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Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy, necessitating the incorporation of robust pre-processing techniques, including data cleaning, preparation, normalization, and addressing imbalances. These steps are crucial for generating a robust dataset for training. Designing frameworks capable of handling such data without compromising efficiency is essential to ensuring robustness. This research aims to propose a novel healthcare framework that selects the best features and enhances performance. This robust deep learning framework, called (R-DLH2O), is designed for forecasting cardiovascular crises. Unlike existing methods, R-DLH2O integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This multi-phase approach ensures superior accuracy and efficiency in crisis prediction, offering a significant advancement in healthcare analytics. H2O is utilized in the R-DLH2O framework for processing big data. The main improvement of this paper lies in the unique form of the Whale Optimization Algorithm (WOA), specifically the Modified WOA (MWOA). The Gaussian distribution approach for random walks was employed with the diffusion strategy to choose the optimal MWOA solution during the growth phase. To validate the R-DLH2O framework, six performance tests were conducted. Surprisingly, the MWOA-2 outperformed other heuristic algorithms in speed, despite exhibiting lower accuracy and scalability. The suggested MWOA was further analyzed using benchmark functions from CEC2005, demonstrating its advantages in accuracy and robustness over WOA. These findings highlight that the framework's processing time is 436 s, mean per-class error is 0.150125, accuracy 95.93%, precision 92.57%, and recall 93.6% across all datasets. These findings highlight the framework's potential to produce significant and robust results, outperforming previous frameworks concerning time and accuracy. [ABSTRACT FROM AUTHOR]

A comprehensive and high-quality lexicon plays a crucial role in traditional text classification approaches. It improves the utilization of linguistic knowledge. Although it is helpful for this task, the lexicon has received little attention in current neural network models. First, obtaining a high-quality lexicon is not easy. Second, an effective automated lexicon extraction method is lacking, and most lexicons are handcrafted, which is very inefficient for big data. Finally, there is no effective way to use a lexicon in a neural network. To address these limitations, we propose a pre-attention mechanism for text classification in this study, which can learn the attention values of various words based on their effects on classification tasks. Words with different attention values can form a domain lexicon. Experiments on three publicly available and authoritative benchmark text classification tasks show that our models obtain competitive results compared with state-of-the-art models. For the same dataset, when we use the pre-attention mechanism to obtain attention values, followed by different neural networks, words with high attention values have a high degree of coincidence, which proves the versatility and portability of the pre-attention mechanism. We can obtain stable lexicons using attention values, which is an inspiring method of information extraction. [ABSTRACT FROM AUTHOR]

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the available sports data mining techniques concentrates on extracting and constructing effective features for basic sports data, which cannot be achieved simply by using data statistics. Especially in the targeted mining of sports data, traditional mining techniques still have shortcomings such as low classification accuracy and insufficient refinement. In order to solve the problem of low accuracy in traditional mining methods, the study combines the random forest algorithm with the artificial raindrop algorithm, and adopts a sports data mining method based on feature selection to achieve effective analysis of sports big data. This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. It uses the information gain index to rank the importance of features and accurately gain the degree of influence of exercise on various indicators of the human body. Through simulation verification, the algorithm proposed by the research institute performs the best in accuracy and FI scores on the training and testing sets, with accuracies of 0.849 ± 0.021 and 0.819 ± 0.022, respectively, and F1 scores of 0.837 ± 0.020 and 0.864 ± 0.021, respectively. This indicates that the algorithm proposed by the research institute has high classification accuracy and performance proves that the Random Forest-based feature selection algorithm established in this study is superior to the existing traditional feature extraction and extraction methods in terms of both performance and accuracy. The proposal of this data analysis method has achieved accurate and efficient utilization of sports big data, which is of great significance for the development of the sports education industry. [ABSTRACT FROM AUTHOR]

Anastomotic leakage in visceral surgery is associated with a large number of known and also unknown or even unmeasurable parameters. Furthermore, the associations between the individual factors are intertwined and complex. According to current data a preoperative prediction is not reliably possible and should be distinguished from intraoperative or postoperative prediction models. Most studies on this topic do not exceed an area under the curve (AUC) of 0.70. A thorough understanding of statistics and prediction models is necessary to correctly interpret the published works. Due to the relatively low incidence rate of anastomotic leakage from a statistical point of view, large datasets are required for adequate prediction. Multimodal data and complex algorithms can potentially handle big data more accurately and improve predictability; however, these models have so far not been applied in the clinical routine. [ABSTRACT FROM AUTHOR]

Introduction: The evaluation of the performance of new methods, expected to provide cheaper and faster results than existing (reference) methods in the health field, is based on comparing the results obtained with this new method to those obtained with the existing method. The primary aim of this study is to examine the correlational and absolute agreement between measurement methods in clinical studies using Bland–Altman analysis and methodological (Ordinary Least Squares, Weighted Ordinary Least Squares, Deming, Weighted Deming, Passing–Bablok, Theil-Sen, and Passing–Bablok for Large Data Sets.) methods, and the secondary aim is to compare the accuracy and precision of Hadlock (I–V) formulas used for fetal weight estimation. Materials and methods: The study was conducted on singleton pregnancies examined in the Prenatal Diagnosis and Treatment Unit of the Department of Obstetrics and Gynecology at Inonu University Faculty of Medicine and who gave birth in the Obstetrics Unit between 01.01.2020 and 01.09.2023, whose gestational ages were confirmed by first-trimester ultrasonography. Estimated fetal weights were calculated using Hadlock (I–V) formulas, and the agreement of these weights with birth weight was evaluated with Bland–Altman method. Results: The comparison of estimated fetal weights calculated using Hadlock formulas with birth weight was analyzed using Bland–Altman analysis, ICC, and CCC values along with regression analyses. According to the mean difference values obtained by Bland–Altman analysis, the estimated fetal birth weights obtained by the Hadlock IV formula were most consistent with the actual birth weights. Conclusions: The estimated fetal weights obtained using the Hadlock IV formula resulted in the closest measurements to the birth weight. This study showcases the efficacy of a new web-based software, Method Comparison Software (MCS), which can be utilized for evaluating the agreement between different methods in clinical measurements. [ABSTRACT FROM AUTHOR]

In real-world scenarios, datasets often lack full supervision due to the high cost associated with acquiring decision labels. Completing datasets by filling in missing labels is essential for preserving the valuable feature information of individual samples. Furthermore, in the era of big data, datasets tend to exhibit high dimensionality, which adds complexity to subsequent data processing. In this study, a new semi-supervised feature selection technique is introduced. Firstly, a fully supervised dataset is created by utilizing a local density decision-labeling algorithm to fill in missing decision labels within the semi-supervised dataset. Next, a fuzzy dependency-based feature selection approach is presented to find and keep the most pertinent characteristics for the finished datasets. Finally, the effectiveness and reliability of our proposed method are validated through a series of rigorous experiments. [ABSTRACT FROM AUTHOR]

