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The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield. Conventional connectivity studies often use methods such as seismic attribute fusion, while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes. Aiming at the above problem in the Bohai A Oilfield, this study proposes a big data analysis method based on the Deep Forest algorithm to predict the sandbody connectivity. Firstly, by compiling the abundant exploration and development sandbodies data in the study area, typical sandbodies with reliable connectivity were selected. Then, sensitive seismic attribute were extracted to obtain training samples. Finally, based on the Deep Forest algorithm, mapping model between attribute combinations and sandbody connectivity was established through machine learning. This method achieves the first quantitative determination of the connectivity for continuous composite sandbodies in the Bohai Oilfield. Compared with conventional connectivity discrimination methods such as high-resolution processing and seismic attribute analysis, this method can combine the sandbody characteristics of the study area in the process of machine learning, and jointly judge connectivity by combining multiple seismic attributes. The study results show that this method has high accuracy and timeliness in predicting connectivity for continuous composite sandbodies. Applied to the Bohai A Oilfield, it successfully identified multiple sandbody connectivity relationships and provided strong support for the subsequent exploration potential assessment and well placement optimization. This method also provides a new idea and method for studying sandbody connectivity under similar complex geological conditions. [ABSTRACT FROM AUTHOR]

Although big data applications in education have different usage areas, they can make important contributions to the evaluation of universities and education systems, decision-making and strategy development, and improvement of the education system. In this study, which is considered to contribute to the academicians who will work on big data in higher education, the subject of big data in higher education has been examined with the bibliometric analysis method. The aim of this study is to reveal the intellectual structure of the studies in the literature on big data in higher education and its development over time. The research method of the study is based on the bibliometric analysis technique, which enables the evaluation and interpretation of the data obtained by examining the literature with different parameters. The research data consists of 477 articles scanned in Scopus and Web Of Science databases published between 2014 and 2023. The articles examined in the study were analyzed under the titles of authors, keywords, the density of the words used, and the journals that published. R Bibliometric analysis program RStudio interface and Biblioshiny package were used for data analysis. The findings of the study are expected to provide guiding information to researchers in the field of big data in higher education. As a result of the study, especially in recent years, educational big data, online learning, industry 4.0 themes have emerged as current subject areas that can be studied. It has been concluded that the big data theme has changed as an online learning, moocs, educational big data theme in recent years. It was concluded that the researchers who published and cited the most in this field were Siemens, Daniel, Chen, Wang and Zhang. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study introduces the novel Fuzzy-Enhanced Predictive Neural System for Student Mental Health (FEPS-MH) approach to mental health assessment and intervention for college students. FEPS-MH synergistically combines a Backpropagation (BP) Neural Network with a Deep Fuzzy-Based Neural Network (DFBNN), leveraging the strengths of both systems to handle the complexities of mental health data in the context of big data analytics. The BP Neural Network, known for its effective learning and generalization capabilities, is integrated with the DFBNN to process imprecise, uncertain, or subjective data, typical in mental health assessments. The core objective of FEPS-MH is to provide a more accurate, robust, and sensitive analysis of mental health states, incorporating the nuanced variations and uncertainties inherent in psychological data. This system is designed to analyze a vast array of data sources, including but not limited to, behavioral patterns, self-reported questionnaires, and social media interactions, to identify potential mental health issues among college students. FEPS-MH's capabilities extend beyond mere assessment; it is also equipped to recommend personalized intervention strategies. Utilizing big data analysis, the system not only predicts potential mental health crises but also suggests tailored intervention approaches based on the unique psychological profile of each student. This study demonstrates the feasibility and effectiveness of FEPS-MH through a series of tests and validations using real-world data. The results indicate a significant improvement in both the accuracy of mental health assessments and the efficacy of suggested interventions. FEPS-MH stands as a promising tool for educational institutions, offering a data-driven, sensitive, and comprehensive approach to student mental health care. Its implementation could revolutionize the field of mental health support in college environments, making it a vital asset for proactive psychological wellness in educational settings. [ABSTRACT FROM AUTHOR]

