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In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]

In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain of scientific paper analysis. Our method employs a novel neural network embedding technique that leverages textual components, such as keywords, titles, abstracts, and full texts, to represent papers in a vector space. By integrating recent advancements in text representation and unsupervised feature aggregation, SPBERT offers a sophisticated approach to encode essential information implicitly, thereby enhancing paper classification and literature retrieval tasks. We applied our method to several real-world academic datasets, demonstrating notable improvements over existing methods. The findings suggest that SPBERT not only provides a more effective representation of scientific papers but also facilitates a deeper understanding of large-scale academic data, paving the way for more informed and accurate scholarly analysis. [ABSTRACT FROM AUTHOR]

Traces of mercury ions in environmental water can harm humans and animals. Paper-based visual detection methods have been widely developed for the rapid detection of mercury ions; however, existing methods are not sensitive enough to be used in real environments. Here, we developed a novel, simple and effective visual fluorescent sensing paper-based chip for the ultrasensitive detection of mercury ions in environmental water. CdTe-quantum-dots-modified silica nanospheres were firmly absorbed by and anchored to the fiber interspaces on the paper's surface to effectively avoid the unevenness caused by liquid evaporation. The fluorescence of quantum dots emitted at 525 nm can be selectively and efficiently quenched with mercury ions, and the ultrasensitive visual fluorescence sensing results attained using this principle can be captured using a smartphone camera. This method has a detection limit of 2.83 µg/L and a fast response time (90 s). We successfully achieved the trace spiking detection of seawater (from three regions), lake water, river water and tap water with recoveries in the range of 96.8–105.4% using this method. This method is effective, low-cost, user-friendly and has good prospects for commercial application. Additionally, the work is expected to be utilized in the automated big data collection of large numbers of environmental samples. [ABSTRACT FROM AUTHOR]

This document provides a summary of a collection of papers that explore the integration of remote sensing and geospatial data for land cover and land use mapping and monitoring. The papers cover various topics, including urban land-use mapping, spatiotemporal change, cropland mapping, forestry, and ecological restoration. The studies demonstrate the benefits of using multiple sources of data and integrating different types of sensors to improve accuracy in land cover classification. The papers also provide technical details and best-practice guidelines for integrating remote sensing into different domains. The collection highlights the importance of this research area and invites further contributions in a second edition of the Special Issue. [Extracted from the article]

This paper evaluates an editorial and seven invaluable and interesting review papers for the Journal of Risk and Financial Management (JRFM). The topics covered include the rising complexity of bank regulatory capital requirements from global guidelines to their United States (US) implementation, connections among big data, computational science, economics, finance, marketing, management and psychology, factors, outcome, and the solutions of supply chain finance, with a review and future directions, time-varying price-volume relationship, adaptive market efficiency, and a survey of the empirical literature, improved covariance matrix estimation for portfolio risk measurement, stock investment and excess returns, with a critical review in the light of the efficient market hypothesis, and a cross section analysis of country equity returns, and a review of the empirical literature. [ABSTRACT FROM AUTHOR]

Researchers from different fields have studied the effects of the COVID-19 pandemic and published their results in peer-reviewed journals indexed in international databases such as Web of Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating the extensive literature on COVID-19 pandemic research, our study conducts a content analysis of the top 1000 cited papers in WoS that delve into the subject by using elements of natural language processing (NLP). Knowing that in WoS, a scientific paper is described by the group Paper = {Abstract, Keyword, Title}; we obtained via NLP methods the word dictionaries with their frequencies of use and the word cloud for the 100 most used words, and we investigated if there is a degree of similarity between the titles of the papers and their abstracts, respectively. Using the Python packages NLTK, TextBlob, VADER, we computed sentiment scores for paper titles and abstracts, analyzed the results, and then, using Azure Machine Learning-Sentiment analysis, extended the range of comparison of sentiment scores. Our proposed analysis method can be applied to any research topic or theme from papers, articles, or projects in various fields of specialization to create a minimal dictionary of terms based on frequency of use, with visual representation by word cloud. Complementing the content analysis in our research with sentiment and similarity analysis highlights the different or similar treatment of the topics addressed in the research, as well as the opinions and feelings conveyed by the authors in relation to the researched issue. [ABSTRACT FROM AUTHOR]

Big data analytics in medicine is driving significant change, as it offers vital information for improving functions, developing cutting-edge solutions and overcoming inefficiencies. With the right archiving and analysis tools, all players in the healthcare system, from hospitals to patients and from medical personnel to the pharmaceutical industry, can yield numerous benefits. Therefore, to analyze and interpret these analytics effectively, so that they can be useful for the advancement of scientific knowledge, we require information sharing, specific skills, training, integration between all system players, unique infrastructures and security. All these characteristics will make it possible to establish and harmonize real big data biobanks, for which it will be appropriate to consider new forms of governance compared to those traditionally conceived for large-sample biobanks. [ABSTRACT FROM AUTHOR]

