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Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data. [ABSTRACT FROM AUTHOR]

In the context of global economic integration and Industry 4.0, digital manufacturing has become crucial. As one of the economic cores of China, the digitization process of the manufacturing industry in the Yangtze River Delta is particularly critical to the overall economic growth. Based on the theory of Industry 4.0 and digital manufacturing, this study deeply analyzes the current digital development of the manufacturing industry in the Yangtze River Delta. More importantly, this paper successfully constructs a fuzzy control model to quantitatively evaluate and guide the process of digital transformation of manufacturing industry in this region. The empirical results of the model reveal how key factors such as capital, talent, technology and data security affect the digitization process, and provide concrete and operational transformation strategies for the Yangtze River Delta region. In addition, combined with industrial advantages, policy support, technological progress and market demand, this paper predicts the digital development prospects of the manufacturing industry in the Yangtze River Delta. Overall, the study not only provides in-depth insights on the digitization of manufacturing in the Yangtze River Delta, but also provides practical guidance for actual operation, which has high theoretical and practical value. [ABSTRACT FROM AUTHOR]

Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community. [ABSTRACT FROM AUTHOR]

Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patientcentric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions. [ABSTRACT FROM AUTHOR]

Background: Telehealth and mHealth apps become increasingly popular in health professions such as physiotherapy calling for increased awareness on functionality, privacy, and data security. Objectives: This work presents a functionality, privacy, and data-security evaluation of four telehealth services commonly used in physiotherapy. Methods: We examined functionality and features, data protection, privacy implementations and data-security with a questionnaire and performed an in-depth investigation of the services. Results: Privacy and security relevant findings such as use of outdated webservers, problems with certificate renewal as well as questionable GDPR compliance were reported. Conclusion: Due to the privacy and security relevant findings in this analysis it can be concluded that there is a need for improvement in design, development, operation as well as regulation of telehealth apps and services. [ABSTRACT FROM AUTHOR]

The Centralized E-Warranty System with Blockchain Security offers a pioneering solution to the challenges entrenched in conventional warranty management methods. This paper delineates a comprehensive platform leveraging mobile technology and real-time data analytics to elevate operational efficiency and customer satisfaction. By facilitating seamless registration, dealer management, and product assignment for companies, this system ensures immediate access to warranty information through automated e-warranty generation through QR code scanning. Furthermore, the optional integration of blockchain technology enhances data security, immutability, and transparency, fostering heightened trust and integrity. This paper holds profound significance as it empowers companies to optimize warranty management practices, reduce operational overhead and provide valuable data insights. Customers benefit from a simplified method to track product warranties, while dealers experience streamlined tasks. The future recommendations encompass continuous system refinement, expansion of blockchain integration, and proactive adaptation to emerging technological advancements. The Centralized E-Warranty Management System stands poised as a transformative force, set to reshape warranty management practices across industries by offering a unified, secure, and efficient platform for warranty tracking and management. [ABSTRACT FROM AUTHOR]

As data and protocols are increasingly generated, accessed, and manipulated electronically, many labs are shifting to storing data in electronic laboratory notebooks (ELNs). ELNs provide clear advantages in readability, shareability, searchability, accessibility, and data security over their paper counterparts. Our interdisciplinary program implemented ELNs across several sections of our gateway molecular biology laboratory course and analyzed the impact on 384 students (13 sections total from Fall 2016 to Spring 2018) using surveys, focus groups, and grade data. We found that students prefer the electronic format, citing the ease of transitioning to lab reports and the longevity of their data. Course grades overall were not affected by the change. Based on our experience, we offer suggestions for smoothing the transition to an ELN system in undergraduate and graduate laboratory courses, including use during remote teaching. [ABSTRACT FROM AUTHOR]

For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidates abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidates data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system. [ABSTRACT FROM AUTHOR]

In this paper we examine the transformation of big data in the banking industry, where data has become a major asset. The advent of big data analytics is bringing about a major shift in how banks operate, interact with customers and make strategic decisions. The article first examines big data, focusing on its key features: volume, velocity, and diversity, then its impact on banking operations and then provides a comprehensive analysis of the origins of big data in its banking sector of the industry, including marketing history, customers and communications, social media engagement, and others. In addition to contributing to the banking industry's vast database, the paper outlines the many ways banks are using big data, from improving risk management to improving customer services to fraud by identifying activities and increasing operational efficiency. Finally, the article addresses big data challenges for banking, such as data privacy and security issues, and provides case studies to illustrate the tangible benefits that banks gain by embracing big data analytics acknowledge its value. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Sciences & Information Technology is the property of Arab Journal of Sciences & Research Publishing (AJSRP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant features, we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities. The innovation of the method is to (1) Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task. (2) Unlike the general supervised model approach, this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships. Specifically, after obtaining the overall features, the address classification and entity clustering tasks are performed separately, and the results are subjected to graph-basedmaximization consensus. The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible. By testing and evaluating over 26,000 Bitcoin addresses, our feature extraction method captures more useful features. In addition, the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%. [ABSTRACT FROM AUTHOR]

