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This paper explores how the combination of artificial intelligence, simulation, and e-collaborative (AISEC) tools can support accessibility in analytics courses within higher education. In the era of online and blended learning, addressing the diverse needs of students with varying linguistic backgrounds and analytical proficiencies poses a significant challenge. This paper discusses how the combination of AISEC tools can contribute to mitigating barriers to accessibility for students undertaking analytics courses. Through a comprehensive review of existing literature and empirical insights from practical implementations, this paper shows the synergistic benefits of using AISEC tools for facilitating interactive engagement in analytics courses. Furthermore, the manuscript outlines practical strategies and best practices derived from real-world experiences carried out in different universities in Spain, Ireland, and Portugal. [ABSTRACT FROM AUTHOR]

With the passage of IRDA Act 1999, Insurance Industry was opened to private companies and as of now we have 70+ Insurance Companies in India including Life, General and Reinsurance Branches. In order to encourage technological innovation IRDAI has constituted Sandbox committee which allows insuretech companies to innovate products and adopt Use and Tile instead of File and Use. Artificial Intelligence and Data Analytics started impacting businesses across spectrum positively. Insurance Industry is no exception. Role of Artificial Intelligence has only quadrupled in recent years. What started as a Sandbox moment approved by regulator has taken a giant leap with the advent of AI, Block chain and Analytics? This paper is to highlight key developments in Insurance Industry adopting Artificial Intelligence, Block chain and Analytics. [ABSTRACT FROM AUTHOR]

This paper explores the potential use and limitations of ChatGPT in a programming course, specifically focusing on its evaluation in a Data Analytics course due to its broad applications. The study reveals that ChatGPT offers valuable assistance to teachers in creating class materials, facilitating teaching-learning activities, and designing assessments. Students also benefit from the technology, as it can generate R programming codes and serve as a learning companion. However, limitations were identified, including the generation of incorrect reference materials, the tendency to produce pattern-like content, and potential misuse. The paper encourages replication of the study in other courses to uncover additional strengths and limitations, contributing to the development of ethical guidelines for responsible utilization of this exciting technology. [ABSTRACT FROM AUTHOR]

This paper presents a review of the field of systems thinking and strategic management The evolution of the areas of interest between systems thinking and strategic management follows similar patterns with more prescriptive developments occurring within the 1960s until 1980s; then, an increasing focus on emergence and transformation emerged in later years. From the review, there seems to exist synergies between multiple strands in Systems Thinking and Strategic Management. Suggestions to facilitate the interaction between both fields are proposed, considering the type of processes to generate strategies and the perspectives employed to visualize the organizations as systems. Hopefully, the paper can contribute to embedding systemic approaches to the strategic management of organizations and society. [ABSTRACT FROM AUTHOR]

In the digital era, multimodal behavioral research has emerged as a pivotal discipline, integrating diverse data sources to comprehensively understand human behavior. This paper defines and distinguishes data governance from mere data management within this context, highlighting its centrality in assuring data quality, ethical handling, and participant protection. Through a meticulous review of the literature and empirical experience, we identify key implementation strategies and elucidate the benefits and risks of data governance frameworks in multimodal research. A demonstrative case study illustrates the practical applications and challenges, revealing enhanced data reliability and research integrity as tangible outcomes. Our findings underscore the critical need for robust data governance, pointing to future advancements in the field, including the development of adaptive governance frameworks, innovative big data analytics solutions, and user-friendly tools. These enhancements are poised to amplify the utility of multimodal data, propelling behavioral science forward. [ABSTRACT FROM AUTHOR]

Aquaculture is a critical source of seafood production, addressing the global demand for fish products. Suggesting a Deep learning-based classification technique for fishes specifically Indian Major Carp (IMC) as Mrigala, Catla and Rohu is the major objective of this paper along with detecting the disease among them. This world inside hydrosphere has their own discrete living manner. Yet they are not untouched by diseases; fishes mostly affected when young carry pathogens which cause various infections naturally or due to environmental pollutants including chemical and hazardous waste. This paper proposed the classification and prediction of diseases of fishes in aquaculture using Deep Learning based customized Convolutional Neural Network with ResNet-50 model. The proposed model performance metric compared with recent state-of-art techniques. ResNet-50 classifies accurately the IMC and type of disease the fishes are suffering from. [ABSTRACT FROM AUTHOR]