The smart and resilient transformation of manufacturing firms has flourished in the last decade. This paper investigates the smart and resilient transformation of manufacturing firms with the application of digital services. It uses a Serbian data set of 136 manufacturing firms from the Digital Servitization Survey from 2022 coordinated by IFIP WG5.7. The authors applied a different method approach through social network analysis, correlation analysis, and interviews with representatives from the manufacturing firms. The main results show that manufacturing firms that apply an adequate combination of services and digital technologies have the highest impact on the network of manufacturing firms. Moreover, the results show that applying digital technologies, such as additive manufacturing/3D printing, big data analytics, and digital twin, increases the gross annual turnover of manufacturing firms in the COVID-19 era. Finally, the results show that for a successful smart and resilient transformation, manufacturing firms need to develop a strategy and motivation oriented to profit to be resilient enough to withstand market change. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal "Technologies" focused on data science and big data in various fields such as biology, physical science, and engineering. The editorial highlights the importance of analyzing large amounts of data generated by digital technologies and the need for data scientists to use artificial intelligence and machine learning to extract valuable knowledge. The special issue includes 12 papers covering topics such as machine learning techniques for customer churn prediction, agile program management in the U.S. Navy, deep learning for cybersecurity in Industry 5.0, self-directed learning during the COVID-19 era, decision tree-based neural networks for data classification, data-driven governance in technology companies, and more. The papers explore different approaches, models, and tools in the context of data science and big data. [Extracted from the article]

Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of the dredger under abnormal working conditions. In this paper, a data-driven method for predicting the vacuum of the underwater pump of the cutter suction dredger (CSD) is proposed with the help of big data, machine learning, data mining, and other technologies, and based on the historical data of "Hua An Long" CSD. The method eliminates anomalous data, standardizes the data set, and then relies on theory and engineering experience to achieve feature extraction using the Spearman correlation coefficient. Then, six machine learning methods were employed in this study to train and predict the data set, namely, lasso regression (lasso), elastic network (Enet), gradient boosting decision tree (including traditional GBDT, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM)), and stacking. The comparison of the indicators obtained through multiple rounds of feature number iteration shows that the LightGBM model has high prediction accuracy, a good running time, and a generalization ability. Therefore, the methodological framework proposed in this paper can help to improve the efficiency of underwater pumps and issue timely warnings in abnormal working conditions. [ABSTRACT FROM AUTHOR]

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) algorithm has been widely used in various optimization problems. Although PSO has been successful in many fields, solving optimization problems in big data applications often requires processing of massive amounts of data, which cannot be handled by traditional PSO on a single machine. There have been several parallel PSO based on Spark, however they are almost proposed for solving numerical optimization problems, and few for big data optimization problems. In this paper, we propose a new Spark-based parallel PSO algorithm to predict the co-authorship of academic papers, which we formulate as an optimization problem from massive academic data. Experimental results show that the proposed parallel PSO can achieve good prediction accuracy. [ABSTRACT FROM AUTHOR]

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles. [ABSTRACT FROM AUTHOR]

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water. [ABSTRACT FROM AUTHOR]

Based on complex network theory, this paper establishes a Boolean competitive relationship network model and a weighted competitive relationship network model for building material enterprises based on big data and performs empirical analyses with construction prefabricated component (PC) enterprises in the Beijing–Tianjin–Hebei region as research samples. Results show the competitive relationship network of PC enterprises in the Beijing–Tianjin–Hebei region is characterised by the development of a small-world network, and its cumulative degree and out-degree (in-degree) intensity distributions are single-scale networks with fast-decaying tails. The network has strong network clustering and assortativity, and it can truly reflect the competitive status and dynamics of the enterprises. [ABSTRACT FROM AUTHOR]

As data-driven models gain importance in driving decisions and processes, recently, it has become increasingly important to visualize the data with both speed and accuracy. A massive volume of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. The visual analytics of educational big data has the capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. However, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense, etc., leverage artificial intelligence and machine learning techniques to visualize data and generate insights from them; however, their applicability in educational advances is limited. This paper focuses on leveraging machine learning and visualization techniques to demonstrate their utility through a practical implementation using K-12 state assessment data compiled from the institutional websites of the States of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad hoc visualization provides contextual analysis in areas of concern for education agencies (EAs). [ABSTRACT FROM AUTHOR]

