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Current blockchain-based cloud (BBC) systems have several security vulnerabilities regarding smart contracts (SC), and several attacks have been reported recently. The SC development lacks standard design processes that follow software lifecycle principles to model secure SC. Secondly, the security mechanisms in the SC are not constantly evolved to resist evolving adversary attacks. BBC systems lack self-adaptive security capability to make spontaneous decisions when adversarial attacks are encountered. To build a self-adaptive secure BBC system that follows standard software development lifecycle principles to model secure SC, we propose the so-called self-adaptive security RE_BBC framework. The framework would utilize the MAPE-BBC adaptation loop to make decisions internally based on the threat models, goal models, and service level agreement (SLA) SC security specifications. The framework identifies vulnerabilities and threats and takes precautionary measures using self-adaptive SC agents. We validated the proposed methodology theoretically and empirically, and statistically proved the research questions and hypothesis using the -test and Mann-Whitney test. Subsequently, we compare our proposed approach with the Security Quality Requirements Engineering approach (SQUARE). The feasibility results and the replicated study results indicate that the proposed approach outperformed the SQUARE approach in terms of artifacts quality, self-adaptive security evaluation quality, efficiency in response time, complexity, and usefulness of the proposed approach for the Healthcare Data Management (HDM) system. SC security developers can immensely benefit from our proposed methodology. They need not reengineer SC from scratch; depending on their security needs and plan, the contract can be adapted to execute a new plan.
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http://dx.doi.org/10.3390/s22103903 | DOI Listing |
J Hazard Mater
September 2025
State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.
Biofilm systems demonstrate significant potential for the efficient degradation of persistent organic pollutants and serve as a crucial reservoir for antibiotic resistance genes (ARGs) due to the widespread misuse of antibiotics. However, the role of biofilm systems under non-antibiotic bisphenol stress in contaminant removal and ARGs dissemination remains uncertain. This research investigates the removal performance of bisphenol S (BPS) as a representative bisphenol and its impact on ARG dynamics in biofilm systems.
View Article and Find Full Text PDFA novel self-adaptive secure end-to-end (E2E) transmission approach is proposed for a radio-over-fiber (RoF) system. The system integrates deep learning (DL) and traditional models across the transmitter, channel, and receiver, forming an E2E transmission framework. The encryption function of the system is embedded into modulation (TransNN) and demodulation (ReceivNN) via E2E optimization.
View Article and Find Full Text PDFJ Mater Chem B
May 2025
College of Biomass Science and Engineering, Sichuan University, Chengdu, 610065, China.
Bacterial infection poses a primary challenge in wound management. However, the commercial non-woven dressings are incapable of treating infected wounds, limiting their clinical applications. Herein, we developed a novel composite dressing, featuring non-woven fabric (NF) decorated with a Zn enhanced semi-interpenetrating network hydrogel (PNGZn@NF), which was achieved by cross-linking graft copolymers composed of acrylic acid and -hydroxysuccinimide with Zn, followed by a coating-heat curing method to securely bond the hydrogel with the NF.
View Article and Find Full Text PDFSensors (Basel)
February 2025
Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK 74104, USA.
Cloud-native computing enhances the deployment of microservice architecture (MSA) applications by improving scalability and resilience, particularly in Beyond 5G (B5G) environments such as Sixth-Generation (6G) networks. This is achieved through the ability to replace traditional hardware dependencies with software-defined solutions. While service meshes enable secure communication for deployed MSAs, they struggle to identify vulnerabilities inherent to microservices.
View Article and Find Full Text PDFPLoS One
January 2025
College of Computer and Information Science, Southwest University, Chongqing, China.
The consumption forecasting of oil and coal can help governments optimize and adjust energy strategies to ensure energy security in China. However, such forecasting is extremely challenging because it is influenced by many complex and uncertain factors. To fill this gap, we propose a hybrid deep learning approach for consumption forecasting of oil and coal in China.
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