Fair East Publishers: E-Journals
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Building Information Modelling (BIM) for construction project management: A literature bibliometric analysis approach
No full textBeing a multidisciplinary sector by nature, construction projects have historically been managed in a complex, dangerous, resource-wasting, imprecise manner that has been found to increase carbon emissions. Building information modelling (BIM) facilitates simulation, collaboration among project stakeholders, and the progression of BIM from 3D spatial representation to 10D industrialized production, all of which enhance the construction project management process throughout the lifecycle of a building. Based on such precedent and benefits, one could want to do bibliometric analysis to find out how many documents have been published on BIM for construction project management. In this study, a bibliometric analysis was employed to further explore the research subject. The Scopus database (www.scopus.com) and widely available tools were used to generate and analyse 246 published documents. Data obtained from the Scopus database was uploaded into the VOSviewer software to conduct further subject-matter analysis to delve deeper into particular documents received from Scopus. Utilizing data retrieved from Scopus, clusters networks analyses of ranking, co-authorship, co-occurrence, co-citation, citation, and bibliography are created and uploaded to the VOSviewer (www.vosviewer.com) tool. Findings reveal that the top countries for literature research and the cluster network of BIM for construction project management publications are the United States, the United Kingdom, Italy, China, Australia, India, Taiwan, Canada, France, Malaysia, and Iran. stating that African scholars need to formalize more of their writings and strengthen collaboration with other industrialized nations on this topic.
Keywords: Building Information Model (BIM), Construction Project Management, Bibliometric Analysis, Vosviewer, Visualisation, Network
Comprehensive analysis of cyber attacks and data breaches in the US health sector: Identifying vulnerabilities and developing proactive defense strategies
No full textCybercriminals increasingly target the US health sector driven by their reliance on digital networks and the lucrative value of healthcare data on the black market. As healthcare providers adopt the use of electronic health records (EHRs), networked medical devices and cloud-based systems, they unintentionally increase their attack surfaces, creating more ways for attackers to infiltrate their systems. These attacks do not only compromise patient data security but also force service interruptions which endangers patient well-being and decreases healthcare trust. Recurring ransomware attacks on hospitals have caused delays in medical care with millions of patients' medical records at risk of identity theft and financial exploitation due to breaches of EHR systems. Despite regulations like Health Insurance Portability and Accountability Act (HIPAA) mandating security standards for safeguarding patient data, these compliance standards usually fail to address the dynamic nature of cyber threats as they evolve. Moreover, most healthcare organizations face issues of limited resources and outdated systems while also lacking proper cybersecurity expertise which further exacerbates their vulnerabilities. The security of healthcare systems is additionally weakened by potential threats posed by third-party vendors and supply chain partners who often serve as gateways for attackers. Hence, this research examines some of the pressing challenges facing the US healthcare sector by analyzing statistical data, attack methodologies, and case studies to identify patterns and common causes of such breaches. This analysis examines the relationship between technical, organizational and regulatory gaps that contribute to this persistent threat landscape. In addition, this research explores security limitations of existing defense measures and provides evidence-based insights into areas where improvements are most needed.
