Data Technology Research Articles

Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research.

Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer's Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders' trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges. CRITICAL RELEVANCE STATEMENT: By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development. KEY POINTS: Global neuroimaging projects are increasingly advancing but still face challenges. Constructing and utilizing neuroimaging projects should follow set rules and guidelines. Effective data management and governance should be developed to support neuroimaging projects.

Research on Big Data Platform Deployment and Maintenance Course Practical Teaching based on "Job-Curriculum-Competition-Certification + Ideological and Political Integration"

With the rapid development of the big data industry, the demand for big data technical talents has increased sharply. The big data technology major in higher vocational colleges aims to cultivate high-quality skilled talents with capabilities in data collection, processing, operation and maintenance, and technical services. The major integrates the core skills of big data implementation and operation and maintenance positions into professional courses and conducts teaching practice based on the "Job-Curriculum-Competition- Certification + Ideological and Political Integration" model. By constructing an education system that integrates knowledge imparting, ability cultivation, and value shaping, ideological and political education is integrated into course teaching, achieving an organic combination of professional teaching and ideological and political education content. At the same time, a blended teaching mode is adopted, focusing on the comprehensive cultivation of students' knowledge, abilities, and qualities, using competitions to promote learning and training, and improving the quality of talent cultivation.

Smart Medicine: The Role of Artificial Intelligence and Machine Learning in Next-Generation Healthcare Innovation

The paper focuses on big data and healthcare pharmaceuticals, which pose both great promise and challenge as they can contribute to well-being and deliver innovation and knowledge to next-generation healthcare products. This paper provides an overview of the benefits that machine learning and big data technology contribute to healthcare systems. Realities such as patient and healthcare professional demands, the number of diseases, insufficient drugs, regulations, legal and ethical guideline restrictions, modus operandi, and technology development take place in the healthcare sector. In addition, big data opportunities and challenges for healthcare companies, research, and healthcare infrastructure are discussed. Finally, a vision of health innovation through personalized medicines as well as smart medicine is presented as a step closer to patients through regulatory, legal, and ethical guidelines, applications, and opportunities that need to be regulated and resolved.

Harnessing genomic technologies for one health solutions in the tropics.

The targeted application of cutting-edge high-throughput molecular data technologies provides an enormous opportunity to address key health, economic and environmental issues in the tropics within the One Health framework. The Earth's tropical regions are projected to contain > 50% of the world's population by 2050 coupled with 80% of its biodiversity however these regions are relatively less developed economically, with agricultural productivity substantially lower than temperate zones, a large percentage of its population having limited health care options and much of its biodiversity understudied and undescribed. The generation of high-throughput molecular data and bespoke bioinformatics capability to address these unique challenges offers an enormous opportunity for people living in the tropics. MAIN: In this review we discuss in depth solutions to challenges to populations living in tropical zones across three critical One Health areas: human health, biodiversity and food production. This review will examine how some of the challenges in the tropics can be addressed through the targeted application of advanced omics and bioinformatics and will discuss how local populations can embrace these technologies through strategic outreach and education ensuring the benefits of the One Health approach is fully realised through local engagement. Within the context of the One Health framework, we will demonstrate how genomic technologies can be utilised to improve the overall quality of life for half the world's population.

Computer big data technology in additive manufacturing and product design in sustainable manufacturing

Computer big data technology in additive manufacturing and product design in sustainable manufacturing

The Influence of Social Change on Human Resource Management Strategies in the Digital Era