Federated Learning is a decentralized machine learning method that allows collaborative model training across several devices or institutions while maintaining the privacy and localization of data. Since the raw data is used locally, this collaborative method enables the development of a strong and precise global model without jeopardizing the privacy and security of sensitive data. The healthcare sector is an important one that focuses on preserving and enhancing people's health through medical services, diagnoses, treatments, and preventative measures. Efficient evaluation of Federated Learning in the Internet of Medical Things (IoMT) enables breakthroughs in medical image analysis, electronic health record analysis, personalized treatment planning, and drug development by enabling institutions to train models locally on sensitive patient information without sharing raw data. This paper presents the role of Federated Learning in healthcare and current trends in Federated Learning-based healthcare. A case study is presented on deep Federated Learning for privacy-preserving in healthcare. Finally, challenges and future research directions are discussed in the paper. [ABSTRACT FROM AUTHOR]

With the continuous progress of the society, Intelligent Connected Vehicles (ICVs) in Internet of Vehicles (IoVs) continuously generate a large amount of data. Considering the data security at the vehicle node end, the introduction of federated learning(FL) into the Internet of Vehicles may be an effective solution. However, there are some challenges in applying existing federated learning frameworks directly to IoVs. For example, the participation of malicious nodes in model training can cause federated model poisoning, and the non-IID data generated at the vehicle side can slow down the convergence speed of FL. To address these challenges, this paper proposes a novel federated learning framework based on trust evaluation. The trust evaluation module of this framework evaluates the trust of vehicle nodes from three levels: the success rate of data transmission, the reliability and real-time performance of the network, and the historical behaviors of the nodes to derive the total trust value of the vehicle nodes in order to select nodes with high trust to participate in model training. The model training module of the framework takes into account the problem of slow model convergence caused by the non-IID nature of the training data at the vehicle node side, which is mitigated with the aid of the Federated Adaptive Weighting algorithm (FedAdp). Simulation results on MNIST and Fashion-MNIST datasets demonstrate that the framework proposed in this paper can achieve 85%-90% test accuracy with training data in IID and non-IID settings and two model attack settings. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0 and big data technology, the Industrial Internet of Things (IIoT) is hampered by inherent issues such as privacy, security, and fault tolerance, which pose certain challenges to the rapid development of IIoT. Blockchain technology has immutability, decentralization, and autonomy, which can greatly improve the inherent defects of the IIoT. In the traditional blockchain, data is stored in a Merkle tree. As data continues to grow, the scale of proofs used to validate it grows, threatening the efficiency, security, and reliability of blockchain-based IIoT. Accordingly, this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data. To solve this problem, a new Vector Commitment (VC) structure, Partition Vector Commitment (PVC), is proposed by improving the traditional VC structure. Secondly, this paper uses PVC instead of the Merkle tree to store big data generated by IIoT. PVC can improve the efficiency of traditional VC in the process of commitment and opening. Finally, this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments. This mechanism can greatly reduce communication loss and maximize the rational use of storage space, which is of great significance for maintaining the security and stability of blockchain-based IIoT. [ABSTRACT FROM AUTHOR]

In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively. [ABSTRACT FROM AUTHOR]

How has China's privacy protection strategy been developed, with its broad scope and stringent requirements for data localization and cross-border data flows? What are the broader geopolitical implications of its divergence from Western models of data privacy? This paper argues that China's privacy protection strategy, characterized by its comprehensive regulatory framework and government access to data, is redefining the contours of global data governance and creating new geopolitical fault lines. Drawing on official documents, laws, regulations, and a case study, this paper highlights the evolution of the regulatory framework in response to emerging challenges posed by technological innovations and the wider geopolitical environment. This paper contributes to the broader discussion regarding the implications of China's privacy protection approach, highlighting potential normative clashes with countries that favor a more open digital economy. China's efforts in developing its own privacy protection strategy have also resulted in the formulation of global standards for data privacy. [ABSTRACT FROM AUTHOR]

