Analytical Integration Research Articles

A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.

Cybersecurity Management: Developing Robust Strategies for Protecting Corporate Information Systems

This growing complexity and sophistication of cyber threats call for a sea change concerning how organizations handle cybersecurity. Traditional isolated, reactive security models no longer protect against evolving digital risk. This abstract provides an innovation-oriented comprehensive methodology for completely transforming the approach of organizations to the protection of their critical information assets. At the heart of the method lies a recognition of the fact that cybersecurity is not strictly a technological challenge but has multifaceted elements that have to be aligned with the overall business objectives operational constraints, and risk tolerance of the organization. One of the crucial innovations is the integration of advanced analytics, blockchain technology, and machine learning techniques that will enable any organization to create a much more accurate and proactive perspective related to its vulnerability to cyber threats. This holistic cybersecurity methodology can transform security posture, strengthen collaborative capabilities, and build a resilient cybersecurity ecosystem through effective implementation and validation. These insights and lessons learned will, no doubt, inspire and guide other organizations toward a more robust, adaptive, and collaborative approach to cybersecurity management as the organization continues to improve and further innovate in the field of best security practices.

Adoption of Precision Agriculture Technologies among Farmers: A Comprehensive Review

Precision agriculture (PA) technologies have spearheaded a revolution in contemporary farming practices, ushering in an era of sustainability, efficiency, and data-driven agricultural production. This comprehensive examination aims to delve deeply into the intricate dynamics surrounding the adoption of precision agriculture technologies among farmers, synthesizing existing literature, identifying key determinants, barriers, and implications. The adoption of precision agriculture technologies is influenced by a myriad of factors spanning socio-economic, institutional, technological, psychological, and attitudinal dimensions. Socio-economic factors such as farm size, income level, access to credit, and demographic characteristics are pivotal in shaping farmers' decisions to adopt PA technologies. Institutional factors, including government policies, extension services, market structures, and support networks, also wield significant influence over adoption patterns. Technological considerations, such as the complexity, compatibility, and perceived benefits of precision agriculture technologies, impact farmers' adoption decisions. Moreover, psychological factors such as risk perception, innovativeness, and attitudes towards change contribute to shaping farmers' willingness to embrace new technologies. The adoption of precision agriculture technologies holds immense promise for enhancing farm productivity, sustainability, and profitability. By enabling precise management of inputs such as water, fertilizers, pesticides, and seeds, PA technologies help optimize resource use, minimize environmental impact, and maximize yield potential. Furthermore, the integration of advanced data analytics, remote sensing, and IoT (Internet of Things) devices facilitates real-time monitoring, decision-making, and predictive analytics, empowering farmers with actionable insights for improved farm management. However, despite the compelling benefits, the adoption of precision agriculture technologies faces several barriers and challenges. High initial investment costs, limited access to capital, inadequate infrastructure, and technical knowledge gaps pose significant hurdles for farmers, particularly those operating in resource-constrained settings. Concerns regarding data privacy, security, and ownership also contribute to farmers' apprehensions about adopting PA technologies, raising questions about data governance and regulatory frameworks. To overcome these barriers and promote the widespread adoption of precision agriculture technologies, concerted efforts are needed from various stakeholders. Farmer education and training programs play a crucial role in enhancing digital literacy, technical skills, and awareness about the potential benefits of PA technologies. Government policies and incentives, including subsidies, grants, and tax incentives, can help alleviate financial barriers and incentivize adoption. Moreover, collaboration between researchers, industry stakeholders, and policymakers is essential to drive technological innovation, develop user-friendly solutions, and address emerging challenges. Investments in infrastructure, such as rural broadband connectivity and sensor networks, are also imperative to ensure the seamless integration and scalability of precision agriculture technologies across diverse farming landscapes.

Integration of analytics in performance management in it organisations

Integration of analytics in performance management in it organisations

Harnessing big data analytics to revolutionize ESG reporting in clean energy initiatives

The integration of Big Data Analytics into Environmental, Social, and Governance (ESG) reporting holds significant potential to revolutionize clean energy initiatives. This paper examines the current challenges in ESG reporting, such as data collection and integration, data quality, regulatory compliance, transparency, and standardization. Big Data Analytics offers advanced techniques, including machine learning, artificial intelligence, predictive analytics, and real-time data processing, which can address these challenges. By leveraging diverse data sources—both structured and unstructured—clean energy projects can achieve more accurate environmental impact assessments, social impact evaluations, and governance analyses. The applications of Big Data Analytics are exemplified through various case studies, such as renewable energy projects, smart grids, and carbon offset programs. The benefits of these applications are vast, including enhanced data accuracy, improved decision-making, increased transparency, better stakeholder engagement, and cost efficiency. However, implementing Big Data Analytics in ESG reporting also presents challenges like data privacy, technological requirements, skills shortage, ethical considerations, and system integration. The future of Big Data Analytics in ESG reporting looks promising with emerging trends like blockchain for data transparency, IoT and sensor technologies, advanced machine learning models, and collaborative platforms. This paper concludes with a call to action for stakeholders to embrace these technologies, highlighting the transformative potential of Big Data Analytics in creating more effective and accountable clean energy initiatives.