Keywords: Cybersecurity, Healthcare Data Breaches, Us Health Sector, Proactive Defense
Concept paper: Strategic healthcare administration and cost excellence for underserved communities (SHACE-UC)
No full textCrafted by a visionary MBA graduate, the Strategic Healthcare Administration and Cost Excellence for Underserved Communities (SHACE-UC) model is a pioneering approach aimed at transforming healthcare delivery in underserved areas. This model seeks to bridge the gap between the need for high-quality patient care and the financial and logistical constraints often present in these communities. By applying advanced business management principles, SHACE-UC aims to enhance healthcare accessibility, quality, and sustainability in regions that are traditionally marginalized. The Strategic Healthcare Administration and Cost Excellence for Underserved Communities (SHACE-UC) model is a revolutionary concept designed by a visionary MBA graduate to revolutionize healthcare delivery in underserved areas. SHACE-UC is built on the premise of bridging the gap between the imperative for high-quality patient care and the prevalent financial and logistical constraints in such communities. By leveraging advanced business management principles, SHACE-UC is poised to elevate healthcare accessibility, quality, and sustainability in regions traditionally marginalized from adequate healthcare services. The SHACE-UC model is a comprehensive approach that integrates strategic healthcare administration with a focus on cost excellence. It recognizes the unique challenges faced by underserved communities and offers tailored solutions to address them effectively. Through meticulous planning and execution, SHACE-UC aims to optimize resource allocation, streamline processes, and enhance operational efficiency within healthcare facilities serving these communities. Key components of the SHACE-UC model include strategic planning, resource optimization, technology integration, and community engagement. By adopting a proactive approach to healthcare management, SHACE-UC endeavors to preemptively address the needs of underserved populations, thereby mitigating health disparities and improving overall health outcomes. Furthermore, SHACE-UC prioritizes collaboration with local stakeholders, including healthcare providers, community leaders, and policymakers, to ensure the alignment of initiatives with the specific needs and cultural nuances of the target communities. This participatory approach fosters trust, promotes inclusivity, and enhances the sustainability of interventions implemented under the SHACE-UC framework. In summary, the Strategic Healthcare Administration and Cost Excellence for Underserved Communities (SHACE-UC) model represents a paradigm shift in healthcare delivery. By harnessing the power of business management principles, SHACE-UC offers a transformative solution to address the healthcare challenges faced by underserved populations. Through innovation, collaboration, and a commitment to excellence, SHACE-UC is poised to make a meaningful and lasting impact on the health and well-being of communities that have long been neglected in the healthcare landscape.
Keywords: Healthcare, Cost, Community
Trauma impact of Covid-19 in the United States
No full textThe COVID-19 pandemic has ushered in a new era of trauma, impacting health, well-being, and cognitive functions, especially among vulnerable populations. Emerging research highlights the role of cumulative trauma exposure in exacerbating COVID-19 stressors. Continuous Traumatic Stress (CTS) offers a framework for understanding trauma proliferation amidst the pandemic, emphasizing the enduring nature of type III traumas. Mounting evidence suggests COVID-19 as a global traumatic stressor, characterized by fear, economic instability, and social disruption. Understanding trauma's complex interplay within the COVID-19 crisis is crucial for addressing its profound effects. Trauma research is pivotal for informing interventions and policies, identifying risk factors, resilience factors, and pathways to recovery. Additionally, trauma research unveils systemic inequities exacerbating trauma's impact, advocating for social justice initiatives.
A mixed-methods approach was employed to comprehensively explore trauma's impact amidst the pandemic, ensuring robustness and reliability in data collection and analysis. The study validates the Continuous Traumatic Stress Type III (CTS) trauma model across diverse samples, delineating distinct trauma profiles and emphasizing accumulation dynamics. Exploration of COVID-19 traumatic stress highlights its additive effect on PTSD symptoms, emphasizing the complex relationship between race/ethnicity, COVID-19 stress, trauma, and mental distress. Investigation into the pandemic's impact on eating behaviors underscores the need for comprehensive approaches to address eating-related concerns during stressful times.
While valuable, the research's generalizability may be limited beyond specific contexts and populations. Future research should strive for more robust study designs, diverse samples, and objective measures to deepen understanding of stress, trauma, and mental health outcomes during the ongoing pandemic.
Keywords: Covid-19, Trauma, Continuous Traumatic Stress (CTS) Post Traumatic Stress Disorder (PTSD), Pandemic, Mental Health, Cumulative Trauma, Eating Behavior
An empirical study of the impact of live streaming quality on purchase intention in Indonesia
Full text linkRapid development and popularity of the internet have increased e-commerce businesses all over the world, particularly in Indonesia. Due to this, the e-commerce sector becomes more competitive and hard to enter. Live-streaming comes as a strategy for e-commerce enterprises to gain and increase engagement and purchase intention of their customers. In light of this phenomenon, we conducted this study to observe the impact of live-streaming quality on purchase intention in Indonesia. We employed the use of the Stimulus-Organism-Response (S-O-R) framework to investigate how external factors can stimulate the desire to engage and develop the intention to purchase goods. Within this constructed framework, we proposed that the stimuli of external factors (interactivity, expertise and attraction, entertainment, and accessibility) can influence the internal desire of an organism (trust and follow intention), which will generate the response through purchase intention. By using questionnaire that we developed to create a live streaming scenario, we acquired 130 respondents from Indonesia. Through the structural equation modeling results, we found that perceived quality values of live streaming contribute to both perceived customers’ trust and follow intention, predicting customers’ intention to purchase.
