Data Technology Research Articles

Research on the Application of Computer Technology in Biostatistics

This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.

Archival Description Turns Truly Collaborative: an Exercise in Records in Contexts Standard

This paper presents an exercise and research conducted by the author at the Faculty of Humanities and Social Sciences at Zagreb University. It aimed to teach archival description subjects to his BA students and link results using the Records in Contexts (RIC) standard, RDF Turtle standard, and a graph database. The exercise spanned five weeks, during which students had to create the archival description of an exemplary records aggregation of notable historian Jaroslav Šidak, provided by the Faculty Archives. This exercise aimed to examine the potential of RIC and linked data technologies to facilitate collaboration among archivists, reduce the description-related work through decentralization, enable re-using and linking descriptions, and visualize results. The author offers a conclusion regarding collaborative work and creating new knowledge from archived heritage. Finally, some further directions regarding these subjects are being proposed.

Research on AIS Data Analysis and Its Integration into Discipline-specific Teaching

The processing of ship AIS (Automatic Identification System) data plays a crucial role in the digital transformation of the shipping industry. From data collection to data storage, data analysis, and data visualization, AIS data processing technology provides robust support for the shipping sector. In terms of data collection, the platform utilizes distributed message queues to aggregate AIS data. For data storage, the platform efficiently manages and stores massive amounts of AIS data using data warehouses. When it comes to data analysis, the platform mines and analyzes AIS data through Lambda, extracting valuable information such as ship trajectories and sailing speeds, thereby supporting areas such as ship management, navigation safety, and marine research. In data visualization, the platform intuitively displays ship navigation status through charts and maps, providing strong support for decision-making. As an essential component of big data applications, AIS data analysis technology is playing an increasingly important role in the shipping industry. The big data technology major of Jiangsu Maritime Institute integrates big data applications like AIS data analysis into its curriculum, taking the upgrading needs of the shipping industry as an entry point. It actively plans the cultivation of composite big data skilled talents under the backdrop of smart shipping, comprehensively serving the upgrading of the shipping industry and promoting the transformation from a major shipping country to a shipping powerhouse.

Proactive pricing strategies for on-street parking management with physics-informed neural networks

Effective pricing is important for on-street parking management and proactive parking pricing is an innovative strategy to achieve optimal parking utilization. For proactive parking pricing, accurately predicting parking occupancy and deriving the price elasticity of parking demand are necessary. In recent years, there have been an increasing number of studies applying big data technology for parking-occupancy prediction. However, existing research has not incorporated economic knowledge into modeling, thus preventing application of the price elasticity of parking demand. In this study, proactive pricing strategies are proposed to adjust on-street parking prices which involve a parking-occupancy prediction model and a price-optimization method. Physics-informed neural networks are employed to achieve accurate prediction of parking occupancy and calculation of parking price elasticity. An elasticity-occupancy parking-management strategy is proposed for on-street parking management which leverages parking occupancy and price elasticity to guide pricing interventions. A case study shows that the parking-occupancy prediction model can make accurate predictions and derive the price elasticity of parking demand. Proactive parking pricing enables drivers to plan their trips in advance, allowing parking occupancy within an optimal range.

Legitimacy and Limits of Administrative Power in the Big Data Era

The advent of the big data era has profoundly transformed the exercise of administrative power, enhancing the efficiency and accuracy of administrative decisions while simultaneously posing challenges to legitimacy and the potential for abuse of power. This study examines the impact of big data technology on administrative power, systematically analyzing the foundations and limits of administrative power's legitimacy. It explores how to achieve a balance between legitimacy and limits of administrative power in the context of big data. By examining the intersection of traditional theories of power legitimacy and big data technology, the study proposes strategies for enhancing transparency in administrative decisions, strengthening public participation, and promoting inter-departmental cooperation. It also looks forward to innovative paths for exercising administrative power in the future. The results of this study are significant for both administrative management practices and theoretical development in the big data era.

Course Reform and Practice of "Python Programming" for Artificial Intelligence and Big Data Applications

With the rapid development of artificial intelligence (AI) and big data technologies, Python has become a widely used programming language in related fields. However, the traditional "Python Programming" course has been unable to meet the demands of applications in AI and big data in terms of content and teaching models. Therefore, this paper presents a comprehensive reform of the "Python Programming" course, optimizing course content by incorporating modules on big data processing and artificial intelligence. Additionally, a project-driven teaching model and a diversified evaluation system have been implemented. By introducing innovative teaching tools and resources, students' practical abilities and innovative thinking have been enhanced. The practical outcomes of the course reform are significant, with students showing notable improvements in their programming skills in AI and big data. In the future, this study will continue to optimize course content to address the ever-changing technological demands.