Utilizing predictive analytics to boost customer loyalty and drive business expansion

In the contemporary business landscape, leveraging data to enhance customer experiences and drive strategic decisions has become paramount. Predictive analytics, a key component of advanced data analytics, offers a powerful tool for businesses seeking to boost customer loyalty and drive business expansion. This review explores the utilization of predictive analytics to understand customer behavior, anticipate their needs, and create personalized experiences that foster loyalty and promote growth. Predictive analytics involves analyzing historical data to identify patterns and trends that can forecast future behaviors and outcomes. By integrating predictive models into customer relationship management (CRM) systems, businesses can gain valuable insights into customer preferences, purchasing habits, and potential churn risks. These insights enable companies to tailor their marketing strategies, optimize customer interactions, and deliver targeted promotions that resonate with individual customers. One significant advantage of predictive analytics is its ability to enhance customer retention. By identifying at-risk customers early, businesses can implement proactive measures to re-engage them, such as personalized offers, targeted communication, and loyalty programs. This not only improves customer satisfaction but also reduces the costs associated with acquiring new customers. Moreover, predictive analytics facilitates market segmentation and helps businesses identify high-value customer segments. By understanding the characteristics and behaviors of these segments, companies can develop focused marketing campaigns and product offerings that cater specifically to their needs. This targeted approach not only maximizes the return on marketing investment but also drives business expansion by capturing new markets and increasing market share. Additionally, predictive analytics can support product development and innovation. By analyzing customer feedback and usage data, businesses can identify emerging trends and unmet needs, guiding the creation of new products and services that align with customer demands. This fosters a culture of innovation and continuous improvement, further strengthening customer loyalty and driving long-term growth. In conclusion, the integration of predictive analytics into business strategies is a powerful means to boost customer loyalty and drive business expansion. By leveraging data-driven insights to personalize customer experiences, anticipate needs, and optimize marketing efforts, businesses can achieve sustainable growth and maintain a competitive edge in the dynamic market landscape.

Ethical Implications and Educational Integration of AI-Driven Predictive Analytics in Healthcare: A Comprehensive Review

Ethical Implications and Educational Integration of AI-Driven Predictive Analytics in Healthcare: A Comprehensive Review

Review on Different Methods for Smart Dam Operation and Water Monitoring

Abstract: This complete literature review digs into the complex world of automated dam operations and water quality management, emphasising key advances and developing techniques in this crucial subject. The study focuses on the integration of sensor technologies, IoT systems, and data analytics to explain how these advances have transformed dam management and water quality preservation. The study gives essential insights into the cutting-edge approaches used in automated dam operations and water quality management by thoroughly examining numerous studies and research findings. Discussions on the use of real-time monitoring systems to track critical water parameters, as well as the use of predictive modelling tools to detect possible problems with water quality early on, are especially relevant. This literature review not only emphasises the importance of technological advancements in improving operational efficiency and environmental stewardship, but it also lays the groundwork for future exploration and refinement of thee methodologies in the pursuit of sustainable water resource management.

Resilience and sustainability of supply chains in the face of natural disasters and climate change

In the fast-paced global business, characterised by increasing competition and the necessity of being sustainable, the agility of supply chains (abbreviated as SCR) with their capability to withstand natural hazards and climate change have emerged as critical factors. This paper analyses the strategies and methods that are used to overcome the problems connected to SCR and sustainability in detail. A review of literature shows a wide variety of strategies of which some are comprehensive development plans, innovative risk management techniques, infrastructure adaptation measures, data analytics integration, adherence to quality management principles, strategic management approaches, collaborative efforts, stakeholder engagement initiatives, compliance with regulatory frameworks, and adoption of advanced technologies. With the use of a mixed method research approach, applying both quantitative surveys and qualitative interviews, this study evaluates the strategies’ effectiveness and their contribution towards SCR and sustainability. The results show the importance of the immediate implementation of the necessary measures and the joint work to reduce the risks and to guarantee operational resilience. Additionally, the paper examines the interdependency across sectors, which demonstrates the significance of collaborative governance, stakeholder involvement and the linkage with regulatory frameworks in building resilient and sustainable supply chains.