In contemporary real-time applications, diminutive devices are increasingly employing a greater portion of the spectrum to transmit data despite the relatively small size of said data. The demand for big data in cloud storage networks is on the rise, as cognitive networks can enable intelligent decision-making with minimal spectrum utilization. The introduction of cognitive networks has facilitated the provision of a novel channel that enables the allocation of low power resources while minimizing path loss. The proposed method involves the integration of three algorithms to examine the process of big data. Whenever big data applications are examined then distance measurement, decisions mechanism and learning techniques from past data is much importance thus algorithms are chosen according to the requirements of big data and cloud storage networks. Further the effect of integration process is examined with three case studies that considers low resource, path loss and weight functions where optimized outcome is achieved in all defined case studies as compared to existing approach. [ABSTRACT FROM AUTHOR]

Until now, automation in nanomaterial research has been largely focused on the automated synthesis of engineered nanoparticles (NPs) including the screening of synthesis parameters and the automation of characterization methods such as electron microscopy. Despite the rapidly increasing number of NP samples analyzed due to increasing requirements on NP quality control, increasing safety concerns, and regulatory requirements, automation has not yet been introduced into workflows of analytical methods utilized for screening, monitoring, and quantifying functional groups (FGs) on NPs. To address this gap, we studied the potential of simple automation tools for the quantification of amino surface groups on different types of aminated NPs, varying in size, chemical composition, and optical properties, with the exemplarily chosen sensitive optical fluorescamine (Fluram) assay. This broadly applied, but reportedly error-prone assay, which utilizes a chromogenic reporter, involves multiple pipetting and dilution steps and photometric or fluorometric detection. In this study, we compared the influence of automated and manual pipetting on the results of this assay, which was automatically read out with a microplate reader. Special emphasis was dedicated to parameters like accuracy, consistency, achievable uncertainties, and speed of analysis and to possible interferences from the NPs. Our results highlight the advantages of automated surface FG quantification and the huge potential of automation for nanotechnology. In the future, this will facilitate process and quality control of NP fabrication, surface modification, and stability monitoring and help to produce large data sets for nanomaterial grouping approaches for sustainable and safe-by-design, performance, and risk assessment studies. [ABSTRACT FROM AUTHOR]

Big Data (i.e., social big data, vehicular big data, healthcare big data etc) points to massive and complex data, that require special technologies and approaches for storage, processing, and analysis. Similarly, big data applications are software and systems utilizing large and complex datasets to extract insights, support decision-making, and address diverse business and societal challenges. Recently, the significance of big data applications has grown immensely for organizations across diverse sectors as they increasingly rely on insights derived from data. The increasing reliance on data insights has rendered traditional technologies and platforms inefficient due to scalability limitations and performance issues. This study contributes by identifying key domains impacted by big data, examining its effect on decision-making, addressing inherent complexities and opportunities, exploring core technologies, and offering solutions for potential concerns. Additionally, it conducts a comparative analysis to demonstrate the superiority of this research. These contributions provide valuable insights into the evolving landscape shaped by big data applications. [ABSTRACT FROM AUTHOR]

Focusing on the applicative setting represented by advanced big data analytics tools over data Cloud infrastructures, this paper introduces and theoretically proofs an innovative privacy-preserving OLAP framework against big query-workloads. The described applicative setting is common for big data analytics tools developed on top of commodity hardware solutions, where, rather than the execution of singleton big data queries, these queries are embedded into big data query programs, such that every elementary query evaluation step refers to one or more queries of the target query-workload. We complete our analytical and theoretical contributions by providing and experimentally assessing an innovative optimized approximate query answering algorithm for providing privacy-preserving approximate answers to OLAP queries. [ABSTRACT FROM AUTHOR]

Purpose of Review: The rising burden of cardiovascular disease (CVD) in Africa is of great concern. Health data sciences is a rapidly developing field which has the potential to improve health outcomes, especially in low-middle income countries with burdened healthcare systems. We aim to explore the current CVD landscape in Africa, highlighting the importance of health data sciences in the region and identifying potential opportunities for application and growth by leveraging health data sciences to improve CVD outcomes. Recent findings: While there have been a number of initiatives aimed at developing health data sciences in Africa over the recent decades, the progress and growth are still in their early stages. Its maximum potential can be leveraged through adequate funding, advanced training programs, focused resource allocation, encouraging bidirectional international partnerships, instituting best ethical practices, and prioritizing data science health research in the region. Summary: The findings of this review explore the current landscape of CVD and highlight the potential benefits and utility of health data sciences to address CVD challenges in Africa. By understanding and overcoming the barriers associated with health data sciences training, research, and application in the region, focused initiatives can be developed to promote research and development. These efforts will allow policymakers to form informed, evidence-based frameworks for the prevention and management of CVDs, and ultimately result in improved CVD outcomes in the region. [ABSTRACT FROM AUTHOR]

The objective of this paper is to predict food consumption patterns for future decades by different social groups while taking generational change into account in the modelling. Using over 20 million observations of households in Switzerland from 1990 to 2017, we develop and apply four forecasting techniques that shift from referenced linear forecasts to population-driven forecasts. Each method considers values of selected household characteristics to define a "social group", derives the proportion of each social group in society for the years 1990–2050, forecasts the future consumption of 75 food items in each social group in its unique way, and weighs these consumption patterns to obtain a future consumption for the total population. Although the results vary for each of the 75 food items and each method, altogether and in general, they define a narrow interval of future consumption development until 2050. All aspects of the approaches and the comparison of the outcomes contribute to knowledge about possible and nontrivial forecasting techniques on big data and foresight about the future of home food consumption. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models have gained significant attention in a variety of applications, from computer vision to natural language processing, and are almost always based on big data. There are a growing number of applications and products with built-in machine learning models, and this is the area where software engineering, artificial intelligence and data science meet. The requirement for a system to operate in a real-world environment poses many challenges, such as how to design for wrong predictions the model may make; How to assure safety and security despite possible mistakes; which qualities matter beyond a model's prediction accuracy; How can we identify and measure important quality requirements, including learning and inference latency, scalability, explainability, fairness, privacy, robustness, and safety. It has become crucial to test thoroughly these models to assess their capabilities and potential errors. Existing software testing methods have been adapted and refined to discover faults in machine learning and deep learning models. This paper covers a taxonomy, a methodologically uniform presentation of all presented solutions to the aforementioned issues, as well as conclusions about possible future development trends. The main contributions of this paper are a classification that closely follows the structure of the ML-pipeline, a precisely defined role of each team member within that pipeline, an overview of trends and challenges in the combination of ML and big data analytics, with uses in the domains of industry and education. [ABSTRACT FROM AUTHOR]