Data Evidence Collection and Legal Norms in Administrative Law Enforcement

With the rapid development of data technology, data evidence collection has become a key means of improving efficiency and accuracy in administrative law enforcement. However, the widespread application of data evidence collection has introduced new challenges related to legal compliance and privacy protection. This paper analyzes the core role of administrative law enforcement in modern society and the practical challenges it faces, explores the specific application scenarios and operational processes of data evidence collection in administrative law enforcement, and evaluates the current legal norms related to data evidence collection. Drawing on international experience, this paper proposes recommendations for improving data evidence collection legal norms in China, aiming to provide legal support and technical assistance for administrative law enforcement practices. The study seeks to offer references and guidance for the reasonable application of data evidence collection in administrative law enforcement and the development of legal norms.

State of continental discharge estimation and modelling: challenges and opportunities for Africa

ABSTRACT Africa’s diverse climates and sparse hydro-meteorological networks create significant challenges in accurately estimating river discharge. Discharge data are crucial for managing water resources and predicting extremes. Our review assesses the data gap, existing methods, and technologies for river discharge estimation in Africa. Limited gauging networks on rivers, including in 63 transboundary basins, hinder accurate discharge modelling, affecting resource management and disaster response. Despite the potential of remote sensing, Geographic Information System (GIS), satellite imagery, and machine learning, their large-scale application for river discharge monitoring in Africa is limited. We propose the use of a monitoring system involving local communities in data collection and decision making, supported by global data centres, enhanced regional data sharing, and strengthened transboundary cooperation. For example, incorporating water data products, including discharge data, in data cubes, such as Digital Earth Africa, could improve monitoring. Strategic investments in hydro-meteorological instrumentation are crucial for strengthening climate resilience.

Monitoring system for physical activity and fitness based on service robots and biomechanics

Exercise is one of the important ways for people to exercise, with characteristics such as sociality and strong participation. Especially with the improvement of the level of economic development and the improvement of the quality of life of the people, more and more people begin to attach importance to the maintenance of their own health. Physical fitness monitoring, as an effective means, is widely used in daily life, especially among the elderly. However, most of the existing monitoring methods are relatively simple, lacking pertinence, and the data collection process is relatively cumbersome and unstable, which cannot meet current needs. Therefore, it is very necessary to explore a new type of equipment that can more comprehensively and accurately monitor various physiological parameters of the human body to replace existing traditional detection technologies. Service Robots are currently the most promising intelligent hardware products. They mainly provide personalized services centered on users, sensing user behavior and implementing intelligent decision-making based on their characteristics, thereby better meeting the needs of different groups of people. This article focused on the research and development of Service Robots, and designed a comprehensive solution for Service Robots based on theories such as the Internet of Things, cloud computing, big data technology, and artificial intelligence. This article compared existing intelligent monitoring systems with fitness monitoring systems based on Service Robots, and proved that the user experience of fitness monitoring with robot participation has improved by about 4.68%. Its application scenarios were richer and its effects were more significant, enabling it to better complete tasks such as analysis and prediction of physical fitness status, real-time warning, etc., reducing the risk of people suffering from diseases, and enhancing individual protection awareness.

Tribute VS "Nanman": The Image Construction of "Magic Mirror Type"

For over 2000 years, feudal dynasties throughout China have referred to southerners as the “Nanman” (means Southern Barbarian). However, the specific situation of the treatment of Nanman titles by various dynasties has not been explored. In addition, the Nanman never stopped paying tribute to the Central Dynasty. No one is involved in whether tribute can earn the respect that the dynasty deserves. Nowadays, big data technology can easily handle massive amounts of literature, and it is entirely possible to analyze the above issues from "official histories" (the 25 historical books) using the method of word frequency research. The words frequency shows that since the unification of Lingnan by Emperor Qin Shi Huang, the feudal dynasties presented a paradox to Lingnan: The products in Lingnan were coveted, and local customs were despised. "Tribute" was used to cover up the predatory nature of Kings, and "Nanman" was used to establish political, national and cultural rank. The number of "tribute" and the meaning of "Nanman" formed the "magic mirror" development relationship in the big tree of feudal autocracy until the end of the Qing Dynasty. But on the one hand, the positive factors of "tribute" were absorbed and used by the western powers to establish their own alliances. On the other hand, they also mislead the public opinion and create trouble. Although "Nanman" has been eradicated politically, the regional discrimination and cultural misreading it contains have not been eradicated and often occurs. "Tribute" ranges from Kyushu to overseas to international relations. The connotation of "Nanman" from the orientation, nation and culture, the meaning of these two words has been spontaneously changing. It is timely to clarify their relationship and help make China revive.