With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict future promising technologies on which new important core technologies will be developed. Therefore, this study aimed to establish science and technology development strategies and support business activities by predicting future promising technologies using big data and deep learning models. The names of the "TOP 10 Emerging Technologies" from 2018 to 2021 selected by the World Economic Forum were used as keywords. Next, patents collected from the United States Patent and Trademark Office and the Science Citation Index (SCI) papers collected from the Web of Science database were analyzed using a time-series forecast. For each technology, the number of patents and SCI papers in 2022, 2023 and 2024 were predicted using the long short-term memory model with the number of patents and SCI papers from 1980 to 2021 as input data. Promising technologies are determined based on the predicted number of patents and SCI papers for the next three years. Keywords characterizing future promising technologies are extracted by analyzing abstracts of patent data collected for each technology and the term frequency-inverse document frequency is measured for each patent abstract. The research results can help business managers make optimal decisions in the present situation and provide researchers with an understanding of the direction of technology development. [ABSTRACT FROM AUTHOR]

With the advent of big data, the swift advancement of diverse algorithmic technologies has enhanced the transaction efficiency of the e-commerce business. Nevertheless, it is crucial to acknowledge that e-commerce platforms might employ algorithmic technology to enforce differential pricing for various consumers with the aim of maximizing profits, thus infringing upon the lawful rights and interests of consumers. This paper focuses on the algorithmic price discrimination commonly observed on e-commerce platforms. To effectively regulate this behavior, the paper utilizes evolutionary game theory (EGT) to analyze the strategies employed by e-commerce platforms, consumers, and market regulators to achieve stability. This research employs a real-life situation and utilizes parametric simulation to visualize and analyze the process and outcomes of the three-party evolutionary game. The results demonstrate the credibility and feasibility of the article's findings. Based on our research, we have reached the following findings: During the process of evolution, the strategic decisions made by the game participants from the three parties will mutually impact each other, and various elements exert varying degrees of influence on the strategic choices made by the game participants from each party. Collaborative governance can enable consumers and market regulators to regulate the discriminatory pricing behavior of e-commerce platforms effectively. This article offers valuable insights into the governance of violations in the e-commerce sector based on robust data and model research. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. The tool supports the emulation or the actual connectivity of a large number of IoT devices. The emulated devices can generate realistic correlated IoT data and stream it via the message queuing telemetry transport (MQTT) protocol to a big data platform. The tool allows students to design ML models with different algorithms for their chosen use cases and train them for decision-making based on the streamed data. Moreover, the paper proposes learning outcomes to be targeted when integrating the tool into an electrical engineering curriculum. The tool is evaluated using a comprehensive survey. The survey results show that the students gained significant knowledge about IoT concepts after using the tool, even though many of them already had prior knowledge of IoT. The results also indicate that the tool noticeably improved the students' practical skills in designing real-world use cases and helped them understand fundamental machine learning analytics with an intuitive user interface. [ABSTRACT FROM AUTHOR]

With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast amount of available data poses a significant challenge. Traditional methods often struggle to capture the nuanced and hierarchical nature of user interests within the tourism domain. This paper pioneers the integration of domain information modeling technology into the realm of online review information mining, presenting a novel approach to constructing a user tourism interest model. Unlike existing methods, which primarily rely on flat or simplistic representations of user data, our approach leverages the hierarchical structure inherent in tourism domain information modeling. By harnessing big data within the tourism domain, we construct hierarchical tourism attributes and apply a conditional random field model along with an affective dictionary to facilitate the hierarchical mining of user travel interest information. This culminates in the establishment of a comprehensive user travel interest model using advanced information modeling techniques. Building upon this foundation, we further propose a dynamic user travel interest model, showcasing its adaptability and responsiveness to changing user preferences. Finally, we validate the accuracy and effectiveness of our model through simulation experiments within a user travel recommendation system, demonstrating significant improvements over traditional methods. [ABSTRACT FROM AUTHOR]

Simple Summary: Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms "machine learning" and "mastitis" were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return. Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area. [ABSTRACT FROM AUTHOR]

In the digital age, China's economic development is transitioning from high speed to high quality. Through the application of digital technology, China's manufacturing industry is moving toward more environmentally friendly and sustainable innovation, which makes it of great significance to study the effect of green investment and big data on innovation. Grounded in strategic management theory, this paper examines the interplay between CEO big data orientation, environmental investment, and their joint impact on technological innovation in manufacturing enterprises. Data are extracted from annual reports of listed Chinese manufacturing companies using computer-assisted text analysis methods and evaluated with negative binomial regression. The results indicate an inverted U-shaped relationship between CEO big data orientation and technological innovation. The results further explain that as CEO big data orientation increases, enterprises with higher levels of green investment will reach the peak of technological innovation performance earlier. According to China's intelligent manufacturing in 2035, we have proposed some methods and suggestions for green investment and big data applications. [ABSTRACT FROM AUTHOR]

