Data-driven Era Research Articles

The dry lab microscopist or prompt microscopist: do we need them?

AbstractIn modern biological microscopy, the explosion of data volume and complexity highlights the urgent need for specialised data management support roles. While traditional microscopy focuses on visual data presentation, the rapid increase in big data acquisition and data mining demands advanced handling and analysis. This gap underscores the need for “dry lab microscopists” or data experts skilled in microscopy data management, software interoperability, and AI-driven solutions. Job markets reflect this demand, pointing to the necessity for dedicated training programs. Integrating these specialists into research institutions is crucial for addressing digital data challenges and maintaining high standards in data integrity and analysis. Their role is essential for advancing research in the data-driven era.

Creative data justice: a decolonial and indigenous framework to assess creativity and artificial intelligence

ABSTRACT In the last decade, the Global South has emerged as a significant player in the data economy due to their majority user base, and studying its role is crucial to comprehend the future of AI. As societies grapple with the implications of AI on creative life, there is an opportunity to reevaluate the creative contributions of Global South cultures, ensuring they are acknowledged and foregrounded in the evolving landscape of human and machine creativity. This paper calls for reimagining and restructuring creative value with the emergence of AI enabled technologies by broadening who and what counts as creative in this data-driven era. To democratize creativity, a decolonial and indigenous framework of cross-cultural creative value is needed which critically intersects and examines the relations between creative labor, rights, and learning. The study of the Global South’s data economies is important not only to harness its potential but also to address the cross-cultural ethics of building Creative AI tools with data from their underrepresented communities. At its core, the creative data justice framework emphasizes the need to challenge the existing power imbalances in global data governance. This paper proposes that fair creative value can be achieved by drawing inspiration from indigenous systems of care as a counterforce to neoliberal values of efficiency and utility. This framework will help scholars, policymakers and designers in their inclusive approaches to creativity in the age of AI.

Cosmic-ray propagation models elucidate the prospects for antinuclei detection

Tentative observations of cosmic-ray antihelium by the AMS-02 collaboration have re-energized the quest to use antinuclei to search for physics beyond the standard model. However, our transition to a data-driven era requires more accurate models of the expected astrophysical antinuclei fluxes. We use a state-of-the-art cosmic-ray propagation model, fit to high-precision antiproton and cosmic-ray nuclei (B, Be, Li) data, to constrain the antinuclei flux from both astrophysical and dark matter annihilation models. We show that astrophysical sources are capable of producing 𝒪(1) antideuteron events and 𝒪(0.1) antihelium-3 events over 15 years of AMS-02 observations. Standard dark matter models could potentially produce higher levels of these antinuclei, but showing a different energy-dependence. Given the uncertainties in these models, dark matter annihilation is still the most promising candidate to explain preliminary AMS-02 results. Meanwhile, any robust detection of antihelium-4 events would require more novel dark matter model building or a new astrophysical production mechanism.

Legal and Economic Analysis of Fair Use in the Process of Artificial Intelligence Creation in Data-Driven Era

In the era of digitalization, AI-generated content has emerged as a pivotal component within the creative industries, thereby instigating extensive deliberations concerning copyright protection and fair use. This study aims to scrutinize and analyze the equitable use of AI-generated content from both a legal and economic standpoint. This paper does a thorough review of the literature to introduce basic ideas about AI content creation and fair use. It then divides existing research into four separate areas: technical analysis, legal case analysis, economic impact analysis, and policy adaptation research. Despite notable advancements in both technological capabilities and legislative frameworks surrounding AI-generated content, there remains a dearth of comprehensive understanding, particularly with regard to its economic implications and policy implementation. Consequently, this study posits a significant research question on how to strike a balance between the fair usage of AI-generated content and upholding copyright protection in order to foster sustainable development within the creative industries. The findings presented herein offer novel perspectives and insights that can inform future policymaking endeavors as well as academic investigations.