The exponential growth of digital data has created new challenges and opportunities to process, store, and analyze large datasets. Cloud computing has transformed the way businesses manage their IT infrastructure by providing a scalable and cost-effective platform for data storage and processing. Because of this, businesses now have access to new opportunities to exploit big data and thus acquire insights into the behavior of their customers, increase the efficiency of their operations, and drive innovation. Furthermore, big data analysis enables the exploration of links between a set of independent data, revealing many aspects that enable the prediction of correct decisions that can aid the achievement of desired goals. With this in mind, the purpose of this article is to investigate the ways in which big data analysis can be put to use in fields such as healthcare, presenting specific instances of effective use along the way. The purpose of this study is to provide a comprehensive overview of the opportunities and application areas offered by big data and cloud computing, as well as to highlight the need for businesses and organizations to adopt these technologies to maintain their competitive edge in the digital age. Furthermore, this paper emphasizes the importance of big data analysis in facilitating decision-making and goal achievement, and it encourages businesses and organizations to adopt these technologies to stay competitive in an increasingly data-driven world. It also discusses the ethical, legal, and security issues that arise when dealing with large amounts of data, as well as ways to address these challenges. [ABSTRACT FROM AUTHOR]

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn. [ABSTRACT FROM AUTHOR]

Big Data (BD) Analytics (BDA) in networking involves acquisition, sharing, pre-processing, storage, analysis, interpretation, and decision-making. Blockchain (BC) technology incorporates a progression of bonded blocks that fundamentally upholds credibility, protects unquestionability, and protects the partial-anonymity of its transactions on account of distributed consensus methods and cryptographic protocols. So as to fulfill the deficiency of a review paper catering to individual and combined use of BC and Artificial Intelligence (BCandAI) for BDA in the networking domain, in this work, we recognize 6 sections in the leading edge BCandAI BDA notion and rigorously analyze each stratagem concerning blockchain attributes, blockchain/AI techniques, network attributes, and the like. We piled up an opening sample of 89 publication citations by culling articles for screening requirements tracked down from cyber libraries, availing a comprehensive and protracted systemology. Established upon this exploration, we highlight that Artificial Intelligence (AI) can be involved in BDA by analyzing BD, while blockchain can facilitate secure transmission and storage of BD due to its inherent security features of unchangeability, non-deniability, etc., preventing data poisoning attacks, and facilitating hybrid on- and off-chain storage due to the challenges of high volume by availing techniques just like offloading and partial storage. Moreover, we highlight that there are BCandAI integrated approaches where blockchain-anchored secure BD storage is availed for secure federated learning or blockchain and cloud computing are availed for BD fusion for analysis, availing AI to generate accurate insights from BD. Rigorous analysis discloses that from all studies, 17.5% use BC alone, 20% avail of the combined BCandAI concept, 62.5% use AI alone, 70% address one or more BDA stages, 10% implement PoW consensus, 12.5% avail of deep learning, and 17.5% choose generic or IoT networks. Finally, we express the potentialities and problems of the proposition of BCandAI-anchored BDA concepts and then offer counsel to overpower them. [ABSTRACT FROM AUTHOR]

The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]

Disruptions can adversely affect profitability, service level, and even the viability of supply networks (SNs). Natural disasters, the COVID-19 pandemic, and geopolitical tensions heightened disruption risks for SNs. We propose a methodological framework to guide the design of resilient SNs, with a focus on efficient resilience. In fact, despite an increasing economic and social interest in SN resilience and a large variety of qualitative resilience frameworks, there is a lack of model-based frameworks to guide decision makers in the complex task of designing resilient SNs. Differently to the existing literature, that mostly focuses on industry- or disruption-specific approaches, the framework of this paper is general, independent from sectors and causes. It consists of three steps to guide practitioners through model-based stress testing of their SNs: it integrates and organises methods from the literature, from the selection of data to be collected to the validation of the resilient and efficient SN. We illustrate the application of descriptive, predictive, and prescriptive analytics at each phase using real-life case studies data supplemented by literature references. Finally, we present an industrial case study with the step-by-step application of the framework highlighting its strengths and objectives. [ABSTRACT FROM AUTHOR]

Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems incorporate new components that are capable of generating better decision making (humidity/temperature/soil sensors, drones for plague detection, smart irrigation, etc.) and also include novel processes for crop control (reproducible environmental conditions, proven strategies for water stress, etc.). At the same time, advances in model-driven development (MDD) simplify software development by introducing domain-specific abstractions of the code that makes application development feasible for domain experts who cannot code. XMDD (eXtreme MDD) makes this way to assemble software even more user-friendly and enables application domain experts who are not programmers to create complex solutions in a more straightforward way. Key to this approach is the introduction of high-level representations of domain-specific functionalities (called SIBs, service-independent building blocks) that encapsulate the programming code and their organisation in reusable libraries, and they are made available in the application development environment. This way, new domain-specific abstractions of the code become easily comprehensible and composable by domain experts. In this paper, we apply these concepts to a smart agriculture solution, producing a proof of concept for the new methodology in this application domain to be used as a portable demonstrator for MDD in IoT and agriculture in the Confirm Research Centre for Smart Manufacturing. Together with model-driven development tools, we leverage here the capabilities of the Nordic Thingy:53 as a multi-protocol IoT prototyping platform. It is an advanced sensing device that handles the data collection and distribution for decision making in the context of the agricultural system and supports edge computing. We demonstrate the importance of high-level abstraction when adopting a complex software development cycle within a multilayered heterogeneous IT ecosystem. [ABSTRACT FROM AUTHOR]

This paper presents aspects concerning the e-learning courses for the Romanian textile sector in the context of digital transition. In the framework of the ADDTEX Erasmus+ project, INCDTP developed three interactive lessons covering digital aspects of the textile industry. The proposed courses for digital transition cover aspects related to data acquisition, visualization, and analytics-based descriptive, diagnostic, predictive, prescriptive, augmented, and real-time analytics, including mathematical and business modelling by artificial intelligence. The proposed courses gradually introduce the learner to digital knowledge, starting from basic principles necessary for technicians, graduates, engineers, professionals, managers, and mentors. In order to achieve the objectives of the ADDTEX project, the developed courses and certification tools for different specialization levels cover digital aspects that could support innovation in the development of new products based on mathematical models and business modelling, promotion of entrepreneurship and improving the quality and relevance of employers’ skills. In addition, data visualization of historical data record stored in data warehouse for analysis and data visualization using specific tools (scatter, combo, list or stacked plots) help in quick visualization of the aspects related to performance and improves the business outcomes. The courses related to digital transition are available on the Addtex project website and offer learners certificates for completing the courses after the courses after completing the course modules and the electronic tests assigned to each module. [ABSTRACT FROM AUTHOR]

This paper focuses on the operation and results of the gas treatment pilot plant for the purification and conditioning of steel mill gases, a key element of the Carbon2Chem® Technical Center in Duisburg, Germany. Using highly sensitive analytics, it is demonstrated that steel mill gas impurities which are detrimental to downstream catalytic conversion are reduced to single‐digit ppb levels. Strategies for adapting the gas purification process to take account of the transformation of steelmaking towards direct reduction are also discussed. [ABSTRACT FROM AUTHOR]

