Healthcare systems face increasing pressure from an aging population, rising rates of chronic disease and comorbidity, workforce shortages, clinician burnout, escalating care costs and fragmented digital infrastructure. Artificial intelligence (AI) has emerged as a transformative enabler to support descriptive, diagnostic, predictive, and prognostic big data analytics for personalized treatment planning, risk stratification, and longitudinal patient monitoring. However, the current AI paradigm is fragmented across clinical domains and is constrained by limited interoperability, insufficient external validation, algorithm opacity, and demographic bias. Governance and regulatory frameworks lag behind technological advancement, impeding AI adoption and eroding stakeholder trust. This narrative review uses a five-pillar framework for AI-enabled precision healthcare composed of (1) multimodal AI that integrates heterogeneous data sources; (2) explainable AI to improve interpretability, clinical accountability, and regulatory transparency; (3) affective computing and human-centered AI to create a therapeutic alliance with patients; (4) privacy-preserving infrastructures including federated learning (FL), differential privacy, and blockchain-enabled auditability to secure interinstitutional collaboration; and (5) adaptive governance systems for equitable, ethical, and sustainable deployment. Peer-reviewed scientific advances published between 2018 and 2026 are examined. The authors in this review argue that responsible and trustworthy AI-enabled precision healthcare should transition from isolated predictive models to complex socio-technical systems. Through the integration of these socio-technical systems into clinical workflows using adaptive ethical governance and privacy-preserving collaborative infrastructures, clinical reasoning and patient-centered care can be improved.
The research focus was motivated by a curiosity about what lies beyond current artificial intelligence (AI) capabilities and an interest in exploring the next frontier of AI evolution. This research is conducted as a literature review, with the purpose of bringing to light ongoing advances in theory of mind (ToM) AI and outlining future AI directions, specifically for readers who are seeking insights into what’s on the horizon in the field of AI. Concretely, the literature review explores the emergence of ToM in AI, tracing its evolution from traditional AI systems towards ToM models capable of comprehending and predicting human mental states. Through a discussion of the current landscape, challenges, and future directions, this literature review clarifies how close AI is to achieving fully-fledged ToM and what’s beyond the ultimate realization of ToM. This research reviews recent peer-reviewed sources: empirical and theoretical/conceptual journal articles, review papers, book chapters, conference proceedings, preprints, textbooks, and extended abstracts that were published within the last five years. The findings indicated that ToM in AI is still under development, with its current applications being either in research or experimental phases. Excellent progress was observed in areas such as emotion recognition, predictive modeling, conversational AI, multi-agent systems, simulation, and cognitive modeling. However, constructing mental models with ToM capabilities remains challenging, particularly in leveraging meta-learning to accurately represent existing intelligent entities, whether artificial or human. The conclusion showed that our world still does not fully grasp complex human thoughts, and creating AI systems that can adapt to our ever-changing minds and infer internal mental states is an ambitious milestone beyond which lies self-awareness, a drastic shift in technology that the world may not be ready for.
The entrepreneurial mindset is increasingly emphasized in higher education, yet faculty perspectives remain underexplored. The purpose of this study is to explore how demographic factors and entrepreneurial exposure influence the entrepreneurial mindset among the faculty of higher educational institutions in Nepal. Employing a quantitative research method and a cross-sectional design, data were collected through an online survey of 248 faculty members selected, using purposive and snowball sampling. Through non-parametric tests (Mann–Whitney U, Kruskal–Wallis H) and CHAID decision tree methods using SPSS 26.0, results indicated that demographic and entrepreneurial experience shaped the entrepreneurial mindset of the faculty. Specifically, the result showed that faculty qualification, teaching level, and participation in entrepreneurship workshops emerged as the most influential variables, demonstrating consistency across multiple entrepreneurial mindset dimensions. While gender, age, faculty qualification, participation in entrepreneurship workshops, experience in teaching entrepreneurship courses, and teaching level show a significant impact on entrepreneurial mindset, teaching experience and the entrepreneurial course studied have no impact on their mindset. The study highlights the combined role of demographics and exposure in shaping faculty entrepreneurial mindset, offering insights for professional development and institutional policy to promote entrepreneurial thinking.