Development of a robust Machine learning model for Ames test outcome prediction

The mutagenicity is an essential parameter for evaluating the safety of pharmaceuticals, chemicals, consumer products, environmentally related compounds and the Ames assay is a significant test for predicting the mutagenicity of chemical compounds. In the data-driven era, developing robust models for efficient mutagenicity prediction before synthesizing and testing in vitro has gained increasing attention. In this study, a machine learning model that could predict Ames mutagenicity based on 2D molecular descriptors was developed. A multistep filtering process that adequately helps in identifying significant descriptors was adopted in this study. Three different sets of descriptors, namely, RDKit, Mordred and CDK were used to train three machine learning algorithms, viz., random forest, xgboost and catboost. The datasets were collected from different resources to develop a robust machine learning model. The robustness of this model was further validated by comparing different available ML and DL models for Ames genotoxicity. Specifically, 12 models, including our xgboost model, were used to validate an external dataset, and our model exhibited excellent performance, with an impressive AUC of 0.97. The codes to predict the genotoxicity of a molecule is available at https://github.com/Naga270588/Genotoxicity.

Research on Construction of Offline Data Warehouse for Ship Shore Power based on DolphinScheduler and Hive

In today's networked, intelligent, and data-driven era, the shore power industry is facing the challenge of rapidly growing data. This paper presents the construction of a professional offline data warehouse system for shore power based on DolphinScheduler and Hive. Firstly, MySQL is adopted as the backend database, combined with Sqoop to synchronize business data to HDFS, ensuring data reliability and integrity. Secondly, the Flume-Kafka-Flume architecture is utilized to achieve real-time collection and caching of user behavior data, providing data support for subsequent analysis. Thirdly, HQL statements are written in Hive to clean, merge, and analyze shore power data, calculating key indicators such as electricity consumption and usage trends. Fourthly, data visualization is achieved through the integration of Superset, displaying data analysis results via a web interface. Fifthly, DolphinScheduler is employed for timed scheduling, ensuring dependency control among various tasks and the smooth operation of the project. This system fully leverages the replication mechanism of HDFS to enhance reliability, dynamically adds nodes to achieve system scalability, and fully utilizes the fault tolerance of the Yarn scheduler. It saves time and computational costs for the shore power industry, realizing higher value and benefits.

Transforming Academic Libraries into Hubs of Innovation and Entrepreneurship: A Case Study of a New Library Model

In a data-driven era, academic libraries must evolve beyond traditional roles as spaces for reading and storing information resources to become platforms promoting learning, research, innovation, future skills, and entrepreneurship. Based on the triple helix model of innovation, this paper highlights the new library model in a case study aimed at transforming traditional academic library services into enterprising, innovation-driven activities. For over ninety years, Thammasat University Library has been located on Rattanakosin Island, an historical area of Thailand. More recently, the library business model has evolved to meet modern needs. This qualitative research uses a case study to explore library strategies to create space meeting requirements of diverse user groups: students, faculty, staff, alumni, businesspeople and the community, including older residents. Service innovations responding to user demands are prioritized by drawing on the expertise of librarians. These changes generate new revenue for the library and enhance the space design and use through the Life Space concept. Co-working spaces, learning areas, inspirational corners, and performance venues make the library a platform for inspiring creativity, learning, life enhancement, and business ventures. The Thammasat University Library has also become an innovative bridge linking university research with small and medium-sized enterprises (SMEs), facilitating knowledge transfer and research commercialization. These findings may serve as guidelines for academic libraries seeking to adapt the Library of Life approach to be centres catering to readers of all generations and stations in life.