Big Data Analytics for Optimizing Multimodal Supply Chains in the Maritime Industry

This research investigates the integration of big data analytics in optimizing multimodal supply chains within the maritime industry. Focusing on efficiency, flexibility, reliability, and visibility, the study examines 100 cadets at a maritime institute, using qualitative research methods and descriptive analysis. Findings reveal a critical need for knowledge and skills, infrastructure and resources, organizational culture and support, and regulatory compliance. The research underscores the importance of investing in training and development, infrastructure, and fostering a culture that values innovation and data-driven decision-making. By addressing these needs, organizations can enhance their readiness to adopt and implement big data analytics, improving the efficiency and sustainability of their supply chains. The study contributes to the understanding of how big data analytics can be leveraged to enhance supply chain performance in the maritime industry.

THE ROLE OF THE LEADER IN THE FORMATION OF CORPORATE CULTURE OF THE ENTERPRISE

Relevance: the role of leadership in shaping the corporate culture of an enterprise is crucial for the success and sustainable development of the organization. Purpose: the article aims to determine the importance of leadership qualities in the process of formation and development of corporate culture of enterprises. Research methods: to achieve this goal, the article uses a methodology that includes analytical review, categorization and integration of data from various sources to better understand the relationship between leadership and corporate culture, which allowed to identify effective approaches to cultivating corporate values. The study found that leaders play a key role in shaping corporate culture through their personal and professional qualities, ability to motivate and unite the team, and ability to implement corporate values in the company's daily operations. Various approaches to management, from strict control to open participation, have a significant impact on the formation of corporate culture, which requires leaders to be flexible in choosing their leadership style based on the needs of the business and its strategic vision. The effectiveness and productivity of corporate culture depend on the ability of leaders to demonstrate diverse qualities that include not only professionalism and personal characteristics, but also social responsibility, value orientations and strategic marketing thinking. Such qualities contribute not only to effective management and team spirit, but also to creating an atmosphere conducive to innovation and longterm development. Corporate culture is a key factor that affects all areas of an organization's life, with people at the center of this process with their relationships, respect and personal growth, which is the basis for creating an effective work environment. Practical significance: the results of the study can be used by managers to formulate an effective strategy for the development of corporate culture.

NiMo 4.0 – Enabling advanced data analytics with AI for environmental governance in the water domain

Abstract In the realm of environmental governance, civil servants confront a plethora of diverse datasets, including time series, geospatial vector data, and raster data. However, unlocking the transformative potential of Artificial Intelligence (AI) models to analyze this data poses the challenge of a widening technical proficiency gap in public administration. This paper explores the intersection of expanding environmental datasets and advanced analytics. Through a real-world project lens, our work aims to guide public administration entities, fostering seamless integration of AI-driven analytics and data-driven decision-making. We present a modular technical architecture that proposes pragmatic solutions that have the potential to empower civil servants. This approach contributes to accelerating environmental governance into an era of more informed and efficient, data-driven practices.

Transforming business scalability and operational flexibility with advanced cloud computing technologies

In the rapidly evolving landscape of digital transformation, cloud computing has emerged as a pivotal technology enabling businesses to achieve unparalleled scalability and operational flexibility. This review explores the transformative impact of advanced cloud computing technologies on business operations, highlighting key innovations and their implications for organizational growth and efficiency. Cloud computing offers a dynamic and scalable environment where resources can be provisioned and managed on-demand, allowing businesses to respond swiftly to changing market conditions and customer demands. By leveraging cloud infrastructure, companies can scale their operations seamlessly without the need for significant upfront investments in physical hardware. This flexibility not only reduces capital expenditure but also enhances the ability to innovate and adapt in a competitive marketplace. Advanced cloud technologies, such as multi-cloud and hybrid cloud solutions, further augment operational flexibility by enabling organizations to optimize their IT environments. Multi-cloud strategies allow businesses to distribute workloads across multiple cloud providers, mitigating the risks associated with vendor lock-in and ensuring high availability and redundancy. Hybrid cloud solutions, which integrate on-premises infrastructure with public and private clouds, provide a balanced approach to managing sensitive data and workloads while benefiting from the scalability of the cloud. Moreover, cloud-native technologies like containerization and serverless computing have revolutionized application development and deployment. Containers encapsulate applications and their dependencies, ensuring consistency across different computing environments and facilitating rapid deployment. Serverless computing reviews the underlying infrastructure, allowing developers to focus solely on code, thus accelerating the development cycle and reducing operational overhead. The integration of advanced analytics and artificial intelligence (AI) with cloud computing further enhances business capabilities. Cloud platforms offer robust analytics tools and AI services that can process vast amounts of data in real-time, providing actionable insights and enabling predictive decision-making. This integration empowers businesses to optimize operations, improve customer experiences, and drive strategic initiatives with data-driven precision. In conclusion, advanced cloud computing technologies are instrumental in transforming business scalability and operational flexibility. By harnessing the power of the cloud, organizations can achieve greater agility, cost efficiency, and innovation, positioning themselves for sustained growth and competitive advantage in the digital age. As cloud technologies continue to evolve, their potential to redefine business operations and drive economic value will only expand, making cloud adoption a critical imperative for modern enterprises. Keywords: Operational Flexibility, Transforming, Business Scalability, Advanced, Cloud Computing Technologies.