The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and job creation. Data science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services. Businesses can also harness the power of artificial intelligence (AI) and big data, and the smart use of digital technologies to enhance productivity and performance, paving the way for innovation. However, integrating data science decisions into an SME requires both skills and IT investments. In most cases, such expenses are beyond the means of SMEs due to their limited resources and restricted access to financing. This paper presents trends and challenges towards effective data-driven decision making for organisations based on a 3-year long study which covered more than 85 UK SMEs, mostly from the West Midlands region of England. In particular, this study attempts to find answers to several key research questions around data science and AI adoption among UK SMEs, and the advantages of digitalisation and data-driven decision making, as well as the challenges hindering their effective utilisation of these technologies. We also present two case studies that demonstrate the potential of digitisation and data science, and use these as examples to unveil challenges and showcase the wealth of currently available opportunities for SMEs. [ABSTRACT FROM AUTHOR]

Under the influence of pervasive digital revolution, the accessibility and analysis of 'big data' can provide useful insights and help various industries evolve. Despite the popularity of big data, the construction industry is lagging behind other industries in adopting big data technologies. This paper fills the knowledge gap by examining the status quo of big data adoption in companies with different sizes and roles, as well as that in projects with different types, and ascertaining the drivers for and challenges in adopting big data. This paper employed a structured questionnaire survey and statistical analyses to investigate the significance of factors influencing the drivers, challenges, and enhancement strategies of big data adoption, and validated the results with post-study interviews with construction professionals. The results show that big data adoption in the construction industry is affected by the size of companies and the work experience of their employees. Technology advancement, competitiveness, and government plan and policy initiatives are identified as the top three drivers of big data adoption in the construction sector. Moreover, a lack of appropriate supporting systems, difficulties in data collection, and the shortage of knowledge and experience are found to be the major challenges in big data adoption. Finally, the identified top three strategies for overcoming these challenges and promoting big data adoption are 'clear organization structure', 'government incentives', and 'the training of information technology (IT) personnel'. The paper suggests the necessity of creating differentiated strategies for big data adoption for companies with different scales and roles, and helps provide useful insights for policy-makers in promoting big data applications. [ABSTRACT FROM AUTHOR]

Scientific and technological innovation (STI) is an important internal driver of social and economic development. Reasonable evaluation of regional scientific and technological innovation (RSTI) capability helps discover shortcomings in the development of urban development and guides the allocation of scientific and technological resources and the formulation of policies to promote innovation. This paper analyzes new opportunities created by big data and artificial intelligence for the evaluation of RSTI capability, and based on this analysis, the collaborative evaluation schemes of multi-entity participation are investigated. In addition, considering the important value of unstructured data in evaluating STI, the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis method are employed to analyze the construction of an evaluation indicator system that integrates scientific and technological news data. To fully utilize the respective advantages of human experts and machine learning in the field of complex issue evaluation, this paper proposes an RSTI capability evaluation model based on AHP-SMO human-machine fusion. This study promotes the integration of science and technology and economy and has theoretical and practical significance. [ABSTRACT FROM AUTHOR]

In an era marked by rapid advancements in information technology, the task of risk assessment for data security within the complex infrastructure of the power grid has become increasingly vital. This paper introduces a novel methodology for dynamic, scenario-adaptive risk assessment, specifically designed to address the entire lifecycle of power data. Integrating hierarchical analysis with fuzzy comprehensive evaluation, our approach provides a flexible and robust framework for assessing and managing risks in various scenarios. This method enables the generation of adaptive weight matrices and precise risk level determinations, ensuring a detailed and responsive analysis of data security at each lifecycle stage. In our study, we applied predictive analytics and anomaly detection to conduct a thorough examination of diverse data scenarios within the power grid, aiming to proactively identify and mitigate potential security threats. The results of this research demonstrate a significant enhancement in the effectiveness of risk detection and management, leading to improved data protection and operational efficiency. This study contributes a scalable, adaptable model for data security risk assessment, meeting the challenges of big data and complex information systems in the power sector. [ABSTRACT FROM AUTHOR]

The use and management of Big Data in the political sphere has posed unprecedented challenges concerning democratic governance, equity, and the rule of law. As Big Data establishes itself as a resource of growing value, it is imperative to address one of the most critical challenges: data sustainability. Data sustainability involves social and ethical considerations relating to the correct use of personal data. Lack of informed consent and transparency in collecting and using personal data raises serious concerns regarding privacy and individual rights. It is necessary to define regulations and public policies that guarantee citizens' digital rights based on ethical and democratic standards associated with data management. This article aims to review the literature in the context of data sustainability to identify how Big Data is used, particularly emphasizing its application in the policy domain and the challenges it poses for democratic governance, equity, and law. We have used systematic mapping methodology to collect relevant papers, finding 28 papers associated with democratic governance and Big Data in the context of data sustainability. From the review of these papers, there appears to be a lack of proposals focusing on applying or implementing democratic governance and Big Data. Furthermore, there seem to be no measures to assess the application of Big Data in democratic governance. From these, the need to move towards the definition of formal models that integrate Big Data practices in democratic governance is identified. [ABSTRACT FROM AUTHOR]

