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Advancements in technology and digitalisation have paved the way for multidimensional possibilities in the availability, extraction, and implantation of data. Consequently, the application of big data in understanding the spectra of different fields of knowledge has been evolving. Entrepreneurship research is one such developing area. Within this field, research has called for the advancement and application of methodological approaches to understand entrepreneurial phenomena. This study attempts to respond to this call by addressing how big data can be applied to entrepreneurship research. Therefore, the use of big data analysis and analytics in entrepreneurship research was explored and several theoretical perspectives and methodological possibilities for applying big data to entrepreneurship research were investigated. Furthermore, benefits, challenges, and ethical considerations were considered with a focus on how to navigate these challenges and implement them. The study concludes with future directions for emerging technologies that can be adopted in entrepreneurship research. Finally, recommendations are offered to researchers on how to apply big data not only to collect information but also to analyse and present it in a meaningful way.

【Objective】The triangular central measuring tank is widely used in irrigation systems for flow measurement and sediment transport monitoring. However, head loss in the tank, influenced by hydraulic and sediment parameters, affects its measurement accuracy and energy efficiency. To improve design and operation of such tanks, this paper investigates the effects of flow rate, shrinkage ratio and sediment content on head loss and develops a predictive model for practical use. 【Method】 The experiments were conducted using six flow rates ranging from 0.031 to 0.093 m3/s, three shrinkage ratios ranging from 0.375 to 0.625, and five sediment concentrations ranging from 0.69 to 3.08 kg/m3. The experimental results were analyzed using the range analysis and the analysis of variance (ANOVA). A predictive model for the head loss was established using dimensional analysis.【Result】 The head loss increased with increasing flow rate and sediment content but decreased as the shrinkage ratio increased. Among the three factors, the shrinkage ratio had the most significant impact on head loss, followed by flow rate, while sediment content had no significant effect on head loss. The proposed prediction model was accurate; the error between measured and predicted head loss ranged from -4.44% to 5.41%. 【Conclusion】Our results established the dependence of head loss on flow rate, shrinkage ratio and sediment content in triangular central measuring tanks. The developed model can reliably predict head loss across a wide range of operational conditions, offering a guidance for hydraulic design and optimization of similar tanks under sediment-laden flow conditions.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes. Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment. Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy. The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that come from educational settings. One of the main problems is predicting the future achievements of students before taking final exams, so we can proactively help students achieve better performance and prevent dropouts. Therefore, many efforts have been made to solve the problem of student performance prediction in the context of educational data mining. In this paper, we provide readers with a comprehensive understanding of student performance prediction and compare approximately 260 studies in the last 20 years with respect to i) major factors highly affecting student performance prediction, ii) kinds of data mining techniques including prediction and feature selection algorithms, and iii) frequently used data mining tools. The findings of the comprehensive analysis show that ANN and Random Forest are mostly used data mining algorithms, while WEKA is found as a trending tool for students’ performance prediction. Students’ academic records and demographic factors are the best attributes to predict performance. The study proves that irrelevant features in the dataset reduce the prediction results and increase model processing time. Therefore, almost half of the studies used feature selection techniques before building prediction models. This study attempts to provide useful and valuable information to researchers interested in advancing educational data mining. The study directs future researchers to achieve highly accurate prediction results in different scenarios using different available inputs or techniques. The study also helps institutions apply data mining techniques to predict and improve student outcomes by providing additional assistance on time.