Application of Adaptive Machine Learning in Non-Stationary Environments

In the current data-driven era, the application of adaptive machine learning in non-stationary environments has become particularly important. This paper explores the basic concepts of adaptive machine learning and its applications in financial market forecasting and industrial equipment monitoring, demonstrating its superior performance in high-noise, dynamic environments. The research results indicate that adaptive machine learning models significantly improve prediction accuracy, response speed, and robustness through strategies such as online learning and incremental learning. In financial market forecasting, the mean squared error (MSE) was reduced to 0.015, and the fault detection accuracy in industrial equipment monitoring increased to 95%. The paper also proposes future research directions, including multi-source data fusion, anomaly detection, computational efficiency enhancement, and model interpretability. Adaptive machine learning technology not only enriches the theoretical framework of machine learning but also provides new solutions for practical applications in various fields, paving the way for further development of intelligent systems.

The anatomy of a data transfer agreement for health research.

In a data-driven era, the exchange and safeguarding of personal information has become paramount. Data transfer agreements (DTAs) serve to guard privacy, defining the rules for sharing and protecting sensitive data. Yet, the complexities surrounding issues such as data privacy, intellectual property, and dispute resolution within these agreements pose challenges that demand careful consideration. Through a scoping review of twenty-four publicly available, English language DTAs relevant to health research, this article undertakes a comprehensive analysis, examining common clauses, their vital components, and charting a course for responsible data sharing through the provision of insights and practical guidance for drafting DTAs. The article underscores the need for attention to detail and an understanding of data protection legislation in order to ensure that DTAs align with the law and maximize legal certainty.

Sowing the seeds for sustainability: A business model innovation perspective on artificial intelligence in green technology startups

In today's data-driven era, ubiquitous concern about environmental issues pushes more startups to engage in business model innovation that promotes environmentally friendly technologies. The goal of these startups is to create technology-based products and services that enhance environmental sustainability. In this context, artificial intelligence promises to be a key instrument to create, capture, and deliver value. However, the existing literature lacks a deep understanding of how startups using AI innovate their business models to achieve a positive environmental impact. Therefore, this paper investigates how green technology startups utilize AI from a business model innovation perspective for environmental sustainability. We conduct a qualitative, exploratory multiple-case study using the Eisenhardt methodology, based on interview data analyzed using qualitative content analysis. We derive five predominant manifestations for AI-driven business model innovation and identify archetypical connections between business model dimensions. Further, we establish three overarching archetypical associations among the cases. In doing so, we contribute to theory and practice by providing a deeper account of how green technology startups attempt to maximize their positive environmental impact through AI. The results of this study also highlight how business model innovation driven by AI can support society in securing a more environmentally sustainable future.

Building a sustainable future with enterprise metaverse in a data-driven era: A technology-organization-environment (TOE) perspective

This research explores organizations' inclination towards adopting the enterprise metaverse using the TOE framework. The outcome of this research reveals that technology roadmapping, organizational fit, and competitive pressure significantly influence enterprise metaverse adoption. Further, enterprise metaverse adoption is also significantly associated with the sustainable performance (environmental, social, economic) of the firm. Moreover, the results highlight that the association between technology roadmapping, organizational fit, competitive pressure, and trading partner readiness towards enterprise metaverse adoption are stronger for organizations having low technology turbulence. Further, the impact of automated decision-making on enterprise metaverse adoption is moderated by leadership support. The findings of this research will enhance the metaverse literature by providing insights into how businesses integrate this technology, shedding light on the technological, organizational and environmental factors crucial for the adoption. Further, this study will provide insights into how businesses can leverage enterprise metaverse capabilities in their organizations, further enhancing overall sustainability.

