Search Results (350)

Background/Objectives: In today’s rapidly evolving healthcare landscape, fostering a culture of innovation and continuous improvement is essential—especially within a nursing association that leads individual and organizational credentialing. Methods: Colleagues from the American Nurses Enterprise (ANE) Innovation Department and the Institute for Nursing Research and Quality Management collaborated to develop the Culture of Innovation and Quality Model^TM. This process involved conducting a literature review, developing a survey instrument, and administering a pilot pre-survey to ANE employees to collect baseline data. Future research will include a comparison with a post-survey after interventions aimed at strengthening the culture of innovation and quality. Results: The results of the pilot pre-survey were high overall and guided the team in identifying areas with the greatest opportunities for improvement. Based on these findings, interventions are being developed that will be implemented at ANE to enhance the practice of and promote the synergy between innovation and quality. Conclusions: Achieving and sustaining high-quality standards of care and advancing the professional development of nurses requires a culture where staff feel safe and have opportunities to create, innovate, improve, and learn. This will help promote an environment where people thrive while ensuring that the nursing profession and practice remain cutting-edge and aligned with emerging technologies and evolving healthcare complexities. The Culture of Innovation and Quality Model^TM may provide a blueprint for organizations who seek to advance innovation and quality knowledge, engagement, and practices and assist their employees in providing better service to colleagues, partners, and customers while adapting to the evolving healthcare environment. Full article

Education 4.0 promotes active, personalized, and competency-based learning aligned with the Sustainable Development Goals (SDGs), yet most current platforms rely on centralized architectures that restrict access, agency, and adaptability. To address this problem, Web3 technologies—including blockchain, decentralized identifiers (DIDs), peer-to-peer storage, and smart contracts—enable the creation of platforms that uphold equity, data sovereignty, and pedagogical flexibility. This paper investigates how the convergence of Education 4.0 and Web3 technologies can drive the development of sustainable, inclusive, and learner-centered digital education systems. We examine two decentralized education platforms, EtherLearn and DeLMS, to assess their design affordances and limitations. Building on these insights, we propose a layered architectural framework grounded in sustainability principles. Our analysis shows that decentralized infrastructures can expand access in underserved regions, increase credential portability, empower learners with greater autonomy, and foster participatory governance through decentralized voting, token-based incentives, and community moderation. Despite these advantages, significant challenges remain around usability, energy efficiency, and regulatory compliance. We conclude by identifying key research priorities at the intersection of sustainable educational technology, digital equity, and decentralized system design. Full article

Phishing attacks are an increasingly common cybersecurity threat and are characterized by deceiving people into giving out their private credentials via emails, websites, and messages. An insight into students’ challenges in recognizing phishing threats can provide valuable information on how AI-based detection systems can be improved to enhance accuracy, reduce false positives, and build user trust in cybersecurity. This study focuses on students’ awareness of phishing attempts and evaluates AI-based phishing detection systems. Questionnaires were circulated amongst students, and responses were evaluated to uncover prevailing patterns and issues. The results indicate that most college students are knowledgeable about phishing methods, but many do not recognize the dangers of phishing. Because of this, AI-based detection systems have potential but also face issues relating to accuracy, false positives, and user faith. This research highlights the importance of bolstering cybersecurity education and ongoing enhancements to AI models to improve phishing detection. Future studies should include a more representative sample, evaluate AI detection systems in real-world settings, and assess longer-term changes in phishing-related awareness. By combining AI-driven solutions with education a safer digital world can created. Full article

Distributed digital identity is an emerging identity management technology aimed at achieving comprehensive interconnectivity between digital objects. However, there is still the problem of privacy leakage in distributed identities, and selective disclosure technology partially solves the privacy issue in distributed identities. Most of the existing selective disclosure algorithms use anonymous credentials or hash functions. Anonymous credential schemes offer high security and meet the requirements of unforgeability and unlinkability, but their exponential operations result in low efficiency. The scheme based on hash functions, although more efficient, is susceptible to man-in-the-middle attacks. This article proposes an efficient selective disclosure scheme based on hash functions and implicit certificates. The attribute values are treated as leaf nodes of the Merkle tree, and the root node is placed in a verifiable credential. According to the implicit certificate algorithm process, a key pair that can use the credential is generated. During the attribute disclosure process, the user autonomously selects the attribute value to be presented and generates a verification path from the attribute to the root node. The verifier checks the Merkle tree verification path. All operations are completed within 10 ms while meeting the unforgeability requirements and resisting man-in-the-middle attacks. This article also utilizes the ZK-SNARK algorithm to hide the validation path of the Merkle tree, enhancing the security of the path during the disclosure process. The experimental results show that the selective disclosure algorithm performs well in both performance and privacy protection, with an efficiency 80% faster than that of existing schemes. This enhances the proposed scheme’s potential and value in the field of identity management; it also holds broad application prospects in fields such as the Internet of Things, finance, and others. Full article