This comparative research explores the potential applications of generative artificial intelligence (AI) methods in creating synthetic electronic health records (EHRs) for training medical AI models. Currently, the growing concerns about healthcare data scarcity, stringent privacy restrictions, and the need for diverse datasets have led to the emergence of synthetic EHRs as a promising solution. This study examines the most advanced generative models, including generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion-based methods, to determine which can produce the most realistic and privacy-protected datasets. The current study quantifies the utility of synthetic data in training AI models by performing an extensive comparison based on statistical similarity, downstream clinical predictive performance, and privacy leakage. In addition, synthetic EHR effectiveness is assessed using a case study of chronic disease prediction during simulated low-resource conditions. The results indicate that synthetic EHRs can improve access to clinical data while also highlighting significant challenges and providing recommendations for further research.
Despite widespread and rapid advancements in natural language processing, most law enforcement case and report management systems still rely on keyword-based search engines. Information retrieval within law enforcement agencies depends on obtaining vast amounts of unstructured text data, including case reports, incident reports, and written statements. Investigators must sift through and analyze this data to find and discover meaningful leads. Keyword-based search engines face challenges with synonyms, equivalent terms, abbreviations, slang, paraphrases, and narrative fluctuation often found in police reports and case files. As a result, investigators may overlook potentially relevant cases unless they manually craft multiple keyword searches. This quantitative study evaluated, using a synthetic dataset generated for this study, whether context-aware models (also known as semantic models or embedding models) reach higher retrieval accuracy than traditional keyword-based search models on law enforcement reports. Results of the study showed that semantic models have significantly better performance than traditional keyword-based search, an improvement of 56 percentage points. Besides investigators, inspectors, and analysts, smarter and enhanced search may benefit other key parties like patrol officers, crime analysts, and prosecutors by allowing faster discovery of related incidents, improved case connection, and richer text analysis. These results highlight the potential to improve operational efficiency, reduce administrative and management burden, and strengthen public safety outcomes.
Financial institutions can operate under capacity and legal limits by using customer behavior prediction to detect anomalous activity, reduce attrition, and personalize services. Using transactional, relational, and demographic characteristics from a de-identified banking and insurance dataset with 120,000 customers observed over a 24-month period, this study creates and assesses an applied machine-learning framework that forecasts four customer outcomes: product uptake, churn risk, claim propensity, and fraud risk. Temporal, behavioral, and engagement indicators such as tenure, product mix, customer-provider network features, and recency-frequency-monetary (RFM) measurements were generated through feature engineering. Logistic Regression, Random Forest, XGBoost, and a stacked ensemble with a logistic meta-learner trained on out-of-fold predictions are among the models that are compared. MAUC, PR-AUC, precision, recall, F1, Brier score, and calibration curves were used to assess model performance on a temporally separated holdout set. The stacked ensemble produced the best overall performance (average AUC ≈ 0.91 and average PR-AUC ≈ 0.64 across tasks) and well-calibrated probabilities (average Brier score ≈ 0.07). Predictions at the cohort and individual levels were interpreted using SHAP explanations, which showed that relative monetary activity, tenure, and recent engagement frequency were consistently the best predictors for all four outcomes. Targeted interventions based on estimated probabilities may boost cross-sell conversion by around 18% and lower churn by about 12%, according to a deployment simulation with basic cost assumptions, while enabling fraud and claims teams to reduce manual review volumes by roughly 30-40% at recall levels over 65% and precision levels above 60%. In order to enhance client outcomes and operational efficiency while adhering to explainable AI and governance standards, the study presents a workable, comprehensible pipeline that financial institutions can incorporate into decision workflows.