Introduction Migraine is a prevalent neurological disorder causing significant disability worldwide. Despite extensive research on specific populations, comprehensive analyses of global trends are remains limited. Methods We extracted incidence, prevalence, and disability-adjusted life years (DALYs) data for migraine from the Global Burden of Disease 2021 database. Trends were analyzed across regions, age groups, sexes, and sociodemographic index (SDI) using estimated annual percentage changes (EAPC). Predictive models (ARIMA) were used to forecast trends to 2050. Results From 1990 to 2021, the global burden of migraine significantly increased: prevalence increased by 58.15%, from 732.56 million to 1.16 billion cases, and incidence increased by 42.06%. The DALYs also increased by 58.27%. There were differences between the sexes: female individuals had higher absolute rates of migraine incidence and prevalence, but male individuals exhibited a four- to five-fold more rapid increase than female individuals in these parameters. Adolescents (< 20 years old) have the fastest growth in prevalence and DALYs. Regionally, high SDI regions having the highest age-standardized rate (ASR) and low SDI regions having the lowest ASR in DALYs. East Asia and Latin America exhibited the most significant increases in migraine burden, whereas Southeast Asia exhibited the most pronounced decrease. Predictive analysis suggests prevalence will continue to rise until 2050, particularly among male individuals and adolescents. Conclusions The global burden of migraine has significantly escalated from 1990 to 2021, with female individuals bearing a greater burden but male individuals showing a faster growth rate. Adolescents also face a rapidly rising prevalence. Disparities across SDI regions, countries, age groups, and sexes emphasize the need for targeted public health strategies. Focused interventions are required to mitigate the growing impact of migraines on global health, particularly among male individuals and adolescents.

Background Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results The performance measures such as precision, accuracy, recall, F 1 score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

In the era of Industry 4.0, integrating digital technologies into industrial processes has become imperative for sustaining growth and fostering innovation. This research paper explores the profound impact of AI-driven intelligent data analytics and predictive analysis on economic efficiency and managerial practices within Industry 4.0. With a focus on knowledge, innovation, technology, and society, this study delves into the transformative potential of these advanced technologies. Intelligent data analytics, powered by artificial intelligence (AI), has revolutionized the way industries harness vast datasets. Uncovering real-time patterns, correlations, and opportunities empowers decision-makers with accurate and timely insights. Predictive analysis, rooted in statistics and machine learning, aids in forecasting trends and managing risks, offering economic stability across sectors. Using a mixed-methods approach, the study combines qualitative interviews with 19 Chinese operations managers and quantitative data from an online survey of 286 managers. The study ranks various Industry 4.0 technologies through ordinal regression based on their impact on environmental sustainability and economic management. Results show that smart sensors, radio-frequency identification, AI, and analytics are the most influential technologies for enhancing economic and environmental outcomes. Conversely, technologies like additive manufacturing and automated robots yield less favorable results. The study also identifies a noticeable gap in professionals’ understanding and adoption of AI and augmented reality. Environmental concerns around the disposal of electronic waste generated by these technologies are also highlighted. The research thus offers significant insights for companies seeking to adopt intelligent data analytics to enhance economic performance and environmental sustainability. On the managerial front, the fusion of these technologies enables agile and responsive frameworks, promoting dynamic strategies in response to changing market dynamics. This culture of continual improvement fosters excellence and foresight in managerial processes. However, challenges exist, including the underutilization of data, data complexity, historical biases, and the need for tailored AI solutions across industries. Ethical considerations, data privacy, and security also pose concerns. Collaborative innovation among stakeholders is crucial to addressing these challenges and seizing opportunities. Governments, academia, and industry players must collaborate to develop technologically advanced, economically viable, and socially responsible solutions. As industries transition to Industry 4.0, this paper advocates a critical approach that embraces technology’s potential while mitigating risks. The future lies in a technologically advanced, economically resilient, and socially inclusive industrial landscape driven by AI-powered knowledge and innovation.

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

Cancer is the second leading cause of mortality worldwide, largely due to low survival rates resulting from diagnosis at advanced stages. This paper focuses on how machine learning (ML) and deep learning (DL) algorithms have evolved over the past decade to improve cancer detection and classification, emphasizing the importance of early diagnosis. Convolutional Neural Networks (CNNs) have demonstrated an accuracy of 89.5% in medical image recognition, highlighting their effectiveness in imaging-based diagnosis. Recent advancements such as YOLOv7 further outperform traditional diagnostic methods by providing more accurate tumor detection. Prognostic analysis using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks has achieved accuracies of 82.3% and 84.7%, respectively. Ensemble methods exhibit superior performance with an impressive accuracy of 91.2%, outperforming individual models. Additionally, data augmentation using Generative Adversarial Networks (GANs) improves precision to 76.8%, underscoring the importance of synthetic data generation in addressing data scarcity. These findings collectively demonstrate the transformative impact of artificial intelligence in oncology and emphasize the significance of integrated, collaborative approaches for achieving improved cancer diagnosis and treatment outcomes.

Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy).