Industry Specific Applications Research Articles

Automating Infrastructure Management: Benefits and Challenges of Ansible and Terraform Implementation Across Sectors

This article examines the implementation and impact of Infrastructure as Code (IaC) practices using Ansible and Terraform across various industries. Through a mixed-methods approach combining case studies, surveys, and quantitative analysis, we investigate how these tools enable more efficient, scalable, and repeatable infrastructure deployments. Our findings reveal significant benefits, including reduced operational costs (average 30% reduction), improved resource utilization (up to 40% increase), and enhanced disaster recovery capabilities (50% faster recovery times). However, challenges such as skills gaps, security concerns, and legacy system integration persist. The article provides insights into industry-specific applications, highlighting how finance, healthcare, and telecommunications sectors leverage these tools to meet unique demands. We present best practices for successful implementation, emphasizing continuous monitoring, data governance, and cross-functional collaboration. This article contributes to the growing body of literature on IaC and offers practical recommendations for organizations seeking to optimize their infrastructure management strategies in an increasingly digital landscape.

Industry-Specific Applications of Site Reliability Engineering

This article explores the application of Site Reliability Engineering (SRE) principles across various industries, demonstrating how SRE practices are being adapted beyond traditional tech companies to sectors such as healthcare, finance, and e-commerce. Originally developed by Google, SRE has evolved into a set of practices that bridge the gap between software development and IT operations, focusing on system reliability, performance optimization, and proactive problem-solving. The article examines specific case studies in each industry, highlighting the challenges faced during SRE implementation and the tangible benefits realized by organizations. By analyzing the impact of SRE on critical systems such as Electronic Health Records, high-frequency trading platforms, and e-commerce infrastructure, the article illustrates how SRE principles can be effectively applied to improve system reliability, enhance customer satisfaction, and increase operational efficiency across diverse business contexts.

Exploring the convergence of Metaverse, Blockchain, and AI: A comprehensive survey of enabling technologies, applications, challenges, and future directions

AbstractThe Metaverse, distinguished by its capacity to integrate the physical and digital realms seamlessly, presents a dynamic virtual environment offering diverse opportunities for engagement across innovation, entertainment, socialization, and commercial endeavors. However, the Metaverse is poised for a transformative evolution through the convergence of contemporary technological advancements, including artificial intelligence (AI), Blockchain, Robotics, augmented reality, virtual reality, and mixed reality. This convergence is anticipated to revolutionize the global digital landscape, introducing novel social, economic, and operational paradigms for organizations and communities. To comprehensively elucidate the future potential of this technological fusion and its implications for digital innovation, this research endeavors to undertake a thorough analysis of scholarly discourse and research pertaining to the Metaverse, AI, Blockchain, and associated technologies. This survey delves into various critical facets of the Metaverse ecosystem, encompassing component analysis, exploration of digital currencies, assessment of AI utilization in virtual environments, and examination of Blockchain's role in enhancing digital content and data security. Leveraging articles retrieved from esteemed digital repositories including ScienceDirect, IEEE Xplore, Springer Nature, Google Scholar, and ACM, published between 2017 and 2023, this study adopts an analytical approach to engage with these materials. Through rigorous examination and discourse, this research aims to provide insights into the emerging trends, challenges, and future directions in the convergence of the Metaverse, Blockchain, and AI.This article is categorized under: Application Areas > Industry Specific Applications

Enhancing management performance in rapidly changing organizations: a conceptual exploration

In today's rapidly evolving organizational landscape, effective management is crucial for survival and success. This study addresses the complex challenges managers face in dynamic environments, aiming to fill gaps in existing literature on strategies for improving management performance. Drawing on diverse secondary data sources, the research identifies key factors influencing management effectiveness, such as adaptive leadership, change management models, and the concept of learning organizations. The review highlights that successful management in rapidly changing organizations requires strategic foresight, adaptability, and a culture of innovation, amidst challenges posed by technological advancements, globalization, and workforce diversity. The findings emphasize the critical role of communication, talent management, and organizational culture in shaping effective management practices. This study enhances understanding of management in dynamic contexts, offering practical implications for both industry and academia. Future research should focus on industry-specific applications, theoretical exploration, and methodological enhancements to further advance management practices. Despite limitations associated with secondary data, this study provides valuable insights into strategies that enhance management performance, ultimately enhancing organizational resilience and competitiveness in a constantly changing environment