One of the key challenges in Big Data for clinical research and healthcare is how to integrate new sources of data, whose relation to disease processes are often not well understood, with multiple classical clinical measurements that have been used by clinicians for years to describe disease processes and interpret therapeutic outcomes. Without such integration, even the most promising data from emerging technologies may have limited, if any, clinical utility. This paper presents an approach to address this challenge, illustrated through an example in Parkinson's Disease (PD) management. We show how data from various sensing sources can be integrated with traditional clinical measurements used in PD; furthermore, we show how leveraging Big Data frameworks, augmented by Artificial Intelligence (AI) algorithms, can distinctively enrich the data resources available to clinicians. We showcase the potential of this approach in a cohort of 50 PD patients who underwent both evaluations with an Integrated Motion Analysis Suite (IMAS) composed of a battery of multimodal, portable, and wearable sensors and traditional Unified Parkinson's Disease Rating Scale (UPDRS)-III evaluations. Through techniques including Principal Component Analysis (PCA), elastic net regression, and clustering analysis we demonstrate how this combined approach can be used to improve clinical motor assessments and to develop personalized treatments. The scalability of our approach enables systematic data generation and analysis on increasingly larger datasets, confirming the integration potential of IMAS, whose use in PD assessments is validated herein, within Big Data paradigms. Compared to existing approaches, our solution offers a more comprehensive, multi-dimensional view of patient data, enabling deeper clinical insights and greater potential for personalized treatment strategies. Additionally, we show how IMAS can be integrated into established clinical practices, facilitating its adoption in routine care and complementing emerging methods, for instance, non-invasive brain stimulation. Future work will aim to augment our data repositories with additional clinical data, such as imaging and biospecimen data, to further broaden and enhance these foundational methodologies, leveraging the full potential of Big Data and AI. [ABSTRACT FROM AUTHOR]

Mechanical equipment is a vital foundational support for promoting national economic development and is widely utilized in key sectors such as aerospace, shipping, construction machinery, energy, petrochemicals, and robotics. With the advancement of artificial intelligence and industrial intelligence, industrial big data and its intelligent analysis provide robust support for fault prediction and health management of equipment. Building on existing research, intelligent diagnostics for mechanical equipment based on deep learning have gained significant attention and application. However, the success of big data relies on comprehensive fault data, which is challenging to obtain in practical applications where continuous equipment operation is essential. Moreover, mechanical equipment often operates under varying conditions, leading to different data distributions for training and testing. This discrepancy can result in low diagnostic accuracy or even failure of deep learning methods. Deep Transfer Learning (DTL) is an emerging machine learning paradigm that not only leverages the advantages of deep learning (DL) in feature representation but also harnesses the strengths of transfer learning (TL) in knowledge transfer. Consequently, DTL techniques can make deep learning-based fault diagnosis methods more reliable, robust, and applicable, leading to extensive development and research in the field of intelligent fault diagnosis. This paper primarily introduces adversarial-based deep transfer learning (ADTL) models, which are fundamentally based on Generative Adversarial Network (GAN). We provide a detailed discussion of the main applications of ADTL and its recent developments in intelligent fault diagnosis, along with some future challenges and prospects. [ABSTRACT FROM AUTHOR]

This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings. [ABSTRACT FROM AUTHOR]

The ability to accurately predict the stock market is a crucial financial topic. The basic assumption is that future stock returns can be somewhat predicted based on publicly available data from the past. Economic variables like interest rates and currency rates, information related to political influences, and company-specific data like statements of income and dividends are some examples of this information. Yet, attempting to forecast stock returns goes against the idea that markets are generally efficient. A prediction model has been created that leverages machine learning, big data analytics, and social media analytics to predict stock market trends regularly. The main objective of this work is to propose a novel approach for stock market prediction for handling big data. This research develops a new SP-PDA (Stock Price Prediction with Political Data Analysis) which includes preprocessing, feature extraction, and prediction. In this work, we employed three types of data (Stock data, News data and Political data). For the stock data, preprocessing was done using Z-score normalization, while tokenization and lemmatization were used for the news and political data. Next, to handle the big data, we used a Map-Reduce architecture. Here, feature extraction from preprocessed stock, news, and political data occurs in the mapper function. The reduction face yields the final extracted feature set. Commodity channel index (CCI), Chaikin Volatility (CV), and Donchian Channel (DC) are examples of technical indicator-based features that are extracted for stock data during the feature extraction phase. Mutual Information (MI), Improved Pointwise mutual information (IPMI), Term Frequency-Inverse Document Frequency (TF-IDF), and correlation features are retrieved for news and political data. Stock market prediction is based on the features extracted. To predict stock prices, we employed an ensemble classification model that incorporates classifiers such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long-short term memory (LSTM), and Deep Maxout. An improved score level fusion is carried out to define the final prediction outcome from the obtained intermediate prediction results. The suggested Dwarf Mongoose Updated War Strategy-based Generalized Normal Distribution (DMUWS-based GND) Optimization Algorithm is used to optimizing the weights of ensemble classifiers. Finally, the performance of the proposed model is evaluated over existing models in terms of error measures like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Squared Log Error (MSLE). The SP-PDA achieved the desired MAE of 0.012, which is low when compared to the other traditional systems for accurate prediction of the stock market. [ABSTRACT FROM AUTHOR]

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies. [ABSTRACT FROM AUTHOR]

This study explores the enhancement of Internet of Things (IoT) product design and development through the integration of ethnography and big data analytics. A new model is proposed, recognizing the limitations of existing big data approaches in capturing nuanced and complex user needs. This model combines in-depth analysis of user attitudes with extensive review data from current IoT product users, aiming to uncover a more comprehensive understanding of the user experience beyond typical quantitative insights provided by big data. An IoT product named 'Zipband' has been developed as a practical application of this integrated research methodology. A combination of qualitative and quantitative research methods, including interviews and term frequency–inverse document frequency (TF-IDF) scheme analysis, was utilized to identify diverse user needs and uncover new opportunities for user experience improvement. This research contributes to the field by introducing a data-driven ethnographic approach that has the potential to inspire convergence research in IoT product design and user experience areas. [ABSTRACT FROM AUTHOR]

In this paper we present a discussion of the basic aspects of the well-known problem of prediction and inference in physics, with specific attention to the role of models, the use of data and the application of recent developments in artificial intelligence. By focussing in the time evolution of dynamic system, it is shown that main difficulties in predictions arise due to the presence of few factors as: the occurrence of chaotic dynamics, the existence of many variables with very different characteristic time-scales and the lack of an accurate understanding of the underlying physical phenomena. It is shown that a crucial role is assigned to the preliminary identification of the proper variables, their selection and the identification of an appropriate level of description (coarse-graining procedure). Moreover, it is discussed the relevance, even in modern practical issues, of old well-known fundamental results, like the Poincaré recurrence theorem, the Kac’s lemma and the Richard’s paradox. [ABSTRACT FROM AUTHOR]

Following the studies of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018), this empirical study uses two scenario-based online experiments. The sample consists of 221 subjects from Germany, differing in both age and gender. The original studies are not replicated one-to-one. New scenarios are constructed as realistically as possible and focused on everyday work situations. They are based on the AI acceptance model of Scheuer (Grundlagen intelligenter KI-Assistenten und deren vertrauensvolle Nutzung. Springer, Wiesbaden, 2020) and are extended by individual descriptive elements of AI systems in comparison to the original studies. The first online experiment examines decisions made by artificial intelligence with varying degrees of impact. In the high-impact scenario, applicants are automatically selected for a job and immediately received an employment contract. In the low-impact scenario, three applicants are automatically invited for another interview. In addition, the relationship between age and risk perception is investigated. The second online experiment tests subjects' perceived trust in decisions made by artificial intelligence, either semi-automatically through the assistance of human experts or fully automatically in comparison. Two task types are distinguished. The task type that requires "human skills"—represented as a performance evaluation situation—and the task type that requires "mechanical skills"—represented as a work distribution situation. In addition, the extent of negative emotions in automated decisions is investigated. The results are related to the findings of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018). Implications for further research activities and practical relevance are discussed. [ABSTRACT FROM AUTHOR]