The paper examines China’s increasing security interests in Africa. It seeks to understand the nature and scope of Chinese engagement in peace and security issues on the continent based on its engagement in international and domestic (African) politics. Through literature analysis and logical reasoning, the author intends to define the implications of China’s new role as a security provider. The paper is based on desk research using primary and secondary data and statistical and comparative analysis of official documents, academic research, and media sources. The methods include literature analysis, logical reasoning, statistical research, comparative analysis, and the inductive method to build general theorems. The paper analyses aspects of China’s security engagement on the African continent: its participation in the United Nations Peacekeeping Operations, small arms exports, and Beijing’s sharing of technology with African partners. China’s engagement in Africa’s security helps to advance its vital economic and political interests, with limited impact on African security. Key lessons for African partners are offered. The last decades have seen an extraordinary increase in China’s economic and political ties with Africa. Security cooperation followed massive Chinese investments and thousands of Chinese nationals working on the continent. China’s security engagement in Africa protects Beijing’s interests regarding access to resources, markets, political influence, and social credibility. It has little to do with a benevolent will to help Africa deal with instability or economic underdevelopment. African partners must make necessary efforts to avoid further dependence on China. [ABSTRACT FROM AUTHOR]

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Remote healthcare monitoring (RHM) has become a pivotal component of modern healthcare, offering a crucial lifeline to numerous patients. Ensuring the integrity and privacy of the data generated and transmitted by IoT devices is of paramount importance. The integration of blockchain technology and smart contracts has emerged as a pioneering solution to fortify the security of internet of things (IoT) data transmissions within the realm of healthcare monitoring. In today's healthcare landscape, the IoT plays a pivotal role in remotely monitoring and managing patients' well-being. Furthermore, blockchain's decentralized and immutable ledger ensures that all IoT data transactions are securely recorded, timestamped, and resistant to unauthorized modifications. This heightened level of data security is critical in healthcare, where the integrity and privacy of patient information are nonnegotiable. This research endeavors to harness the power of blockchain and smart contracts to establish a robust and tamper-proof framework for healthcare IoT data. Employing smart contracts, which are self-executing agreements programmed with predefined rules, enables us to automate and validate data transactions within the IoT ecosystem. These contracts execute automatically when specific conditions are met, eliminating the need for manual intervention and oversight. This automation not only streamlines the process of data processing but also enhances its accuracy and reliability by reducing the risk of human error. Additionally, smart contracts provide a transparent and tamper-proof mechanism for verifying the validity of transactions, thereby mitigating the risk of fraudulent activities. By leveraging smart contracts, organizations can ensure the integrity and efficiency of data transactions within the IoT ecosystem, leading to improved trust, transparency, and security. Our experiments demonstrate the application of a blockchain approach to secure transmissions in IoT for RHM, as will be illustrated in the paper. This showcases the practical applicability of blockchain technology in real-world scenarios. [ABSTRACT FROM AUTHOR]

With the widespread adoption of blockchain technology, its public ledger characteristic enhances transaction transparency but also amplifies the risk of privacy breaches. Attackers can infer users' real identities and behaviors by analyzing public transaction patterns and address relationships, posing a severe threat to users' privacy and security, and thus hindering further advancements in blockchain applications. To address this challenge, covert communication has emerged as an effective strategy for safeguarding the privacy of blockchain users and preventing information leakage. But existing blockchain-based covert communication schemes rely solely on the immutability of blockchain itself for robustness and suffer from low transmission efficiency. To tackle these issues, this paper proposes a stealthy communication model with blockchain smart contract for bidding systems. The model initiates by preprocessing sensitive information using a secret-sharing algorithm-the Shamir (t, n) threshold scheme-and subsequently embeds this information into bidding amounts, facilitating the covert transfer of sensitive data. We implemented and deployed this model on the Ethereum platform and conducted comprehensive performance evaluations. To assess the stealthiness of our approach, we employed a suite of statistical tests including the CDF, the Kolmogorov–Smirnov test, Welch's t-test and K–L divergence. These analyses confirmed that amounts carrying concealed information were statistically indistinguishable from regular transactions, thus validating the effectiveness of our solution in maintaining the anonymity and confidentiality of information transmission within the blockchain ecosystem. [ABSTRACT FROM AUTHOR]

In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security. [ABSTRACT FROM AUTHOR]

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)