Analytical Applications Research Articles

Evaluation of survival weighted Pareto distribution: Analytical properties and applications to industrial and aeronautics data

In statistical modeling, lifetime datasets play an important and significant role. Weighted distributions can be useful to attain the essential purposes relating to the developed distributions’ flexibility and to improve further compliances in data forming; they provide the extension of distributions. The current study suggests a new best-fit probability distribution model based on Pareto distribution. The new model is called Survival Weighted Pareto (SWP) distribution model. Furthermore, the statistical expressions for different properties, which include moments, inverse moments, quantile function, and order statistics, are reported. Moreover, to check the performance and the behavior of different estimators using different sample sizes, a simulation study, which is based on SWP distribution, is performed. According to the simulation results, it is established that the proposed model is a most useful fitted probability model. In addition, an application of the SWP model for three datasets is provided to check the goodness of fit measures of the model.

Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics

The rapid growth of graph data in various domains has propelled the need for efficient distributed graph processing techniques in cloud computing environments. This paper presents a comprehensive review of distributed graph processing for graph analytics of massive size in the context of cloud computing. The paper begins by highlighting the challenges associated with distributed graph processing, including load balancing, communication overhead, scalability, and partitioning strategies. It provides an overview of existing frameworks and tools specifically designed for distributed graph processing in cloud environments. Furthermore, the review encompasses various techniques and algorithms employed in distributed graph processing. The paper also reviews recent research advancements in optimizing distributed graph processing in cloud computing. To provide practical insights, the paper presents a comparative analysis of representative large-scale graph analytics applications implemented on different cloud computing platforms. Performance, scalability, and efficiency metrics are evaluated under varying workload sizes and graph characteristics. Overall, this comprehensive review paper serves as a highly prized asset for researchers and large-scale graph analytics professionals who are practitioners in the field. It provides a holistic understanding of the state-of-the-art distributed graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud environments.

Mysore sentinel-2: deep learning for image classification with optimizer exploration

The classification of Sentinel-2 image is presented in this work using a tile based methodology. The Mysore district of India's Karnataka state serves as the subject region of this research. By tiling Sentinel-2 images, we were able to construct a distinct dataset and get approximately 3,000 training samples for the five classes. These images are manually labelled and geo-referenced. Three different optimizers were employed in a thorough analysis with deep learning models such as ResNet50, MobileNetV2, ShuffleNet, and VGG16 to achieve better performance metrics. With a classification accuracy of 98.1% on RESNet50 using ADAM surpassed the others. This facilitates investigating various geographical data analytics applications of the study region.

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.

An Application of TRIZ Inventive Approach in Development Strategy into Fourth-party Logistics

Purpose: The study aims to affirm the application of TRIZ in logistics service and then develop innovative strategies for Vietnamese local logistics service providers (LSPs) to transform into fourth-party logistics (4PL) through TRIZ inventive methodology. Methodology/Approach: The integration of Savransky’s methodology into TRIZ proposals enables to identify the core correlated dependencies of selected parameters. The analyses of the theoretical background and the application of the TRIZ matrix are comprehensively conducted to create innovation for three important attributes of 4PL. Findings: The research results propose seven strategies through Savransky’s suggested steps from the analytical application of TRIZ. The established recommendations are categorised into short-term and long-term strategies and can be flexibly applied depending on the corporate growth process. Research Limitation/implication: TRIZ approach was initially constructed for solving engineering problems. As a result, several inventive principles in the contradiction matrix were not entirely appropriate to the service area. In such cases, the authors can’t apply all suggested solutions from the contradiction matrix. Originality/Value of paper: The study provides local LSPs in Vietnam with comprehensive strategies for improving their capabilities to meet the requirements of 4PLs’ power.

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.