Despite extensive research on early childhood education (ECE) quality at the preschool level, toddler settings remain comparatively understudied, particularly in Chile and Latin America. Research suggests that quality ECE strengthens child development, while low-quality services can be harmful. ECE quality comprises structural features like ratios and classroom resources, and process features related to interactions within classrooms. This study examines how process and structural quality indicators are related in nurseries serving disadvantaged backgrounds. Data were collected from 51 Chilean urban classrooms serving children aged 12–24 months. Classrooms were evaluated using the Classroom Assessment Scoring System (CLASS) for toddlers, questionnaires, and checklists. Latent Profile Analysis identified process quality patterns, while multinomial regression examined associations with structural quality indicators. The results revealed low-to-moderate process quality across classrooms (M = 4.78 for Emotional and Behavioral Support; M = 2.35 for Engaged Support for Learning), with three distinct quality clusters emerging. Marginally significant differences were found between high- and low-performing clusters regarding classroom space (p = 0.06), number of toys (p = 0.08), and staff educational credentials (p = 0.01–0.07). No significant differences emerged for group sizes or adult-to-child ratios, which are heavily regulated in Chile. These findings underscore the need to strengthen quality assurance mechanisms ensuring all children access quality ECE. Full article

The growing need for secure and privacy-preserving identity management in industrial environments has exposed the limitations of traditional, centralized authentication systems. In this context, SIBERIA was developed as a modular solution that empowers users to control their own digital identities, while ensuring robust protection of critical services. The system is designed in alignment with European standards and regulations, including EBSI, eIDAS 2.0, and the GDPR. SIBERIA integrates a Self-Sovereign Identity (SSI) framework with a decentralized blockchain-based infrastructure for the issuance and verification of Verifiable Credentials (VCs). It incorporates multi-factor authentication by combining a voice biometric module, enhanced with spoofing-aware techniques to detect synthetic or replayed audio, and a behavioral biometrics module that provides continuous authentication by monitoring user interaction patterns. The system enables secure and user-centric identity management in industrial contexts, ensuring high resistance to impersonation and credential theft while maintaining regulatory compliance. SIBERIA demonstrates that it is possible to achieve both strong security and user autonomy in digital identity systems by leveraging decentralized technologies and advanced biometric verification methods. Full article

According to the latest Verizon DBIR report, credential abuse, including password reuse and human factors in password creation, remains the leading attack vector. It was revealed that most users change their passwords only when they forget them, and 35% of respondents find mandatory password rotation policies inconvenient. These findings highlight the importance of combining technical solutions with user-focused education to strengthen password security. In this research, the “human factor in the creation of usernames and passwords” is considered a vulnerability, as identifying the patterns or rules used by users in password generation can significantly reduce the number of possible combinations that attackers need to try in order to gain access to personal data. The proposed method based on an LSTM model operates at a character level, detecting recurrent structures and generating generalized masks that reflect the most common components in password creation. Open datasets of 31,000 compromised passwords from real-world leaks were used to train the model and it achieved over 90% test accuracy without signs of overfitting. A new method of evaluating the individual password creation habits of users and automatically fetching context-rich keywords from a user’s public web and social media footprint via a keyword-extraction algorithm is developed, and this approach is incorporated into a web application that allows clients to locally fine-tune an LSTM model locally, run it through ONNX, and carry out all inference on-device, ensuring complete data confidentiality and adherence to privacy regulations. Full article