Research on Hybrid Teaching Driven by Big Data Application Development Competition

With the increasing emphasis placed by the country on vocational education, it has become a key focus for vocational colleges to encourage their students to participate in national vocational skills competitions, through which they can enhance their professional qualities, theoretical knowledge, practical skills, and employability. The big data technology major of Jiangsu Maritime Institute has analyzed the knowledge and skill points required in big data application development skills competitions, and has constructed a project-driven hybrid teaching approach based on the Chaoxing Cloud Course Platform, which utilizes a SPOC (Small Private Online Course) approach. By aligning with the workflows and tasks of big data technology-related job positions, the major integrates skills competition projects into its curriculum, realizing a learner-centered, integrated online and offline learning approach. Through teaching practice, this teaching method has achieved good results, improving the quality of teaching and students' learning enthusiasm, and providing new methods and perspectives for the reform of big data technology professional courses in higher vocational education.

Research on Education and Teaching of Artificial Intelligence Enabling Intelligent Construction

This study is committed to in-depth analysis of the practical application of artificial intelligence in intelligent construction teaching and its empowerment effect. An efficient and intelligent teaching platform is built, which can intelligently recommend relevant learning resources according to students’ learning progress and feedback, and track students’ learning situation in real time. This personalized learning style not only improves the learning efficiency of students, but also enables them to better adapt and master the knowledge and skills in the field of intelligent construction. The introduction of artificial intelligence technology has innovated the method of intelligent construction teaching. Based on the learning analysis technology of big data, we can conduct in-depth analysis of students’ learning behaviors and achievements, so as to grasp students’ learning status and needs more accurately. These achievements not only show the great potential of artificial intelligence technology in the field of education, but also provide new ideas and methods for the reform and innovation of intelligent construction teaching.

The foresight methodology for transitional shale gas reservoirs prediction based on a knowledge graph

The sedimentary environment in the eastern Ordos area of China is complex and contains a large number of transitional facies environments. Yet, there are many characteristics such as large vertical lithology changes, complex lateral sedimentary environment changes, small monolith thickness and large organic matter content changes, which lead to large uncertainty in the prediction of favorable areas for transitional shale gas. As the intricate reservoirs continue to unfold, the conventional linear prediction methods find themselves facing an arduous path to meet the demands of development. The ever-evolving complexity of these reservoirs has outpaced the capabilities of these traditional approaches. It becomes apparent that a more comprehensive and adaptable approach is necessary to navigate the intricacies of these reservoirs and unlock their hidden potential. Therefore, we put forward a method of introducing knowledge graph into transitional shale gas reservoir prediction in the eastern Ordos region by using big data technology. Because artificial intelligence big data relies heavily on data tags, it is particularly important for the construction of tags. Firstly, a top-down knowledge graph in the field of reservoir prediction is constructed to determine the key parameters used in prediction, namely porosity, total organic carbon and brittleness index. Secondly, the decision tree knowledge graph optimization label is constructed in a bottom-up way. The key parameter of this prediction is the knowledge graph obtained according to the professional knowledge of reservoir prediction, so as to optimize the school label of U-net and reduce the workload of artificial judgment. The results of the combination of the two methods are applied to 11 wells in Daji area of Ordos, and the experimental results are consistent with the actual situation of the reservoir. Based on the foundation of theoretical knowledge, this method enhances the efficiency and accuracy of interpretation and evaluation. It provides fundamental and technical support for the selection of favorable areas in shale gas exploration and the evaluation of exploration and development prospects, particularly in transitional shale gas areas, which is innovative and advanced in the field.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Государственное публичное управление цифровой средой: реализация идеи в условиях выявления и устранения конституционных рисков

Deployment of contemporary digital technologies creates new dynamics for the people, society and state and introduces considerable changes to the public administration system. Application of big data technologies significantly expands the opportunities for the development of new economic models, international cooperation and competition. However, increased involvement of people in the digital environment not only reveals new forms and methods of innovative administration of the state but is also associated with serious constitutional risks and threats for the state sovereignty. Consecutive constitutionalization predetermines efficiency of implementation of constitutional laws and activities of public government authorities. Efficient public administration of the digital environment can be reached through development of a high-quality regulatory framework characterized by relative stability and constitutional orientation.

Digital-Twin Predictive Control of Nonlinear Systems With Time Delays, Unknown Dynamics, and Communication Delays.

