Articles

Smartphone use among adolescents has surged globally, reshaping communication and learning patterns, especially in developing countries. However, the implications of such digital habits on students in rural or under-resourced areas remain underexplored. This study aims to examine the patterns of smartphone usage and its effects on learning among high school students in Tarutung, a developing region of North Sumatra, Indonesia. Utilizing a quantitative descriptive approach, data were collected from 358 students using structured questionnaires. The results show that 96.05% of students own personal smartphones regardless of socioeconomic background, with an average daily usage of 4 hours and 45 minutes. While 91.81% believe smartphones support their learning, 25.99% report declining academic performance. Alarmingly, 20.62% of students admitted involvement in cyberbullying activities, highlighting a critical digital risk impacting the school environment and student well-being. The study concludes that although smartphones offer educational benefits, their misuse can lead to negative academic, social, and psychological outcomes. This study recommends digital literacy curricula and structured cooperation between parents and educators to prevent risks while optimizing educational opportunities in smartphone use.

<b>Background:</b> Educational attainment is a well-established predictor of physical health outcomes, including body mass index (BMI). However, according to the theory of Minorities' Diminished Returns (MDRs), the health benefits of education tend to be weaker for ethnic minorities compared to non-Latino Whites, due to structural inequalities and social disadvantages. <b>Objective:</b><b> </b>This study examines whether the association between educational attainment and BMI is weaker among Latino individuals compared to non-Latino individuals, in line with the MDRs framework. <b>Methods:</b><b> </b>Data were drawn from the 2014 wave of the Understanding America Study (UAS), a nationally representative internet-based panel. Body mass index (BMI) was the outcome of interest. Linear regression models were used to analyze the association between educational attainment and BMI, with an interaction term for ethnicity to explore differences in the relationship between Latino and non-Latino people. Models were adjusted for age, sex, marital status, and labor market participation and results were presented as beta coefficients, p-values, and 95% confidence intervals (CIs). <b>Results:</b><b> </b>Higher educational attainment was associated with lower BMI for both Latino and non-Latino participants (p < 0.001). However, the interaction between educational attainment and ethnicity was significant (p < 0.05), indicating that Latino individuals experienced smaller reductions in BMI because of higher education compared to non-Latino people. <b>Conclusion:</b><b> </b>This study provides evidence of diminished returns from educational attainment on BMI among Latino individuals. These findings support the MDRs framework, suggesting that structural barriers may limit the health benefits of education for Latino populations. While education is a key determinant of physical and mental health, its benefits are not equitably distributed across ethnic groups. Structural inequalities, chronic stress, poor neighborhood environments, and adverse educational and occupational conditions likely contribute to this disparity. Addressing these underlying factors through targeted policy interventions is necessary to promote health equity for Latino populations.

<b>Background: </b>The dual use of e-cigarettes and combustible cigarettes poses significant public health concerns due to the compounded risks associated with the use of both products. Understanding the predictors of dual use can inform targeted interventions and tobacco control strategies aimed at reducing nicotine dependence and health risks among adults.<b> </b><b>Objective: </b>This study aims to identify the sociodemographic predictors of dual use of e-cigarettes and combustible cigarettes among U.S. adults using baseline data from the Population Assessment of Tobacco and Health (PATH) Study.<b> </b><b>Methods: </b>We analyzed baseline data from the PATH Study, focusing on adult participants who reported the use of both e-cigarettes and combustible cigarettes. Logistic regression models were used to identify the associations between dual use and key sociodemographic variables, including age, gender, race/ethnicity, and education level.<b> </b><b>Results: </b>The analysis revealed that dual use of e-cigarettes and combustible cigarettes was predominantly observed among young, female, non-Latino, White, and highly educated adults. Younger adults were more likely to engage in dual use compared to older age groups. Females showed higher rates of dual use compared to males. Non-Latino White individuals were more likely to be dual users than individuals from other racial/ethnic backgrounds. Additionally, higher educational attainment was associated with increased dual use, contrary to traditional smoking patterns.<b> </b><b>Conclusion: </b>The findings highlight specific demographic groups that are at higher risk of dual use of e-cigarettes and combustible cigarettes, particularly younger, highly educated, non-Latino White females. These insights suggest the need for tailored public health interventions that address the unique needs and behaviors of these populations. Future research should explore the underlying motivations and contextual factors contributing to dual use to enhance the effectiveness of tobacco control policies and cessation programs.