As a new generation of mobile communication networks, 6G security faces many new security challenges. Vehicle to Everything (V2X) will be an important part of 6G. In V2X, connected and automated vehicles (CAVs) need to frequently share data with other vehicles and infrastructures. Therefore, identity revocation technology in the authentication is an important way to secure CAVs and other 6G scenario applications. This paper proposes an efficient credential revocation scheme with a four-layer architecture. First, a rapid pre-filtration layer is constructed based on the cuckoo filter, responsible for the initial screening of credentials. Secondly, a directed routing layer and the precision judgement layer are designed based on the consistency hash and the dynamic RSA accumulator. By proposing the dynamic expansion of the RSA accumulator and load-balancing algorithm, a smaller and more stable revocation delay can be achieved when many users and terminal devices access 6G. Finally, a trusted storage layer is built based on the blockchain, and the key revocation parameters are uploaded to the blockchain to achieve a tamper-proof revocation mechanism and trusted data traceability. Based on this architecture, this paper also proposes a detailed identity credential revocation and verification process. Compared to existing solutions, this paper’s solution has a combined average improvement of 59.14% in the performance of the latency of the cancellation of the inspection, and the system has excellent load balancing, with a standard deviation of only 11.62, and the maximum deviation is controlled within the range of ±4%. Full article

The Internet of Vehicles (IoVs) uses vehicles as the main carrier to communicate with other entities, promoting efficient transmission and sharing of traffic data. Using real identities for communication may leak private data, so pseudonyms are commonly used as identity credentials. However, existing anonymous authentication schemes have limitations, including large vehicle storage demands, information redundancy, time-dependent pseudonym updates, and public–private key updates coupled with pseudonym changes. To address these issues, we propose a certificateless strong anonymous privacy protection authentication scheme that allows vehicles to autonomously generate and dynamically update pseudonyms. Additionally, the trusted authority transmits each entity’s partial private key via a session key, eliminating reliance on secure channels during transmission. Based on the elliptic curve discrete logarithm problem, the scheme’s existential unforgeability is proven in the random oracle model. Performance analysis shows that it outperforms existing schemes in computational cost and communication overhead, with the total computational cost reduced by 70.29–91.18% and communication overhead reduced by 27.75–82.55%, making it more suitable for privacy-sensitive and delay-critical IoV environments. Full article

There is a growing pressure on industry to secure environments and demonstrate their commitment in taking right steps to secure their products. This is because of the growing number of security compromises in the IT industry, Operational Technology environment, Internet of Things environment and smart home devices. These compromises are not just about data breaches or data exfiltration, but also about unauthorised access to devices that are not configured correctly and vulnerabilities in software components, which usually lead to insecure authentication and authorisation. Incorrect configurations are usually in the form of devices being made available on the Internet (public domain), reusable credentials, access granted without verifying the requestor, and easily available credentials like default credentials. Organisations seeking to address the dual pressure of demonstrating steps in the right direction and addressing unauthorised access to resources can find a viable approach in the form of the zero trust concept. Zero trust principles are about moving security controls closer to the data, applications, assets and services and are based on the principle of “never trust, always verify”. As it stands today, zero trust research has advanced far beyond the concept of “never trust, always verify”. This paper provides the culmination of a literature review of research conducted in the space of smart home devices and IoT and the applicability of the zero trust five-step implementation process to secure them. We discuss the history of zero trust, the tenets of zero trust, the five-step implementation process for zero trust, and its adoption for smart home devices and Internet of Things, and we provide suggestions for future research. Full article

The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals. Full article

The use of Verifiable Credentials under the new eIDAS Regulation introduces privacy concerns, particularly during revocation status checks. This paper proposes a privacy-preserving revocation mechanism tailored to the European Digital Identity Wallet and its Architecture and Reference Framework. Our method publishes a daily randomized revocation list as a cascaded Bloom filter, enhancing unlinkability by randomizing revocation indexes derived from ARF guidelines. The implementation extends open-source components developed by the European Committee. This work demonstrates a practical, privacy-centric approach to revocation in digital identity systems, supporting the advancement of privacy-preserving technologies. Full article