With the advancement of computing technology and big data technology, digital twins have gradually been applied in various fields, such as manufacturing, energy, and healthcare. This article studies the predictive control of nonlinear dynamic systems using digital twins. Based on a digital-twin control system framework, predictive control is discussed for three different nonlinear systems with time delays: 1) known nonlinear systems; 2) unknown nonlinear systems; and 3) unknown nonlinear cyber-physical systems. Both a digital-twin predictive control strategy and a digital-twin control predictor are proposed to compensate for time delays and communication delays actively. With the strategy and predictor, the digital-twin controller of a time-delay nonlinear system can be designed to achieve the desired performance based on the nonlinear system without time delays, which vastly simplifies the controller design procedure. A digital model is constructed using data to deal with unknown nonlinear dynamics. The three different closed-loop digital-twin predictive control systems are analyzed to derive a unified stability criterion. The simulation results show how the proposed digital-twin predictive control method performs well for nonlinear systems with time delays, unknown dynamics, and/or communication delays.

Healthcare Data Privacy Protection Through the Lens of Big Data

In the era of rapid development of big data, there are more and more fields that have a close connection with big data technology, of which the medical field is a particularly important part. Nowadays, citizens reflect that the medical information they had uploaded to the cloud platform has been leaked frequently, even in some regular hospitals and other government-provided platforms, which has aroused citizens' doubts and dissatisfaction. This paper will combine the two perspectives of the field of medical information and big data technology to provide new countermeasures and solutions for solving the problem of medical data leakage from the perspective of big data technology and combining with citizens' awareness of privacy protection.

Using Big Data Analytics to Identify Trends and Group Crimes through Clustering

The incidence of crime in a city presents a challenge in the absence of trend analysis that impacts citizen security. The objective of this research was to analyze and visualize crime trends in the area, using the concepts and fundamentals of Big Data Analytics, Data Mining and Clustering, the problem is addressed with a quantitative approach, using the CRISP-DM process, Principal Component Analysis (PCA) and the K-Means algorithm for clustering. Validation is performed with the Elbow Score and the Average Silhouette method, ensuring the robustness of the data clustering. The results show that crimes against property, such as robbery and theft, are the most frequent. Four crime clusters are identified, each associated with a specific category, providing a detailed view of crime distribution. Comparison with previous studies highlights the effectiveness of Big Data technologies in reducing crime, providing a solid basis for more accurate security strategies.

Challenges and opportunities for enhancing groundwater data access and usability in low- and middle-income countries: insights and recommendations from WaSH researchers and practitioners

ABSTRACT Groundwater provides more than half of all water for drinking and irrigation worldwide. Monitoring and managing groundwater resources is essential to achieve the Sustainable Development Goals. However, groundwater resources need to be better monitored. We explored practical approaches to improve groundwater monitoring for informed decision-making, focusing on low- and middle-income country settings. Case studies from a 2023 UNC Water and Health Conference side event highlighted innovative approaches, including open-source data modeling, groundwater monitoring in humanitarian contexts, and using in situ sensors to monitor drought resilience. The case studies described field data integration, remote sensing, and technology (e.g., Modflow-USG, ModelMuse, Drought Resilience Impact Platform). Barriers to groundwater data use were identified by side event participants, including inadequate data access, insufficient collaboration and leadership, and challenges in integrating diverse data and technology. Side event recommendations included creating a central groundwater monitoring data repository, building greater hydrogeology capacity in the WaSH sector, and increasing funding for data collection and monitoring. We urge government leaders to develop regulations and build internal capacity for sustainable groundwater management. This comprehensive approach aims to address challenges and promote informed decision-making for the long-term sustainability of groundwater resources.

The AI and Big Data Equitable Transformation Theory (ABETT): A Pathway to Inclusive Development and Technological Empowerment

The rapid advancement of artificial intelligence (AI) and big data technology has created new potential for economic growth, social development, and creativity. However, their incorporation into local economies presents issues regarding ethical governance, skill development, access to infrastructure, and equitable benefit sharing. This paper presents the AI and Big Data Equitable Transformation Theory (ABETT), a comprehensive framework for guiding the responsible and inclusive use of AI and big data in various scenarios. The ABETT model has six basic constructs: policy and ethical governance, skills development and education, scalable and equitable infrastructure access, collaborative innovation and knowledge exchange, sustainable AI and data for development, and economic growth and competitiveness. The ABETT framework was created, validated, and enhanced using a qualitative research design that included a systematic literature assessment, expert interviews, and case study analysis. The results support the model's relevance and applicability, illustrating how its elements work together to generate ethical and sustainable technological innovation. Comparisons with previous theories show that ABETT adds value by proposing a comprehensive and customizable pathway for AI and big data adoption that emphasizes collaboration, equity, and sustainability. The paper discusses the model's policy and practical implications, limitations, and future research recommendations. ABETT provides a practical roadmap for stakeholders who want to use AI and big data to promote inclusive growth, sustainable development, and equitable technical advancement.