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Container ports operate in more challenging and volatile environments at present times. Events such as US-China trade tensions and the COVID-19 pandemic severely affect numerous container ports at various levels. Strategies pursued by container ports are key to port development and management amidst these challenges. Drawing on configuration theory, this research employs Fuzzy-set Qualitative Comparative Analysis to investigate the relation between port strategies and container throughput. The research contributes to the literature by proposing an approach to account for complexity of the port sector and offers insights into strategies adopted by major container ports. The research further identifies 10 port strategies and proposed indicators that can represent the essence of these strategies. Being able to represent strategies in a quantitative format is important for strategy analysis and performance evaluation. Results reveal that major container ports employ a combination of strategies which address both the supply and demand-side aspects of the port business. Growing digitalization and digitization coupled with advancements in information capture, diagnostics capabilities and predictive abilities means a greater role for data analytics to influence container port strategy and performance. Implications for port managers, policy makers and researchers from the perspective of port policy and management are proposed.
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The COVID-19 pandemic caused unemployment rates to reach record highs, adding to an already unequally divided system (Kawohl & Nordt, 2020). Minorities' unemployment rates in the United States were significantly higher in 2020 than the white unemployment rate, regardless of educational attainment. This study draws upon U.S. census data after the onset of the pandemic to investigate the relationship between educational attainment, race, and employment rates in the United States. Logistic regression revealed that the probability of being employed in 2020 was higher for whites than minorities and significantly higher for those with higher levels of education. Based on these preliminary results, we discuss the relationships among race, educational attainment, and employment, and suggest routes for further inquiry. © Issues in Information Systems.
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PurposeThe abrupt outbreak of coronavirus disease (COVID-19) hit every nation in 2020–2021, causing a worldwide pandemic. The worldwide COVID-19 epidemic, described as a "black swan”, has severely disrupted manufacturing firms' supply chain. The purpose of this study is to investigate how supply chain data analytics enable the effective deployment of agility, adaptability and alignment (3As) strategies, resulting in improving post-COVID disruption performance. It also analyses the indirect effect of supply chain data analytics on disruption performance through the 3As supply chain strategies.Design/methodology/approachThe hypothesis and theoretical framework were tested using a questionnaire survey. The authors employed structural equation modelling through the SMART PLS version 3.2.7 to analyse data from 163 textile firms located in Pakistan.FindingsThe results revealed that the supply chain data analytics contributed positively and significantly to the agility and adaptability, while all 3As supply chain strategies impacted the PPERF substantially. Further, the connection between supply chain data analytics (SCDA) and disruption performance has substantially been influenced through 3As supply chain strategies.Practical implicationsThe results imply that in the event of low likelihood, high effect disruptions, managers and decision-makers should focus their efforts on integrating data analytics capabilities with 3As supply chain policies to ensure long-term company success.Originality/valueThis research sheds fresh light on the importance of data analytics in effectively implementing 3As strategies for sustaining company performance amid COVID-19 disruptions.
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The pandemic forced many education providers to pivot rapidly their models of education to increased online provision, raising concerns that this may accentuate effects of digital poverty on education. Digital footprints created by learning analytics systems contain a wealth of information about student engagement. Combining these data with student demographics can provide significant insights into the behaviours of different groups. Here we present a comparison of students' data from disadvantaged versus non-disadvantaged backgrounds on four different engagement measures. Our results showed some indications of effects of disadvantage on student engagement in a UK university, but with differential effects for asynchronously versus synchronously delivered digital material. Pre-pandemic, students from disadvantaged backgrounds attended more live teaching, watched more pre-recorded lectures, and checked out more library books than students from non-disadvantaged backgrounds. Peri-pandemic, where teaching was almost entirely online, these differences either disappeared (attendance and library book checkouts), or even reversed such that disadvantaged students viewed significantly fewer pre-recorded lectures. These findings have important implications for future research on student engagement and for institutions wishing to provide equitable opportunities to their students, both peri- and post-pandemic.
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In this interview, Sujay Jadhav, global vice president, study start-up, Oracle Health Sciences, touches on how COVID has affected study start-up and what new perspectives it has forced the industry to have on its own challenges. [...]assessing site ability to leverage telehealth will be a factor in site selection. Andy Studna is an Assistant Editor for Applied Clinical Trials Sujay Jadhav Global Vice President, Study Start-Up, Oracle Health Sciences Problems with startup, more than any other phase of a clinical trial, have the greatest potential to increase timelines and budgets.
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The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.