Theoretical perspectives on predictive analytics in it service management: Enhancing service quality

This paper explores the theoretical perspectives underpinning the application of predictive analytics in IT service management (ITSM) to enhance service quality. It begins with an introduction to predictive analytics in the context of ITSM and the significance of improving service quality in IT operations. Theoretical frameworks such as Systems Theory, Information Theory, Decision Theory, and Machine Learning Theory are discussed to provide a comprehensive understanding of the underlying principles guiding predictive analytics in ITSM. The paper examines the practical applications, challenges, and benefits of predictive analytics in ITSM, emphasizing its role in anticipatory problem resolution, proactive service improvements, and predictive maintenance. Case studies and examples of successful implementations are presented to illustrate real-world applications and best practices. Additionally, future directions and emerging trends in predictive analytics technology are explored, along with their potential impact on ITSM practices and ethical considerations. Overall, this paper contributes to the theoretical foundation and practical insights for leveraging predictive analytics to enhance service quality in ITSM.

A review: Exploratory analysis of recent advancement in green analytical chemistry application

It is always a concern to ensure personnel and environment safety in the field of chemistry which has caused to development of green analytical chemistry methods. Green chemistry aims to create an eco-friendly environment in laboratories by using various analytical methods/strategies to reduce the use of toxic solvents which are harmful to humans and the environment. It is a way that protect the environment by using green solvents and methods. Green analytical chemistry is a rapid analytical technique that describes the separation, identification, and quantification of an analyte in drugs, environments, and humans. Various green methodologies such as automation, miniaturization, precipitations, and passivation are utilized in the recovery of solvents and reagents. Green analytical chemistry aims to create an eco-friendly environment in the laboratories by using various analytical methods/strategies to reduce the use of toxic solvents which are harmful to the environment/humans as well as to decrease the amount of waste generated. In this review, we explore different green solvents that can replace other toxic solvents used during extraction processes. In this review, the various extraction methods and analytical techniques used to analyze different components have been discussed.

Real-time surveillance of zoonotic and pig pathogens using surface-enhanced Raman scattering system

The shift towards large-scale intensive pig farming methods has led to an increase in cases of complex, mixed infections, and secondary infections. Early diagnosis and disease prevention have become crucial for effective pig farm management. However, clinical diagnosis may be complicated by antibiotic treatment and atypical disease symptoms. To ensure accurate pathology diagnosis, it is essential to integrate robust laboratory diagnostics with traditional methods. Surface-enhanced Raman scattering (SERS) spectroscopy has emerged as a potentially powerful technique for whole-organism fingerprinting, enabling rapid identification of bacteria. Biosensors utilizing SERS offer promising capabilities for sensitive and quick detection of bacterial pathogens, thus reducing diagnosis time. In this study, we aimed to characterize and evaluate a SERS-based diagnostic system for detecting and identifying bacteria in specific pathogen free (SPF) mice, focusing on two bacterial zoonoses and swine bacteria present in pooled swine sera, feces, and meat. We compared the spectra of bacteria recovered from the specimens to those of pure cultured bacteria and conducted principal component analysis to determine the bacterial molecular fingerprint. Our results demonstrated successful detection, identification, and classification of these bacteria in mice specimens (sera and feces) and swine specimens (sera, feces, and meat) using SERS. SERS provided reproducible molecular spectroscopic signatures suitable for analytical applications. This approach presents a new and potent tool for real-time surveillance of animal bacterial pathogens in clinical settings.

Moral Engagement and Disengagement in Health Care AI Development

Background Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms. However, little is known regarding the perspectives of developers themselves regarding their obligations to mitigate harms. Methods We conducted 40 semi-structured interviews with developers of ML predictive analytics applications for health care in the United States. Results Participants varied widely in their perspectives on personal responsibility and included examples of both moral engagement and disengagement, albeit in a variety of forms. While most (70%) of participants made a statement indicative of moral engagement, most of these statements reflected an awareness of moral issues, while only a subset of these included additional elements of engagement such as recognizing responsibility, alignment with personal values, addressing conflicts of interests, and opportunities for action. Further, we identified eight distinct categories of moral disengagement reflecting efforts to minimize potential harms or deflect personal responsibility for preventing or mitigating harms. Conclusions These findings suggest possible facilitators and barriers to the development of ethical ML that could act by encouraging moral engagement or discouraging moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them.