The leadership practices of Mahatma Gandhi and Sir Winston Churchill are examined through Greenleaf’s servant leadership. The nonviolent and grassroots approach of Mahatma Gandhi and the decisive, crisis driven leadership of Sir Winston Churchill are compared to each other, demonstrating core servant leadership traits despite the vastly different context like social and economic conditions. The study also includes the comparative analysis of various factors like context, timing, social and economic conditions that influenced the leadership styles. Results showed servant leadership is highlighted through Gandhi’s approach to uplift communities through ethical commitment and Churchill’s empathetic yet pragmatic decisive leadership style. In conclusion, this research uses a triangulation methodology to fill a scholarly gap by integrating Greenleaf’s framework with preexisting data, and theoretical concepts. It also suggests global leaders utilize hybrid servant leadership approaches to tackle complex modern business challenges. Keywords: Servant leadership, Greenleaf, Gandhi, Churchill, comparative analysis, triangulation
Abstract Electronic commerce or e-commerce has a significant impact on the global economic environment. However, recent developments show technology and applications are increasingly paying more attention to mobile computing, the wireless Web, and mobile commerce. Because of this, much research has been done on the acceptance of mobile banking, or cyber-banking, as a significant channel for distribution. For many, though, this method is still relatively new. Thus, cyber-banking is examined and summarized in the current qualitative study in multiple areas, such as age, gender, education level, occupation, and technology expertise. The most significant difficulty was estimating how users will use it. A total of 180 respondents completed the questionnaire. Since 21% did not use cyber-banking, they were excluded from further analysis. Data analysis was performed on the remaining 142 surveys. The survey had versatile and open results because it was done online and offline. Via qualitative analysis, the results demonstrated that adopting cyber-banking was uneven in specific ways since it frequently depends on the acceptance of technology and its advancements. It also showed that most people's attitudes, perceived utility, and compatibility with their devices and lifestyles were critical factors in their decision to use cyber-banking services in their daily lives. Keywords: Cyber-banking, bank digitalization, mobile banking, banking technology, online assistance service
The stock market prices and volume volatility in Mergers and Acquisitions (M&A) create stock market inefficiencies, erodes regular and institutional investors' confidence in the stock market which makes it even more difficult for companies to raise capital and grow, and leads to market illiquidity which is another main catalyst of the stock market failure. The purpose of this review paper is to discuss how merger and acquisition have an impact on the stock market prices and volume volatility. Understanding the relationship between certain variables, like M&A events and the stock prices and volume volatility is what motivated this review study. Using a thematic methodology, this review study organizes information by content before analyzing, synthesizing, and interpreting the findings. The study shows that M&A public announcements can increase the price and volume volatility; therefore, M&A might have a positive correlation with the stock price movement and the volatility. Keywords: Market efficiency, volume volatility, stock price movement, M&A, market inefficiency
The objective of this exploratory research paper is to investigate how organizations can utilize cloud computing models, Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS), to scale up their growth and efficiency, lower costs and improve scalability. Using the Technology-Organization-Environment (TOE) framework, this research investigates the strategic advantages, adoption of drivers, and limitations of cloud computing. A qualitative research methodology was used, gathering primary data (100) from food service representatives via questionnaires and evaluating them using a structured framework. Results showed that cloud adoption lowered costs and had a positive environmental effect while improving operational efficiency, scalability, and mobility. Businesses gained from automatic backups and real-time data accessibility, which promote business continuity. However, issues with liability, compliance, and data security presented difficulties, particularly for smaller businesses. Large corporations fully integrated cloud computing (100%), whereas medium and small firms had lower acceptance rates (60% and 25%, respectively), according to a comparative examination of cloud adoption across all company sizes. Additionally, investment patterns indicated that larger businesses spent far more on cloud solutions. In this study, the research paper emphasizes how cloud computing promotes digital transformation, lowers carbon emissions, and maximizes the use of IT resources. Strategic adoption can improve business sustainability even while security threats and regulatory issues still exist. To optimize advantages, organizations must match their cloud plans with their operational objectives. To reduce adoption hurdles, future studies could examine sophisticated security systems and legislative initiatives. Keywords: Cloud computing, SaaS, IaaS, PaaS, software, business
