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data analytics for Catalysis Predictions: Are We Ready Yet?

ACS CATALYSIS 2024年第10期14卷 8073-8086页

作者： Zhang, Difan Smith, Brett Wu, Haiyi Nguyen, Manh-Thuong Rousseau, Roger Glezakou, Vassiliki-Alexandra Pacific Northwest Natl Lab Phys Sci Div Richland WA 99352 USA Oak Ridge Natl Lab Chem Sci Div Oak Ridge TN 37830 USA

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C-X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C-X cleavage reactions that are common in production, upcycling, or reactive separation: the C-C cleavage in cyclopropyl alcohol, the C-H cleavage in hydroacylation reactions, the C-O cleavage in beta-O-4 linkages, and the C-N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. We also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

关键词： catalysis bond cleavage data analytics PCA reaction conditions

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data analytics for Product Segmentation and Demand Forecasting of a Local Retail Store Using Python

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 226-232页

作者： Mishra, Arun Kumar Sinha, Megha Vinoba Bhave Univ Univ Coll Engn & Technol UCET Dept Comp Sci & Engn Hazaribagh 825301 Jharkhand India Sarala Birla Univ Dept Comp Sci & Engn Ranchi 835103 Jharkhand India

In today's competitive business environment, understanding customers' expectations and choices is a necessity for the successful operations of a retail store. Forecasting demand also plays an important role in maintaining inventory at an optimum level. The work utilises data analytics for product segmentation and demand forecasting in a local retail store. Python is being used as a programming language for data analytics. Historical sales data of a local store has been used to categorise products into different segments. Statistical techniques and a k-means clustering algorithm have been used to understand different segments of the product. Machine learning algorithms and time series models have been used to forecast future sales trends. The business insights allow the retail store to meet customers' expectations, manage inventory at an optimum level and enhance supply chain efficiency. The present work seeks to illustrate how data-driven tactics can enhance operational decision-making in retail.

关键词： data analytics product segmentation demand forecasting multicriteria ABC classification seasonality

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data analytics in the electricity market: a systematic literature review

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2025年第1期16卷 1-35页

作者： Imani, Mahmood Hosseini Bompard, Ettore Colella, Pietro Huang, Tao Politecn Torino Dept Energy Corso Duca Abruzzi 24 I-10129 Turin Italy

In the last decade, data analytics studies have covered a wide range of fields across the entire value chain in the electricity sector, from production and transmission to the electricity market, distribution, and load consumption. It is essential to integrate and organize the wide range of current scientific publications to effectively allow researchers and specialists to implement and progress cutting-edge methodologies in the future. Because of the electricity market's significance in the value chain of the electricity sector, in this study, we structure a systematic literature review of the data analytics-related works following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) framework to categorize the more common applications and approaches in the electricity market field. After refining the identified studies from the Web of Science database using the inclusion and exclusion criteria, 925 articles were chosen as the final pool of literature. Investigation of the extracted studies reveals that the application of data analytics in the electricity market can be clustered into four distinct groups: Prediction, Demand Side Management (DSM), Analysis of the market power, and Market simulation. Within the categorized applications, Prediction with 67% is the most frequent application of data analytics in the electricity market, followed by market simulation (14%), analysis of the market power (9%), DSM (7%), and other applications (3%).

关键词： data analytics Electricity market Systematic literature review (SLR) PRISMA Machine learning

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data analytics for Societal Challenges: Examining Student Participation in the National School Lunch Program

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JOURNAL OF BUSINESS analytics 2024年第3期7卷 137-150页

作者： Northey, Gavin Desai, Alpana M. Alaska Dept Educ & Early Dev Child Nutr Programs Juneau AK USA Univ Alaska Anchorage Coll Business & Publ Policy Dept Management Mkt Logist & Business Analyt Anchorage AK 99508 USA

Rising child hunger rates in the United States underscore the critical role of school meals in providing reliable food access for many children. This study investigates factors shaping student participation in the National School Lunch Program (NSLP), aiming to support UN Sustainable Development Goal 2 of Zero Hunger amidst the COVID-19 aftermath. While the NSLP promotes student health, participation gaps persist, necessitating targeted interventions. Leveraging data analytics, this research examines socioeconomic and school-specific variables to uncover participation patterns. Focused on a major US school district, the study aims to identify factors influencing middle and high school student meal participation, offering actionable insights for decision-makers. Key findings emphasize the pivotal role of free and reduced-price meal eligibility as the most influential factor for student participation, with other factors such as meal quality, transaction frequency, operating cycle, cuisine, and weather playing a secondary role.