Social change in the digital era has had a significant impact on various aspects of life, including the world of work and human resource management (HR). This study aims to explore the influence of social change on HR management strategies in the digital era, as well as identify the challenges and opportunities that arise in the process of organizational adaptation. Using a qualitative approach and literature review method, this study analyzes the latest scientific literature from various trusted sources, including academic journals, textbooks, and industry reports. The results of the study indicate that social change has driven transformation in various aspects of HR management, including recruitment, retention, employee development, performance management, and organizational culture. Digitalization, shifting workforce demographics, and changing employee expectations are the main factors influencing HR strategies. Key findings include: (1) adoption of AI and big data technologies in the recruitment and selection process, (2) increased focus on work flexibility and work-life balance, (3) transformation of employee development programs towards a continuous learning and personalization model, (4) shift from traditional performance management to a more agile and real-time feedback-based approach, and (5) the importance of building an inclusive and adaptive organizational culture. The study also uncovers the ethical and social challenges that arise from the digitalization of HR practices, as well as the importance of bridging the digital divide in the workforce. In conclusion, successful organizations are those that are able to adapt their HR strategies to meet the challenges of the digital era, while maintaining a focus on the human factor as the core of effective HR management.

Probabilistic Linguistic TODIM Method with Probabilistic Linguistic Entropy Weight and Hamming Distance for Teaching Reform Plan Evaluation

In the context of the construction of new liberal arts, the integration and intersection of disciplines have become a new trend in the development of higher education. How to promote the teaching reform of big data technology and application courses in the new liberal arts construction scenario has become an important issue in enhancing students’ digital talent literacy and social adaptability. In this study, an extended probabilistic linguistic TODIM (an acronym in Portuguese for interactive multi-criteria decision making) with probabilistic linguistic entropy weight and Hamming distance is presented for teaching reform plan evaluation for the core course “big data technology and applications” in the digital economy major. Firstly, probabilistic linguistic entropy weight, based on the entropy of the additive linguistic term set, is applied to generate weight information. Secondly, parameter sensitivity analysis is carried out to prove the stabilization and effectiveness of the extended TODIM approach. Thirdly, this extended approach can integrate the psychological factors and cognitive behaviors of decision-makers for effectively responding to education management in the new liberal arts construction scenario. Finally, a case study on teaching reform plan evaluation is carried out, and a comparative analysis with different criteria weights and different methods is conducted to verify the extended approach. The results indicate that the extended approach can provide an effective technical tool for scientific decision-making, especially in the teaching reform plan evaluation scenario in order to promote high-quality development of education.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Applications of integrating artificial intelligence and big data: A comprehensive analysis

Abstract The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.

The role of big data in transforming financial management in U.S. healthcare: A conceptual framework

This paper explores the transformative role of big data in financial management within the U.S. healthcare sector, highlighting its capacity to address long-standing financial inefficiencies, enhance decision-making, and improve regulatory compliance. Big data technologies, such as predictive analytics and real-time financial reporting, provide healthcare institutions with the tools to forecast financial trends, optimize budgeting, and mitigate risks. Despite the significant benefits, the adoption of big data in healthcare finance faces challenges, including data privacy concerns, regulatory complexities, and technological limitations. The paper examines these barriers and enablers, offering strategic recommendations for healthcare institutions and policymakers to optimize the use of big data. By improving data governance, upgrading infrastructure, and fostering collaboration across departments, healthcare organizations can maximize the impact of big data on their financial operations. The review concludes with a call for continued policy support and financial incentives to encourage the broader adoption of big data in healthcare finance, ultimately promoting cost efficiency and operational resilience in the sector. Keywords: Big Data, Healthcare Financial Management, Predictive Analytics, Data Governance, Regulatory Compliance, Financial Decision-Making.

Movie Recommendation System Based on Word Embedding

Abstract. Amidst the swift progression of Internet and big data technologies, recommendation systems have emerged as crucial conduits linking users to products across various digital platforms. This study delves into the deployment of neural collaborative filtering within the realm of movie recommendation systems, with the objective of constructing a system of high precision. Utilizing the MovieLens dataset, this investigation applies one-hot encoding and embedding techniques within the PyTorch-Lightning framework to effectively model user behaviors and predict cinematic preferences. The neural collaborative filtering (NCF) model leverages deep learning to extract latent features of users and items, exhibiting a notable enhancement in performance compared to traditional collaborative filtering approaches. Empirical results indicate that the NCF model attains an accuracy of 86% on the validation dataset, underscoring its efficacy in extensive recommendation scenarios.