Within the rapidly evolving panorama of the virtual financial system, the function of training in fostering innovation and entrepreneurship has become more and more vital. This study aims to explore how big records analysis and digital sharing techniques can be leveraged to complement innovation and entrepreneurship schooling. The observation is grounded within the context of the virtual economy generation, characterised with the aid of the proliferation of virtual technologies and the exponential boom of facts. The middle objective is to research how instructional techniques can be more desirable thru the utility of large records analytics and digital sharing, thereby preparing college students extra effectively for entrepreneurial roles inside the digital age. The studies employ a deep learning technique, combining quantitative information evaluation with qualitative insights. Primary statistics could be accumulated through surveys and interviews with educators and marketers, even as secondary statistics will be sourced from existing educational literature and case research. The observe will awareness on key regions which includes the effect of huge facts on expertise marketplace developments and client conduct, the function of virtual sharing in fostering collaborative learning and innovation, and the integration of these technologies into curriculum design and pedagogical practices. Anticipated results include a set of recommendations for academic institutions on integrating large statistics and digital sharing tools into entrepreneurship training. The study targets to offer insights into how this technology can enhance college students' analytical and innovative skills, put together them for the challenges of the digital economic system, and foster a tradition of innovation and entrepreneurial attitude. [ABSTRACT FROM AUTHOR]

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today's big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies. [ABSTRACT FROM AUTHOR]

With the rapid advancement of big data and information technology, the analysis of student emotional fluctuations has emerged as a new frontier in the field of educational psychology. This study aims to explore the patterns of student emotional fluctuations through big data analysis techniques and to use these patterns to predict students' psychological tendencies, thereby providing educators with real-time, accurate teaching references. The importance of student emotions in the learning process and the potential of big data technology in perceiving and analyzing student emotions are elucidated in the background section. The current state of study discusses the limitations of traditional methods in analyzing student emotions, such as small sample sizes, short data collection time spans, and the lack of timeliness and accuracy in analysis. A student emotional fluctuation identification model based on big data is first established, capable of integrating multi-source data and effectively capturing subtle changes in student emotions. Furthermore, a psychological tendency mixed-frequency prediction model is constructed, utilizing the mixed data sampling (MIDAS) mixed-frequency model, aimed at achieving accurate predictions of trends in student emotional fluctuations. The development and validation of these two models demonstrate the application value of big data analysis in the field of educational psychology, supporting personalized learning and promoting the effectiveness of student emotional management and teaching interventions. [ABSTRACT FROM AUTHOR]

Detection of abnormal power consumption is very important to reduce irregular power consumption and economic losses. To reduce power wastage, it is necessary to understand the power consumption habits of users and detect unusual usage behavior promptly. Researchers have developed various methods to detect abnormal electricity consumption behavior. However, it does not provide enough consideration to identify the relationships between different time stamps and multi-scale characteristics of the power utilization series. Therefore, a grey relation projection–based better clustering learning technique is proposed for assessing power consumption abnormalities. Power data from a power consumption information gathering system is analyzed with a diagnosis model for anomalous electricity use by customers constructed using big data techniques and machine learning-related algorithms. Density-based clustering is offered as a means of analyzing instances of excessive electricity use. First, consumers in the same area are grouped based on the similarities between their electricity usage patterns. Then, using density clustering, we identify the most extreme cases of electricity use amongst consumers who are otherwise comparable. Using a comparable user electricity consumption framework and the historical electricity usage approach, we can determine the degree to which these anomalies match the norm. Finally, the threshold value allows for discrimination of anomalous electricity usage with the level of comprehensive support. The proposed DBSCAN approach attains the PR-AUC values of 0.96, and AUC attains 0.911, results from simulations demonstrate that this approach can detect aberrant patterns of electricity consumption. [ABSTRACT FROM AUTHOR]

The editorial discusses the exponential growth in scientific research publications, highlighting the challenges and opportunities it presents. The surge in publications is attributed to factors like the expansion of the global research community and the pressure to publish in academia. While the increase in research output can lead to more knowledge and discoveries, it also poses challenges such as information overload, concerns about research quality, and the need for new approaches to research evaluation. The editorial emphasizes the importance of adapting to the evolving research landscape to ensure genuine progress and quality academic research. [Extracted from the article]

Chinese culture continues to influence the world. As one of the important carriers of cultural communication film and television dramas carry rich historical and cultural information. With historical culture exquisite costume design and gripping drama stories ancient costume film and television dramas have deeply attracted the audience and aroused strong resonance. Meanwhile the rapid development of technologies such as artificial intelligence has continuously impacted the design and cultural fields and has put forward higher requirements for the development and research of related industries. How to adapt to the development trend of the times strengthen the dissemination of traditional culture and achieve industrial transformation is a difficult problem that the Chinese ancient costume film and television drama clothing industry has to face at this stage. This paper selects big data analysis methods uses CONCOR analysis and LDA topic modeling and conducts research from two levels research hotspots and industry development trends. In order to use objective experimental results from the two levels of research and industry the paper analyzes the hot topics related to Chinese ancient costume film and television drama clothing further revealing the future development trend of Chinese ancient costume film and television drama clothing industry and providing objective literature reference for enterprises and related researchers. Research hotspot data were collected by using CNKI database as the data source of literature ' film and television costumes were selected as the search keyword to collect nine research documents in the field of Chinese ancient costume film and television costumes in the past five years and the research content was examined. Social opinion hotspot research data were collected by using TEXTOM-v5. 5 program and with ' Chinese ancient costume film and television costumes as the keyword relevant websites posts public accounts news and other data released by Chinese social media platforms were collected in the past three years from May 24 2021 to May 23 2024. Research results show that in the past five years related research has mainly focused on four aspects design concepts and aesthetic pursuits the dissemination and promotion of traditional clothing culture the development requirements of costume design for ancient costume film and television dramas and the shaping and performance of roles. In terms of industry development trends with the further advancement of the national cultural communication strategy the future Chinese ancient costume film and television costume industry will usher in new trends such as digital development new trends in cross-industry linkage industrial transformation new opportunities for cultural communication and brand building and new design requirements that focus on tradition and innovation. This study hopes to provide useful empirical data for related research and industries in the field of costumes for Chinese ancient film and television dramas. However in the process of collating and confirming a large amount of data it takes considerable time and effort so there are limitations in improving research efficiency. [ABSTRACT FROM AUTHOR]

With the popularity of Instagram, the hashtag (#) symbol has emerged as an important marketing tool. By using hashtags, brands can increase their visibility and reach among Instagrammers. The purpose of this research is to examine how fashion brands utilize hashtags and captions on Instagram and how Instagrammers respond to different hashtag and caption patterns. A computational analysis of a large collection of hashtags and captions posted with fashion images was conducted to identify patterns of utilizing hashtags and captions in terms of frequency, form and content. Specifically, 65,107 hashtags attached to 29,557 fashion images posted on Instagram by ten global fashion brands were analysed. This study also examined how customer engagement levels vary for each variable. Several important findings emerged from this study: (1) while most fashion images were posted with hashtags and captions, indiscriminate use of hashtags (i.e. hashtags that are too frequent or too long) were likely to lower the number of likes and comments; (2) using a hashtag in combination with a caption or only using a caption could be more effective than using a hashtag alone in increasing customer engagement levels; and (3) hashtags containing promotional messages, in general, were less effective in promoting likes and comments. [ABSTRACT FROM AUTHOR]