Evaluation of Explainable Artificial Intelligence using TOPSIS Method

Explainable Artificial Intelligence (XAI) refers to the development of AI systems are transparent, explainable and their comprehensible for results can provide explanations or predictions. As AI technologies, particularly machine learning models, become more complex and sophisticated, there is a growing need to ensure that their decisions can be comprehended and trusted by humans, especially health, Finance and such as criminal justice in important domains. Evaluating Explainable Artificial Intelligence (XAI) is essential to ensure transparency, accountability, and user trust in AI systems. Interpretability is a key factor, examining how easily the model's internal mechanisms can be understood. Model transparency, feature importance, and the clarity of visualizations contribute to this aspect. Differentiate between post-hoc and intrinsic explanations, considering whether the model inherently provides interpretable insights. The distinction between local and global explanations is crucial, as it determines whether explanations focus on individual predictions or the overall model behavior. Robustness and consistency are assessed through stability and sensitivity analysis, ensuring that explanations remain reliable across similar instances. Additionally, ethical considerations, such as fairness and transparency in decision-making, must be addressed to uncover and mitigate biases. User feedback and the relevance of explanations to the specific use case contribute to a comprehensive evaluation, fostering the development of XAI systems that are not only technically robust but also ethically sound and user-friendly. The significance of research in Explainable Artificial Intelligence (XAI) lies in addressing critical challenges associated with the adoption and deployment of AI systems in various domains. As AI technologies, particularly complex machine learning models, become integral to decision-making processes in areas such as healthcare, finance, and criminal justice, the need for transparency and interpretability becomes paramount. Topsis involves optimizing from an advantageous standpoint by simultaneously minimizing the distance to and maximizing the distance from a reference point, which is defined in relation to solutions within a set of alternative options and numerous identification criteria. The importance of Topsis criteria lies in the potential to integrate comparative weights. This study conducts a comprehensive review of Topsis, exploring various weighing schemes and employing different distance measurements. Numerous applications of Topsis are examined, particularly its utilization in comparing results for a diverse set of multiple criteria data with varying weights. Interpretable Machine Learning Models, Human-Centric Design in XAI, Ethical Implications of XAI, Industry-specific Applications of XAI and Hybrid Approaches for Model Interpretability. Interpretability Metrics, Human-Subjective Evaluation, Algorithmic Robustness and Real-world Impact. the Ranking of Evaluation Explainable Artificial Intelligence. Industry-specific Applications of XAI is got the first rank whereas is the Ethical Implications of XAI is having the Lowest rank.

Latest Innovations in Internet of Things (IoT): Digital Transformation Across Industries

This research article explores recent advances in IoT technology and its huge impact on various sectors. This research method uses a qualitative research approach involving in-depth interviews and thematic analysis. This research explores key IoT innovations such as wearable devices, smart medical devices, predictive maintenance systems, and IoT-based transportation solutions. The research findings highlight how these innovations are driving digital transformation across industries, leading to improved healthcare delivery, increased manufacturing efficiency, optimized transportation operations, sustainable agricultural practices, and personalized retail experiences. This study also aligns these empirical findings with theoretical frameworks regarding IoT, digital transformation, and industry-specific applications, emphasizing the strategic importance of leveraging IoT as a catalyst for innovation, competitiveness, and value creation. This research provides valuable insights for businesses, policymakers and researchers looking to leverage IoT technology to drive digital transformation and achieve sustainable growth across industries.