Time-series classification in smart manufacturing systems: An experimental evaluation of state-of-the-art machine learning algorithms

Manufacturing is transformed towards smart manufacturing, entering a new data-driven era fueled by digital technologies. The resulting Smart Manufacturing Systems (SMS) gather extensive amounts of diverse data, thanks to the growing number of sensors and rapid advances in sensing technologies. Among the various data types available in SMS settings, time-series data plays a pivotal role. Hence, Time-Series Classification (TSC) emerges as a crucial task in this domain. Over the past decade, researchers have introduced numerous methods for TSC, necessitating not only algorithmic development and analysis but also validation and empirical comparison. This dual approach holds substantial value for practitioners by streamlining choices and revealing insights into models’ strengths and weaknesses. The objective of this study is to fill this gap by providing a rigorous experimental evaluation of the state-of-the-art Machine Learning (ML) and Deep Learning (DL) algorithms for TSC tasks in manufacturing and industrial settings. We first explored and compiled a comprehensive list of more than 92 state-of-the-art algorithms from both TSC and manufacturing literature. Following this, we methodologically selected the 36 most representative algorithms from this list. To evaluate their performance across various manufacturing classification tasks, we curated a set of 22 manufacturing datasets, representative of different characteristics that cover diverse manufacturing problems. Subsequently, we implemented and evaluated the algorithms on the manufacturing benchmark datasets, and analyzed the results for each dataset. Based on the results, ResNet, DrCIF, InceptionTime, and ARSENAL emerged as the top-performing algorithms, boasting an average accuracy of over 96.6 % across all 22 manufacturing TSC datasets. These findings underscore the robustness, efficiency, scalability, and effectiveness of convolutional kernels in capturing temporal features in time-series data collected from manufacturing systems for TSC tasks, as three out of the top four performing algorithms leverage these kernels for feature extraction. Additionally, LSTM, BiLSTM, and TS-LSTM algorithms deserve recognition for their effectiveness in capturing features within manufacturing time-series data using RNN-based structures.

Hybrid Physical–Virtual Digital Twin System for Additive Manufacturing

This study presents a hybrid physical–virtual digital twin system to enhance additive manufacturing (AM). The system aims to achieve real-time monitoring, predictive modeling, and improved 3D printing control. It integrates sensing-driven and simulation-driven digital twins into a comprehensive hybrid digital twin environment (HDTE) tailored for the Creality Ender-5 Plus 3D printer within the Unity 3D virtual environment. The AM process simulation employs a three-dimensional transient finite element model, enabling precise predictions of thermodynamics, residual stress, final distortion, and solidification parameters. To enhance real-time insights, external sensors like thermocouples, thermal cameras, and LIDAR ranging sensors continuously collect temperature and location data during 3D printing. Experimental results confirm the HDTE’s accuracy. Position assessment experiments show their ability to replicate component positions under varying conditions, while temperature assessment experiments demonstrate their capacity to mimic temperature variations promptly and accurately. The study also provides a detailed analysis of nodal temperature distribution within a 3D printed plate, indicating potential for predictive defect detection and print parameter optimization. Additionally, the study integrates a closed-loop control mechanism, ensuring rigorous quality control, although a detailed exposition of this system is reserved for future publications. This paper bridges the gap between virtual design and physical AM production, offering real-time monitoring, predictive capabilities, and automated control. These advancements enhance efficiency, reduce material waste, and promote sustainability. As this system evolves, it promises to revolutionize additive manufacturing, ushering in a data-driven era of intelligent 3D printing across industries.

Strategic Implementation of Big Data Automation for Wastage Management Reporting Using Analytical Hierarchy Process in The Tobacco Industry

In today's data-driven era, big data automation is crucial, often referred to as "the new oil." Industries, particularly the fast-moving consumer goods (FMCG) sector like the tobacco industry, must undergo digital transformation to stay competitive. The integration of big data automation with reporting processes is significantly correlated, as it can automate repetitive reporting tasks, enhancing efficiency. This automation enables decision-makers to make faster and more accurate decisions. This research focuses on assessing the capacity and factors involved in the collaboration between the operations department and the digital team to automate repetitive reporting processes by integrating big data from various sources such as SAP and Microsoft Forms. The study employs a combination of qualitative and quantitative methods, along with the Analytic Hierarchy Process (AHP), to identify optimal business solutions. Insights from this research prioritize big data automation and reporting projects to meet business needs. Results indicate among four alternative project groups, the Central Data Wastage project is the top priority with a score of 51.7%, followed by SMD Wastage at 25.2%, PMD Wastage at 14.7%, and FMD Wastage at 8.4%. Five stakeholders participated in this research, including a product manager, business user, business analyst, and two developers. These participants contributed to assessing criteria, sub-criteria, and alternative project groups. This research not only helps prioritize projects but also facilitates seamless digitalization within the operations team, fostering synergy with the digital team.

Data Ownership and Digital Governance: Promoting Enterprise Cooperation and Sustainable Development

In the contemporary data-driven era, the triad of data ownership, digital governance, and government-enterprise cooperation is woven intricately into the fabric of the digital landscape. At the core of this interconnected web lies the imperative to strike a delicate balance between safeguarding data rights, fostering data sharing, and upholding data security, all pillars underpinning the sustainable growth of digital ecosystems. This paper aims to illuminate the profound interplay between data ownership complexities, enterprise management strategies, and the overarching domain of digital governance. Furthermore, it delineates the landscape of pertinent legal frameworks that govern this intricate terrain. By embracing a nuanced, scenario-driven approach to data protection and by nurturing collaborations among diverse stakeholders, this article embarks on a quest to unravel how the implementation of effective cooperation models can serve as catalysts for the sustainable evolution of the digital economy. Simultaneously, these models advocate for responsible data governance, ensuring that the fundamental values of individual privacy and corporate interests remain duly safeguarded.

Big data applications in portfolio management: A review of techniques and strategies

In the ever-evolving landscape of finance, the advent of big data has ushered in a paradigm shift, transforming the art and science of portfolio management from its traditional moorings to a future where data reigns supreme. This paper delves into the intricate dance between big data and portfolio management, aiming to demystify the journey from historical practices to the forefront of technological innovation in finance. Through a meticulous examination, the study sets out to map the evolution of portfolio management in the digital age, scrutinize the technological foundations essential for big data analytics, and explore the symbiotic relationship between big data capabilities and investment strategies. Employing a systematic literature review as its methodological backbone, the research navigates through the complexities of integrating big data into portfolio management, offering a panoramic view of the current landscape and the challenges that lie ahead. The findings illuminate the transformative impact of big data analytics, highlighting its potential to enhance decision-making, optimize risk assessment, and unearth new investment opportunities. However, the journey is fraught with challenges, including data privacy concerns, ethical considerations, and the need for continuous technological adaptation. In conclusion, the study advocates for a proactive embrace of big data, urging portfolio managers to cultivate a culture of innovation, ethical vigilance, and lifelong learning. As the financial sector stands on the brink of a data-driven era, this paper calls for a strategic reorientation towards the integration of big data analytics in portfolio management, paving the way for a future where informed decision-making and strategic foresight are paramount.

BIG DATA ANALYTICS: A REVIEW OF ITS TRANSFORMATIVE ROLE IN MODERN BUSINESS INTELLIGENCE

In the dynamic landscape of modern business intelligence, Big Data Analytics has emerged as a transformative force, reshaping the way organizations derive insights from vast and diverse datasets. This paper provides a concise overview of the key themes explored in the comprehensive review of Big Data Analytics and its impact on modern business intelligence. Big Data Analytics represents a paradigm shift in decision-making processes, offering organizations the capability to harness the full potential of their data assets. The review delves into the multifaceted role of Big Data Analytics, emphasizing its significance in strategic planning, risk management, operational optimization, and customer-centric initiatives. Strategic planning takes a quantum leap forward as organizations leverage predictive analytics to anticipate market trends. The integration of analytics-derived insights aligns decision-making with overarching organizational objectives, driving a more informed and forward-thinking approach to strategic initiatives. Risk management becomes more proactive with the integration of Big Data Analytics, particularly in fraud detection and prevention. The ability to process large volumes of data in real-time enhances vigilance, mitigating financial risks associated with fraudulent activities cenario modeling further empowers organizations to assess and address potential risks before they materialize. Operational optimization becomes a focal point as analytics uncovers inefficiencies in manufacturing processes, supply chains, and retail operations. Real-time decision-making in retail, enabled by data analytics, ensures agility and responsiveness to changing market dynamics and customer preferences. Customer-centric initiatives are elevated through personalized marketing campaigns and predictive analytics in customer support. The review explores how Big Data Analytics enables organizations to craft personalized experiences, enhancing customer satisfaction and loyalty. The study encapsulates the transformative journey of Big Data Analytics in modern business intelligence, emphasizing its role in navigating strategic complexities, mitigating risks, optimizing operations, and placing the customer at the center of decision-making processes. The comprehensive review provides insights for organizations seeking to harness the transformative potential of Big Data Analytics in the data-driven era.
 Keywords: Big Data, Business Intelligence, Data Analytics, Modern Business, Review.