Meniere's disease (MD) is a chronic inner ear disorder characterized by tinnitus, ear fullness, episodic vertigo, and fluctuating hearing loss, which significantly impacts quality of life and poses management challenges. Recent evidence suggests that upper respiratory infections (URIs) may contribute to MD's onset. This study examines the potential link between URIs and MD using data from the Korean National Health Insurance Service-National Sample Cohort (2002–2019). We analyzed 19,721 individuals with MD and 78,884 matched controls, adjusting for demographic factors and comorbidities using propensity score matching. Our results showed that individuals with a URI within one year prior to the index date exhibited a 2.01-fold greater likelihood of developing MD (95% confidence interval [CI] = 1.91–2.11, p < 0.001), while those with URIs within two years demonstrated a 1.54-fold higher probability (95% CI = 1.50–1.59, p < 0.001). Furthermore, we found that even remote URIs occurring up to two years before the index date significantly increased the risk of developing MD, underscoring the need for long-term patient follow-up. Overall, our study suggests that individuals with a history of URI may have an elevated risk of developing MD over multiple time frames, regardless of demographic or health profiles. [ABSTRACT FROM AUTHOR]

With the development of information technology, big data analysis has been highlighted in operations and management. From this viewpoint, this paper studies a buyer's optimal purchasing decisions in combined procurement. For combined procurement, a buyer first signs a long-term contract with a supplier to guarantee a certain level of commodity supply, and can then replenish the commodities from the spot market if necessary. The optimal purchasing quantity in the long-term contract is examined to maximise the buyer's expected profit from combined procurement. In view of the imperfectness in the spot market, the spot trading liquidity is considered in the buyer's optimal purchasing decision. The properties of the two optimal purchasing quantities are examined and several interesting results are obtained. For example, it is illustrated that a buyer's expected profit may decrease in the spot capacity, a result that has never appeared in the existing literature, which reveals the importance of a buyer's optimal order decision in the presence of spot replenishment. Numerical results and sensitivity analysis are performed to verify the results. Management insights are suggested for a buyer's optimal purchasing decisions in combined procurement with a long-term contract and spot replenishment. [ABSTRACT FROM AUTHOR]

Abstract This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as “lead,” “bottled water,” “water filters,” “pneumonia,” and “Legionnaires’ Disease,” we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of “Big Data,” led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research.

Abstract The morphological characteristics of the foot arch and the plantar soft tissue thickness are pivotal in assessing foot health, which is associated with various foot and ankle pathologies. By applying deep learning image segmentation techniques to lateral weight-bearing X-ray images, this study investigates the correlation between foot arch morphology (FAM) and plantar soft tissue thickness (PSTT), examining influences of age and sex. Specifically, we use the DeepLab V3+ network model to accurately delineate the boundaries of the first metatarsal, talus, calcaneus, navicular bones, and overall foot, enabling rapid and automated measurements of FAM and PSTT. A retrospective dataset containing 1497 X-ray images is analyzed to explore associations between FAM, PSTT, and various demographic factors. Our findings contribute novel insights into foot morphology, offering robust tools for clinical assessments and interventions. The enhanced detection and diagnostic capabilities provided by precise data support facilitate population-based studies and the leveraging of big data in clinical settings.

Joon Young Choi,1 Chin Kook Rhee2 1Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; 2Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of KoreaCorrespondence: Chin Kook Rhee, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea, Tel +82 2 2258 6067, Fax +82 2 599 3589, Email chinkook77@gmail.comAbstract: Asthma is a chronic inflammatory airway disease with significant burden; exacerbations can severely affect quality of life and healthcare costs. Advances in big data analysis and artificial intelligence have made it easier to predict future exacerbations more accurately. This study used an integrated dataset of Korean National Health Insurance, meteorological, air pollution, and viral data from national public databases to develop a model to predict asthma exacerbations on a daily basis in South Korea. We merged these sources and applied random forest, AdaBoost, XGBoost, and LightGBM machine learning models to compare their performances at predicting future exacerbations. Of the models, XGBoost (AUROC of 0.68 and accuracy of 0.96) and LightGBM (AUROC of 0.67 and accuracy of 0.96) were the most promising. Common important variables were the number of visits and exacerbations per year, and medical resource utilization, including the prescription of asthma medications. Comorbid diabetes, hypertension, gastroesophageal reflux, arthritis, metabolic syndrome, osteoporosis, and ischemic heart disease were also associated with elevated exacerbation risk. The models examined in this study highlight the importance of previous exacerbations, use of medical resources, and comorbidities in the prediction of future exacerbations in patients with asthma.Keywords: asthma, machine learning, big data analysis, South Korea, XGBoost, LightGBM