<b>Background: </b>Educational attainment is a well-established social determinant of various domains of cognitive function across the lifespan. However, the theory of Minorities' Diminished Returns (MDRs) suggests that the health benefits of educational attainment tend to be weaker for ethnic minorities compared to non-Latino Whites. This phenomenon may reflect the impact of structural inequalities, social stratification, and historical disadvantage. <b>Objective: </b>This study examines whether the association between educational attainment and numeracy score, one domain of cognitive function, is weaker in Latino individuals compared to non-Latino individuals, as predicted by the MDRs framework. <b>Methods: </b>Data were drawn from the 2014 wave of the Understanding America Study (UAS), a national internet-based panel. Numeracy score, a domain of the cognitive function was measured using an 8-item measure. Linear regression models were used to analyze the association between educational attainment and numeracy score, with an interaction term for ethnicity x educational attainment to explore differences between Latino and non-Latino participants. Models were adjusted for age, gender, marital status, immigration, and employment, and results were presented as beta coefficients, p-values, and 95% confidence intervals (CIs). <b>Results: </b>Overall, 5,659 participants entered our analysis.<b> </b>Higher educational attainment was positively associated with higher numeracy score for both Latino and non-Latino participants (p < 0.001). However, the interaction between education and ethnicity was significant (p < 0.05), indicating that Latino individuals experienced smaller numeracy benefits from education compared to non-Latino individuals. These results support the MDRs framework, suggesting that structural barriers may reduce the numeracy returns of education for Latino individuals. <b>Conclusion: </b>This study provides evidence of diminished returns of educational attainment in terms of numeracy scores among Latino individuals. While education is a key determinant of cognitive abilities such as numeracy, its benefits are not equitably distributed across ethnic groups. Structural inequalities particularly in educational opportunities likely contribute to this disparity. Addressing these underlying factors through targeted policy interventions is necessary to promote cognitive equity for Latino populations.

<b>Background</b>: In the landscape of education, the decision-making process that leads students to pursue or reject nursing as a career is a multifaceted phenomenon shaped by a plethora of influences ranging from personal experiences to societal norms. <b>Aim</b>: To explore non-nursing students' insights on nursing education, seeking to shed light on the considerations and challenges that influence their views on nursing education. <b>Materials &#x26; Methods</b>: A qualitative approach using thematic analysis were utilized. Lincoln and Guba's framework for rigor and trustworthiness directed the validation process. Semi-structured interviews based on vetted questionnaires yielded the data<b>. Results</b>: Analysis of interviews with ten (10) non-nursing college students revealed three key themes: 1) initial insights, 2) factors influencing their insights, and 3) difficulty of nursing education. Non-nursing students view nursing education as multifaceted and rigorous, recognizing the profession's complexity but have reservations about the heavy workload, intense clinical demands, and health risks, particularly highlighted by the pandemic, which contributes to their reluctance to choose nursing as a career path. <b>Implications</b>: Addressing perceptions, enhancing curricula, offering mentorship, and providing emotional support, nursing education can be improved, steering more students towards a career in nursing. <b>Conclusion</b>: Non-nursing students respect the complexity of the nursing profession but are deterred by its demands and risks, indicating a need for educational reforms to better convey the role, value, and opportunities within nursing to encourage more students into the field.

Myeloproliferative neoplasms (MPNs) are hematological disorders affecting myeloid stem cells. They are classified as Philadelphia (Ph) chromosome positive-chronic myeloid leukemia, and Ph-negative polycythemia vera, essential thrombocythemia, primary myelofibrosis, chronic neutrophilic leukemia, chronic eosinophilic leukemia, juvenile myelomonocytic leukemia, and MPN unclassifiable. This review is mainly focused on the Ph-negative MPNs namely, PV, ET, and PMF. These affect both males and females with a slight male predominance, with patients mainly presenting in the seventh decade. Patients often present with thrombotic events resulting in complications that lower survival rates. The major driver mutations that have been identified in MPNs are <i>JAK2 </i>Exon 14, <i>JAK2 </i>Exon 12, <i>MPL </i>Exon 10, and<i> CALR </i>Exon 9. The importance of these driver mutations gives due recognition to their inclusion into the 2022 diagnostic criteria of the MPN WHO Classification. However, other non-driver mutations have also been reported, especially in triple-negative cases. These mutations lead to downstream constitutive activation of the JAK/STAT signaling pathway, as well as the MAPK, and PI3K/Akt pathways. Insights into the molecular pathogenesis of MPN and its association with <i>JAK2</i>, <i>CALR</i>, and <i>MPL</i> mutations have identified JAK2 as a rational therapeutic target. Thus, as an approach to MPN therapy, JAK2 inhibitors, such as ruxolitinib, have been shown to effectively inhibit JAK2, and are currently in clinical trials in combination with other drug classes. This review comprehensively examines the molecular markers of the main Ph-negative MPNs, as well as diagnosis and treatment options.

The current financial services sector is realising considerable changes in its operations due to development in technology and embracing of digital platforms. This evolution is changing the established concepts of business, consumers and channels of delivery of services. Financial services firms are changing the way they work through digital transformation due to developments in technology, changes in customer needs, and an increase in emphasis on sustainability. Understanding the opportunities, risks, and new trends in digital transformation is the focus of this paper. Opportunities include efficient real-time decision-making processes, increased transparency and better process controls, which are balanced by the threats of change management, dubious organization-technology fit, and high implementation costs. The study also examines recent advancements, including the application of machine learning and artificial intelligence, developments in mobile and online banking, integration of blockchain, and increasing focus on security and personalised banking. A literature review yields some findings from different studies on rural financial services, the evolution of the blockchain, drivers of digital transformation, cloud-based learning approaches, and emerging sustainability practices. All of these results suggest that more strategic planning, analytics, and more focus on ensuring that organisational objectives are met with transformations should be pursued. Hence, this research findings add to the existing literature in determining how innovative and digital technologies are likely to transform the financial services sector and advance sustainability.

Generative AI refers to deep learning technology that can automatically produce original text, images, audio, video, and other outputs. With its emerging capabilities, Generative AI can radically change the dynamics of key operational processes in most industries. In this document, we illustrate how it is possible to integrate Generative AI technologies into the Financial Reporting System (FRS) of a corporation. The integration will allow the FRS to deliver on demand concise and lucid insights to its associated users on what is happening in the corporation and different aspects of the corporation performance assessment, such as its liquidity, solvency, profitability, organizational structure, and share buy back decision. The integration will also facilitate the delivery of what-if analyses associated with different strategic and tactical decisions taken by the corporation management, such as capital budgeting and profit distribution decisions. The unique added value of several attributes of these insightful analytics is automating the responses to ongoing requests of the FRS users on the corporation. Generative AI capabilities are rapidly expanding. The integration can be applied not only for the corporate FRS but any FRS at the national or global levels delivered by a central bank or an accounting standards setter. Any of these FRS can be made into a unique “hub” for the integrated Generative AI technologies. An equally innovative possible generalized integration could put any corporate process at the center and its supporting FRS tasks and deliverables in its periphery.

Self-service business intelligence (BI) platforms have become essential applications for exploring, analyzing, and visualizing business data in various domains. Here, we envisage that the business intelligence platform will perform automatic and autonomous data analytics with minimal to no user interaction. We aim to offer a data-driven, intelligent, and scalable infrastructure that amplifies the advantages of BI systems and discovers hidden and complex insights from very large business datasets, which a business analyst can miss during manual exploratory data analysis. Towards our future vision of autonomous analytics, we propose a collective machine learning model repository with an integration layer for user-defined analytical goals within the BI platform. The proposed architecture can effectively reduce the cognitive load on users for repetitive tasks, democratizing data science expertise across data workers and facilitating a less experienced user community to develop and use advanced machine learning and statistical algorithms.

Emerging smart medical instruments combined with advanced smart industrial equipment facilitate the collection of vast volumes of critical data. This data not only enables significantly more accurate and cost-effective diagnosis and maintenance but also enriches the datasets available for AI algorithms, leading to improved insights and outcomes. The integration of high-speed and ultra-reliable telecommunications infrastructure is crucial, as it supports the cloud model. This model allows for off-device aggregation in the cloud, which effectively offloads infrastructure demands and provides an extended runway for future technological improvements before the deployment of the next generation of devices. However, in certain scenarios, latency and bandwidth limitations present significant challenges. These limitations require that a substantial amount of AI and machine learning processing is conducted directly on the transmitted data, which places rigorous demands on both the processing subsystems and the communications links themselves. The current project directly addresses the accelerator side of this multifaceted issue. It will carry out comprehensive end-to-end demonstrations leveraging pilot 5G networks and telemedicine facilities, collaborating closely with major industry participants to showcase the capabilities and potential of this innovative technology. This collaborative effort is essential to pushing the boundaries of what is possible in smart medical instruments and industrial applications [1].

Factory owners and vehicle manufacturers increasingly opt for predictive analytics to inform their decisions. While predictive analytics have been proven to provide insights into the initiation of maintenance measures before a machine actually fails, the right models and features could have a significant impact on the budget spent and resources allocated. This means that financially oriented questions need to at least partially guide the decisions in the planning phase of data science projects. Data-driven approaches will play an increasingly important role, but only a few of the firms that were confident performed logistic regression models for predictive maintenance. Also, from the available knowledge, data-driven classification models connecting vehicle component failures and the occurrence of delays at the assembly line have not been published. This paper utilizes a real-world data-driven approach using classification models in predictive analytics by vehicle manufacturers and thereby links the financial implications of such data science projects to their results. We expand the existing literature on predictive maintenance and possess a unique dataset of newly launched series of vehicles, presented as-is. Our research context is of interest to researchers and practitioners in the automotive industry that manage and plan the final vehicle assembly with just-in-time principles, factoring the consequences of component failures on the assembly process. Key findings of this paper highlight that while minor tweaking of the models is possible, their potential input in decision-making processes for budget optimization is limited.

Scalable architecture principles for data warehousing are introduced to support population health management and predictive analytics. These principles are validated through the design of an accompanying Data Pipeline that allows the integration of non-traditional data sources, the use of real-time data for descriptive analytics dashboards, and support for the generation of supervised Machine Learning models. Several analytical capabilities have been implemented to exemplify the practical application of the principles, including predictive models for Risk Stratification in health care. Optimal cost-effectiveness and performance considerations ensure the practical relevance of the architectural principles and associated Data Pipeline. In recent years, the availability of Low-Cost Data Storage services and the increasing popularity of Streaming technologies opened new possibilities for the storage and processing of Streaming data on a near-real-time basis. These technologies can help Developing Countries in tackling many relevant issues such as Urban Planning, Environmental Management, Migration Policies, etc. A multi-tier approach combining Cloud-based Storage with Data Warehousing and Data Mining technologies can offer an interesting architecture to exploit Big Data related to populations.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.

This study aims to achieve a new understanding of how, why, and when consumer behavior is shaped, enacted, and experienced inside and across integrated digital ecosystems related to large-scale trackable goods, all in service of helping marketers optimize their business performance in the new economy. The pioneering understanding begins by exploring what motivates the choices of a homogeneous group of consumers to organize their consumption of national and store brand varieties of consumer package goods in a certain manner. Thereafter, the essay explores how, if at all, the other digital activities of consumers across various product-related digital spaces and on various platforms build expertise and interest in these products such that they exert an effect on the purchase choices for these products. The essay then advances to asking how online information seeking, in various product-related digital spaces, on various platforms, and from various sources, and taking place at various points in the purchase journey affects online-offline dynamics in purchasing these products. Thereafter, the research examines how paid digital communication in various product-related digital spheres and forms, enabled by consumer advertising engagement on various platforms, boosts the offline sales of these products. Finally, by employing a new methodology that combines consumer scanning data, self-reported online activity data, and transaction data collected from an ad-tech partner, the research presents a fresh set of marketing action levers and performance outcomes on chosen products. Along the way, progress is made on four under-investigated topics in the advertising literature – the role of consumer actors and their expertise in the online-offline purchasing dynamics for ads, advertising engagement, consumer digital spaces, and consumer digital activity investment.