The Game Process and Achievement of EU and US Cross Border Data Flow Rules

In today's data-driven era, the governance of data rules has become a focal point for countries worldwide. This paper elucidates the significant differences between the United States and Europe, both major players in this domain, in terms of legislation and value positions. They also operate within distinct social data environments and possess varying national interest needs. These influence the direction and results of the game between the two sides in the data privacy framework. The paper also encompasses many far-reaching factors involved in this process, such as the political relationships between the United States and Europe, and the economic conditions on both sides. In the contest for dominance in data governance rules, the United States and Europe have been constantly interacting, refining regulations through ongoing friction, and to some extent, driving the development of the data industry. Meanwhile, the world still needs a more mature and stable regulatory framework for data governance rules, indicating the necessity for further significant progress in this area.

Research on Management and Optimization of Big Data Computing Engine Based on Cloud Native Technology IT Architecture

In the wave of digital transformation, we are in a data-driven era. With the explosive growth of data volume, traditional big data processing platforms are facing more and more challenges, including insufficient computational efficiency and resource utilization. At the same time, cloud native technology has emerged as a key factor in filling this gap due to its flexibility, automated management, and cross platform compatibility. Although cloud native technology has broad prospects, its application in the field of big data is still in its early stages, and there are still many problems that need to be solved. Among them, the unity of cluster management, the collaboration between operation and development, and the efficiency of technology architecture transformation are the main challenges currently faced. To address these issues, we will conduct in-depth research around cloud native technology, big data computing engine technology, and containerized services, exploring and implementing a big data computing engine management and optimization solution based on cloud native technology. By proposing and implementing cloud native deployment methods, containerized construction methods, and a one-stop big data application management and development platform, the aim is to provide new ideas and methods for solving many problems in traditional big data computing engine management, and to contribute to the development and innovation of the big data field.

Research on the Cultivation of Core Literacy of Physical Education Professionals in Colleges and Universities Based on the Background of Data Mining

Abstract In the context of the data-driven era, it is essential to explore practical ways to cultivate the core literacy of sports professionals in colleges and universities. This study aims to analyze the core literacy of physical education majors in depth by using data mining technology to seek a more scientific and systematic cultivation strategy. The study used the decision tree CART algorithm and the improved Apriori algorithm to analyze the physical fitness data of the students in the School of Physical Education of S University. The CART algorithm clustered the biological fitness data of male and female students, and it was found that male students’ primary physical fitness deficiencies were concentrated in the upper body strength and standing long jump events. In contrast, female students showed deficiencies in endurance and lower body explosive strength. The improved Apriori algorithm reveals the association rules between different physical fitness items, for example, there is a strong association between boys’ 50-meter running performance and pull-up performance. There are apparent differences in the influencing factors of physical fitness between male and female students, which need to be targeted to design training programs. It was found through association rule mining that specific physical testing programs significantly affect students’ physical fitness quality. This study provides a new path for cultivating core quality of physical education professionals based on data mining, which offers scientific basis and practical guidance for physical education in colleges and universities.