With the development of China’s digital economy, Internet traffic has shown a steady growth trend. In view of the problem that the uneven distribution of Internet traffic will affect the long-term stable development of the network, the methods of Internet traffic acquisition and identification were discussed. The network architecture for traffic collection, and the software architecture for traffic analysis were constructed. A feasible market-oriented strategy for regulating and channeling Internet traffic was proposed. The propoced strategy has achieved preliminary results through the practice of operators, and it provides a solution and reference basis to guide the distribution of Internet traffic in a proper way.

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises. [ABSTRACT FROM AUTHOR]

This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as "lead," "bottled water," "water filters," "pneumonia," and "Legionnaires' Disease," we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of "Big Data," led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research. [ABSTRACT FROM AUTHOR]

Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to control the spread of infection, and maintain proactive nursing rounds. However, recruiting and retaining qualified caregivers and medical staff has long been a challenge. Therefore, using advanced technology to ensure the safety and security of residents is highly desirable. In this work, we first demonstrate the applicability of indoor tracking applications in a nursing home, such as resident and asset tracking, nursing assistant management, visitor tracking, infection control, and vital-sign monitoring. To monitor the locations of residents and staff, Bluetooth tags were used, providing real-time data for location tracking. We then conduct a series of quantitative analyses to illustrate how indoor tracking data can support the management of nursing homes, including characterizing residents' activities in daily living and assessing the performance and workload of nursing assistants. Finally, we use qualitative research to evaluate the acceptability of an indoor positioning system in the nursing home. The results show that the implemented indoor positioning applications can improve the quality of healthcare and working efficiency, thereby providing a safer and more secure living environment for residents. [ABSTRACT FROM AUTHOR]

With globalization and technological progress, the demand for language translation is increasing. Especially in the fields of education and research, accurate and efficient translation is considered essential. However, most existing translation models still have many limitations, such as inadequacies in dealing with cultural and contextual differences. This study aims to solve this problem by combining big data analysis, machine learning and translation theory, and proposes a comprehensive translation quality evaluation model. On the basis of screening and constructing a representative sample database, pre-processing and standardization, feature selection is carried out by combining multi-dimensional features such as grammatical complexity and cultural adaptability factors, and different machine learning algorithms are used for model construction and parameter optimization. Finally, by training and testing the model, the performance and effectiveness of the model are evaluated, and a comprehensive evaluation standard is constructed. The results show that this model can not only effectively improve the translation quality, but also has a high system application and universality. [ABSTRACT FROM AUTHOR]

With the development of the times, computers have been continuously developed, and data analysis based on intelligent algorithms has been gradually applied. Big data analysis has become an indispensable part of various enterprises and institutions, which can help enterprises better analyze the market, optimize the overall resource allocation and decision-making efficiency, and promote the sustainable development of the overall enterprises and institutions. The purpose of this experiment is to use intelligent algorithm and big data analysis technology to cluster hotel data, so as to find the patterns and associations hidden in the data and provide decision support for the hotel industry. [ABSTRACT FROM AUTHOR]