The Impact of Data Analytics and Mining in Transforming the Decision-Making System

In an increasingly data-driven world, effective decision-making hinges on the ability to use the power of information and technology supporting it. This research paper explores the transformative role of data analytics and mining in shaping the once upon a time decision support systems through a profound understanding and analysis of the existing research. This paper highlights the significance of leveraging data analytics and mining techniques to extract valuable insights from vast datasets. By uncovering hidden patterns and trends within the data, businesses can make informed and strategic choices that drive success and outperform their competitors. The practical applications of data analytics and mining are vast, including domains such as customer relationship management, fraud detection, and market analysis. The paper discusses the various ways in which these techniques enable organizations to obtain a competitive edge in the market by unlocking valuable data-driven insights. Real-world case studies are used to illustrate the tangible benefits of this approach. Overall, the research paper showcases the critical role of data analytics and mining in encouraging a data-driven decision-making culture across the business world. By equipping businesses with the analytical tools to unveil the true potential of their data, this approach paves the way for improved performance, sustained success and can guide organizations towards achieving their long-term objectives. Keywords: Data Analytics, Data Mining, Decision Making, Data, Technology, Information.

Study on thermal radius and capacity of multiple deep borehole heat exchangers: Analytical solution, algorithm and application based on Response Factor Matrix method (RFM)

The deep borehole heat exchanger systems (DBHE) are offering potential low carbon heating for buildings. However, the study of DBHE is hindered by both modeling and system complexity. Although some analytical models of DBHE are presented in literature, the involved expression of complex integral calculation on the contrary slows down the computation speed and many calculations are redundant in algorithm. In this study, a Response Factor Matrix method (RFM) is proposed based on simple analytical expression and well-designed data structure for DBHE. The RFM can be prefabricated and then used in each time step updates of temperature field. A superposition algorithm is designed for fast computation of DBHE array. The proposed model and algorithms are verified by measurements from two independent engineering projects. Based on the new tool, both thermal radius and heating capacity of DBHE array are investigated in details. The step-ascending pattern of thermal radius is recognized. The correlation of borehole spacing and system heating capacity is identified. In the end, the computational efficiency of the new tool is justified and some outlooks are proposed for a further development and applications.

An integrated method for rapid extraction of fluorescent whitening agents from selected cosmetics by in situ formation of deep eutectic solvent

A novel extraction methodology was developed based on deep eutectic solvent (DES) in situ formation. The solvent was synthesized from a binary mixture of L-cysteine, functioning as a hydrogen bond acceptor, and 2-methylphenol, serving as a hydrogen bond donor. The DES’s inherent molecular structures and extensive hydrogen bonding networks were characterized using the Fourier-transform infrared spectroscopy (FT-IR), while its hydrophobic nature was assessed by determining molecular polarity. Its extraction efficiency was demonstrated through the extraction of fluorescent whitening agents (FWAs) in cosmetic products, acting as a model analytical application. This was accomplished using vortex-assisted emulsification solid–liquid extraction (SLE) method, followed by high-performance liquid chromatography-fluorescence detection. Response surface methodology was employed to optimize extraction conditions in a multivariate experimental design. This approach streamlined the multi-operation extraction process via ingeniously integrating extraction and clean-up into a single step, resulting in significant reductions in extraction time required and high sample throughputs of approximately 40 samples per hour. The methodology was validated and exhibited excellent linearity in concentration ranges of 0.01–24 mg/L for five FWAs and 0.01–40 mg/L for other three FWAs, with correlation coefficients (r) ranging from 0.9985 to 1.000. The limits of detection and quantification were in the range of 0.03–0.26 mg kg−1 and 0.10–0.87 mg kg−1, respectively. The reliability and practicality of the proposed method were affirmed by quantifying eight FWAs in ninety-four commercially available facial masks and whitening creams, yielding recoveries values between 90.0 % and 106.0 % with relative standard deviations (RSDs) of 1.0–4.3 %. Inorganic ions and organic compounds present in the samples had minimal impact on FWAs detection following sample pre-treatment. This procedure showcased exceptional extraction capabilities towards a diverse array of FWAs with the aid of multiple interactions, suggesting significant potential for increasing sample throughput while reducing organic solvent and material consumptions compared with conventional SLE methods. The low-toxicity DES is an excellent alternative to traditional extractants typically applied to SLE techniques.