At the start of this review, 168 million individuals required humanitarian assistance, at the conclusion of the research, the number had risen to 235 million. Humanitarian aid is critical not just for dealing with a pandemic that occurs once every century, but more for assisting amid civil conflicts, surging natural disasters, as well as other kinds of emergencies. Technology's dependability to support humanitarian and disaster operations has never been more pertinent and significant than it is right now. The ever-increasing volume of data, as well as innovations in the field of data analytics, present an incentive for the humanitarian sector. Given that the interaction between big data and humanitarian and disaster operations is crucial in the coming days, this systematic literature review offers a comprehensive overview of big data analytics in a humanitarian and disaster setting. In addition to presenting the descriptive aspects of the literature reviewed, the results explain review of existent reviews, the current state of research by disaster categories, disaster phases, disaster locations, and the big data sources used. A framework is also created to understand why researchers employ various big data sources in different crisis situations. The study, in particular, uncovered a considerable research disparity in the disaster group, disaster phase, and disaster regions, emphasising how the focus is on reactionary interventions rather than preventative approaches. These measures will merely compound the crisis, and so is the reality in many COVID-19-affected countries. Implications for practice and policy-making are also discussed. [ABSTRACT FROM AUTHOR]

With the increasing volume, velocity, and variety of data generated by various sources, big data has become a critical challenge for many organizations. Cloud computing provides an efficient and cost-effective solution to address the challenges of big data analysis. In this paper, we review the state-of-the-art cloud computing technologies for big data analysis, including cloud storage, cloud computing platforms, and cloud-based big data analytics tools. We also identify the major challenges and solutions for cloud-based big data analysis. [ABSTRACT FROM AUTHOR]

Financial risks associated with Fintech have been increasing with its significant growth in recent years. Aiming at addressing the problem of identifying risks in online lending investment under a financial technology platform, we develop a Q&A text risk recognition model based on attention mechanism and Bi-directional Long Short-Term Memory. First, the Q&A pairing on the text data set is carried out, and the matching data set is selected for the next analysis. Secondly, the online loan investment platform is assessed by the named entity recognition of the question text. Finally, the risk level of the corresponding investment platform is evaluated based on the answer text. The experimental results show that the proposed model has achieved improved precision, recall, F1-score, and accuracy compared with other models. Our proposed model can be applied to identify the risks from the text posted on online loan investment platforms and can be used to guide investors' investment and improve the management of financial technology platforms. [ABSTRACT FROM AUTHOR]

Our research reveals the continued and evolving role of the human factor in decision making in digitalized retail supply chains. We compare managerial roles in a pre‐ and post‐COVID era through conducting in‐depth interviews of 25 executives spanning the retail supply chain ecosystem. We use grounded theory to develop four main contributions. First, we find that the involvement of managerial judgment is found to be progressively greater moving up the retail supply chain, away from the customer and the demand signal. Second, integration of analytics and judgment is now the primary method of decision making, and we identify elements needed for success. Third, we develop an essential framework for a successful integration process. Fourth, we isolate the necessary components of a successful process for analytics/artificial intelligence (AI) implementation. Our paper offers important insights into how analytics and AI are—and should be used—in judgment and decision making and opportunities for researchers to understand the changing role of the human factor in digitalized retail supply chains. [ABSTRACT FROM AUTHOR]

This paper explores how the combination of artificial intelligence, simulation, and e-collaborative (AISEC) tools can support accessibility in analytics courses within higher education. In the era of online and blended learning, addressing the diverse needs of students with varying linguistic backgrounds and analytical proficiencies poses a significant challenge. This paper discusses how the combination of AISEC tools can contribute to mitigating barriers to accessibility for students undertaking analytics courses. Through a comprehensive review of existing literature and empirical insights from practical implementations, this paper shows the synergistic benefits of using AISEC tools for facilitating interactive engagement in analytics courses. Furthermore, the manuscript outlines practical strategies and best practices derived from real-world experiences carried out in different universities in Spain, Ireland, and Portugal.