The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters. [ABSTRACT FROM AUTHOR]

Understanding urban vibrancy has been considered crucial to promoting human activities and interactions in public open spaces. Recent advancements in urban big data have facilitated the potential to understand and measure vibrancy patterns throughout cities. While streets are considered the center stage of human activity, previous studies have often overlooked their multifaceted nature and their association with urban vibrancy. In this study, we incorporate multi-source big data and combine a set of features that comprehensively describe the scale, function, and topology of street segments in two Seoul districts: Jung-gu and Gangnam-gu. Using these features, we employ a machine learning clustering technique to classify them into five distinct typologies. Then, with street-level aggregated mobile phone tracking data, we investigate whether street typology characteristics are associated with urban vibrancy with respect to age groups, time of day, and day types (weekends/weekdays). The results show varying relationships between street characteristics with age-, time- and day-vibrancy measures by the identified street typology. Further, we contrast the results of the two districts to evaluate urban vibrancy differences in organic and planned urban layouts. This study enables a more nuanced understanding of urban streets to better comprehend their impact on people's use of street space. The derived novel insights could assist planners and designers to better pinpoint street management solutions for different age- and time-dependent needs based on the complexities in urban vibrancy dynamics. [ABSTRACT FROM AUTHOR]

Random sample partition (RSP) is a newly developed big data representation and management model to deal with big data approximate computation problems. Academic research and practical applications have confirmed that RSP is an efficient solution for big data processing and analysis. However, a challenge for implementing RSP is determining an appropriate sample size for RSP data blocks. While a large sample size increases the burden of big data computation, a small size will lead to insufficient distribution information for RSP data blocks. To address this problem, this paper presents a novel density estimation-based method (DEM) to determine the optimal sample size for RSP data blocks. First, a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz (DKW) inequality by using the fixed-point iteration (FPI) method. Second, a practical sample size is determined by minimizing the validation error of a kernel density estimator (KDE) constructed on RSP data blocks for an increasing sample size. Finally, a series of persuasive experiments are conducted to validate the feasibility, rationality, and effectiveness of DEM. Experimental results show that (1) the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality; (2) the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function (p.d.f.); and (3) DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of p.d.f. estimation. This demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation. [ABSTRACT FROM AUTHOR]

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The Internet of Things (IoT) is a powerful technology adopted in various industries. Applications of the IoT aim to enhance automation, productivity, and user comfort in a cloud and distributive computing environment. Cloud computing automatically stores and analyzes the large amounts of data generated by IoT-based applications. Cloud computing has become a crucial component of the information age through easier data administration and storage. Currently, government agencies, commercial enterprises, and end users are rapidly migrating their data to cloud environments. This may require end-user authentication, greater safety, and data recovery in the event of an attack. A few issues were discovered by authors after analysis and assessments of various aspects of the published research papers. The research analysis reveals that the existing methods need to be further improved to address the contemporary dangers related to data security and privacy. Based on the research reports, it can be stated that safe end-to-end data transmission in a cloud-IoT environment requires modifications and advancements in the design of reliable protocols. Upcoming technologies like blockchain, machine learning, fog, and edge computing mitigate data over the cloud to some level. The study provides a thorough analysis of security threats including their categorization, and potential countermeasures to safeguard our cloud-IoT data. Additionally, the authors have summarized cutting-edge approaches like machine learning and blockchain technologies being used in the data security privacy areas. Further, this paper discusses the existing problems related to data privacy and security in the cloud-IoT environment in today’s world and their possible future directions. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has posed unprecedented pressure to health care systems, and interrupted health care delivery and access including HIV care in the United States' Deep South, which endures a double epidemic of HIV and COVID-19. Ryan White programs cover HIV care services for over half of PLWH in the Deep South. Given the important role of Ryan White programs, examining the visitation changes to Ryan White facilities during the pandemic offers insights into the impact of the pandemic on HIV healthcare utilization. Objectives: Analyze the geographic distribution of HIV facility visitors at the county level before and during the pandemic in the nine US states of Deep South (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas) to reveal the geographic and racial disparity in visitation disruption caused by the pandemic. Methods: We first extracted mobile device-based visitation data for Ryan White HIV facilities in the Deep South during 2019 and 2020. To quantify the disruption in visitations during 2020, we calculated the visitation reduction rate (VRR) for each county, using 2019 data as the baseline. Next, we conducted a spatial analysis of the VRR values to uncover geographical disparities in visitation interruptions. To investigate racial disparities, we performed spatial regression analyses with VRR as the dependent variable, and the percentages of Black, Hispanic, and Asian populations as the independent variables. In this analysis, we controlled for potential confounders. Results: Geographic disparities in visitation reduction were observed, with all nine Deep South states experiencing significant drops. Georgia experienced the highest visitation loss (VRR = -0.58), followed by Texas (-0.47), Alabama (0.47), and Tennessee (-0.46), while South Carolina had the smallest reductions (-0.11). All the regression models consistently revealed racial disparities in visitation interruption. That is, counties with a higher proportion of Black population tended to have higher RW facility visitation reductions. Conclusions: Our analysis revealed distinct geographic disparities in visitation interruptions at Ryan White HIV facilities in the Deep South during the COVID-19 pandemic in 2020. Furthermore, we found that the Black/African American population experienced a greater disruption at the county level in the Deep South during this period. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic has imposed unprecedented pressure on health systems and has interrupted public health efforts for other major health conditions, including HIV. It is critical to comprehensively understand how the pandemic has affected the delivery and utilization of HIV-related services and what are the effective strategies that may mitigate the negative impacts of COVID-19 and resultant interruptions. The current study thus aims to comprehensively investigate HIV service interruptions during the pandemic following a socioecological model, to assess their impacts on various outcomes of the HIV prevention and treatment cascade and to identify resilience resources for buffering impacts of interruptions on HIV treatment cascade outcomes. We will assess HIV service interruptions in South Carolina (SC) since 2020 using operational report data from Ryan White HIV clinics and HIV service utilization data (including telehealth use) based on statewide electronic health records (EHR) and cellphone-based place visitation data. We will further explore how HIV service interruptions affect HIV prevention and treatment cascade outcomes at appropriate geospatial units based on the integration of multi-type, multi-source datasets (e.g., EHR, geospatial data). Finally, we will identify institutional-, community-, and structural-level factors (e.g., resilience resources) that may mitigate the adverse impacts of HIV service interruptions based on the triangulation of quantitative (i.e., EHR data, geospatial data, online survey data) and qualitative (i.e., in-depth interviews with clinic leaders, healthcare providers, people living with HIV, and HIV clinic operational reports) data regarding health system infrastructure, social capital, and organizational preparedness. Our proposed research can lead to a better understanding of complicated HIV service interruptions in SC and resilience factors that can mitigate the negative effects of such interruptions on various HIV treatment cascade outcomes. The multilevel resilience resources identified through data triangulation will assist SC health departments and communities in developing strategic plans in response to this evolving pandemic and other future public health emergencies (e.g., monkeypox, disasters caused by climate change). The research findings can also inform public health policymaking and the practices of other Deep South states with similar sociocultural contexts in developing resilient healthcare systems and communities and advancing epidemic preparedness. [ABSTRACT FROM AUTHOR]

