metrics, and federated learning to enhance scalability and adaptability.

This study investigates how perceptions of corporate social responsibility (CSR) moderate the relationship between organizational commitment and key employee outcomes – motivation, engagement, and job satisfaction. Using quantitative correlational design, validated instruments (Meyer and Allen’s three-component model of organizational commitment, the Work Extrinsic and Intrinsic Motivation Scale, the Utrecht Work Engagement Scale, and the Minnesota Satisfaction Questionnaire) were administered via structured surveys across multiple organizations. The survey was conducted in the United States in 2024 with a sample of 271 employees (N = 271). Pearson correlations and moderation tests assessed the strength and direction of these relationships across employee subgroups. Results show that stronger CSR perceptions significantly amplify the positive effects of organizational commitment on motivation, engagement, and job satisfaction, while weaker CSR perceptions attenuate these links. The findings address a gap in the literature by isolating employees’ CSR perceptions as a boundary condition and provide evidence-based guidance for leaders seeking to align CSR strategy with HR initiatives. Practically, credible CSR initiatives can magnify the returns of commitment programs, improving employee well-being, engagement, and sustainable organizational performance.

Regional disparities in road infrastructure access in Kenya hinder socioeconomic development and challenge progress toward the Vision 2030 goals. This study addresses spatial inequality in transport provision by developing a transport network need index (TNNI) based on socioeconomic indicators and comparing it with existing road network density, termed transport network provision (TNP). The difference between TNNI and TNP yields the index of disparity between needs and provision (IDNP), identifying counties that are over- or under-served relative to their development needs. Machine learning models are employed to predict road density based on socioeconomic features, with the best-performing model selected through accuracy metrics. Shapley additive explanations (SHAP) are used to interpret the model and determine the empirical importance of each input feature. These SHAP-based weights are compared with literature-based weights used in TNNI to assess disparities in factor prioritization. Results highlight critical spatial disparities, with counties such as Turkana, Wajir, and Mandera falling into the most under-served category (IDNP >0.8). SHAP analysis reveals that population density is the dominant driver of current road provision (56.8% weight), while poverty and unemployment, despite their policy relevance, are underrepresented in actual infrastructure allocation. This twofold analysis—identifying both regional service gaps through IDNP and mismatches in factor prioritization—provides critical insights for policymakers. By pinpointing under-served regions and highlighting which socioeconomic drivers are being overlooked, the study offers a robust, data-driven framework for guiding equitable infrastructure investments, advancing transport justice, and supporting diversity and inclusion in Kenya’s road transport planning.

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks.

This article presents a curated collection of 12 teaching innovations presented at the Association for Business Communication 89th conference in Tulsa, Oklahoma, as well as online, in October 2024. These MFA presenters demonstrated teaching ideas specifically on improving students’ speaking skills. This My Favorite Assignment 32nd edition introduces readers to these classroom approaches in teaching speaking skills in business contexts. Teaching support materials—instructions to students, stimulus materials, slides, rubrics, frequently asked questions, links, and sample student projects—are downloadable from the Association for Business Communication website.