This classroom exploration article focuses on a solution to the problem of traditional Western-focused art history survey courses limiting students’ exposure to the art and architecture of Africa, Asia, Central and South America, and Oceania. While most current art history survey courses claim a global focus, the Western focus is still present with the art and architecture of Africa, Asia, Central and South America, and Oceania being given less than a third of the focus given to Western art. In order for these art history survey courses to be globally focused instead of Western-focused and offer a more inclusive perspective on art history, they need to be redesigned to possess a chronological design approach versus the traditional regional/chronological design approach. A chronological design approach allows for equal representation of art from different regions, fostering a more balanced and inclusive understanding of global art history. The redesign of ARH 101 Art from Prehistory Through Middle Ages for Estrella Mountain Community College (EMCC) occurred over the summer of 2024 with a launch in the Fall of 2024. The redesign of the course involved the use of the chronological design approach, a new Open Educational Resource (OER) textbook, The Met Heilbrunn Timeline of Art History (2023), to complement the new course design, and assignments that offer student choice and the need to analyze art from different periods and regions. Keywords: Art history survey courses, chronological design approach, global inclusivity
Due to the rise of industrialization and world trade, numerous global companies are venturing into the food marketing industry, which is seeing rapid growth and intense competition worldwide. This review article extensively studies Kellogg's Pringles, a leading brand in the snack industry. It sets the stage with an introduction to marketing management, and it discusses Kellogg's acquisition of Pringles, which is later followed by a company overview that encompasses the firm's history, products, and market position. The SWOT analysis indicates Pringles' strengths, weaknesses, opportunities, and threats, such as brand identity, worldwide presence, and competitive arena. Moreover, the PESTEL analysis looks into the external forces affecting Pringles' operations, such as regulatory, economic, and technological factors. This study delved into Pringles' marketing strategy, utilizing the marketing mix elements: product, price, promotion, and distribution. Through an in-depth analysis, the research focused on how Pringles is positioned within the snack food industry and, more importantly, how it creates and maintains its competitive advantage. Monumental achievements demonstrate the company's focus on product innovation, dynamic pricing strategies, one-of-a-kind promotional campaigns, and wide distribution. The study has underlined the brand's ability to employ these factors in maintaining its market supremacy and recommended ways of increasing its marketing strategy in the future. Keywords: Branding, marketing strategy, Kellogg, Pringles, SWOT analysis, PESTEL analysis
The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks
Artificial Intelligence (AI) is steadily becoming the new normal in doing business through increasing operational performance, improving customer relations, and increasing predictive accuracy. This quantitative exploratory research employed a mixed-methods approach, integrating qualitative insights into organizational trends, best practices, and challenges with quantitative assessments of performance measures, cost savings, and business outcomes. Several surveys were administered to a diverse group of business professionals. The study, situated within the field of applied research, explores how AI facilitates business growth through change and proposes best practices for successful integration. It also studies what happens during transition periods when organizations emphasize artificial intelligence, NLP, and robotic process automation as top technologies since they contribute to completing work tasks, analyzing large data sets, and improving individual communication with clients. Besides potentially generated cost savings and a long-term increase in business value, there are several obstacles that organizations face if implementing AI, such as high initial costs and a market that requires professional knowledge on the topic. If implemented correctly, AI technologies hold huge potential for businesses going through transitions and taking advantage of AI’s strengths. For this reason, it is important to describe and analyze trends like AI technology integration accurately. This article suggests best practices for applying AI in business development during transformations. Keywords: Artificial intelligence, machine learning, business transitions, predictive analytics, robotic process automation, cost reduction, AI adoption strategies
This study employs a quantitative research approach to investigate the relationship between social media use and adolescent mental health. The evolution of social media has revolutionized communication, becoming an integral part of daily life. Numerous studies have shown that adolescents (ages 12-19) spend significant time on social media platforms, impacting mental health. (Kaur et al., 2022) in India. In the case of Nepal, adolescent students spend a good amount of time on social media. However, the impact of using social media by adolescent students is not systematically investigated. (Kharel, 2023) This research study aims to examine the relationship between social media use and adolescent mental health. It has conducted a structured survey with 260 participants from Kathmandu Valley and Dang Valley in Nepal. It has defined various factors as well-being, psychological, risk, value, and perceived factors. Responses were recorded on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Cronbach’s alpha test (0.94) confirms strong internal consistency. While conducting the sampling, a 95% confidence level was assumed, with a desired margin of error set at 5% and an expected population proportion of 0.2. Results indicate no significant gender differences (ANOVA p = 0.56), but linear regression analysis reveals a mental health outcome of 12.5. Cronbach’s alpha test (0.94) confirms strong internal consistency. The findings indicate no significant gender differences but emphasize the need for targeted interventions to mitigate social media's negative impact on adolescent mental health. Mental health professionals should focus on early detection, its impact, and preventive strategies to support adolescent mental health. Keywords: Social media impact, adolescent mental health, social media and mental health, positive and negative impact of social media, mental health survey
