A man in his 60s with multiple vascular comorbidities presented with sudden, painless vision loss in one eye. Although he had a high risk for atherosclerotic events, initial evaluation for stroke was negative for acute ischemia, but found to have markedly elevated inflammatory markers. Accordingly, giant cell arteritis was investigated and Ophthalmologic findings and fulfillment of the 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria supported the diagnosis of giant cell arteritis, despite a negative temporal artery biopsy. Management included high-dose glucocorticoids and delayed tocilizumab initiation due to the need for multiple vascular surgeries. Vision loss was irreversible, but systemic symptoms resolved and vascular interventions were successful. This case highlights the diagnostic and management complexities of biopsy-negative giant cell arteritis in patients with severe atherosclerotic vascular disease, emphasizing the importance of clinical judgment and established classification criteria when imaging and biopsy results are inconclusive.

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

Background: School characteristics — including poverty levels, teacher experience, graduation rates, and college enrollment — are essential determinants of students’ academic outcomes and long-term success. Families often use their socioeconomic resources, such as parental education and household income, to secure access to high-quality schools with favorable attributes. However, Minorities’ Diminished Returns (MDRs) theory suggests that Black families may not experience the same benefits of high family SES due to structural barriers. This study examines the association between family SES and school characteristics, focusing on racial disparities in access to high-quality educational environments. Objective: To investigate the relationship between family SES (parental education and household income) and multiple school characteristics (poverty, teacher experience, graduation rates, and college enrollment), and to assess racial differences in these associations. Methods: Data from the Adolescent Brain Cognitive Development (ABCD) study, a national sample of US adolescents, was analyzed. We used multivariate regression models to examine associations between family SES and school characteristics and to test for interactions by race, specifically comparing Black and White adolescents. Results: Higher family SES was associated with positive school characteristics overall, including lower school poverty, greater teacher experience, and increased graduation and college enrollment rates. However, these positive effects of high family SES on school characteristics were significantly weaker for Black adolescents than for White adolescents. Black adolescents from high-income families were more likely than White adolescents from similar backgrounds to attend schools with higher poverty rates, less experienced teachers, and reduced graduation and college enrollment rates. Conclusion: Our findings highlight persistent racial inequities in access to educational opportunities, even among families with comparable socioeconomic resources. The diminished returns of family SES for Black adolescents underscore the role of structural barriers in limiting access to high-quality schools. These findings emphasize the need for policy interventions to address systemic inequalities that hinder Black families from fully leveraging their SES to access favorable educational environments.

The benefits of physical activity are immense and indisputable; however, the inclusion of Physical Education Activity Programs (PEAP) within the college setting, specifically the University Undergraduate Core Curriculum, is largely disregarded and viewed as unnecessary. Increased physical activity has shown to have a positive relationship throughout the human development process. Health promotion programs should target people of all ages, since the risk of chronic disease starts in childhood and increases with age. In addition to the physical activity benefits associated with human development, decrease disease rate, and extended lifespan, physical activity has also been thoroughly researched and documented to have positive effects on academic achievement. Despite the evidence identified through the results of the research, PEAP at the college level are consistently being ignored and removed from university undergraduate core curriculum. The purpose for university exclusion of PEAP is unknown; however, addressing the gap within the literature may provide justification for re-installment; research has not yet identified the impact of PEAP on the academic achievement of undergraduate students. A positive outcome could be gained by increasing the curricular value of compulsory PK-12 health and physical education requirements.

Ghana's Free Senior High School (SHS) policy has been touted as one of the most significant educational reforms in recent years, fueling the nation's achievement of Goal 4 of the United Nations Sustainable Development Goals (SDGs). While many studies have been conducted on the policy’s impact on beneficiaries, little is known about its impact on tertiary institutions. The purpose of this study is to evaluate the socioeconomic effects of the Free SHS policy on tertiary institutions in Ghana's Western Region. We also provide suggestions on how to improve the policy's efficacy and lessen any obstacles to its incorporation into higher education. Methodologically, 103 employees of Takoradi Technical University and the University of Mines and Technology were sampled using a purposive sampling technique. According to the report, despite the lack of adequate facilities such as lecture halls, dorms, and residence halls on campus, the Free SHS policy has increased enrollment at the postsecondary level. This has resulted in a high student-to-lecturer ratio, increased utility bills, a high cost of living on campus, and a rise in the opening of businesses on campus. To address some of these socioeconomic impacts, respondents proposed that the government should improve infrastructure in the country's higher institutions, hire more lecturers, boost incentives for lecturers, and foster partnerships with private institutions to support infrastructure development.

A user is considered to be someone who would be actually using the system. The definition of a user is contextual and for any specific software project it depends on the software development methodology being developed. Satisfaction is the fulfillment of one’s wishes, expectations, needs or the pleasure that is derived from using a software product therefore “user satisfaction “is a reflection of quality from different perspective than measuring defects or code structure. Measurement in software engineering makes comparison possible and when you compare you have a choice and quality is quantified in terms of values. Therefore, this paper aims to explore the impact of software measurement scales on user satisfaction.

Machine Learning (ML) and Artificial Intelligence (AI) are having an increasingly transformative impact on all industries and are already used in many mission-critical use cases in production, bringing considerable value. Data engineering, which combines ETL pipelines with other workflows managing data and machine learning operations, is also significantly impacted. The Intelligent Data Engineering and Automation framework offers the groundwork for intelligent automation processes. However, ML/AI are not the only disruptive forces; new Big Data technologies inspired by Web2.0 companies are also reshaping the Internet. Companies having the largest Big Data footprints not only provide applications with a Big Data operational model but also source their competitive advantage from data in the form of AI services and, consequently, impact the cost/performance equilibrium of ETL pipelines. All these technologies and reasons help explain why the traditional ETL pipeline design should adapt to current and emerging technologies and may be enhanced through artificial intelligence.

Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.