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Clinical trial patient recruitment is arguably the most difficult aspect of pharmaceutical development, because it involves a variety of factors beyond study sponsors' control. The aggregation of data across 80 hospitals and 20 systems, for the purpose of understanding patients, doing feasibility studies, or engaging in decentralized recruitment, is the trend we're seeing." Nimita Limaye, PhD, is the vice president of research for the life sciences R&D strategy and technology division at the International Data Corporation (IDC), a market research and advisory firm specializing in the technology industry and headquartered in Boston, Mass. Limaye says the rise of social media-based patient recruitment has opened the door for sponsors and investigators to mine real-world data and to give patients a more central focus in research.
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Using learning analytics and dispositional learning analytics in teaching is difficult. Examples of their use are required for higher educational institutions and teachers. In this paper, we present a flipped learning approach in online settings (due to COVID-19) with particular emphasis on learning analytics and dispositional learning analytics. For this, an understanding of flipped approaches (i.e., flipped classroom and flipped learning) as well as the role of technology in the teaching context is required and presented. The role of technology includes (1) a digital learning system, (2) a conferencing system, (3) the collection and use of learning analytics and dispositional learning analytics, and (4) content-specific technology. Additionally, our aim is to present students' course feedback results from quantitative research methods course practices (2020, 2021) for preservice teachers (i.e., students;N = 70). The content is highly challenging for these students, causing fear, frustration, anxiety, and boredom. Generally, the results for pedagogy were positive, but the results of students' learning perceptions were lower. Based on the approach and results, discussion with new insights is provided. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)
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PurposeAmid the COVID-19 contamination, people are bound to use contactless FinTech payment services. Because of restrictions on physical movement and avoidance of touching physical money, people willingly choose mobile payment, resulting in enormous growth in FinTech payment service industries. Because of this, this study aims to examine the effect of factors affecting Gen X and Millennials users to use FinTech payment services.Design/methodology/approachThe authors used 328 responses collected through convenience sampling of Indian users aged between 26 and 57 years in the Delhi-NCR region who are users of FinTech payment services.FindingsThe authors' findings verified that in India, perceived COVID-19 risk, perceived severity for COVID, individual mobility, subjective norms, perceived ease of use and perceived usefulness have statistically significant impacts on FinTech payment services during the COVID-19 pandemic. Structural equation modelling was used to study the proposed research model. Overall, the model predicted 76.9 % of the variation in intention to use FinTech payment services by the abovesaid variables by Indian users during a pandemic.Practical implicationsThis study will provide valuable insight to all FinTech service providers and stakeholders in planning and designing the concerned policy. It will be able to draw the attention of users more.Originality/valueThis research added a valuable theory to the existing technology adoption model (TAM) theory. It demonstrated the utility of the above variables in adopting and using FinTech payment services, which will help service providers to develop future strategies because of the COVID-19 pandemic.
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Today, in Uzbekistan, the number of retail store chains is increasing. In their work, the latest technological achievements are used in order to satisfy the demands and needs of our people. Especially in the conditions of the COVID-19 pandemic, it has been highlighted that retail enterprises operating on the basis of network marketing, based on the needs and demands of the population, are operating in the form of large supermarkets and small stores. In this article, based on the latest information, we analyzed the brands "Korzinka", "Makro", "Havas", "Carrefour"operating in Uzbekistan. © 2022 ACM.
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Big data is a very large size of datasets which come from many different sources and are in a wide variety of forms. Due to its enormous potential, big data has gained popularity in recent years. Big data enables us to investigate and reinvent numerous fields, including the healthcare industry, education, and others. Big data specifically in the healthcare sector comes from a variety of sources, including patient medical information, hospital records, findings from physical exams, and the outcomes of medical devices. Covid19 recently, one of the most neglected areas to concentrate on has come under scrutiny due to the pandemic: healthcare management. Patient duration of stay in a hospital is one crucial statistic to monitor and forecast if one wishes to increase the effectiveness of healthcare management in a hospital, even if there are many use cases for data science in healthcare management. At the time of admission, this metric aids hospitals in identifying patients who are at high Length of Stay namely LS risk (patients who will stay longer). Once identified, patients at high risk for LS can have their treatment plans improved to reduce LS and reduce the risk of infection in staff or visitors. Additionally, prior awareness of LS might help with planning logistics like room and bed allotment. The aim of the suggested system is to precisely anticipate the length of stay for each patient on an individual basis so that hospitals can use this knowledge for better functioning and resource allocation using data analytics. This would contribute to improving treatments and services. © 2023 IEEE.