With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more indispensable. In this paper, we designed a graph-based multi-view clustering method based on graph learning to explore the mega-city region structures from multi-source data. An example of clustering analysis consists of the people flow network, cargo flow network, and information flow network, covering 88 cities from Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Jiangsu Province, Anhui Province, Shanghai, and Zhejiang Province in China is used to illustrate the applicability of the idea in super mega-city region scale studies. Utilizing the proposed clustering method, a unified network representation is calculated, and 5 mega-city regions, Beijing-Tianjin-Hebei Cluster, Henan Cluster, Shandong Cluster, Shanghai-Jiangsu-Anhui Cluster, and Zhejiang Cluster, are detected based on intercity flow networks. City-to-city flows, including Luan-Taizhou, Lianyungang-Chuzhou, and Xuzhou-Bengbu of the people network, Shanghai-Hangzhou, Suzhou-Shanghai, and Shanghai-Ningbo of the cargo network, Shanghai-Hangzhou, Bozhou-Jinhua, and Huaibei-Bozhou of the information network, are suggested to be further enhanced to facilitate the ongoing nationwide constructions of urban agglomerations in China. The multi-view clustering method proved to be a helpful calculation framework for mega-city region analysis, which would also be considered as a substantial foundation for further urban explorations with more advanced graph learning techniques. [ABSTRACT FROM AUTHOR]

Under the current big topic of enterprise management, how to cultivate, use, and maintain talents is the critical problem of modern human resource management. Based on big data mining, cloud computing, and other related model optimization technologies, this study explores the crucial significance of enterprise culture construction and enterprise human resource management in achieving the business objectives of the entire enterprise. The establishment of this model also means that the enterprise's profit margin and business indicators have been improved and developed under a certain model. The case results demonstrate that the optimized competency model is significant to studying corporate culture management system behavior, policy analysis, and improving corporate human resource management policies. [ABSTRACT FROM AUTHOR]

With the development of highways, there has been an increase in toll evasion by unscrupulous individuals who employ various means to avoid paying fees. This has made it essential for highway management to focus on effective methods to prevent and manage toll evasion, as well as regulate toll-related operations. However, traditional auditing techniques are heavily reliant on manual toll data and lack sufficient auxiliary data such as video and pictures, making big data analysis and in-depth data analysis difficult. Consequently, these methods can only identify a portion of evasion behavior, with a significant number of potential evasion cases remaining unidentified. To address this issue, we propose utilizing vehicle re-identification to enable cross-camera identification and continuous tracking of vehicles, allowing for accurate calculation of their operating mileage. However, vehicle re-identification using images captured by highway cameras is a challenging task, primarily due to the low resolution of the images and the lack of relevant datasets. To tackle these problems, we construct a large-scale, low-resolution dataset based on images captured by cameras on highways. We also propose a Part-guided Feature-combination Network (PFNet) to analyze the dataset. PFNet adopts a three-stage approach, wherein each stage of the network requires independent training. In the first stage, the Pyramid Scene Parsing Network (PSPNet) is improved to segment the entire vehicle into parts. In the second stage, based on a comparative learning approach, the Siamese Feature Extraction Module (SFEM) module was created to extract features of pairs of vehicle parts. To train the SFEM, pairs of positive samples, which consist of the same vehicle component pairs captured by different cameras, and pairs of negative samples, which consist of different vehicle component pairs are needed. To ensure that the positive sample pairs are closer to each other in the embedding space, and the negative samples are as far away as possible from each other, contrast loss is utilized. Moreover, The SFEM incorporates VGG16 and a Multi-period Convolutional Block Attention Module (MCBAM), which enhances the learning ability of the conventional Convolutional Block Attention Module (CBAM). In the third stage, information propagation among the vehicle parts is achieved using a Multi-layer Perceptron (MLP), which calculates the similarity after combining the features of all parts. Our self-built test set demonstrates that PFNet is effective for continuous monitoring of vehicles on highways under multiple cameras, resulting in improved accuracy of vehicle re-identification, with Top1 accuracy of 93.61% and Top5 accuracy of 99.44%. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and big data analytics are powerful tools when combined for disease detection and healthcare applications. They enable the processing of vast amounts of medical data to uncover patterns, make predictions, and assist healthcare professionals in diagnosing diseases and improving patient care. Existing disease detection model often faces two challenging issues namely feature selection and insufficient hyperparameter tuning, which considerabley affects the performance and generalization abilities of DL models in disease detection. To resolve these issues, this article focuses on the design of a new Chaotic Red Deer Optimizer with Deep Learning based big data analytics for Disease Classification (CRDODL-BDADC) technique in the healthcare environment. The CRDODL-BDADC technique resolves the high dimensionality problem by the use of CRDO based feature selection approach. For disease detection process, convolutional bidirectional gated recurrent unit (CBGRU) is applied for disease diagnosis and its hyperparameters are selected by using Seeker Optimization Algorithm (SOA). Furthermore, Map Reduce is applied to manage healthcare BD. A widespread set of simulation outcomes occur on the PCOS database, from the Kaggle repository to illustrate the greater performance of the CRDODL-BDADC technique. The simulation values highlighted the enhanced efficiency of the CRDODL-BDADC method over other recent approaches under various measures. [ABSTRACT FROM AUTHOR]

The accumulation of waste has a profound impact on the environment, prompting a crucial discussion about effective waste management strategies aligned with Oman Vision 2040's sustainability goals. The consequences of municipal solid waste generation have multifaceted impacts on the environment, public health, and overall well-being of communities. Addressing these consequences requires a holistic approach that includes the integration of sustainable waste management technologies to foster a circular economy. This paper emphasizes the necessity for a paradigm shift in waste management methodologies, emphasizing the importance of Hyperspectral Imaging and Big Data Analytics into municipal solid waste management processes. This paper explores the synergistic relationship between hyperspectral imaging which is capable of precise material identification, and big data analytics to facilitate comprehensive data analysis. The integration aims to optimize waste segregation, resource recovery, and recycling processes. The utilization of data-driven insights enables predictive modeling and the identification of trends thereby facilitating more efficient and sustainable waste management practices. The harnessing of big data analytics empowers stakeholders to make informed decisions in waste management to achieve long-term environmental, and economic sustainability. [ABSTRACT FROM AUTHOR]

The importance of technology and managerial risk management in banks has increased due to the financial crisis. Banks are the most affected since there are so many of them with poor financial standing. Due to this problem, an unstable and inefficient financial system causes economic stagnation in both the banking sector and overall economy. Data envelopment analysis (DEA) has been used to examine decision-making units (DMUs) performance to enhance efficiency. Currently, with the rapid growth of big data, adding more DMUs will likely require a large amount of memory and CPU time on the computer system, which will be the biggest challenge. As a result, machine learning (ML) approaches have been used to analyze financial institution performance, but many of them have variances in predictions or model stability, making measuring bank efficiency extremely difficult. For this, ensemble learning is commonly used to evaluate the performance of financial institutions in this context. This paper presents a robust super learner ensemble technique for assessing bank efficiency, with four machine learning models serving as base learners. These models are the support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and AdaBoost classifier (ADA) which represent the base learners and their results utilized to train the meta-learner. The super learner (SL) approach is an extension of the stacking technique, which generates an ensemble based on cross-validation. One important benefit of this cross-validation theory-based technique is that it can overcome the overfitting issue that plagues most other ensemble approaches. When SL and base learners were compared for their forecasting abilities using different statistical standards, the results showed that the SL is superior to the base learners, where different variable combinations were used. The SL had accuracy (ACC) of 0.8636–0.9545 and F1-score (F1) of 0.9143–0.9714, while the basic learners had ACC of 0.5909–0.8182 and F1 of 0.6897–0.9143. So, SL is highly recommended for improving the accuracy of financial data forecasts, even with limited financial data. [ABSTRACT FROM AUTHOR]

In recent times, IDS (Intrusion Detection System) has become a significant tool for improvising network security through the detection of abnormal and normal data. It is vital as it permits one to identify and respond to incoming malicious traffic. The intruders have also enhanced the inclusion of attacks in systems with a recent increase in data. Concurrently, ML (Machine Learning) algorithms can learn from corresponding data that has been afforded. With the provision of new data, the accuracy and efficacy of the ML model to take decisions to enhance with training. However, with the evolution of big data, ML has turned incapable of handling huge data interpretation issues which made most of the conventional systems explore high FP (False Positive) rates and low accuracy rates. This gave rise to pyspark which serves as a platform for addressing these issues that the ML method fails to solve. ML in pyspark is a scale and easy to use. Considering this, the present research intends to propose ML-based algorithms for classifying intrusion detection in a pyspark environment. This study proposes a security framework named Fuzzy K-Means with M-KMP (Modified-Knuth Morris Pratt) wherein the clustering is accomplished by Fuzzy K-means which is capable of exploring data points that potentially relate to multiple clusters. Whereas, M-KMP achieves information matching on the clustered data for assessment of the information occurrence on the allocated threat data that will serve as an assistance for security developers in attack prevention. The efficiency of this proposed work is confirmed through the results. [ABSTRACT FROM AUTHOR]

In the digitalized world, efficient big data clustering is necessary for massive data generation. The clustering algorithm plays an important role in resolving the computational complexity. The big data arriving from various sources are being processed using the MapReduce framework (MRF) by the knowledge of the clustering algorithms. Moreover, the clustering algorithm is useful for mining the significant information from the dataset. Generally, there are several difficulties in applying the clustering approach to big data as its new challenges are based on computation cost and reasonable time. Hence, this research introduced the Competitive Jaya Leader Harris Hawks Optimization assisted Entropy Weighted Power K-Means Clustering (CJayaLHHO_EWPKMC) for big data clustering. In addition, the overall processing of the devised method for big data clustering is carried out in the MapReduce (MR) framework. In mapper, the feature selection is done using Support vector Machine-Recursive Feature Elimination (SVM-RFE) assisted Jaya Leader Harris Hawks Optimization (JayaLHHO). In the reducer, the big data clustering is established using the EWPKMC method, wherein the weight of EWPKMC is modified with the CJayaLHHO algorithm such that the clustering outcome is attained. The proposed method is scalable, simple, cost-effective, and able to integrate with other technologies. The experimental result portrays that the developed method attained a superior presentation than the conventional methods based on the clustering accuracy is 0.937, the Jaccard coefficient is 0.913, and the rand coefficient is 0.912. [ABSTRACT FROM AUTHOR]

This paper investigates the role of digitalization in enhancing wastewater treatment processes, emphasizing its potential to optimize resource utilization, reduce energy consumption, and improve water quality. By examining the implementation of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), the study demonstrates how these tools enable real-time monitoring, predictive maintenance, and intelligent decision-making in wastewater treatment operations. The paper provides a comparative analysis based on key performance indicators (MAPE, RMSE, R2) to evaluate the effectiveness of these digital solutions. Additionally, it discusses the benefits and challenges associated with integrating digital tools in wastewater treatment plants (WWTPs), including cost, complexity, and data security concerns. The study also addresses the impact of digitalization on carbon neutrality goals, highlighting how data-driven approaches can enhance resource allocation and management. By offering insights into current practices and future directions, this paper aims to contribute to the advancement of sustainable wastewater treatment and support the achievement of UN SDG#6, ensuring clean water and sanitation for all. [ABSTRACT FROM AUTHOR]

A collection of large data sets from different orogenic belts was compiled for a correlation between organic matter (OM) versus clay mineral (CM) indices calibrated with the vitrinite reflectance, (VR) vs Kübler-Indices (KI) method. Data selection was based on a normal geothermal gradient (25 to 35 °C/km) as determined in previous studies, e.g. by maturity modelling and clay mineral reaction progress calculations. In the Lower Austroalpine (Eastern Switzerland, European Alps) a 20 myr lasting metamorphic overprint caused an OM–CM thermal equilibrium among the indices used. The observed correlation enables to determine gradual changes in metamorphic factors such as pressure, temperature and time causing sensitive shifts of the gradient slope in the range of normal gradients. For New Caledonia, an identical correlation has been determined. Prior to re-equilibration of the VR/KI indices, sediments in New Caledonia of diagenetic to incipient metamorphic grade underwent a high-pressure subduction event. VR/KI indices are in or close to equilibrium, while slight differences in OM vs CM indices allow for a better understanding of polyphase conditions, especially with respect to pressure. Temperature estimations are identical despite of their poly-phase metamorphic history, which was mainly controlled by the last orogenic thermal event lasting > 5 to < 10 myr. In the eastern Helvetic Alps and Northern Calcareous Alps similar correlations were found with slightly different slopes. Comparison between different regions is possible when using KI standardization and same data discrimination. In both parts of the Alps a complex thermal history of short durations (< 5.0 myr for the Northern Calcareous Alps to 10 myr for the Helvetic Alps) caused similar VR/KI trends, but disequilibrium is suggested by weaker regression parameters. The following correlation is calculated for a moderate geotherm (55 to 74 mWm2, mean = 61 mWm2) and normal temperature gradient conditions (25 to 35 °Ckm−1): KI = 1.134e−0.305VR, (R2 = 0.880, n = 462) with VR given as %Rmax, KI as Δ°2θ (limited to values between 0.2 to 1.0 Δ°2θ). With increasing depth (z) a VR gradient of 1.4 ± 0.2%Rmaxkm−1 is determined and a KI gradient of 0.09 ± 0.002 Δ°2θ km−1 is observed. The study illustrates that a normal geotherm can be described by VR/KI correlation, even if different heating episodes may occur. For the detection of a poly-phase or plurifacial thermal history, several indices of clay minerals and organic matter with very different kinetics should be used, as e.g. demonstrated by strong differences in smectite content at equal VR/KI values versus structural depth. A specific interest is given to the correlation of vitrinite like solid bitumen reflectance as an alternative method to VR, the persistent preservation of liptinite macerals and the stability range of clay minerals and sub-greenschist facies critical minerals compared with VR/KI data. Until now, despite the Alps in this study, systematic liptinite maceral studies have not been published in other orogenic settings. [ABSTRACT FROM AUTHOR]

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods. [ABSTRACT FROM AUTHOR]

The Paris Agreement represents the notable characteristics of global environmental governance, namely multi-actor and multi-level governance. Despite these changes, limited empirical studies have investigated the actors in global environmental governance who participate in political discourse on climate change. To comprehend whether or not the Paris Agreement's actors represent the transformation of global environmental governance, we adopted a big-data approach to analyze Twitter's climate-change discourse. The classification of actors provided an understanding of the diversity of the actors involved in providing public information. We also calculated the closeness centrality and identified the changes in the actors' influence, comparing the changes in the 5 years since the Agreement was adopted. The results showed an increased diversity of actor types. Non-state actors are not homogeneous in their changes, and Intergovernmental Organizations exhibited an increased influence. The findings indicated that the actors participating in the climate-change discourse post-Paris Agreement partially reflect the changes in global environmental governance. [ABSTRACT FROM AUTHOR]

The E-Health Big Data Evidence Innovation Network (FeederNet) in Korea, based on the observational medical outcomes partnership (OMOP) common data model (CDM), had 72.3% participation from tertiary hospitals handling severe diseases as of October 2022. While this contributes to the activation of multi-institutional research, concerns about the comprehensiveness of device data persist due to the adoption of national health insurance corporation (NHIC) claim codes as device identifiers in the medical device field. This study critically evaluated the effectiveness and compatibility of NHIC claim codes and unique device identifier (UDI) within FeederNet to identify the optimal identifier for efficient Post-market surveillance (PMS). Specifically, this study addressed three main questions: (1) the number of UDIs classified as NHIC-covered items, (2) the number of UDIs included in each NHIC claim code, and (3) the number of NHIC claim codes each UDI covers. Among the 1,979,655 UDIs registered domestically, only 36.02% (712,983) were classified as covered by National Health Insurance. NHIC-covered medical devices were limited to categories (A) medical devices, (B) medical supplies, and (C) dental materials, excluding most software and in vitro diagnostics (IVD). Multiple UDIs could be registered under a single NHIC claim code, and a single UDI could be registered under multiple NHIC claim codes. Only 32.62% (13,756/42,171) of NHIC claim codes had registered UDIs, with an average of 53 UDIs per claim code. Of the UDIs listed as NHIC covered, 92.39% (659,046/713,341) had one claim code, while 7.25% (51,652) had multiple claim codes. Additionally, 2643 UDIs were listed as NHIC covered but had no registered claim codes. Due to this complex relationship, NHIC claim code-based PMS may pool safe and unsafe models or disperse problematic models across multiple claim codes, leading to a lower problem rate or insignificant differences between claim codes, thus reducing signal detection sensitivity compared to UDI-based PMS. In conclusion, NHIC claim code-based PMS has limitations in granularity and signal detection sensitivity, necessitating the adoption of UDI-based PMS to address these issues. The UDI system can enhance the accuracy of medical device identification and tracking, playing a crucial role in generating real-world evidence (RWE) by integrating data from various sources. Future research should explore specific strategies for integrating and utilizing UDI with NHIC claim codes, contributing to the implementation of a more reliable and comprehensive PMS in Korea's healthcare system. [ABSTRACT FROM AUTHOR]

Statins, widely used cardiovascular drugs that lower cholesterol by inhibiting HMG-CoA reductase, have been increasingly recognized for their potential anticancer properties. This study elucidates the underlying mechanism, revealing that statins exploit Synthetic Lethality, a principle where the co-occurrence of two non-lethal events leads to cell death. Our computational analysis of approximately 37,000 SL pairs identified statins as potential drugs targeting genes involved in SL pairs with metastatic genes. In vitro validation on various cancer cell lines confirmed the anticancer efficacy of statins. This data-driven drug repurposing strategy provides a molecular basis for the anticancer effects of statins, offering translational opportunities in oncology. [ABSTRACT FROM AUTHOR]

Due to the scattered distribution and poor clustering of abnormal clusters in energy big data, the ability to detect anomalies is poor. Therefore, a high-energy data anomaly clustering detection method based on redundant convolutional encoding is proposed. Quantitative analysis of the coupling characteristics of electrical thermal gas optical time series for multi energy users based on Copula function, and incorporating quantitative values into multi energy feature indicators to extract the energy consumption behavior characteristics of multi energy users. Utilize redundant convolutional codecs to recombine and structurally encode abnormal features of energy big data, and capture multi energy coupling time features using coupling time capsule layers. Then, coupling time features are synthesized through fully connected linear regression layers to generate anomalous clustering feature components, and the energy time series data is then transformed into feature values of the time series in three-dimensional space. Based on this, a comprehensive energy system and massive multi energy user energy big data anomaly clustering analysis are carried out to determine the optimal number of multi energy users. Then, based on linear layers, the electricity heat gas light load characteristic map of multi energy users is transformed into one-dimensional form, and an energy big data anomaly clustering detection model is constructed to complete anomaly detection. The simulation results show that the proposed method has excellent feature clustering performance, detection accuracy above 98.7%, fast convergence speed, and an error rate below 0.1, which has reliable application value. [ABSTRACT FROM AUTHOR]

The article attempts to overcome the lack of protectionism policy underlying the existing approach to managing sustainable business development, which is due to the fact that such a policy limits the influence of the external environment on business, as well as eliminates its incentives to adaptation and reduces business flexibility. The research methodology is based on the use of the method of regression analysis. Based on the representative international statistics by IMD and the UN for 2021 (60 countries), we use regression analysis to determine the dependence of the indicators of the sustainable development of the business—GDP and SDG 9—on the factors of protectionism and the use of Big Data and analytics based on AI. The reliability of the econometric modeling results is verified by determining their correspondence to the Gauss–Markov theorem. The key conclusion is that change management based on smart technologies contributes to sustainable business development, expressed in an increase in GDP and the result of SDG 9 implementation. The significance of the managerial aspect of the article is that, using the progressive methodology of SAP–LAP, we compile a SAP–LAP model of change management based on smart technologies for sustainable business development, which makes it possible to increase its flexibility and adaptability. The main result of this paper consists in the substantiation of the critical role of the flexibility of business as the main condition for its sustainable development. The paper's novelty lies in the development of a new approach to the management of the sustainable development of the business, which specific feature is change management based on smart technologies—which ensures increased flexibility of the business. The state economic policy implications are connected with the necessity to refuse protectionism in favor of the deregulation of markets to allow competition to create natural stimuli for the increase in the flexibility of business through change management. [ABSTRACT FROM AUTHOR]

The observation of geomagnetic field variations is an important approach to studying earthquake precursors. Since 1987, the China Earthquake Administration has explored this seismomagnetic relationship. In particular, they studied local magnetic field anomalies over the Chinese mainland for earthquake prediction. Owing to the years of research on the seismomagnetic relationship, earthquake prediction experts have concluded that the compressive magnetic effect, tectonic magnetic effect, electric magnetic fluid effect, and other factors contribute to preearthquake magnetic anomalies. However, this involves a small magnitude of magnetic field changes. It is difficult to relate them to the abnormal changes of the extremely large magnetic field in regions with extreme earthquakes owing to the high cost of professional geomagnetic equipment, thereby limiting large-scale deployment. Moreover, it is difficult to obtain strong magnetic field changes before an earthquake. The Tianjin Earthquake Agency has developed low-cost geomagnetic field observation equipment through the Beijing–Tianjin–Hebei geomagnetic equipment test project. The new system was used to test the availability of equipment and determine the findings based on big data. [ABSTRACT FROM AUTHOR]

With the emergence of the big data era, the need for sampling methods that select samples based on the order of the observed units is felt more than ever. In order to meet this necessity, a new sequential unequal probability sampling method is proposed. The decision to select or not each unit is made based on the order in which the units appear. A variant of this method allows a selection of a sample from a stream. This method consists in using sliding windows which are a kind of strata of controllable size. This method also allows the sample to be spread in a controlled manner throughout the population. A special case of the method with windows of size one leads to deciding on each sampling unit immediately after observing it. The implementation of size one windows is simple and will be presented here based on an algorithm with a single condition. Also, by selecting the windows of size two, we will have one of the optimal stream sampling methods, which results in a well-spread stream sample with positive second-order inclusion probabilities. [ABSTRACT FROM AUTHOR]

The particle swarm optimization-K-Means algorithm is proposed by the related researchers to improve the clustering accuracy of the K-Means algorithm. However, the particle swarm optimization-K-Means algorithm brings more burden to the computation, and the computational efficiency is low when dealing with large data sets. To solve this problem, a parallel particle swarm K-Means algorithm based on MapReduce with multi-threading is proposed. The algorithm performs parallel computation by dividing the particle swarm into several equal-sized sub-populations based on the number of available nodes in the cluster and distributing them to each node. It uses a multi-threaded execution in the evaluation stage, which has the highest computational complexity in the evolutionary process. Experiments show that although splitting the population will affect the optimization effect to some extent, the proposed still can effectively optimize the clustering results of the K-Means algorithm, and the computational efficiency is significantly improved compared with serial particle swarm optimization k-means algorithm and MapReduce-based non-multithreaded particle swarm optimization k-means algorithm, in the experiment with the largest dataset and a configuration of 16 nodes, the proposed algorithm is 58 times faster than the serial algorithm. Furthermore, the computing efficiency can be improved in the clusters with more CPU cores. [ABSTRACT FROM AUTHOR]

Feature Selection (FS), a pre-processing step used in the majority of big data processing applications, aims to eliminate irrelevant and redundant features from the data. Its purpose is to select a final set of data characteristics that best represent the data as a whole. To achieve this, it explores every potential solution in order to identify the optimal subset. Meta-heuristic algorithms have been found to be particularly effective in solving FS problems, especially for high-dimensional datasets. This work adopts a recently developed meta-heuristic called the White Shark Optimizer (WSO) due to its simplicity and low computational overhead. However, WSO faces challenges in effectively balancing exploration and exploitation, particularly in complex multi-peak search problems. It tends to converge prematurely and get stuck in local optima, which can lead to poor search performance when dealing with FS problems. To overcome these issues, this paper presents three enhanced binary variants of WSO for well-known FS problems. These variants are as follows: (1) Binary Distribution-based WSO (BDWSO), where the algorithm refines the positions of white sharks by considering both the average and standard deviation of the current shark, the local best shark, and the global best shark. This strategy is designed to alleviate issues of premature convergence and stagnation during iterations , (2) Binary Sine Cosine WSO (BSCWSO), which uses sine and cosine adaptive functions for the social and cognitive components of the position update rule, and (3) Binary Hybrid Sine Cosine WSO (BHSCWSO), which employs sine and cosine acceleration factors to regulate local search and achieve convergence to the global optimal solution. Additionally, the population was initialized using the Opposition-Based Learning (OBL) mechanism, and the sine map was used to modify the inertia weight of WSO. These revised variants of WSO were established to have a better harmony between the exploration and exploitation facets. The proposed methods were extensively compared to the fundamental binary WSO and other well-known algorithms in the field. The experimental findings and comparisons demonstrate that the proposed methods outperform the conventional and most evaluated similar algorithms in terms of robustness and solution quality. In terms of classification accuracy, number of selected features, specificity, sensitivity, and fitness values, the proposed BHSCWSO optimizer performed better than all other proposed peer optimizers, including BSWO, BDWSO, and BSCWSO, in 11, 8, 13, 18, and 10 datasets, respectively. The proposed BHSCWSO optimizer showed performance levels of more than 90% in terms of accuracy, sensitivity, and specificity metric measures on 15, 14, and 11 of the 24 datasets deemed. [ABSTRACT FROM AUTHOR]

In the age of Industry 4.0 and smart manufacturing, traditional production processes are undergoing significant changes due to the integration of advanced technologies like automation, robotics, big data analytics, and machine learning, which are enhancing productivity and manufacturing efficiency. The center of this evolution is the introduction of the digital twin (DT), the digital replicas of physical assets that blend real-time data with advanced analytics and simulations. Ensuring synchronization between the digital replica and its physical counterpart is crucial for successfully implementing digital twins. This paper addresses the challenge of achieving and quantifying synchronization within DTs, focusing on replicating physical system behavior and measuring deviations or delays. The study delves into the critical aspects of synchronization within digital twin applications, focusing on its implications for a robotic assembly system. The research successfully harnessed YOLOv8 to facilitate real-time event tracking and synchronization characterization, highlighting the potential of object detection deep-learning models in enhancing synchronization accuracy and, consequently, the efficiency and reliability of manufacturing processes. [ABSTRACT FROM AUTHOR]

Perioperative anesthetic management entails a multitude of decision-making processes within complex medical scenarios. These demand the continuous and dynamic execution of precise decisions which poses significant challenges. In the age of big data, the exponential growth in data volume from diverse sources has revolutionized many fields, including healthcare, finance, and marketing. Machine learning has emerged as a powerful tool for analyzing big data, enabling the handling of large datasets and uncovering intricate patterns and relationships. The application of big data and artificial intelligence algorithms is gradually being integrated, enabling effective task completion in various stages of perioperative management, including risk prediction, decision support, and auxiliary examination. Through in-depth analysis of big data, healthcare professionals can gain insights into patient prognoses. This review provides a comprehensive overview of the distinctive features of perioperative big data and its applications in anesthesia management during the perioperative period. [ABSTRACT FROM AUTHOR]