Metamaterials are usually designed using biomimetic technology based on natural biological characteristics or topology optimization based on prior knowledge. Although satisfactory results can be achieved to a certain extent, there are still many performance limitations. For overcoming the above limitations, this paper proposes a rapid metamaterials design method based on the generation of random topological patterns. This method realizes the combined big data simulation and structure optimization of structure-electromagnetic properties, which makes up for the shortcomings of traditional design methods. The electromagnetic properties of the proposed metamaterials are verified by experiments. The reflection coefficient of the designed absorbing metamaterial unit is all lower than −15 dB over 12–16 GHz. Compared with the metal floor, the radar cross section (RCS) of the designed metamaterial is reduced by a minimum of 14.5 dB and a maximum of 27.6 dB over the operating band. The performance parameters of metamaterial obtained based on the random topology design method are consistent with the simulation design results, which further verifies the reliability of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

This paper aims to find the relationship between PM10 (particulate matter) concentrations in Seoul and Beijing for a sustainable environment. The influence of Beijing's PM10 concentrations on those of Korea has been extensively discussed since 2019. Previous studies have presented conflicting opinions on the origin of particulate matter in Korea. Some argued that the particulate matter in Korea had its origin in foreign countries, while others emphasized that particulate matter concentrations in Korea were affected by Korean domestic factors. Thus, we try to answer this controversial question based on actual data. For this, we analyze the correlation between concentrations of particulate matter (PM10) in Seoul and Beijing using hourly data for Shunyi, Beijing and Jung-gu, Seoul from March 2014 to February 2017 and examine how particulate matter in Seoul has been affected by particulate matter in Beijing, taking into consideration wind speed and wind direction, through regression analysis. We hypothesize that particulate matter concentrations in Beijing have affected concentrations in Seoul, and that this would be confirmed if there were a correlation between concentrations of particulate matter in Beijing and concentrations in Seoul when the wind blew from China to Korea. The results of this paper are as follows: (i) When considering the 42-h delay and wind direction, the correlation between Seoul_PM10 and Beijing_PM10 becomes more pronounced. (ii) Regression analysis confirms the influence of Beijing's factors on Seoul's PM10 concentrations. (iii) Interaction effects between Beijing PM10 concentration and Beijing wind speed are also identified, indicating a stronger impact when the wind blows more strongly from Beijing. This paper contributes to understanding the dynamics of fine dust in Korea, emphasizing the importance of considering external factors, especially from China. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology. [ABSTRACT FROM AUTHOR]

With the advent of the big data era, the size and complexity of data continue to increase, which makes the requirement for data privacy and security increasingly urgent. However, traditional encryption methods cannot meet the demand for efficient searching in large-scale datasets. To solve this problem and enable users to search within encryped data and without decrypting the entire dataset, trapdoor functions and other cryptograhic techniques are introduced in searchable encryption. However, searchable encryption still cannot meet the needs in the real world. Therefore, researchers have introduced the concept of attribute-based encryption into searchable encryption, resulting in attribute-based searchable encryption (ABSE). This approach aims to achieve efficient search by attributes in encrypted datasets. ABSE has a wide range of applications in the fields of privacy protection, data sharing, and cloud computing. In this paper, we describe the trends in development, focusing on enhancing security, improving computational efficiency, and increasing flexibility. We also present the related schemes. In addition, several common application areas are introduced and the relevant schemes proposed by researchers are summarized. Moreover, the challenges and future directions of ABSE are discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper explores the importance of smart manufacturing in the context of Industry 4.0, highlighting the crucial role of Manufacturing Execution Systems (MESs) in facilitating Industry 4.0, particularly in data capture and process management. It is worth noting that Small and Medium Enterprises (SMEs) face several obstacles, unlike large companies that have the resources to adopt these principles. This text explores the challenges that SMEs encounter when adopting Industry 4.0, considering budget constraints and technology transfer difficulties. The potential benefits of such projects are often difficult to measure during the initial stages, but they can facilitate the digital transformation of small businesses. To support this thesis, this paper presents an example of MES implementation in a manufacturing SME, showcasing the creation of a comprehensive data monitoring and industrial performance assessment system. This paper aims to introduce a systematic approach for integrating a Key Performance Indicator (KPI) framework using MESs within an SME. This paper highlights the importance of transitioning from big data to smart data to achieve outcomes in terms of operational efficiency, cost analysis, workload management, resource utilisation, knowledge dissemination, and enhanced operator engagement. [ABSTRACT FROM AUTHOR]

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine. [ABSTRACT FROM AUTHOR]