Keywords: Live-Streaming Quality, Purchase Intention, Perceived Trust, Follow Intention, S-O-R Model
Artificial intelligence and international IP law: Reconciling innovation with equitable access in the U.S. and Global South
Full text linkThe rapid advancement of artificial intelligence technologies has fundamentally transformed the landscape of intellectual property law, creating unprecedented challenges for global regulatory frameworks and raising critical questions about innovation equity between developed and developing nations. This study examines the complex intersection of artificial intelligence and international intellectual property law, with particular focus on the disparities between the United States and Global South countries in accessing and benefiting from AI-driven innovations. The research reveals significant tensions between protecting intellectual property rights to incentivize innovation and ensuring equitable access to AI technologies that can drive economic development and social progress in developing nations. The analysis demonstrates that current international intellectual property frameworks, primarily designed for traditional innovations, are inadequately equipped to address the unique characteristics of AI technologies, including their reliance on massive datasets, algorithmic transparency concerns, and the potential for widespread societal impact. The United States, with its robust intellectual property enforcement mechanisms and advanced technological infrastructure, maintains significant advantages in AI innovation and commercialization, while Global South countries face substantial barriers to accessing, adapting, and benefiting from these technologies. These disparities are exacerbated by restrictive licensing practices, patent thickets, and the concentration of AI development within a small number of multinational corporations based in developed countries. The research identifies several critical areas where international intellectual property law must evolve to better balance innovation incentives with equitable access. These include the development of differential patent terms for AI technologies based on their potential social impact, the creation of compulsory licensing mechanisms for essential AI innovations, and the establishment of technology transfer frameworks that facilitate knowledge sharing between developed and developing nations. The study also examines the role of open-source AI initiatives and collaborative innovation models in promoting more equitable access to artificial intelligence technologies while maintaining incentives for continued research and development. Furthermore, the analysis reveals that effective reconciliation of innovation protection with equitable access requires comprehensive reforms to international intellectual property treaties, including the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) and the World Intellectual Property Organization frameworks. The research proposes a multi-stakeholder approach involving governments, international organizations, private sector entities, and civil society organizations to develop more inclusive intellectual property policies that recognize the unique characteristics of AI technologies and their potential to address global development challenges.
Keywords: Artificial Intelligence, Intellectual Property Law, Global South, Innovation Equity, Technology Transfer, Patent Reform, TRIPS Agreement, AI Governance, Digital Divide, International Trade Law
A performance-driven surveillance architecture for anomaly detection and response in subsea systems using PI Processbook and AI models
Full text linkThis paper presents a performance-driven surveillance architecture that enhances anomaly detection and operational decision-making in subsea oil and gas systems through the integration of PI ProcessBook and artificial intelligence (AI) models. Subsea operations generate vast amounts of time-series data from distributed sensors monitoring pressure, temperature, flow rates, and system states. While PI ProcessBook provides a widely adopted platform for visualizing and analyzing such data, its functionality is primarily limited to human interpretation and threshold-based alerts. To address the limitations of manual monitoring and improve responsiveness, this research proposes a unified architecture that embeds AI-based pattern recognition within the PI ProcessBook interface. The proposed system includes structured data acquisition pipelines, real-time preprocessing, and the deployment of supervised and unsupervised AI models for identifying deviations from normal operating conditions. These models continuously monitor telemetry and feed anomaly insights back into PI ProcessBook in the form of graphical alerts and contextual diagnostics. The architecture supports operator decision-making by reducing information overload, enhancing detection accuracy, and improving response time. Analytical insights demonstrate improvements in anomaly sensitivity, false positive reduction, and operator confidence. The integration also supports predictive maintenance strategies and contributes to digital transformation in offshore production environments. Recommendations for future development include adaptive learning, system-wide data fusion, and autonomous response capabilities.