Background: Physician rating websites (PRWs) are increasingly used by patients to assess healthcare providers, yet little is known about the factors influencing patient feedback in ophthalmology across different healthcare systems. Objective: This study aimed to identify variables associated with patient reviews of ophthalmologists, with a particular focus on soft skill-related feedback, and to compare trends between Polish and British healthcare systems. Methods: We analyzed data on 461 verified ophthalmologists—261 from ZnanyLekarz.pl (Poland) and 200 from Doctify.com (UK)—collected between March and July 2024 with the highest number of reviews. Physician characteristics, including sex, academic title, years of experience, practice setting, specialization, and online presence, were examined. Review content was categorized into soft skills, professional abilities, or both. Statistical and multivariate analyses were performed to determine factors influencing (a) soft review frequency and (b) the profile of a top-rated ophthalmologist, defined as one with the highest overall ratings and a minimum of 100 reviews. Results: We analyzed a total of 70,176 patient reviews—55,786 from a Polish physician rating website (PRW) and 14,390 from a British PRW. Polish ophthalmologists received significantly more reviews than their British counterparts, with a median of 141 reviews per physician compared to 44 in the UK (p < 0.001). Feedback focused on soft skills accounted for 74% of Polish reviews and 59% of British reviews. Key predictors of soft skill-focused reviews included female gender, higher average ratings, practice in pediatric ophthalmology, and affiliation with the private sector. In contrast, fewer soft reviews were associated with area of surgical expertise, public sector employment, and practicing in the UK. Academic title also influenced the content of reviews: physicians holding PhDs received more feedback focused on soft skills, while full professors received less and years of experience had no significant effect. In the multivariate model assessing predictors of being a top-rated ophthalmologist for the whole cohort, each additional review containing substantive content or soft skill-related feedback increased the odds by 29% and 14%, respectively (p < 0.001 for both). Conclusions: Patient reviews are largely influenced by perceived interpersonal qualities rather than professional credentials or clinical experience. The active solicitation of patient feedback, as implemented on Polish physician rating platforms, results in over three times the volume of reviews compared to the British approach, which relies on passive voluntary submission without direct prompts. Full article

An Identity Management System (IDMS) is responsible for managing and organizing identities and credentials exchanged between users, Identity Providers (IDPs), and Service Providers (SPs). The primary goal of IDMS is to ensure the confidentiality and privacy of users’ personal data. Traditional IDMS relies on a third party to store user information and authenticate the user. However, this approach poses threats to user privacy and increases the risk of single point of failure (SPOF), user tracking, and data unavailability. In contrast, decentralized IDMSs that use blockchain technology offer potential solutions to these issues as they offer powerful features including immutability, transparency, anonymity, and decentralization. Despite its advantages, blockchain technology also suffers from limitations related to performance, third-party control, weak authentication, and data leakages. Furthermore, some blockchain-based IDMSs still exhibit centralization issues, which can compromise user privacy and create SPOF risks. This study proposes a decentralized IDMS that leverages blockchain and smart contract technologies to address the shortcomings of traditional IDMSs. The proposed system also utilizes the Interplanetary file system (IPFS) to enhance the scalability and performance by reducing the on-chain storage load. Additionally, the proposed IDMS employs the Elliptic Curve Integrated Encryption Scheme (ECIES) to provide an extra layer of security to protect users’ sensitive information while improving the performance of the systems’ transactions. Security analysis and experimental results demonstrated that the proposed IDMS offers significant security and performance advantages compared to its counterparts. Full article

Scholars traditionally hold influential positions due to their cultural capital, derived from academic degrees, scholarly publications, and professional credentials. However, the rise of digital platforms has disrupted this hierarchy, placing scholars into new roles as online public intellectuals who engage in political advocacy and mobilize knowledge through public discourse. This transformation calls attention to how public intellectuals’ visibility and influence have become entangled with platform logics, leading to a reconsideration of “digital cultural capital”. Drawing theoretical insights from critical platform studies, celebrity studies, and marketing research, this article conceptually addresses three questions: (1) how traditional cultural capital transforms digitally; (2) how public intellectuals accumulate digital cultural capital; and (3) what are the risks of knowledge mobilization on platforms? This article proposes that traditional academic credentials are no longer sufficient to maintain public intellectuals’ influence, whereas visibility metrics—such as views, likes, shares, and follower counts—emerge as a digital form of “cultural capital from below”. Public intellectuals, thus, must engage in “code-switching” to navigate platform-mediated knowledge mobilization. Nevertheless, the populist tendencies embedded in cultural capital from below and the platform algorithms that enable it risk marginalizing less visible knowledge forms. Eventually, this article calls for future empirical research on how digital cultural capital and code-switching operate across geopolitical contexts, particularly within marginalized communities shaped by distinct platform logics and populist dynamics. Full article