Studying how objects are positioned is vital for improving technologies like robots, cameras, and virtual reality. In our earlier papers, we introduced a bio-inspired artificial visual system for orientation detection, demonstrating its superiority over traditional systems with higher recognition rates, greater biological resemblance, and increased resistance to noise. In this paper, we propose a hardware-based orientation detection system (ODS). The ODS is implemented by a multiple dendritic neuron model (DNM), and a neuronal pruning scheme for the DNM is proposed. After performing the neuronal pruning, only the synapses in the direct and inverse connections states are retained. The former can be realized by a comparator, and the latter can be replaced by a combination of a comparator and a logic NOT gate. For the dendritic function, the connection of synapses on dendrites can be realized with logic AND gates. Then, the output of the neuron is equivalent to a logic OR gate. Compared with other machine learning methods, this logic circuit circumvents floating-point arithmetic and therefore requires very little computing resources to perform complex classification. Furthermore, the ODS can be designed based on experience, so no learning process is required. The superiority of ODS is verified by experiments on binary, grayscale, and color image datasets. The ability to process data rapidly owing to advantages such as parallel computation and simple hardware implementation allows the ODS to be desirable in the era of big data. It is worth mentioning that the experimental results are corroborated with anatomical, physiological, and neuroscientific studies, which may provide us with a new insight for understanding the complex functions in the human brain. [ABSTRACT FROM AUTHOR]

The Special Issue on "Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process" received several state-of-the-art research papers related to the application of big data analysis and advanced analytics in three different areas of sustainable production processes: prognostics and health management (PHM), supply chain management (SCM), and safety policy in the workplace. On a similar subject, in the paper "A Time-Series Data Generation Method to Predict Remaining Useful Life" [[5]], a time series generation method, which is useful when there are not enough fault-labeled data available, is presented by Ahn, Yun, and Hur. We live in the big data era, in which a large amount of information is continuously created, registering all kinds of events, such as those generated in the design, planning, control, and execution of manufacturing, logistics, and supply chain processes. [Extracted from the article]

The transition from a linear economy (LE) to a circular economy (CE) is not just about mitigating the negative impacts of LE, but also about considering changes in infrastructure, while leveraging the power of technology to reduce resource production and consumption and waste generation, and improve long-term resilience. The existing research suggests that digital technologies (DTs) have great potential to drive the CE. However, despite the exponential growth and increasing interest in studies on DTs and the CE from year 2016 onwards, few systematic studies on the application of DTs to enable the CE have been found. In addition, the current status and development direction of the DT-driven CE is unclear, and the potential of DTs to support CE implementation is under-researched. Therefore, the aim of this paper is to explore the potential of DTs to drive the CE. This paper set out to analyze the current status and development of the DT-driven CE and examine future development trends in the field. Using a systematic literature review approach, this paper is the first attempt to use a mixed method, i.e., to combine macro-quantitative bibliometric methods with a micro-qualitative content analysis method to explore the DT-driven CE. The results, which include the research background, co-occurrence clusters, research hotspots, and development trends of keyword co-occurrence network visualization and keyword burst detection, are presented from a macro perspective using two bibliometric analysis softwares. In addition, the use of 13 specific DTs in the CE is analyzed according to seven disciplinary areas (Environmental Sciences and Ecology, Engineering, Science and Technology and Other Topics, Business Economics, Computer Science, Operations Research and Management Science, and Construction and Building Technology) of greatest interest from a micro-qualitative point of view. Further, future trends and challenges facing DT-driven CE development are explored and feasible directions for solutions are proposed. [ABSTRACT FROM AUTHOR]

Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0), offering a crucial analytical tool for the vast data streams generated by modern industrial processes. This literature review systematically consolidates existing research on the predictive analysis of time series within the framework of Industry 4.0, illustrating its critical role in enhancing operational foresight and strategic planning. Tracing the evolution from the first to the fourth industrial revolution, the paper delineates how each phase has incrementally set the stage for today's data-centric manufacturing paradigms. It critically examines how emergent technologies such as the Internet of things (IoT), artificial intelligence (AI), cloud computing, and big data analytics converge in the context of Industry 4.0 to transform time series data into actionable insights. Specifically, the review explores applications in predictive maintenance, production optimization, sales forecasting, and anomaly detection, underscoring the transformative impact of accurate time series forecasting on industrial operations. The paper culminates in a call to action for the strategic dissemination and management of these technologies, proposing a pathway for leveraging time series prediction to drive societal and economic advancement. Serving as a foundational compendium, this article aims to inform and guide ongoing research and practice at the intersection of time series prediction and Industry 4.0. [ABSTRACT FROM AUTHOR]