Over the last three decades, business-to-business (B2B) sales practices have undergone significant change. This study evaluates the current responsibilities, skills, and knowledge sets required in B2B sales professionals. It identifies roles, qualifications, competencies, and the B2B sales position taxonomy. To achieve these objectives, we conducted three studies. The first study included an analysis of 565 job descriptions to identify the current task and skill requirements of B2B salespeople. The second survey included 380 B2B salespeople and incentivized the development of a new up-to-date taxonomy for sales positions. Our third and final study consisted of 33 semi-structured interviews with B2B sales professionals, and was designed to predict the future skillsets required in B2B sales professionals. Our results discuss the conception and current status of the B2B sales profession, and primarily highlight the role of future B2B salespeople. Lastly, this study offers a set of B2B sales-related recommendations based on our results. [ABSTRACT FROM AUTHOR]

This paper explores the potential use and limitations of ChatGPT in a programming course, specifically focusing on its evaluation in a Data Analytics course due to its broad applications. The study reveals that ChatGPT offers valuable assistance to teachers in creating class materials, facilitating teaching-learning activities, and designing assessments. Students also benefit from the technology, as it can generate R programming codes and serve as a learning companion. However, limitations were identified, including the generation of incorrect reference materials, the tendency to produce pattern-like content, and potential misuse. The paper encourages replication of the study in other courses to uncover additional strengths and limitations, contributing to the development of ethical guidelines for responsible utilization of this exciting technology.

This paper presents a review of the field of systems thinking and strategic management The evolution of the areas of interest between systems thinking and strategic management follows similar patterns with more prescriptive developments occurring within the 1960s until 1980s; then, an increasing focus on emergence and transformation emerged in later years. From the review, there seems to exist synergies between multiple strands in Systems Thinking and Strategic Management. Suggestions to facilitate the interaction between both fields are proposed, considering the type of processes to generate strategies and the perspectives employed to visualize the organizations as systems. Hopefully, the paper can contribute to embedding systemic approaches to the strategic management of organizations and society.

Player movement is a fundamental component of evaluating performance in most team sports. Movement can be evaluated across multiple scales, referring to the function of anatomical structures through various planes of motion or an individual regulating their field position based on the movement of opposition players. Developments in commercially available tracking systems have afforded end users the ability to investigate the spatiotemporal features of movement in fine detail. These advancements, in conjunction with overlaid contextual information, have provided insights into the strategies adopted by players in relation to their movement. Understanding movement beyond its semantic value allows practitioners to make informed decisions surrounding performance evaluation and training design. This investigation proposes a framework to guide the analysis of player movement within team sports environments. The framework describes how operational standards for assessing movement can be designed in reference to theory and a set training philosophy. Such practice allows for the spatial and temporal complexities within team sports to be described and could potentially lead to betterapplied outcomes through greater interdisciplinary collaboration and an improved holistic understanding of movement. To inform its development, this study evaluates the current research and identifies several open questions to guide future investigations. [ABSTRACT FROM AUTHOR]

Background: Almost 80 years after implementation of the industrial ethanol fractionation process, based on the pioneering work of Edwin J. Cohn's research group, it has not lost its importance in providing life-saving biotherapies to patients. The focus has shifted from albumin, which was first used to treat intensive care patients, to immunoglobulin G preparations. Nowadays the latter enables effective, life-long treatment of patients suffering from immunodeficiencies. Ethanol concentration, pH, temperature, protein, and salt concentration are diligently varied during the Cohn fractionation process to finally yield fractions in which the main plasma proteins are enriched at adequate purity. Total protein concentration of the intermediates, probably not as important as ethanol levels and pH, has nevertheless an indisputable influence on the process' performance Objectives: Total protein measurement is therefore a valuable in process-control for versatile process performance monitoring. For this purpose, we investigated the application of analytical digital refractometry. Design: For our pilot study, we used six consecutive batches of nine relevant intermediates covering Takeda's whole industrial-scale Cohn Fractionation process. Methods: Total protein concentrations of the intermediates were measured with the method of Kjehldal to obtain reference data and digital analytical refractometry, using 10-kDa ultrafiltration to obtain a protein free permeate serving as a sample-specific blank. Results and Conclusions: Specific refractive index increment dn/dc values, allowing the calculation of the total protein concentrations by refractive index measurement, were obtained for the Cohn Fractionation intermediates investigated. Thus, analytical digital refractometry, a simple, fast, and robust technique, was shown to be fit for this purpose. [ABSTRACT FROM AUTHOR]