The main objective of this intervention research was to enrich the teaching strategies of a group of English as a Foreign Language (EFL) teachers through four online workshops on digital learning materials (DLMs). Pre-survey questionnaires were administered to determine to what extent participants master certain DLMs and the instructional purposes they consider when incorporating DLMs into their classes. The data collected paved the way for designing and developing these workshops. Zoom was the virtual classroom for this intervention research, and Google Classroom functioned as the Learning Management System (LMS). Goals and indicators were set to evaluate their effectiveness along with feedback forms conducted at the end of each workshop. Results showed that all the goals and most indicators set to reach these goals were successfully met. In addition, participants’ responses indicated that the workshop content was beneficial for teaching EFL in a virtual environment, and they plan to integrate it into their classes. Finally, incorporating interactive activities into the workshop content fosters engagement. Keywords: Online workshops, digital learning materials, virtual classroom, English as a foreign language
Understanding operational resilience during disruptive events is critical in the dynamic global logistics field. This qualitative study explores the challenges faced by a logistics company during the COVID-19 pandemic based on surveys and interviews with twelve logistics management experts. A thematic analysis was used to identify recurring themes regarding logistics disruptions and response strategies. The data revealed internal disruptions such as delays in pickup or delivery, inaccurate delivery information, and communication challenges with drivers. External disruptions include supply-demand imbalances, freight rate volatility, port congestion, and unexpected supplier shutdowns. Strategies to enhance logistics resilience are discussed, emphasizing strategic decision-making, robust leadership, digitalization for improved communication and supply chain visibility, and agility in adapting to change. These findings provide a thorough understanding of logistics disruptions and offer practical recommendations for professionals to navigate challenges and strengthen their logistics operations.
Blockchain technology offers a promising way to improve business processes by providing a secure and transparent transaction platform. However, using this technology brings its own set of challenges, especially when trying to balance user privacy with legal and regulatory needs. This article explores the challenges of keeping user information private, adhering to regulatory frameworks, and fulfilling legal requirements on the blockchain. A key point in this research is the challenge of keeping or maintaining confidentiality while being transparent. The article also discusses the issues of applying legal rules to a system not controlled by one central authority, the risks of privacy and security breaches, and the need to follow data protection laws. The article highlights how some blockchain-based companies have tackled these challenges, mainly through smart blockchain management and innovative technology, by looking at real-world examples from major companies like IBM, Bitpay, Ripple, and Coinbase. The systematic literature review (SLR) methodology involved reviewing literature from the past 15 years (2008-2023) from trusted sources like Google Scholar, ACM Digital Library, IEEE, Springer, and Science Direct. The findings indicate that cutting-edge technologies prioritizing privacy, such as zero-knowledge proofs, ring signatures, and encryption methods, would enable Bitcoin (BTC) platform operations to maintain or balance privacy and transparency. Furthermore, the study indicates the importance of clear privacy guidelines, adhering to relevant regulations, working closely with regulators and law enforcement, and educating users. In summary, it is crucial to approach blockchain carefully, prioritizing user privacy while meeting all legal and regulatory requirements.
The purpose of this quantitative research was to identify the factors causing labor shortages in the hospitality industry in the post-pandemic era. Specifically, it examined the effects of work-life balance, employee compensation, government-issued unemployment benefits, and job insecurity on employees' turnover intentions. The research methodology employed in this study was a quantitative survey, with a sample size of 385 participants from the hotel, restaurant, bar industry, and food service sector. The findings indicated work-life balance, employee compensation, and job insecurity had a significant impact on employees' turnover intentions, as the null hypotheses for these factors were rejected. However, the government-issued unemployment benefits (EDD) did not show any significant impact, indicating further research is needed to gain deeper insights into the potential influence of these benefits. These findings contribute to the understanding of the challenges faced by the hospitality industry in retaining employees and highlight the importance of addressing work-life balance, compensation, and job insecurity to mitigate employee turnover.
The present study intends to explore the relationship between financial inclusion and entrepreneurship development in India. Entrepreneurship development is essential for the economic growth of an economy. Financial inclusion by providing easy credit availability at affordable cost aids in entrepreneurship development. To measure the level of financial inclusion three basic parameters i.e., availability of banking services, penetration of banking services and usage of banking services is used. The level of entrepreneurship is measured through the number of new businesses registered per 1000 individuals of the age group 15 to 64. By employing multiple regression model, the study found a positive relationship between financial inclusion and entrepreneurship development in India. This finding underscores the significance of financial inclusion in not only aiding business initiation and expansion but also in catalysing job creation, boosting economic growth, and alleviating poverty.