THE INTERSECTION OF GEOLOGY AND BUSINESS SUSTAINABILITY: A DATA-DRIVEN REVIEW OF U.S. CORPORATE ENVIRONMENTAL STRATEGIES

In the contemporary business landscape, the intersection of geology and corporate sustainability emerges as a critical area of exploration, particularly within the U.S. context. This study delves into this intersection, aiming to elucidate how geological insights are integrated into corporate environmental strategies and the ensuing implications for sustainable business practices. The background of the study is rooted in the growing emphasis on environmental stewardship in business operations, where geological considerations are becoming increasingly significant. The paper's aim is to dissect the convergence of geology and corporate sustainability, assessing the influence of geological data on corporate environmental strategies. The scope encompasses a qualitative thematic analysis of recent literature, focusing on current trends, regional variations and industry-specific applications of geological insights. It also explores the economic and environmental impacts of integrating geology into business decisions, highlighting its role in risk management and sustainable innovation. The study concludes that geological data is a strategic asset in business, driving innovation, shaping risk management strategies and influencing long-term economic and environmental outcomes. It recommends that businesses embrace geological data as a core component of their sustainability strategies and policymakers create conducive environments for this integration. Future research is encouraged to further explore this dynamic field. The study provides valuable insights for businesses, policymakers and researchers, underscoring the necessity of integrating geological insights into corporate strategies for sustainable development.
 Keywords: Geological Data, Corporate Sustainability, Environmental Strategies, Business Innovation, Risk Management, Sustainable Development.

REVIEW ON THE EVOLUTION AND IMPACT OF IOT-DRIVEN PREDICTIVE MAINTENANCE: ASSESSING ADVANCEMENTS, THEIR ROLE IN ENHANCING SYSTEM LONGEVITY, AND SUSTAINABLE OPERATIONS IN BOTH MECHANICAL AND ELECTRICAL REALMS

This study provides a comprehensive review of the evolution and impact of Internet of Things (IoT)-driven predictive maintenance, focusing on advancements in technology, their role in enhancing system longevity, and promoting sustainable operations in mechanical and electrical systems. The primary objective was to assess how IoT integration has transformed traditional maintenance approaches, leading to improved system durability and reliability. Utilizing a systematic literature review methodology, the study involved sourcing data from peer-reviewed journals, conference proceedings, and industry reports. A content analysis approach was employed to analyze the data, focusing on themes such as technological advancements, sustainability considerations, and industry-specific applications of IoT in predictive maintenance. Key findings reveal significant advancements in IoT applications, particularly the integration of advanced data analytics, artificial intelligence, and machine learning in predictive maintenance strategies. These advancements have led to more accurate and timely maintenance interventions, contributing to enhanced system longevity and operational efficiency. The study also highlights the emergence of green IoT practices and the challenges and opportunities in the future landscape of IoT in predictive maintenance. The study concludes that IoT-driven predictive maintenance is pivotal for sustainable industrial operations, with opportunities lying in addressing challenges through innovative solutions and robust regulatory frameworks. Recommendations for industry and policy include fostering sustainable IoT practices and prioritizing energy efficiency. Future research directions involve exploring the integration of IoT with emerging technologies and investigating the long-term environmental impacts of IoT deployments.
 Keywords: Predictive Maintenance, System Longevity, Sustainable Operations, Internet of Things.

Advances in modified silica nanoparticles utilization for various applications: Now and future

The review article explores the multifaceted applications of silica nanoparticles (SNPs) across diverse industries, emphasizing their catalytic role in transformative advancements. Green nanotechnology principles are crucial for sustainable SNP synthesis, with a focus on utilizing natural extracts and bio-agents. Standardization and enhanced collaboration between industry and academia are pivotal for realizing the broader potential of SNPs. In the biomedical realm, SNPs exhibit exceptional capabilities in drug delivery and diagnostics, promising significant medical advancements. Safe integration necessitates collaborative efforts in safety assessments, long-term studies, and standardized testing. The exploration of SNP-based advanced coatings hints at industry-specific applications, with a recommendation for continued research into new capabilities and compatibility. SNPs in Li-ion batteries show promise for energy storage, urging further investigation into scalability and long-term performance. Agriculture benefits from SNP applications in precision farming, emphasizing the need for environmentally conscious formulations. In nanocomposite materials, SNPs enhance mechanical properties, advocating collaborative research for standardization and optimization. The adaptability of SNP-based smart coatings in aerospace and automotive industries requires exploration of new functionalities and seamless integration. In conclusion, SNPs hold promising prospects in healthcare, energy storage, and agriculture, emphasizing the necessity of collaborative efforts, sustained research, and a commitment to responsible and innovative SNP integration for a technologically advanced and environmentally conscious future.