Recent advancements in Enterprise Information Systems (EISs) have transitioned from primarily supporting operational and tactical processes to enabling strategic decision-making through integrated analytics. This systematic review critically examines global literature from 2010 to 2023, focusing on the factors influencing the adoption of analytical components in EISs and assessing their impact on strategic decision-making in organizations. Following the PRISMA 2020 guidelines, we meticulously selected and reviewed articles from the Scopus database, employing a robust taxonomy based on the technology–organization–environment (TOE) framework to categorize findings. Our methodology involved a thorough screening of 234 studies, leading to a final analysis of 45 peer-reviewed articles that met our stringent criteria. These studies collectively underscore a significant gap in organizational capabilities, notably in the business ecosystems surrounding EISs, which hampers the effective adoption and utilization of advanced analytics. The results highlight a distinct need for improved understanding and implementation strategies for integrated analytics within EISs to enhance strategic decision-making processes. This review identifies critical factors for integrating analytics into Enterprise Information Systems (EISs), emphasizing technological, organizational, and environmental dimensions. It highlights a significant gap in models guiding ERP systems with Business Intelligence (BI) capabilities and underscores the need for robust research to enhance strategic decision-making through analytics. [ABSTRACT FROM AUTHOR]

The Titanic disaster is a topic that continues to fascinate. As the importance of analytics continues to grow in industry, data literacy skills have become increasingly important in business education. This project allows students to use the passenger data from the Titanic to build their data literacy skills using an engaging, experiential topic. The project requires students to extract, transform, describe, analyze, and draw conclusions regarding the factors that impacted survival on the Titanic. The project can be deployed using various application software and tools. We describe how the assignment can be completed using Excel, Tableau, and Python using the Pandas library. [ABSTRACT FROM AUTHOR]

The focus of this study was to assess the skill requirements for data analytics positions and to understand data analysis employment expectations for new graduates. Furthermore, this work seeks to highlight issues relevant to curriculum management in university degree programs. 786 job postings were analyzed for domain-related, soft skills, as well as degree requirements. Soft skills, often referred to as people skills, comprised the largest part of the results (11 of the top 21 skills). Results revealed the most frequent soft skills were related to communication and teams or teamwork. The most frequent domain skills were related to visualization, data cleaning, data extraction and programming. Implications for curriculum based on results are discussed, and suggestions for future research are provided. [ABSTRACT FROM AUTHOR]

Purpose: The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply chain in the "new normal" environment. Design/methodology/approach: A systematic literature review was conducted by extracting research articles related to analytics in the healthcare supply chain from Scopus. The author used a hybrid review approach that combines bibliometric analysis with a theories, contexts, characteristics, and methodology (TCCM) framework-based review to identify various themes of analytics in the healthcare supply chain. Findings: The hybrid review strategy yielded results that focus on prevalent theories, contexts, characteristics, and methodologies in the field of healthcare supply chain analytics. Future research should explore the resulting antecedents, decision-making processes and outcomes (ADO) framework, which integrates technological, economic, and societal concerns and outcomes. Future research agendas could also seek to apply theoretical perspectives in the field of analytics in the healthcare supply chain. Originality/value: The result of a review of selected studies adds to the current body of work and contributes to the growth of research in the field of analytics in the healthcare supply chain. It also provides new directions to healthcare supply chain managers and academic scholars. [ABSTRACT FROM AUTHOR]

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017–2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions. • This paper examines the trajectory of AI-based CDSS application in dementia care. • It focuses on GAPD's seven action areas, highlighting AI's role in improving diagnosis and care. • It identifies limitations in data volume and cost as barriers to their development and usage in practice. • A model utilising digital twin technology is presented as a potential approach to addressing these identified limitations. [ABSTRACT FROM AUTHOR]

In the digital era, the volume of data continues to increase rapidly, big data and analytics are important tools for companies to utilize the data and increase competitive advantage. The purpose of this research is to find out how big data and analytics can be applied to increase competitive advantage. This research uses qualitative research methods. The data collection technique in this research is a literature study. The data that has been collected is then analyzed in three stages, namely data reduction, data presentation and conclusion drawing. The results showed that big data and analytics are important tools for companies to increase competitive advantage in this digital era including through personalization of customer experience, operational optimization, and business strategy development. The implementation of big data and analytics is expected to have an impact on companies so that they can make better decisions, develop new products and services, improve operating efficiency, and strengthen relationships with customers. [ABSTRACT FROM AUTHOR]

Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods. Methods: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null treatment effect, including the COI in the outcome generation model, and four missingness mechanisms for the COI were simulated: completely at random (MCAR), at random (MAR), and two not at random (MNAR) mechanisms, where missingness was dependent on an unmeasured confounder and on the value of the COI itself. We evaluated the ability of three groups of diagnostics to differentiate between mechanisms: 1)-differences in characteristics between patients with or without the observed COI (using averaged standardized mean differences [ASMD]), 2)-predictive ability of the missingness indicator based on observed covariates, and 3)-association of the missingness indicator with the outcome. We then compared analytic methods including "complete case", inverse probability weighting, single and multiple imputation in their ability to recover true treatment effects. Results: The diagnostics successfully identified characteristic patterns of simulated missingness mechanisms. For MAR, but not MCAR, the patient characteristics showed substantial differences (median ASMD 0.20 vs 0.05) and consequently, discrimination of the prediction models for missingness was also higher (0.59 vs 0.50). For MNAR, but not MAR or MCAR, missingness was significantly associated with the outcome even in models adjusting for other observed covariates. Comparing analytic methods, multiple imputation using a random forest algorithm resulted in the lowest root-mean-squared-error. Conclusion: Principled diagnostics provided reliable insights into missingness mechanisms. When assumptions allow, multiple imputation with nonparametric models could help reduce bias. [ABSTRACT FROM AUTHOR]

The fifth generation of mobile networks is going to transform the way we live, work, and communicate. The shift from 4G to 5G would be a crucial enabler for the growth of smart cities due to its superior speed and minimal latency. Extant literature indicates that 5G technology holds immense potential in implementing and managing smart cities and would provide benefit to citizens and corporates alike. The purpose of this study is to examine the potential of 5G computing as a smart city accelerator, in terms of enabling data driven decisions and its effects on various economic sectors. Based on primary data collected from 392 respondents based in Delhi NCR, the study attempts to explore the attitude of people towards 5G technology with respect to Connectivity, Infrastructure and Application for development of smart cities in India [ABSTRACT FROM AUTHOR]

In the intersecting fields of data mining (DM) and sports analytics, the impact of socioeconomic, demographic, and injury-related factors on sports performance and economics has been extensively explored. A novel methodology is proposed and evaluated in this study, aiming to identify essential attributes and metrics that influence the salaries and performance of NBA players. Feature selection techniques are utilized for estimating the financial impacts of injuries, while clustering algorithms are applied to analyse the relationship between player age, position, and advanced performance metrics. Through the application of PCA-driven pattern recognition and exploratory-based categorization, a detailed examination of the effects on earnings and performance is conducted. Findings indicate that peak performance is typically achieved between the ages of 27 and 29, whereas the highest salaries are received between the ages of 29 and 34. Additionally, musculoskeletal injuries are identified as the source of half of the financial costs related to health problems in the NBA. The association between demographics and financial analytics, particularly focusing on the position and age of NBA players, is also investigated, offering new insights into the economic implications of player attributes and health. [ABSTRACT FROM AUTHOR]

With the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making process of humans, using insights obtained from group-level interpretation of ML models for designing individual interventions may lead to mixed results. The present study proposes a hybrid ML framework by integrating established predictive and explainable ML approaches for decision support systems involving the prediction of human decisions and designing individualized interventions accordingly. The proposed framework is aimed at providing actionable insights for designing individualized interventions. It was showcased in the context of college students' attrition problem using a large and feature-rich integrated data set of freshman college students containing information about their demographics, educational, financial, and socioeconomic factors. A comparison of feature importance scores at the group- vs. individual-level revealed that while group-level insights might be useful for adjusting long-term strategies, using them as a one-size-fits-all strategy to design and implement individual interventions is subject to suboptimal outcomes. [ABSTRACT FROM AUTHOR]