Cold chain logistics has become a core link in ensuring drug quality and safety, especially for temperature sensitive drugs such as vaccines and biologics. This paper aims to develop a drug cold chain transportation risk assessment and early warning system based on big data, utilizing advanced technologies such as big data analysis and machine learning to achieve real-time monitoring, intelligent analysis, and early warning of various risk factors in the drug cold chain transportation process. This paper proposes a comprehensive architecture that includes data collection layer, data transmission layer, data processing and storage layer, analysis engine, warning and response module, user interface, and system integration interface. This paper collects key environmental data in real-time through sensors, such as temperature and humidity, and uses a secure data transmission layer to transmit the data to the central processing system. The construction of the risk assessment model is based on a series of quantifiable risk indicators, including temperature deviation, humidity deviation, location deviation, etc., and through data standardization and weight allocation, combined with a machine learning model to perform risk scoring and grading. The early warning mechanism implementation part includes early warning rules and threshold settings as well as real-time monitoring and dynamic warning to ensure that the system can respond to potential risks in a timely manner. The results of the system performance evaluation show that the risk evolution prediction deviation rate remains at a very low level, with a maximum of 1.4%, the false alarm rate is very low, with a maximum of 0.15% and a minimum of 0.03%, and the risk situational awareness sensitivity coefficient remains at a high level, with a minimum of 0.8. These results indicate that the developed system performs well in terms of risk prediction accuracy, warning timeliness and risk perception sensitivity. [ABSTRACT FROM AUTHOR]

The current study aimed to address the limitations of the terror management theory literature by using big data analysis to examine the theory's predictions in the COVID-19 pandemic. Specifically, Google Trends were examined before and after the first COVID-19 case was identified in Singapore. The results showed that there was a significant increase in mortality salience, intergroup conflict, and prosocial behavior, and a significant decrease in materialism after the first COVID-19 case was identified. However, no significant differences were found for anxiety. Limitations include the assumption that search terms reflect intentions that would eventually lead to a relevant behavior and the lack of data from other sources to corroborate with the results from Google Trends. Future research could use data from other sources to examine the effects of COVID-19 on theoretically relevant behaviors. [ABSTRACT FROM AUTHOR]

Online supplementary education has been prevalent in recent years due to the advent of technology (e.g., live streaming) and the COVID-19 pandemic. However, the performance of students in this mode of education varies greatly, and the underlying reasons are yet to be investigated. This study aims to understand the impact of various factors and giving their quantified importance, including student information, family information, and course information by applying Machine Learning method to one of the world's largest online learning platforms. Big data analysis is employed for this purpose via leveraging the abundance of data generated from online education platforms, providing insights that are not attainable through conventional methods such as panel surveys or questionnaires used in offline education. The findings indicate that the most significant factor affecting student performance is the disparity in access to educational resources and the socioeconomic status of families. Finally, we predict students' online learning performance by using explainable machine learning with different groups of features. Our results indicate approximately 70% accuracy in predicting students' performance progress. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early-stage detection of chronic kidney disease (CKD) is crucial in research to enable timely intervention, enhance understanding of disease progression, reduce healthcare costs and support public health initiatives. The traditional approaches on early-stage chronic kidney disease detection often suffer from slow convergence and not integrate advanced technologies, impacting their effectiveness. Additionally, security and privacy concerns related to patient data are ineffectively addressed. To overcome these issues, this research incorporates novel optimized artificial intelligence-based approaches. The main aim is to enhance detection process through enhanced hybrid mud ring network (EHMRN), a novel detection technique combining light gradient boosting machine and MobileNet, involving extensive data collection, including a large dataset of 100,000 instances. The introduced network is optimized through the mud ring optimization to attain enhanced performance. Incorporating spark ensures secure cloud-based storage, enhancing privacy and compliance with healthcare data regulations. This approach represents a significant advancement in primary stage detection more effectively and promptly. The results show that the introduced approach outperforms traditional approaches in terms of accuracy (99.96%), F1-score (99.91%), precision (100%), specificity (99.98%), recall (100%) and execution time (0.09s). [ABSTRACT FROM AUTHOR]