A new gridded spatio-temporal big data fusion method is proposed for the organization and management of cropland big data, which could serve the analysis application of cropland quality evaluation and other analyses of geographic big data. Compared with traditional big data fusion methods, this method maps the spatio-temporal and attribute features of multi-source data to grid cells in order to achieve the structural unity and orderly organization of spatio-temporal big data with format differences, semantic ambiguities, and different coordinate projections. Firstly, this paper constructs a dissected cropland big data fusion model and completes the design of a conceptual model and logic model, constructs a cropland data organization model based on DGGS (discrete global grid system) and Hash coding, and realizes the unified management of vector data, raster data and text data by using multilevel grids. Secondly, this paper researches the evaluation methods of grid-scale adaptability, and generates distributed multilevel grid datasets to meet the needs of cropland area quality evaluation. Finally, typical data such as soil organic matter data, road network data, cropland area data, and statistic data in Da'an County, China, were selected to carry out the experiment. The experiment verifies that the method could not only realize the unified organization and efficient management of cultivated land big data with multimodal characteristics, but also support the evaluation of cropland quality. [ABSTRACT FROM AUTHOR]

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis. [ABSTRACT FROM AUTHOR]

With the rapid development of modern geochemical analysis techniques, massive volumes of data are being generated from various sources and forms, and geochemical data acquisition and analysis have become important tools for studying geochemical processes and environmental changes. However, geochemical data have high-dimensional, nonlinear characteristics, and traditional geochemical data analysis methods have struggled to meet the demands of modern science. Nowadays, the development of big data and artificial intelligence technologies has provided new ideas and methods for geochemical data analysis. However, geochemical research involves numerous fields such as petrology, ore deposit, mineralogy, and others, each with its specific research methods and objectives, making it difficult to strike a balance between depth and breadth of investigation. Additionally, due to limitations in data sources and collection methods, existing studies often focus on a specific discipline or issue, lacking a comprehensive understanding of the bigger picture and foresight for the future. To assist geochemists in identifying research hotspots in the field and exploring solutions to the aforementioned issues, this article comprehensively reviews related studies in recent years, elaborates on the necessity and challenges of combining geochemistry and artificial intelligence, and analyzes the characteristics and research hotspots of the global collaboration network in this field. The study reveals that the investigation into artificial intelligence techniques to address geochemical issues is progressing swiftly. Joint research papers serve as the primary means of contact within a worldwide collaborative network. The primary areas of focus in the ongoing research on the integration of geochemistry and artificial intelligence include methodologies for analyzing geochemical data, environmental modifications, and mineral prospectivity mapping. Geochemical data analysis is currently a significant focus of research, encompassing a range of methods including machine learning and deep learning. Predicting mineral resources for deep space, deep Earth, and deep sea is also a pressing topic in contemporary research. This paper explores the factors driving research interest and future trends, identifies current research challenges, and considers opportunities for future research. [ABSTRACT FROM AUTHOR]

Featured Application: The paper describes the possible application of particular data visualization to understand the interactions of the variables usually considered in scoping reviews. The approach, borrowed from other disciplines, can support the medical scientists in revealing and analyzing the data collected by the literature search. The method can be applied to every medical field and can be implemented in relation to the research questions. Water-based rehabilitation is a well-known approach that is useful for the prevention and management of many conditions. Its application in the elderly is quite common in clinical practice, but scientific evidence is limited. We conducted a scoping review on geriatric water-based rehabilitation using a methodology borrowed from data science to investigate and discuss the extensive literature data. We searched the papers on PubMed and we used the abstracts to collect different data. We imported them into an electronic database and we used its filters to build different graphical representations. The filters allowed the selections of specific modalities of a variable and the following visualization of the values of the other variables linked to that selected modality. A total of 49 papers were found and they confirmed the safety and usefulness of water-based rehabilitation. Our analysis was able to show the relationships among the variables and the differences in the elements considered for the analysis. The current literature shows some limitations, especially concerning the article types in some specific diseases and the outcome measurements. Future research can overcome these limitations by collecting more data on the diseases that affect old people, even with the use of precise outcome measures. Our described methodology can be potentially beneficial and other studies may confirm its utility. [ABSTRACT FROM AUTHOR]

Sustainable Cloud Computing is the modern era's most popular technology. It is improving daily, offering billions of people sustainable services. Currently, three deployment models are available: (1) public, (2) private, and (3) hybrid cloud. Recently, each deployment model has undergone extensive research. However, relatively little work has been carried out regarding clients' adoption of sustainable public cloud computing (PCC). We are particularly interested in this area because PCC is widely used worldwide. As evident from the literature, there is no up-to-date systematic literature review (SLR) on the challenges clients confront in PCC. There is a gap that needs urgent attention in this area. We produced an SLR by examining the existing cloud computing models in this research. We concentrated on the challenges encountered by clients during user adoption of a sustainable PCC. We uncovered a total of 29 obstacles that clients confront when adopting sustainable PCC. In 2020, 18 of the 29 challenges were reported. This demonstrates the tremendous threat that PCC still faces. Nineteen of these are considered critical challenges to us. We consider a challenge a critical challenge if its occurrence in the final selected sample of the paper is greater than 20%. These challenges will negatively affect client adoption in PCC. Furthermore, we performed three different analyses on the critical challenges. Our analysis may indicate that these challenges are significant for all the continents. These challenges vary with the passage of time and with the venue of publication. Our results will assist the client's organization in understanding the issue. Furthermore, it will also help the vendor's organization determine the potential solutions to the highlighted challenges. [ABSTRACT FROM AUTHOR]