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Digital transformation, which significantly impacts our personal, social, and economic spheres of life, is regarded by many as the most significant development of recent decades. In an industrial context, based on a systematic literature review of 262 papers selected from the ProQuest database, using the methodology of David and Han, this paper discusses Industry 4.0 technologies as the key drivers and/or enablers of digital transformation for business practices, models, processes, and routines in the current digital age. After carrying out a systematic literature review considering key Industry 4.0 technologies, we discuss the individual and collective ways in which competitiveness in contemporary organizations and institutions is enhanced. Specifically, we discuss how these technologies contribute as antecedents, drivers, and enablers of environmental and social sustainability, corporate growth and diversification, reshoring, mass customization, B2B cooperation, supply chain integration, Lean Six Sigma, quality of governance, innovations, and knowledge related to dealing with challenges arising from global pandemics such as COVID-19. A few challenges related to the effective adoption and implementation of Industry 4.0 are also highlighted, along with some suggestions to overcome them.
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India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.
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"With the advance of data science enabling factors such as easy access to scalable memory and computing resources;our growing competence in collecting, storing, and contextualizing data;advances in robotics;[and] the quickly evolving method landscape driven by the open-source community, the benefits of automation and simulation are becoming accessible in the notoriously complicated realm of biopharma manufacturing," says Marcel von der Haar, head of product strategy data analytics at Sartorius. "Plug-and-play" capabilities of automation systems, which enable flexible manufacturing and faster technology transfer, are more important than ever, he says. Walvax Biotech's new COVID-19 mRNA vaccine plant in China is another example of an intelligent and digital plant;it uses Honeywell's batch process control, building and energy management solution systems, and digital twins to monitor assets (5). "Automation brings in the data for machine learning to model the dynamic processes of cell growth and map it against the multiple dimensions provided by advanced sensors," explains Brandl.
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This paper aims to visualise three financial distress outlooks using computer simulations. The financial distress exposure for airport operations in Malaysia between 1991 and 2021 is given by Altman Z”-score and modelled by the multivariate generalized linear model (MGLM). Seven determinants contributing to the financial distress from literature are examined. The determinant series are fitted individually by using linear model with time series components and autoregressive integrated moving average models to forecast values for the next 10 financial years. Future short- to long-term memory effects following COVID-19 are apparent in time series plots. In the simulations, the MGLM procedure utilised Gaussian, gamma, and Cauchy probability distributions associated with expectations and challenges of doing business as well as uncertainties in the economy. The underlying trends of realistic, optimistic, and pessimistic financial distress outlooks insinuate that the increasing risk of financial distress of airport operations in Malaysia is expected to continue for the next decade.
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Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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As virtual reality (VR) is labeled by many as 'an ultimate empathy machine,' immersive VR applications have the potential to assist in empathy training for mental healthcare such as depression [21]. In responding to the increasing numbers of diagnosed depression throughout COVID-19, a first-person VR adventure game called 'Schwer' was designed and prototyped by the authors' research team to provide a social support environment for depression treatment. To continue the study and assess the training effectiveness for an appropriate level of empathy, this current article includes a brief survey on data analytics models and features to accumulate evidence for the next phase of the study, an interactive game-level design for the 'Reconstruction' stage, and a preliminary study with data collection. The preliminary study was conducted with a post-game interview to evaluate the design of the levels and their effectiveness in empathy training. Results showed that the game was rated as immersive by all participants. Feedback on the avatar design indicated that two out of three of the non-player characters (NPCs) have made the intended effect. Participants showed mostly positive opinion towards their experienced empathy and provided feedback on innovative teleport mechanism and game interaction. The findings from the literature review and the results of the preliminary study will be used to further improve the existing system and add the data analytics model training. The long-term research goal is to contribute to the healthcare field by developing a dynamic AI-based biofeedback immersive VR system in assisting depression prevention. © 2023 IEEE.
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During the COVID-19 Pandemic, many universities in Thailand were mostly locked down and classrooms were also transformed into a fully online format. It was challenging for teachers to manage online learning and especially to track student behavior since the teacher could not observe and notify students. To alleviate this problem, one solution that has become increasingly important is the prediction of student performance based on their log data. This study, therefore, aims to analyze student behavior data by applying Predictive Analytics through Moodle Log for approximately 54,803 events. Six Machine Learning Classifiers (Neural Network, Random Forest, Decision Tree, Logistic Regression, Linear Regression, and Support Vector Machine) were applied to predict student performance. Further, we attained a comparison of the effectiveness of early prediction for four stages at 25%, 50%, 75%, and 100% of the course. The prediction models could guide future studies, motivate self-preparation and reduce dropout rates. In the experiment, the model with 5-fold cross-validation was evaluated. Results indicated that the Decision Tree performed best at 81.10% upon course completion. Meanwhile, the SVM had the best result at 86.90% at the first stage, at 25% of the course, and Linear Regression performed with the best efficiency at the middle stages at 70.80%, and 80.20% respectively. The results could be applied to other courses and on a larger e-learning systems log that has similar student activity conditions and this could contribute to more accurate student performance prediction © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.
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This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.
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Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The ${{\sf assess}}$assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube;(ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance;(iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.