BIG DATA AND STATISTICAL APPLICATIONS

In recent years, modern developments in computer and cloud technologies have led to a significant increase in the quantity and speed of generated and stored information. This increase in the amount of information has introduced a new concept into our lives called "Big Data". Big Data offers significant advantages, especially for governments and businesses, in making informed decisions about their futures. However, the magnitude and diversity of this emerging data also bring about some challenges. Traditional database systems have been inadequate in processing these data formats, necessitating the need for new tools and techniques to overcome this challenge. Today, a wide range of techniques and technologies have been developed to collect, process, analyze, and visualize “Big Data”. These techniques and technologies encompass and draw upon various fields such as statistics, computer science, applied mathematics, and economics. Keywords: big data, visualization of data, structured data, big data technologies, big data techniques.

Industrialization and innovation strategy of event resources in ice and snow sports under the background of big data

This article examines the industrialization and innovative strategy of event resources in ice and snow sports in the context of big data. Based on this analysis, the article proposes a path and development strategy for the Chinese sports industry, which includes encouraging the industrialization of the nation’s ice and snow sports industry. It is important to evaluate ice and snow sports in order to improve competitive results and optimize training techniques using big data technologies. Despite using a variety of techniques to evaluate performance, their inability to fully capture the complex patterns found in ice and snow sports events presents difficulties. In order to assess and enhance sports talents, therefore we proposed a new method using ISSE-Apriori algorithms. The pre-processed data gathered before extracting the most important and relevant features. The experiment results section analyzes the resources of ice and snow sports cities. The results examine sports event strategies, taking into account predictions and actual accuracy results, the GDP growth rate, the efficiency of industrial development, and a comparison of Apriori before and after enhancement. The experimental result is validating using measures like recall, accuracy, and precision. In addition, we conducted a comparative analysis with the current approaches to confirm the efficiency and robustness of the proposed methodology. The Suggested approach is implemented with Python Software. The Suggested Approach’s performance is measured in terms of RMSE (0.3524), MAE (0.1832), MAPE (4.24) with large dataset. The results stated that the proposed methodology has provided an accuracy of 98.42%.

Effectiveness of Integrated Marketing and Financial Strategies in Healthcare

In recent years, the healthcare sector has experienced a shift toward integrated marketing and financial strategies to drive better patient outcomes, streamline operational costs, and improve service delivery. This paper examines the effectiveness of these strategies by exploring how they align to optimize healthcare systems. It focuses on the benefits, challenges, and implications of integrating financial planning with marketing outreach, emphasizing the role of data analytics, technology, and consumer engagement in enhancing both patient satisfaction and financial performance. Through a detailed analysis of current literature, industry examples, and statistical insights, the paper aims to provide a comprehensive overview of how an integrated approach influences revenue cycles, operational efficiency, and patient engagement in healthcare.

Optimizing ambulance nurse performance in southern Tuscany: insights from a Delphi study

This study aims to enhance the performance of ambulance nurses in Southern Tuscany through a Delphi method analysis. Conducted from January to December 2022, the research engaged 16 expert participants from a Tuscan health agency. Data on 89 interventions performed by "India" ambulances, which are nurse-led emergency vehicles, were collected. The Delphi method facilitated structured communication among the experts over three rounds, resulting in consensus on nine key intervention proposals. These proposals span areas including staff training, measurement and data collection methods, technologies and materials, and work environments. Key recommendations include prioritizing high-fidelity simulation training, improving data collection for clinical risk, and standardizing equipment across all emergency units. The study highlights the importance of continuous professional development, technological integration, and supportive work environments to optimize the effectiveness of pre-hospital emergency care. These findings provide a practical roadmap for enhancing the competencies and performance of ambulance nurses, ultimately improving patient outcomes in emergency situations.

Development of methodology for calculating flooded area and flood volume in small urban areas based on unmanned aerial vehicle images

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers.