The state‐of‐art review of ultra‐precision machining using text mining: Identification of main themes and recommendations for the future direction

AbstractUltra‐precision machining (UPM), one of the most advanced machining techniques that can produce exact components, significantly impacts the technological community. The significance of UPM attracts the attention of academic and industrial partners. As a result of the rapid development of UPM caused by technological advancement, it is necessary to revisit the current stages and evolution of UPM to sustain and advance this technology. The state of the art in UPM is first investigated systematically in this study by identifying the current four major UPM themes. The UPM thematic network is then built, along with a structural analysis of the network, to determine the interactions between each theme and the primary roles of theme members responsible for the interactions. Furthermore, the “bridge” role is assigned to the specific UPM theme content. On the other hand, Sentiment analysis is conducted to determine how the academic community at UPM feels about the themes for UPM research to focus on those themes with a need for more confidence. Considering the above findings, the future perspective of UPM and suggestions for its advancement are discussed and provided. This study provides a comprehensive understanding and the current state‐of‐the‐art review of UPM technology by a text mining technique to critically analyze its research content, as well as suggestions to enhance UPM development by focusing on its current challenges, thereby assisting academia and institutions in leveraging this technology to benefit society.This article is categorized under: Algorithmic Development > Text Mining Application Areas > Science and Technology Application Areas > Industry Specific Applications

Super-resolution in thin section of lacustrine shale reservoirs and its application in mineral and pore segmentation

Lacustrine shale reservoirs present intricate attributes such as the prevalence of lamination, rapid sedimentary phase transitions, and pronounced heterogeneity. These factors introduce substantial challenges in analyzing and comprehending reservoir characteristics. Thin-section imaging offers a direct medium to observe these traits, yet the intrinsic compromise between image resolution and field of view impedes the concurrent capture of comprehensive details and contextual overview. This study delves into the application of super-resolution (SR) techniques to augment the segmentation of thin-section images from lacustrine shale, an unconventional reservoir. SR application furnishes high-resolution images, facilitating a robust analysis of morphology, texture, edge properties, and target classification. Utilizing data from the lacustrine shale reservoir of the Ordos Basin, we evaluate our methodology and assess the impact of SR enhancement on segmentation. Quantitative results indicate significant improvements, with the Jaccard index for shale increasing from 0.4790 (Low-Resolution) to 0.7803 (ESRGAN) in the Y channel of the YCrCb color space after level set segmentation, exemplifying the efficacy of SR in shale gas and oil reservoirs. This research underscores the necessity to consider lacustrine shale's unique features while formulating and implementing SR techniques for improved information extraction. Furthermore, it highlights SR's potential for propelling future research and industry-specific applications.

ABCD Analysis of Industries Using High-Performance Computing

Purpose: High-Performance Computing (HPC) is the technique of increasing the overall processing capacity of the system, and achieving high performance in solving large problems in science, engineering, or business. This research paper investigated the High-Performance Computing industry using exploratory research methodology and a listing of ABCD analysis is done. Along with this, the identification of various applications and grand challenges in HPC and various Software Tools required for industry-specific application areas of HPC is done. Design/Methodology/Approach: Qualitative exploratory research methodology to perform Industry Analysis of High-Performance Computing using ABCD Analysis. This research paper is written with the literature review of High-Performance Computing (HPC) industries and various software tools available to use in those industries. Findings/Results: Finding of this research paper is the identification of various grand challenges in HPC industries like healthcare and medical field, engineering and design, aerospace sector, urban planning, and infrastructure, finance, and business. It also lists different HPC software tools that can be used to solve these challenges, along with the findings of ABCD analysis of HPC industry. Originality/Value: Listing of ABCD Analysis (Advantages, Benefits, Constraints, and Disadvantages) of High-Performance Computing industry. Paper Type: Industry Analysis research paper