Research on the application of machine learning in business analytics: Cases of Amazon and eBay

With the rapid development of the Internet and the rise of e-commerce, commercial enterprises are faced with a large amount of data and a complex market environment. In this situation, machine learning, as a powerful tool, is widely used in the field of business analysis. In this dissertation, we take Amazon and eBay as examples to study the application of machine learning in the company's business analytics, focusing on its role in market prediction, customer behavior analysis and operation optimization. By analyzing the relevant cases, we find that machine learning technology plays an important role in helping companies make more accurate decisions and improve efficiency. Studying the application of Amazon machine learning in business analytics can promote in-depth research on the application of machine learning in business in academia, and promote the application and development of machine learning technology in other business scenarios. Overall, the application of machine learning in business analytics can help companies understand customer behavior, optimize operations, and improve sales results. However, there are still some challenges, such as data quality, algorithm selection and privacy protection. Therefore, further research and innovation are necessary to advance the development of machine learning applications in business analytics.

Efficiency Unleashed: Harnessing AI for Agile Project Management

This research paper explores the application of predictive analytics for real-time monitoring and feedback in project management. Through case studies of leading organizations such as Amazon, Uber, and NASA, the paper examines how predictive analytics enables organizations to optimize operations, improve efficiency, and achieve business objectives. The paper discusses the implementation of predictive analytics in various industries, including e-commerce, transportation, and space exploration, highlighting the benefits of real-time monitoring and feedback for enhancing decision-making, reducing costs, and improving customer satisfaction. By leveraging predictive analytics, organizations can anticipate trends, identify issues early, and make data-driven decisions to drive continuous improvement and innovation in project management practices.

HARNESSING BUSINESS ANALYTICS FOR GAINING COMPETITIVE ADVANTAGE IN EMERGING MARKETS: A SYSTEMATIC REVIEW OF APPROACHES AND OUTCOMES

This study systematically reviews the impact of business analytics on achieving competitive advantage in emerging markets, focusing on the integration of advanced analytical tools and strategies within organizational processes. Employing a systematic literature review and content analysis methodology, this research scrutinizes peer-reviewed articles, conference papers, and grey literature from 2018 to 2023, sourced from databases such as Web of Science, Scopus, and IEEE Xplore. The inclusion criteria targeted studies that explore the application, outcomes, and strategic implications of business analytics, while exclusion criteria filtered out non-peer-reviewed sources, non-English literature, and studies outside the scope of emerging markets or published before 2018. Key findings reveal that business analytics significantly enhances strategic decision-making, operational efficiency, and innovation, providing firms in emerging markets with a robust foundation for sustaining competitive advantage. The study identifies the critical role of data-driven insights in transforming organizational strategies and operations. However, challenges such as data privacy, security, and the digital skills gap pose potential barriers to the full realization of business analytics benefits. The study concludes with strategic recommendations for practitioners and policymakers to foster a culture of data literacy and develop supportive frameworks for data privacy and security. Future research directions are proposed to bridge existing gaps in literature, particularly focusing on the long-term impacts of business analytics, ethical considerations, and the integration of emerging technologies. This research contributes to the understanding of how business analytics can be leveraged to navigate the complexities of emerging markets, offering a roadmap for organizations seeking to harness data for competitive advantage.
 Keywords: Business Analytics, Competitive Advantage, Emerging Markets, Data-Driven Decision-Making.

The modern alchemy of clinical pathology: turning the output of microbiology laboratory operations into gold.