关键词： data analytics childhood hunger CRISP-DM school lunch programmes food insecurity sustainable development goals

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data analytics for geochemical and petrogenetic study of an igneous province: A case study on Andean andesite, South America

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JOURNAL OF EARTH SYSTEM SCIENCE 2024年第4期133卷 177页

作者： Khare, Sunil Kumar Univ Petr & Energy Studies Dept Petr Engn & Earth Sci Dehra Dun 248007 India

Genesis of Phanerozoic Andean andesite rocks is related to the subduction of the oceanic Nazca plate beneath the South American continental plate along the west coast of South America. Exploratory data analytics is done on whole-rock geochemical data of 3311 andesite rock samples collected from the Andes orogenic belt, extending from Argentina, Chile, Bolivia, Peru, and Ecuador to Colombia. Python programming has been used for the visualisation and interpretation of large volumes of geochemical data, and data-driven tectonomagmatic inferences for volcanism extending across the South American continent have been drawn with the help of data analytics. Interelemental diagrams with Zr as a fractionation index reveal relative immobility and incompatibility of several major elements, large ion lithophile elements, high field strength elements, and rare earth elements. The interelemental diagrams, correlation matrix and heat maps drawn for major elements reveal that except K2O, TiO2 and SiO2, all major elements decrease with fractionation. Major element variation trends indicate that plagioclase and pyroxene were the major fractionating phases. The density plots give insight into the range of variation and data density of major and trace elements. Petrogenetic study reveals calc-alkaline, basaltic andesite to andesite, and back-arc tectonomagmatic environment for these volcanic rocks. Mantle source of Andean magma was enriched to primitive upper mantle. Around 25-30% partial melting of the upper mantle led to the genesis of the most primitive Andean magma.

关键词： data analytics geochemistry petrogenesis Andes andesite subduction

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data analytics-based auditing: a case study of fraud detection in the banking context

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ANNALS OF OPERATIONS RESEARCH 2024年第2-3期340卷 1161-1188页

作者： Kamdjoug, Jean Robert Kala Sando, Hyacinthe Djanan Kala, Jules Raymond Teutio, Arielle Ornela Ndassi Tiwari, Sunil Wamba, Samuel Fosso ESSCA Sch Management 1 Rue Joseph Lakanal F-49003 Angers France Catholic Univ Cent Africa Management Catholic Inst Yaounde Fac Social Sci BP 11628 Yaounde Cameroon CIE Ctr Rech Intelligence Artificielle Abidjan Cote Ivoire Univ Bristol Business Sch Howard House Bristol BS8 1SD England TBS Business Sch 1 Pl Alphonse Jourdain F-31068 Toulouse France

For a long time, decision-making in auditing was limited to a risk-oriented recommendation and consisted of the rigorous analysis of a sample of data. The new trend in the audit decision process focuses on the use of decision support systems (DSSs) founded on data analytics (DA) to better concentrate on in-depth analysis. This study examines how DA can improve the audit decision-making approach in the banking sector. We show that DA techniques can improve the quality of audit decision-making within banks and highlight the advantages associated with mastering these techniques, which results in a more effective and efficient audit of digital banking transactions. We propose an artifact-based data analytics-driven decision support system (DA-DSS) for an automatic fraud detection system supported by DA. The proposed DA-DSS artifact with a random forest classifier at its core is a promising innovation in the field of electronic transaction fraud detection. The results show that the random forest classifier can accurately classify the data generated by this artifact with an accuracy varying from 88 to 93% using transaction data collected from 2021 to 2022. Other classifiers including k-nearest neighbors (KNN) are also used, with a classification rate ranging from 66 to 88% for the same transaction datasets. These results show that the proposed DA-DSS with random forest can significantly improve auditing by reducing the time required for data analysis and increasing the results' accuracy. Management can use the proposed artifact to enhance and speed up the decision-making process within their organization.

关键词： data analytics Decision-making in audit Digital banking Design science Fraud detection

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data analytics in quality 4.0: literature review and future research directions

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2023年第5期36卷 678-701页

作者： Bousdekis, Alexandros Lepenioti, Katerina Apostolou, Dimitris Mentzas, Gregoris Natl Tech Univ Athens NTUA Inst Commun & Comp Syst ICCS Informat Management Unit IMU Athens Greece Univ Piraeus Dept Informat Piraeus Greece

The quality level in manufacturing processes increasingly concerns manufacturing firms, as they respond to pressures such as increasing complexity and variety of products, more complex value chains and shortened time-to-market. Quality management is becoming increasingly challenging as model variety, and highly complex products harbour the danger of distributing defective products in the market. data analytics has started gathering the interest of quality researchers and practitioners, who investigate approaches, algorithms, and methods for supporting the manufacturing quality procedures in the context of Industry 4.0. This trend is facilitated by the wide expansion of sensory technology and the accelerated adoption of information systems by the manufacturing firms. Since quality and process control has been identified as one of the major challenges with a high potential of big data analytics, in this paper we investigated the manufacturing quality research field from a data analytics perspective. Specifically, we examined the existing literature, we provided clarity to the Quality 4.0 research field, we synthesized the literature review outcomes, and we identified the research gaps and challenges. On top of them, we proposed directions for future research.