In line with the qualitative and quantitative growth of academic papers, it is critical to understand the factors driving citations in scholarly articles. This study discovered the up-to-date academic structure in the tourism and hospitality literature and tested the comprehensive sets of factors driving citation counts using articles published in first-tier hospitality and tourism journals found on the Web of Science. To further test the effects of research topic structure on citation counts, unsupervised topic modeling was conducted with 9910 tourism and hospitality papers published in 12 journals over 10 years. Articles specific to online media and the sharing economy have received numerous citations and that recently published papers with particular research topics (e.g., rural tourism and eco-tourism) were frequently cited. This study makes a major contribution to hospitality and tourism literature by testing the effects of topic structure and topic originality discovered by text mining on citation counts. [ABSTRACT FROM AUTHOR]

The increasing availability of vast quantities of data from various sources significantly impacts the insurance industry, although this industry has always been data driven. It accelerates manual processes and enables new products or business models. On the other hand, it also burdens insurance analysts and other users that need to cope with this development parallel to other global changes. A novel information system (IS) for artificial intelligence (AI)-supported big data analysis, introduced within this paper, shall help to overcome user overload and to empower human data analysts in the insurance industry. The IS research's focus lies neither in novel algorithms nor datasets but in concepts that combine AI and big data analysis for synergies, such as usability enhancements. For this purpose, this paper systematically designs and implements an AI2VIS4BigData reference model to help information systems conform to automatically detect anomalies and increase its users' confidence and efficiency. Practical relevance is assured by an interview with an insurance analyst to verify the demand for the developed system and derive all requirements from two insurance industry user stories. A core contribution is the introduction of the IS. Another significant contribution is an extension of the AI2VIS4BigData service-based architecture and user interface (UI) concept on AI and machine learning (ML)-based user empowerment and data transformation. The implemented prototype was applied to synthetic data to enable the evaluation of the system. The quantitative and qualitative evaluations confirm the system's usability and applicability to the insurance domain yet reveal the need for improvements toward bigger quantities of data and further evaluations with a more extensive user group. [ABSTRACT FROM AUTHOR]

Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' cooperation ability and lifelong learning ability. Based on students' interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency–inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In recent years, online ride-hailing has become an indispensable part of residents' travel mode. Therefore, the prediction of online ride-hailing travel demand has become extremely important. In the era of big data, the application of big data in the field of transportation is becoming more extensive. Based on the open data of ride-hailing trips in Haikou City, Hainan Province, provided by the Didi platform and combined with the rainfall data of Haikou City, this paper proposes a gate recurrent unit (GRU) model considering rainfall factors and rest days factors for short-term trip demand prediction. The K-fold cross-validation method is adopted to adjust the parameters of the model to the optimal ones through the training set. The improved GRU model is compared with the original GRU model and other classic models, and the model is evaluated by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score indexes. Finally, it is proved that the GRU model proposed in this paper greatly improves the prediction accuracy of short-term online ride-hailing travel demand. [ABSTRACT FROM AUTHOR]

Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP). [ABSTRACT FROM AUTHOR]

With the ubiquitous use of digital technologies and the consequent data deluge, official statistics faces new challenges and opportunities. In this context, strengthening official statistics through effective data governance will be crucial to ensure reliability, quality, and access to data. This paper presents a comprehensive framework for digital data governance for official statistics, addressing key components, such as data collection and management, processing and analysis, data sharing and dissemination, as well as privacy and ethical considerations. The framework integrates principles of data governance into digital statistical processes, enabling statistical organizations to navigate the complexities of the digital environment. Drawing on case studies and best practices, the paper highlights successful implementations of digital data governance in official statistics. The paper concludes by discussing future trends and directions, including emerging technologies and opportunities for advancing digital data governance. [ABSTRACT FROM AUTHOR]

Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict carbon emissions from private cars in various provinces and cities in China. Secondly, by simulating different scenarios of carbon tax, carbon trading, and their policy mix, the propensity score matching model is constructed to explore the effects of the policy mix on carbon emission reduction from private cars and social welfare while conducting regional heterogeneity analysis. Finally, policy proposals are proposed to promote carbon emission reduction from private cars and enhance social welfare in China. The results indicate that the environmental policy mix has a significant positive impact on carbon emission reduction from private cars and social welfare. Furthermore, in the regional heterogeneity analysis, the implementation of the policy mix in eastern regions has a significant positive effect on both carbon emission reduction from private cars and social welfare, while in central and western regions, it shows a significant positive impact on social welfare but has no significant effect on carbon emission reduction in the private car sector. [ABSTRACT FROM AUTHOR]