The clinical microbiology laboratory generates a huge amount of high-quality data that play a vital role in clinical care. With proper extraction, cleaning, analysis, and validation pipelines, these data can serve multiple other purposes that include supporting laboratory operations, understanding local epidemiology, informing hospital-specific policies, and public health surveillance. In this review, I use one of the core activities of the microbiology laboratory, antimicrobial susceptibility testing (AST), to illustrate several potential applications of next-generation data analytics. The first involves continuous monitoring of commercial AST systems using comparisons of minimum inhibitory concentration (MIC) distributions over time to trigger re-verification when statistically significant differences are detected. An extension of this is temporal analysis of joint MIC distributions to understand performance for multidrug-resistant organisms. More sophisticated analyses involve linking microbiologic data to clinical metadata to gain insight into the clinical validity of AST data and to inform treatment policies. The elements of a robust, validated analysis engine using routine data streams already exist, but numerous challenges must be overcome to make it a reality. Most importantly, it will require the sustained collaboration and advocacy of hospital leadership, microbiologists, clinicians, antimicrobial stewardship, data scientists, and regulatory agencies. Though no small feat, achieving this vision would provide an important resource for microbiology laboratories facing a rapidly evolving practice landscape and further cement its role as an integral part of a learning health system.

Designing an Educational Metaverse: A Case Study of NTUniverse

An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. In this work, a virtual environment is created that duplicates real-world situations and enables immersive and interactive learning in the educational metaverse. For this purpose, we built a digital twin of the Nanyang Technological University (NTU) campus as a foundation, called NTUniverse. It is designed as an educational metaverse in which various academic and analytical applications are digitized as 3D content embedded within this virtual campus. The approach to digitally twinning educational systems and embedding them within virtual campuses enables remote and collaborative learning as well as professional technical skills training. It also makes feasible the analysis of abstract concepts, complicated structures, dynamic processes, and sensitive industrial procedures virtually, which is otherwise challenging if not impossible to perform in the real world. The work offers important insights into the behaviors and interactions of systems in the metaverse by evaluating design choices and user interests. NTUniverse is an attempt to explore a novel approach that addresses remote education and training challenges. Three efforts with NTUniverse will be discussed in this work, including (1) digitalization of the NTU campus; (2) campus train modelling and simulation; and (3) science, technology, engineering and mathematics education.

Applications of Learning Analytics in Latin America

Learning analytics (LA) is a key research domain within educational technology. This field harnesses data analysis, unlocking tangible advantages for educators and learners. Despite extensive research, geographical disparities in adoption remain, with certain regions still in developing stages. Within this scenario, Latin America has been adopting LA to address specific educational challenges, including reducing quality disparities and dropout rates. However, the region confronts limitations for adoption, such as infrastructure, a lack of widespread data literacy, and regulatory constraints, all of which impede broader implementation. In this context, this special issue, titled “Applications of Learning Analytics in Latin America,” intends to present a comprehensive perspective on LA’s deployment across the continent, highlighting its current state and potential growth. In this editorial, we curate a selection of relevant papers, each delving into diverse facets of LA’s application within Latin American contexts. These papers exemplify practical implementations and discuss the implications and possible future trajectories for LA research, especially tailored for the Latin American academic community.

Advanced herbal technology for the development of herbal drug molecule-A review

This review article explores the interface between conventional herbal knowledge and contemporary scientific discoveries as it digs into the most cutting-edge advancements in herbal technology. Despite the fact that over 80% of the world’s population relies on herbal products and medicine for a healthy lifestyle, people are only recently becoming interested in herbal medicine We examine the advancements in herbal technology. Despite the fact that over 80% of the world’s population relies on the herbal products and medicine for a healthy lifestyle, people are only recently becoming interested in herbal medicine we examine the advancements made in boosting the effectiveness, bioavailability, and sustainability of herbal products, from novel extraction methods to the incorporation of nanotechnology in herbal formulations. Additionally, the application of data analytics and artificial intelligence to the development of customized herbal therapies is looked at this review attempts to provide a detailed overview of the developing environment of herbal technology by combining data from several disciplines, illuminating its potential to transform healthcare and contribute to a more sustained future. Due to the numerous benefits herbal formulations have as therapeutic agents for various diseases. The qualitative analysis of a marker or bioactive compound and other major constituent is a major challenge to scientists. Standardization is an important step to for the establishment of a consistent biological activity a result of the increase in the use of herbal products, which has also given rise to various forms of abuse and adulteration of the product leading to the consumer and manufacture this disappointment and in some instant fatal consequence., , .