Measuring the Integration of Data Analytics in the Computerized Accounting Curriculum

This study demonstrates a framework for integrating data analytics into an existing undergraduate accounting curriculum, that students are introduced to data analytics progressively while not requiring additional semester credit units. The Qasim model proposes course learning outcomes related to data analysis with specific course levels from lecturers and students of accounting study program. The findings showed that data analysis materials are identified as existing and impact changes in learning materials. Empirically, there are differences in the level of understanding and mastery of skills in data analysis tools between lecturers and students. The course that needed to develop the adoption of data analysis material is intermediate financial accounting expansively. This model can be a reference for lecturers and curriculum committees when updating the accounting curriculum to include data analytics integrating.

Safeguarding Patient Confidentiality in Telemedicine: A Systematic Review of Privacy and Security Risks, and Best Practices for Data Protection

The COVID-19 pandemic accelerated telemedicine adoption, showcasing its potential in improving healthcare delivery. However, privacy and security risks pose challenges, impeding widespread acceptance. The aim is to investigate the integration of data analytics, data analysis, and data cleaning in telemedicine, focusing on patient data privacy and security, with the goal of proposing strategies to mitigate risks and uphold confidentiality. Utilizing a qualitative approach, privacy and security challenges in telemedicine were investigated. Multiple databases, including PubMed, Embase, and Cochrane Library, were searched from 2018-2023. Inclusion criteria involved English-language, peer-reviewed empirical studies focusing on telemedicine privacy and security. Out of 770 unique records screened, eight studies were included. Full-text review and risk of bias assessment were conducted using CASP tool. Privacy and security, technology hurdles for providers, patient trust, professional training, physical assessment challenges, and disparities among special populations were identified. Environmental, technological, and operational factors contribute to privacy and security risks in telehealth. Technology challenges like restricted access to telehealth tools and poor internet hinder adoption. Data analytics in telemedicine facilitates healthcare transformation, addressing privacy and security while optimizing patient outcomes through advanced analytics techniques and structured data analytics lifecycles. The integration of data analytics in telemedicine shows promise for healthcare transformation by providing insights into patient behavior and policy impacts, while ensuring data privacy and security. Addressing barriers, accelerated by the COVID-19 pandemic, requires infrastructure enhancements and global research efforts for inclusive telehealth ecosystems.

Nonlinear analysis of laminated composite shells with piezoelectric patches under displacement‐dependent pressure loads

AbstractIn this paper, the three‐dimensional stress field in nonlinear laminated composite shells with piezoelectric patches subjected to displacement‐dependent loads is analyzed. To solve the problem, the geometrically exact (GeX) hybrid‐mixed four‐node solid‐shell element has been developed. The term GeX means that the middle surface is described by analytical functions, including spline functions, and, therefore, the coefficients of the first and second fundamental forms are taken exactly at the element nodes. The laminated solid‐shell element formulation is based on the choice of an arbitrary number of sampling surfaces (SaS) parallel to the middle surface and located at the Chebyshev polynomial nodes within the layers, in order to introduce the displacements and electric potentials of these surfaces as unknown functions. The outer surfaces and interfaces are also included into a set of SaS. Due to analytical integration utilized to evaluate the tangent stiffness matrix, the proposed GeX piezoelectric solid‐shell element under nonconservative loading exhibits superior performance in the case of coarse meshes and allows the use of only one load step in all considered benchmark problems. Therefore, it can be recommended for the large deformation analysis of doubly curved laminated composite shells with piezoelectric patches, since the SaS formulation allows one to find three‐dimensional solutions of electroelasticity with a prescribed accuracy.

Lipid extraction alters amino acid composition and bulk, but not amino acid, carbon and nitrogen isotope values.

Concerns exist over observed shifts in value and variance of nitrogen isotopes following physicochemical extraction of lipids from organic matter. The mechanisms behind these apparent changes in bulk tissue δ15N values are not fully understood yet have major implications for analytical costs and integrity of data interpretations. Changes in proximate analysis, amino acid composition, C:N ratios, bulk tissue and amino acid δ13C and δ15N values, and resulting isotope-based food web metrics were compared between lipid-intact and lipid-extracted muscle tissue of fishes spanning <1% to >20% muscle fat content to identify mechanisms of nitrogen isotope fractionation associated with physicochemical lipid extraction. Bulk δ13C and δ15N values increased and %N, C:N ratios and crude protein content decreased following lipid extraction. Resulting bulk isotope niche spacing and overlap varied significantly between lipid-intact and lipid-extracted tissues. While amino acid composition significantly changed during lipid extraction, particularly for lipid-associated amino acids (e.g., Glu, Lys, Ser), individual amino acid δ13C and δ15N values, and their associated compound-specific isotope analysis of amino acids (CSIA-AA)-based food web metrics, did not. Physicochemical lipid extraction caused significant tissue composition changes (e.g., leaching of amino acids and 15N-deplete nitrogenous waste) that affected δ13C and δ15N values and tissue %C and %N beyond simply removing lipids. However, lipid extraction did not alter individual amino acid δ13C or δ15N values or their associated CSIA-AA-based food web metrics.

Smart Agriculture: A Comprehensive Survey on IoT-Enabled Plant Disease Detection and Agricultural Automation

This research paper is dedicated to the comprehensive review and discussion of diverse techniques employed in plant disease detection within the realm of agriculture. Emphasizing notable contributions and showcasing innovative methodologies, the research work takes a critical turn to address the myriad issues and challenges intricately woven into the integration of IoT data analytics in agriculture. The paper meticulously unravels the complexities associated with plant disease detection in the era dominated by IoT and data analytics. Serving as more than just a repository of current methodologies and technologies, this work actively illuminates the challenges that await further exploration. The insights derived from this exploration will provide a substantial foundation for emerging researchers. By shedding light on the evolving landscape of plant disease detection and the nuances of IoT integration in agriculture, this paper empowers researchers to actively contribute to the resilience and sustainability of agricultural practices in the face of ongoing challenges.

A geometrically nonlinear Hellinger–Reissner shell element for the postbuckling analysis of variable stiffness composite laminate structures

Variable stiffness (VS) composite laminates provide larger freedom to design thin-walled structures than constant stiffness (CS) composite laminates. They showed to allow the redistributing of stresses, improving buckling and post-buckling performance and, therefore, reducing material weight and costs. This work extends a recently developed mixed shell element, MISS-4C, to the postbuckling analysis of VS composite laminate structures. MISS-4C has a linear elastic closed-form solution for the stress interpolation of symmetric composite materials. Its stress field interpolation is obtained by the minimum number of parameters, making it an isostatic element. Moreover, its kinematic is only assumed along its contour, leading to an efficient evaluation of all operators obtained through analytical integration along the element contour. MISS-4C uses a corotational approach within a fast multi-modal Koiter algorithm to efficiently obtain the initial post-buckling response of VS composite laminate structures.First, the element performance is investigated by analysing a carbon fibre VS composite laminate plate subjected to compressive stresses. Numerical results obtained with MISS-4C are compared with those obtained with the MISS-4 element, showing good accuracy and a high convergence rate. Subsequently, the structural response of a glass fibre VS composite laminate girder of a short-length bridge is optimised through a multi-objective optimisation that exploits the robustness of the MISS-4C element and the efficiency of the multi-modal Koiter algorithm.

ENHANCING FASHION FORECASTING ACCURACY THROUGH CONSUMER DATA ANALYTICS: INSIGHTS FROM CURRENT LITERATURE

The fashion industry is characterized by its fast-paced nature and constant evolution of consumer preferences, making accurate fashion forecasting essential for brands to remain competitive. Traditional forecasting methods, which rely heavily on historical sales data and expert intuition, are increasingly being complemented or replaced by advanced consumer data analytics. This article explores the integration of consumer data analytics into fashion forecasting, drawing insights from recent literature. By examining methodologies such as machine learning, big data analytics, and AI, as well as utilizing diverse data sources including social media, online shopping behaviors, and mobile data, this study highlights the significant improvements in trend prediction accuracy and operational efficiency. Key findings indicate that data-driven approaches provide more precise and real-time insights into consumer preferences, enabling brands to better anticipate market demands and optimize inventory management. The discussion underscores the transformative potential of consumer data analytics in enhancing the overall effectiveness of fashion forecasting.