The irruption of advanced technologies and the limited knowledge of software architectures are making it difficult for many small and medium-sized manufacturing companies to keep up with what is being called the fourth industrial revolution (Industry 4.0, Industry of the Future). Container orchestration platforms provide layers of simplification for key requirements such as interoperability, security, and privacy, and provide mechanisms that allow companies and technology providers to focus on their specific functionalities and goals, instead of investing considerable time and effort in the underlying platform on which the solution will operate. This article focuses on these platforms and the issues when developing them, and proposes a risk- and goal-oriented hybrid meta-framework for security and privacy analysis. The meta-framework uses well-known security and privacy standards and frameworks as a reference and can be used to understand assets and requirements and, in particular, to select and configure countermeasures. For practical evaluation of the meta-framework, it was applied to a real case. This case shows how the needs of the KITT4SME project platform were analyzed to support, among others, four manufacturing pilot cases and to define the key security and privacy features that should be introduced when implementing a software platform for easy uptake by small and medium enterprises.
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http://dx.doi.org/10.1016/j.heliyon.2024.e26446 | DOI Listing |
BMC Health Serv Res
January 2025
Te Aka Whai Ora (Māori Health Authority), Auckland, New Zealand.
Background: Breast cancer screening in Aotearoa New Zealand (NZ) still has persistent inequitable coverage by ethnicity, especially for Indigenous Māori women. This project aimed to undertake systematic data linkage to identify and invite eligible Māori women to participate in breast screening.
Methods: This is a cross-sectional observational study conducted in Northern New Zealand between 1/01/2020 and 30/06/2021.
BMC Bioinformatics
January 2025
Solu Healthcare Oy, Kalevankatu 31 A 13, 00100, Helsinki, Finland.
Background: Genomic surveillance is extensively used for tracking public health outbreaks and healthcare-associated pathogens. Despite advancements in bioinformatics pipelines, there are still significant challenges in terms of infrastructure, expertise, and security when it comes to continuous surveillance. The existing pipelines often require the user to set up and manage their own infrastructure and are not designed for continuous surveillance that demands integration of new and regularly generated sequencing data with previous analyses.
View Article and Find Full Text PDFPLoS One
January 2025
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
The explosion of Internet-of-Thing enables several interconnected devices but also gives rise chance for unauthorized parties to compromise sensitive information through wireless communication systems. Covert communication therefore has emerged as a potential candidate for ensuring data privacy in conjunction with physical layer transmission to render two lines of defense. In this paper, we aim to enhance the individual transmission of nearby users in non-orthogonal multiple access (NOMA) systems under scenarios of an eavesdropper who monitors covert transmission before decoding covert information.
View Article and Find Full Text PDFDiabetes Metab Syndr Obes
January 2025
School of Marxism, Capital Normal University, Beijing, People's Republic of China.
With the aging of China's population and lifestyle changes, the number of patients with type 2 diabetes (T2D) has surged, posing a significant challenge to the public health system. This study explores the application and effectiveness of artificial intelligence (AI) technology in T2D management from a Chinese perspective. AI demonstrates substantial potential in personalized treatment planning, real-time monitoring and early warning, telemedicine, and health management.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, Riyadh, 11633, Saudi Arabia.
The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments.
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