This study investigated how indoor and outdoor environments in Nature Cafes, serving as nature-based solutions (NBS), influence customer approach behavior using the SOR model and a mixed-method approach. Analyzing 5,456 reviews, the study identified key indoor and outdoor variables. Results revealed that green spaces within cafes and outdoor natural environments as NBS were associated with improved self-rated mental health and connectedness. The study used fsQCA analysis to determine an optimized factor combination, highlighting the moderating role of product knowledge. The findings offer a valuable framework for maximizing NBS in approach behavior, particularly in the natural cafe setting. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic required swift responses from governments at all levels. Government agencies were faced with the immense task of mitigating the health, social, and economic effects of COVID-19. These actions and responses included developing mobile phone location tracking systems and 'electronic fences' alongside the use of big data analytics. Whether intentionally or not, this led to questions about the rise of the 'biosurveillance state'. In this paper, we examine the extent to which digital democracy has emerged as a contested concept in Taiwan. Furthermore, we ask: to what extent is the use of digital surveillance for disease control and prevention justifiable, and to what extent can personal privacy be sacrificed when adopting digital surveillance measures with the aim of securing collective safety? We compare Taiwanese citizens' concerns about personal privacy with those in other democracies, such as the UK, and those in the EU and North America. [ABSTRACT FROM AUTHOR]

Aiming to address the issues of low data execution efficiency and slow storage speed on traditional tourism cloud service platforms, a rural tourism management cloud service platform based on an embedded system is proposed. The embedded device components, such as the ARM processor, NOR Flash memory, and RTL network chip, are utilized to optimize the hardware structure of the cloud service platform. This optimization aims to enhance data processing and storage efficiency, as well as improve the reliability and durability of system operation. The unstructured feature grasping algorithm is employed to gather rural tourism resource data for target demand characterization. Associated algorithms are then utilized to develop data analysis, service recommendation, and tourism management modules, creating a comprehensive tourism cloud service platform. Additionally, the shortest path algorithm is used to enhance database storage speed, improve business docking efficiency, establish a big data monitoring center, comprehensively monitor changes in the scenic area and related industries, and enhance platform service efficiency. It has been proven through experiments that the latency of the tourism management cloud service platform, based on the embedded system, is reduced by almost 46.7% compared to the traditional approach, and the throughput is increased to 0.69 b/s. The practical application effect is positive, as it contributes to the development of tourism resources and the economic growth of rural areas. It also brings about economic and social benefits. [ABSTRACT FROM AUTHOR]

The coronavirus, which first appeared in December 2019, has had a great economic and social impact not only on the world but also on Korea as of 2024, and has a great impact on the cultural and artistic fields. This study was conducted to identify the perception of online non-face-to-face arts and physical education practical training among the fields of arts and culture education and to suggest directions for online arts education. To this end, online big data was collected based on the keywords of 'art education', 'culture and art performance', and 'art and physical training', and after purification/morphological analysis of the collected data, text mining, matrix analysis, discourse analysis, Network analysis and sentiment analysis were performed. The big data collection of online arts and physical training was done through portals and SNS, and was targeted at Naver, Daum, Google, YouTube, and Twitter. The period consisted of 7,837 data from March 2022 to March 2023 to investigate the general public's perception of arts education after the COVID-19 pandemic. This study is expected to be a basic material to present the direction of online education for art culture by analyzing the perceptions of art majors as well as ordinary people interested in art and physical education through big data. [ABSTRACT FROM AUTHOR]