关键词： machine learning predictive quality Industry 4 0 data analytics

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data analytics and Visualization of Adaptive Collaboration Simulations

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第1期10卷 84-93页

作者： Wachowiak-Smolikova, Renata Zhu, Haibin Nipissing Univ Dept Comp Sci & Math North Bay ON P1B 8L7 Canada

Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.

关键词： Collaboration data models data visualization Analytical models Adaptive systems Adaptation models data analysis Adaptive collaboration (AC) adaptive systems big data data analytics dynamic modeling role-based collaboration (RBC) visual analytics (VA)

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data analytics for Risk of Hospitalization of Cardiac Patients

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IETE JOURNAL OF RESEARCH 2023年第6期69卷 3193-3202页

作者： Chandralekha, M. Shenbagavadivu, N. Anna Univ Dept Comp Applicat Univ Coll Engn Bharthidasan Inst Technol BIT Campus Tiruchirappalli 620024 India

Recognizing cardiac patients with high risk of hospitalization could enable to offer timely and life-saving care. The accumulation of healthcare data and utilization of data analytics to develop risk prediction models from healthcare data could facilitate personalized treatment care and predict the risk of emergency. Healthcare providers use different prediction tools to improve clinical decision making as there is a relation between hospitalization and disease diagnosis, disease complications and disease treatment. Several factors constitute to the hospitalization of cardiac patients such as age, gender, disease type, disease complication, associated disease conditions and so on. In this paper, a prediction model is developed to predict the risk of hospitalization of cardiac patients and the significance of each factor that contributes to the risk of hospitalization of cardiac patients is measured. The proposed model is designed to discover and validate the factors that are associated with the high risk of hospitalization in cardiac patients.

关键词： Cardiac risk factors data analytics Disease diagnosis Healthcare Predictive analytics Prediction model Risk of hospitalization

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data analytics, Digital Transformation, and Cybersecurity Perspectives in Healthcare 1st

Data Analytics, Digital Transformation, and Cybersecurity Pe...

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1st International Workshop on Secure and Resilient Digital Transformation of Healthcare (SUNRISE)

作者： Barik, Kousik Misra, Sanjay Chockalingam, Sabarathinam Hoffmann, Mario Univ Alcala Dept Comp Sci Alcala De Henares Spain Inst Energy Technol Dept Appl Data Sci Halden Norway Inst Energy Technol Dept Risk & Secur Halden Norway

ISBN: (纸本)9783031558283;9783031558290

data analytics personalize treatments and improve outcomes in a fast-changing digital world. The infusion of digital technologies into healthcare processes and workflows offers enhancements in areas such as flexibility, scalability, reliability, agility, cost-effectiveness, and the overall quality of healthcare services and operations. However, the heightened dependence on these digital advancements underscores the imperative for robust cybersecurity measures. Safeguarding patient data, healthcare systems, and infrastructure from potential cyber threats becomes paramount in ensuring the integrity and security of the healthcare ecosystem. This study addresses data security concerns in the health organization, a digital health system. This study aims to explore the connections between interdependence, uncertainty, knowledge, and security in the digital transformation of healthcare organizations. The data is collected from healthcare organization managers' responses based on a questionnaire. The gathered data is used to test six hypotheses. A comprehensive questionnaire with Likert-scale responses serves as our research instrument. Furthermore, SmartPLS is used for statistical analysis and validating structural and measurement frameworks. Path coefficients and boot-strap confidence intervals support the hypothesis that digital uncertainty understanding improves interdependence, cybersecurity, and digital transformation. The findings indicate a strong positive relation between the healthcare organization's understanding of interdependence, uncertainty, and cybersecurity knowledge and its expenditures in cybersecurity solutions. This analysis shows that hypotheses H1, H3, and H4 significantly impact digital transformation security layers, with the greatest values (0.542), offering policymakers important information for improving digital resilience.

关键词： Cyber resilience data analytics Digital Transformation Healthcare

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