Keywords: Subsea Surveillance, PI ProcessBook, Anomaly Detection, Artificial Intelligence, Performance Monitoring, Predictive Maintenance
AI-Driven continuous compliance and threat intelligence model for adaptive GRC in complex digital ecosystems
Full text linkThe rapid evolution of digital ecosystems—characterized by multi-cloud infrastructures, IoT proliferation, and distributed data flows—has fundamentally altered the governance, risk, and compliance (GRC) landscape. Traditional GRC frameworks, rooted in periodic audits and reactive controls, are increasingly inadequate for addressing the scale, speed, and sophistication of modern cyber threats. This review paper examines the emergence of AI-driven continuous compliance and threat intelligence models as adaptive solutions for managing GRC in complex digital environments. It synthesizes existing literature on regulatory mapping, continuous auditing, and real-time threat intelligence integration, while identifying key limitations of siloed, manual approaches. The study highlights how artificial intelligence and machine learning can enable proactive risk identification, predictive analytics, and automated remediation, transforming GRC into a continuous and intelligent function. Furthermore, the paper explores technical methodologies such as natural language processing for regulatory interpretation, anomaly detection algorithms for compliance monitoring, and predictive modeling for risk forecasting. By analyzing current advancements, challenges, and research gaps, this review proposes a conceptual framework that positions AI as a catalyst for adaptive, resilient, and future-ready GRC architectures. The findings underscore the critical need for intelligent, real-time governance models to ensure organizational sustainability in the face of regulatory volatility and cyber risk escalation.
Keywords: Continuous Compliance, Threat Intelligence, Adaptive GRC, Artificial Intelligence in Governance, Predictive Risk Analysis, Automated Remediation
Stress, depression, and anxiety prevalence of teenagers in Manipur
Full text linkThis is in line with other research showing that youth in conflict-affected areas like Manipur may exhibit violent and delinquent behaviours as a result of environmental stresses and sociopolitical tensions. Developing successful intervention techniques requires an awareness of the relationships between behavioural difficulties and mental health disorders, particularly in the context of Manipur, where socio-political tensions, economic inequities, and environmental stresses are prevalent. The results imply that there are, in fact, correlations between different kinds of behavioural issues and mental health issues among teenagers in Manipur
Keywords: Adolescents. Stress, Depression, and Anxiety
A conceptual model for multivariate data integration in reservoir modeling: exploring the role of neural networks in petrophysical analysis
Full text linkReservoir modeling is crucial for optimizing oil and gas production, as it helps characterize subsurface properties like porosity, permeability, and fluid saturation. Integrating diverse data sources such as well logs, seismic data, and core analyses presents a significant challenge due to their differing scales, resolutions, and data types. Traditional methods often struggle to accurately integrate these data sources, resulting in suboptimal predictive accuracy. This review proposes a conceptual model that leverages neural networks to integrate multivariate data for enhanced reservoir characterization. Neural networks, with their ability to handle nonlinear relationships and large, complex datasets, offer a transformative approach to unify diverse data inputs, resulting in more accurate predictions of reservoir properties. The model focuses on using feedforward neural networks (FNNs), convolutional neural networks (CNNs), and autoencoders to merge well logs, seismic interpretations, and core sample data. This integration aims to improve the identification of productive zones, reservoir boundaries, and fluid distributions, thereby enhancing reservoir models. Additionally, the review explores how neural networks can manage data gaps, predict missing values, and quantify uncertainty, which are common challenges in reservoir studies. By automating data processing and facilitating real-time analysis, neural networks can accelerate decision-making in reservoir management and field development. This framework highlights the potential of AI-driven techniques to revolutionize reservoir modeling, offering a pathway to more efficient, accurate, and adaptive resource management. The conceptual model aims to advance predictive capabilities in reservoir analysis, addressing the complexities of multivariate data integration and opening doors for future innovations in the energy industry.
Keywords: Conceptual Model, Multivariate Data, Reservoir Modeling, Petrophysical Analysis