Insider Trading in the Clinical Trial Setting

The Securities and Exchange Commission’s enforcement agenda regularly includes charges of trading based on material nonpublic information about a clinical drug trial conducted to obtain FDA approval to market a new drug. Almost half of the recent cases have been accompanied by a criminal indictment. In an academic setting, researchers and those who advise them, unlike employees of public companies, are not generally given training about the risks of securities trading. Recent developments in the law might be applied in connection with clinical trial information in ways that would not have been foreseen until recently. It is time for a comprehensive analysis of how the law of insider trading may be applied to the important endeavor of clinical drug trials. Clinical trials should not be sullied by conflicts created by unlawful trading. This Article begins with the basic principles of insider trading, followed by a summary of how clinical trials are conducted and regulated. It then turns to the ways in which nonpublic information material to the stock price of the sponsor of the trial might be used to trade or tip. The analysis that follows identifies a number of situations where use or disclosure of material nonpublic information about a trial could be unlawful. Employees of sponsors of the trial, clinical investigators, and even the subjectsparticipating in a trial could run afoul of the prohibition on insider trading, such as when information has been used in breach of a confidentiality agreement or exchanged between an investigator and a trial participant, exchanged among trial participants, gleaned from attending medical conferences, and obtained in other settings specific to this industry. Presenting the full potential reach of the law facilitates taking steps to avoid violating the law. The analysis demonstrates that anyone involved in a clinical drug trial must be attuned to the risks of misusing material nonpublic information, including in scenarios not yet pursued by the SEC. By understanding the potential reach of the law, those involved in or who learn information about a specific trial can take steps to avoid or minimize the risk of liability. The Article concludes with recommendations to achieve this end. The Article thus presents a complete case study of an industry-specific application of insider trading law, with recommendations that can be applied to other industries.

Exploring the recycling potential of HDPE films reinforced with flax fiber for making sustainable decorative tiles

The goal of the research was to assess the recycling potential of waste high-density polyethylene (HDPE) films taken as reinforcement with natural fibers to fabricate decorative tiles with improved mechanical properties. Initially, the density of the composite was determined. Further, hardness, quasi-mechanical and impact properties were evaluated for the HDPE/natural fiber composite samples by testing the composites tensile, flexural loads and impact. In addition, to evaluate the thermal characteristics of the composite, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) study were performed. Water absorption and sound absorption properties were determined for industry specific applications. Scanning Electron Microscopic analysis (SEM) was performed for analyzing the microstructure and also HDPE matrix and natural fibers bonding exists in the composite. The findings confirmed that adding natural fibers to the HDPE matrix impressively increased both tensile and flexural strengths by up to 25%. The impact strength was also enhanced by up to 38%, whereas the hardness and density values remained relatively unchanged. Improved interfacial bonding between the HDPE matrix and natural fibers had been identified by SEM analysis, which was a factor in the composite material's enhanced mechanical characteristics. These findings suggest that waste HDPE films/natural fibers composite can be effectively recycled to fabricate decorative tiles with improved mechanical properties, thereby offering a sustainable and eco-friendly alternative to traditional decorative tile materials.

How can quantum technologies be applied in healthcare, medicine and the life sciences?

Quantum technologies, including computing, communication/security and sensing, have significantly advanced over the last years. Industry-specific applications are now being intensely researched and healthcare, medicine and the life sciences represent one of the focus areas.