The Socio-Economic Situation of a Population as a Determinant of Childhood Obesity: Answers from Big Data

Big data tools are currently a major tool for assessing population changes. There is a causal relationship between low economic levels and a higher prevalence of conditions associated with malnutrition and obesity. One of the causes of low income in different areas could be a higher unemployment rate and a confluence of people who are migrating for economic reasons. Objective: To assess in a child population the effect of the unemployment rate, average income and immigration rate as a possible effect of increasing the prevalence of malnutrition associated with childhood obesity. Material and methods: Data collected from computerized clinical history episodes, studying the variables of sex, age, weight, height, of a pediatric population (year 2022), comparing it with the average income of their residential district, unemployment rate and immigration rate. Use of big data methods for the study of variables. Using the Cole-Green LMS algorithm with penalized likelihood, implemented in the RefCurv 0.4.2 software (2020), which allows managing large amounts of data. The hyperparameters have been selected using the BIC (Bayesian Information Criterion). To calculate population deviations from the reference, the reference was taken as being above 1.5 standard deviations from the average according to age. Results: 66,975 computerised episodes of children under 16 years of age and a total of 1,205,000 variables studied. The data and comparative graphs between districts of the population studied are represented with respect to the variables analysed. There are significant differences, with an increase in the rate of overweight in those areas with lower economic income and higher unemployment and immigration rates. Big data technology allows for more efficient population studies, selecting populations most in need of health intervention, optimizing scarce health resources.

How does digital transformation affect corporate governance paradigms? A synthesis of the literature

At present, digital transformation has become a vital way for companies to achieve sustainable growth. This paper reviews the literature on the correlation between digital transformation and corporate governance paradigm, analyzes the specific impact mechanism of AI technology and big data technology in the field of corporate governance, and explores the influence effect of the most popular ChatGPT technology on corporate governance from the perspective of business practice. It is found that digital transformation has an important impact on stakeholder management, information disclosure, green governance and other aspects of corporate governance. The purpose of the study is to provide a new reference for the construction of corporate governance paradigm and help companies achieve long-term development in the digital wave.

The Impact of National Big Data Pilot Zones on the Persistence of Green Innovation: A Moderating Perspective Based on Green Finance

Enhancing the persistence of corporate green innovation is crucial for promoting sustainable societal development. As big data technologies and related policies advance, digitalization emerges as an effective enabler of green innovation in enterprises. This study, grounded in resource-based theory and corporate ecology theory, examines the impact of national-level big data pilot zone reforms on the persistence of corporate green innovation, utilizing data from China’s A-share-listed companies from 2011 to 2021. It also explores the moderating effect of green finance development. Empirical results indicate that (1) the implementation of national-level big data pilot zones positively influences the persistence of green innovation, with robustness and endogeneity tests confirming these findings. (2) The development of regional green finance positively moderates this relationship. (3) A mediation analysis reveals that national-level big data policies enhance innovation persistence by alleviating financing constraints, promoting talent aggregation, and facilitating industrial upgrading. (4) The heterogeneity analysis shows that the positive impact of these reforms is more pronounced in non-state-owned, non-heavy polluting firms, those receiving high subsidies, and in regions with stringent environmental regulations. This study offers a novel theoretical perspective on and empirical evidence for digital transformation from the angle of national policy, supporting societal green development and contributing to policy enhancement aimed at achieving sustainable growth.

How to Promote Data Sharing of Financial Service Providers Based on Federated Learning Technology

Data sharing between financial service providers (FSPs) can promote the reduction of financial risks, but the reality is that the FSPs may be unwilling to share data with each other due to data security problems. The emergence of federated data technology provides a convenient way to address this issue. In this article, the author proposes using a federated data sharing model for FSPs and two dynamic evolutionary games for evaluating influencing factors. Cooperation results when both FSPs reach a certain level of mutual payoff. The reward and punishment mechanisms are important means by which FSPs actively implement real data sharing. The interaction between the FSP and the federated technology service provider (FTSP) demonstrates the practicality of this new technology. The results show that the data sharing improved the data trust and supported regulatory mechanisms.