Digital technology has changed every process of the fashion industry significantly. Using big data analysis methods such as text-mining, network, CONCOR, and content analyses, this study aims to understand the impact of digital technology trends from the fashion design perspective. The influence of digital technology on fashion design elements (e.g., color, print and graphic, textiles, and style and details) was evident through various keywords related to digital technology, humans, and nature, and the relationships between these keywords were confirmed. The analysis of the implicit meanings and directions of the derived keywords resulted in four clusters: (1) human- and nature-oriented design in the digital world as a new reality; (2) new textiles reflecting digital technology; (3) sustainable design technology; and (4) new utility fashion in the digital space. This study proposed a new design research methodology in which big data were incorporated and could be applied to educational curricula, allowing students to derive practical design elements through big data analysis and serving as a guide for planning and developing technology-inspired designs. Practically, it provided specific information on the direction of digital-technology-inspired fashion design trends, which could assist fashion designers and aspiring entrepreneurs in planning. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the policy considerations that should be taken into account regarding the practical introduction of affirmative action policies in the field of education. For this purpose, we analysed the 100 most relevant YouTube videos produced between 2015 and 2023 using network analysis, the aim being to utilize the material they provide on affirmative action so as to reflect this in future education policies. As a result, nine key policy considerations that should be considered when introducing affirmative action policies in the field of education were derived. [ABSTRACT FROM AUTHOR]

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With the social economy's rapid progress and the popularization of environmental awareness, ecological enterprises have gradually become a crucial trend in the development of modern enterprises. This work intends to promote the development of ecological enterprises to a higher level. This work first analyzes the management mode of ecological enterprises in the context of big data in China. Then, it establishes various indicators to analyze the role of sustainable technological innovation in enterprise development and the impact of digital empowerment on enterprise development. Finally, this work takes China's manufacturing industry and ecological enterprises in Hubei Province as examples to summarize the digital empowerment of sustainable technological innovation management of ecological enterprises under the background of big data. The final result indicates that sustainable technological innovation significantly reduces ecological enterprises' resource consumption and waste emissions. Additionally, it has a significant positive effect on improving enterprise output value and economic benefits. The digital empowerment of enterprises has a significant driving effect on sustainable technological innovation, with a digital driving coefficient of 26. This work provides a feasible scheme for the specific application of big data analysis in the technology innovation management of ecological enterprises, including market demand analysis, environmental monitoring and governance, technology assessment and risk management. This work expounds the role of big data analysis technology in improving decision-making efficiency, optimizing resource allocation and enhancing the competitiveness of enterprises in the digital empowerment of ecological enterprises. [ABSTRACT FROM AUTHOR]

Big data analytics for clinical decision-making has been proposed for various clinical sectors because clinical decisions are more evidence-based and promising. Healthcare data is so vast and readily available that big data analytics has completely transformed this sector and opened up many new prospects. The smart sensor-based big data analysis recommendation system has significant privacy and security concerns when using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access, which might lead to identity theft and privacy violations, increases when sending and storing sensitive medical data on the cloud. Our effort will improve patient care and well-being by creating an anomaly detection system based on machine learning specifically for medical images and providing timely treatments and notifications. Current anomaly detection methods in healthcare systems, such as artificial intelligence and big data analytics-intracerebral hemorrhage (AIBDA-ICH) and parallel conformer neural network (PCNN), face several challenges, including high resource consumption, inefficient feature selection, and an inability to handle temporal data effectively for real-time monitoring. Techniques like support vector machines (SVM) and the hidden Markov model (HMM) struggle with computational overhead and scalability in large datasets, limiting their performance in critical healthcare applications. Additionally, existing methods often fail to provide accurate anomaly detection with low latency, making them unsuitable for time-sensitive environments. We infer the extraction, feature selection, attack detection, and data collection and processing procedures to anticipate anomaly inpatient data. We transfer the data, take care of missing values, and sanitize it using the pre-processing mechanism. We employed the recursive feature elimination (RFE) and dynamic principal component analysis (DPCA) algorithms for feature selection and extraction. In addition, we applied the Auto-encoded genetic recurrent neural network (AGRNN) approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and root mean square error (RMSE) are some metrics used to evaluate the proposed task.