Wastes to profit: a circular economy approach to value-addition in livestock industries

The livestock sector is a fundamental part of the modern global economy and provides food, clothing, furnishings, and various other products. So as to ensure its resilience to changes in consumer expectations, cost of production, and environmental sustainability, the sector must shift to a circular economy model. Current strategies to recover value from wastes and low-value co-products from livestock industries yield limited value; hence, new technologies are required to upgrade wastes and co-products, and generate high-value products that can feed into the livestock value chain. Anaerobic digestion can convert high organic-content waste to biogas for energy and a stable nutrient-rich digestate that can be used as fertiliser. Microbial technologies can transform wastes to produce nutritionally advanced feeds. New materials from waste can also be produced for livestock industry-specific applications. While aiming to add commercial value, the successful implementation of these technologies will also address the environmental and productivity issues that are increasingly valued by producers and consumers.

Converter based distributed drive system with enhanced dynamic response

In this paper optimized converter based motor drive system is presented with enhanced dynamic response. A detailed design including source, storage and motor drive is proposed for various operating modes. The controlled distribution system is experimented to meet a guideline of the industry specific applications. Furthermore, compensation is provided from the power regenerated inside the drive. The storage with cascaded converters can supply the additional energy requirements when the drive is subjected to transients. The optimized controller is designed to control the individual phase voltage, speed and the firing angle of cascaded and lead converter. A voltage balancing control with isolated power regenerative control system is established to achieve the smooth power flow in the designed drive system. The experimental output voltage, Power flow and speed control are presented to explain the systems dynamic performance.

Internet of Things and data analytics: A current review

AbstractWith the advent of Internet and computing, we entered into an era with more people exchanging information over the Internet using devices like desktops, laptops, tablets, mobile phones, and similar data transmitting and receiving gadgets. This was a host centric communication approach. Internet of Things (IoT) is the next stage of technological advancement in computation, networking and communication with physical objects around the world getting connected to the network and exchanging data. This is an information centric approach. Thus it can be defined as an expanding physical network of dynamically increasing physical objects. The objects share information derived from their environments, reliably, and securely over the communication medium leveraging multiple protocols. The protocols involved are getting standardized to address the compatibility and interoperability issues. Each object connected is uniquely identified and controlled in the network. IoT finds its applications in many fields as environment monitoring, logistics, health care, automobile, controlled industrial environment, smart cities, and many more. As the devices, their cardinality and alignment, the data type, the data rate and specifics of the domain of the IoT applications vary; there is also a need to define architectures that incorporate the devices, communication mediums, storage, and analysis capabilities and the consumers of the derived output. Advancements in data analytics, machine learning, and digital technologies offer possibilities of derive meaningful intelligence for actionable output and in creating useful and context aware applications.This article is categorized under: Algorithmic Development > Web Mining Application Areas > Internet Application Areas > Data Mining Software Tools Application Areas > Industry Specific Applications

Behavioral fingerprinting of Internet‐of‐Things devices

AbstractRapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain.This article is categorized under: Application Areas > Science and Technology Application Areas > Internet Technologies > Machine Learning Application Areas > Industry Specific Applications

A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers

The constant need to assess loans makes risk evaluation a very important problem for the banking sector. A crucial function of the banks is to fund households and companies from various industries in the economy. Risk is taken by the banks as soon as a loan is given to an entity. Currently, there are sector‐and‐experience based methods of analysis employed by the banks to estimate the risks to be taken. For the credit process, there exist a large number of studies in the literature on scoring individual clients but there are very few studies on scoring small and medium enterprises (SME) commercial corporate customers. In this study, we propose an objective risk measurement method for the lending process of SME commercial corporate customers and performed classification task of data mining by collecting current customer data on credit evaluation process of a bank. For this purpose, we first create a risk measure by looking into the risks identified for existing customers by the analysts of a bank. These scores are used as target variable in the classification process. Then, we extract rules for estimating these scores using Weka software. We used six different algorithms, and compared results in terms of test accuracy, the number of rules, recall, precision and Kappa statistic. We obtained high accuracy rates on real life data by our approach. As a result, we showed that an objective evaluation strategy is possible to use in the lending process for SME commercial corporate customers in the banking system using data mining.This article is categorized under: Application Areas > Business and Industry Technologies > Classification Application Areas > Industry Specific Applications