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Blockchain technology, a key component of Industry 4.0, offers significant benefits to manufacturing and supply chains, including enhanced security, transparency, and traceability. In supply chain management, these technologies support real-time monitoring, predictive analytics, automated quality control, and end-to-end traceability, transforming conventional logistics and operational workflows. However, while the modern supply chain ecosystem facilitates global trade, it remains susceptible to challenges such as a lack of transparency, compromised data integrity, and regulatory non-compliance. Even minor vulnerabilities can result in fraud, business disruptions, and loss of visibility. The increase in these risks necessitates secure, intelligent, responsible frameworks that align with Industry 4.0 standards. To address these challenges, this work introduces a novel dual-contract-based architecture that is integrated with role-based strategies. This approach focuses on enhancing traceability, accountability, and interoperability in unified supply inspection chains. In addition, three smart contract (SC) optimization mechanisms: separation of logic ownership (SLO), resource isolation control (RIC), DUal-layer VALidation (DUAL), are compared to analyze the performance. Key performance metrics such as average latency, packet size, computational overhead, gas fees, transaction costs, and execution costs are considered, alongside a rank-based accountability scoring system for inspection teams. The performance analysis shows that the DUAL optimization mechanism outperforms in reducing packet size, gas fees, execution costs, and transaction costs, though it comes with a higher traceability latency due to enhanced security measures implemented. Overall, the RIC mechanism provides a balance across all performance indicators. In addition, the proposed role-based ranking model provides a more effective, transparent, and reliable inspection process in supply chain management environments by employing unified-contract-based systems.

Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security.

Serverless computing and a function execution model, Function-as-a-Service (FaaS), are currently receiving considerable attention from both academia and industry. One of the reasons for the success of serverless computing is its straightforward interface that abstracts complex internals of cloud computing resource usage and configurations. However, this approach may result in hiding too much information about how underlying cloud resources would work, entailing that users cannot predict how their applications will perform, especially for IO-heavy ones. To address this issue, we evaluate several aspects of network and disk IO performance with realistic workloads using public FaaS systems. Our analysis reveals that current public FaaS systems do not provide appropriate levels of IO performance differentiation, and the ability to isolate network resource allocation during concurrent execution is rarely offered by service providers. Based on the results presented in this paper, we insist that it must be mandatory for network and disk IO resource performance of FaaS to be more visible and predictable, as is the case for memory and CPU, in order to expand serverless computing applications to data-intensive ones.

As cloud data centers are dramatically growing, various applications are moved to cloud data centers owing to cost benefits for maintenance and hardware resources. However, latency-critical workloads among them suffer from some problems to fully achieve the cost-effectiveness. The latency-critical workloads should show latencies in a stable manner, to be predicted, for strictly meeting QoSs. However, if they are executed with other workloads to save the cost, they experience QoS violation due to the contention for the hardware resources shared with co-location workloads. In order to guarantee QoSs and to improve the hardware resource utilization, we proposed a memory bandwidth management method with an effective prediction model using machine learning. The prediction model estimates the amount of memory bandwidth that will be allocated to the latency-critical workload based on a REP decision tree. To construct this model, we first collect data and train the model with the data. The generated model can estimate the amount of memory bandwidth for meeting the SLO of the latency-critical workload no matter what batch processing workloads are collocated. The use of our approach achieves up to 99% SLO assurance and improves the server utilization up to 6.8 × on average.

The effectiveness of isolation strategies against emerging infectious diseases (EIDs) is critically undermined by two interacting factors: Limited resource capacity and imperfect public compliance, yet their combined impact remains poorly quantified. We develop an ordinary differential equation (ODE) model incorporating a saturation function for resource limits and a compliance parameter ($ \\epsilon $) to quantify their nonlinear interaction. Theoretical analysis reveals a resource-driven backward bifurcation, indicating that reducing a basic reproduction number $ R_0 $ below 1 is necessary but may be insufficient for disease elimination when isolation capacity is critically low. Numerically, we identify a counterintuitive paradox: High compliance amplifies the infection risk when isolation resources are severely constrained. The simulation results classify the dynamic regimes under various parameter settings and reveal the qualitative impact of different isolation strategies. The study finds that increasing isolation resources, combined with a certain level of compliance, significantly reduces the infection risk and aids in disease control. Notably, specific transmission patterns emerge when isolation resources are inadequate, resulting in elevated infection risks even when compliance is high. Our results underscore the imperative of synchronizing resource allocation with behavioral interventions, particularly during early outbreak stages, providing a framework for precision public health strategies.

This paper proposes a dynamic resource isolation framework FNN-Cloud based on fuzzy neural network (FNN), which aims to solve the limitations of static policies and the lack of ability to handle uncertain demands in cloud computing environments. FNN-Cloud is designed for multi-tenant scenarios. It uses fuzzy logic to quantify uncertain resource demands and dynamically adjusts isolation thresholds through neural networks to optimize resource utilization and maintain service level agreement (SLA) compliance. In terms of computational methods, the framework uses a double hidden layer back propagation (BP) neural network combined with an adaptive moment estimation (Adam) optimizer and a dynamic loss function (SLA violation loss + resource utilization loss) for online learning. At the same time, it uses triangular membership functions to fuzzify key indicators such as CPU utilization and memory pressure, and uses a 3×3 fuzzy rule base to handle multi-dimensional resource coupling relationships. In terms of experiments, 8 physical nodes are deployed on the OpenStack test platform to simulate three typical workloads: Web services, data analysis, and mixed workloads, and compared with static thresholds, long short-term memory networks (LSTM), and deep Q networks (DQN). Test data shows that FNN-Cloud outperforms the baseline model in CPU usage (28.3%--34.7%), memory usage (31.5%--37.2%), and SLA violation rate (2.1%--4.5%), while reducing P99 latency by 62.3% and controlling the policy response time within 51.4 milliseconds. The system demonstrates efficient and robust dynamic isolation capabilities through a fuzzy priority arbitration mechanism and a neural prediction-driven pre-isolation strategy, providing a reproducible intelligent optimization solution for cloud computing resource management.

Single-ISA heterogeneous multi-core processors trade-off power with performance; however, threads that co-run on shared resources suffer from resource contention, which induces performance degradation and energy inefficiency. The authors introduce a novel approach to optimise the co-scheduling of multi-threaded applications on heterogeneous processors. The approach is based on the concept of stakes function, which represents the trade-off between isolation and sharing of resources. The authors also develop a co-scheduling algorithm that use stakes functions to optimise resource usage while mitigating resource contention, thus improving performance and energy efficiency. They validated the approach using applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite, obtaining up to 12.88% performance speed-up, 13.65% energy speed-up and 28.29% energy delay speed-up with respect to the standard Linux heterogeneous multi-processing scheduler.

In a virtualized environment, different virtual networks can operate over the same physical infrastructure. Each virtual network has its own protocols and share the available resources, thus highlighting the need of resource isolation mechanisms. Investigating the isolation mechanisms provided by FlowVisor, we have discovered vulnerabilities previously unknown regarding addressing space isolation. We show that, in the presence of a malicious controller, FlowVisor’s isolation can be broken allowing different attacks. This paper addresses these vulnerabilities by proposing an Action Slicing mechanism, that allows FlowVisor to limit which actions can be used by each virtual network controller, thus extending the virtual network definition. Our experimental results show that using the proposed Action Slicing mechanism can effectively neutralize the discovered vulnerabilities.

Natural antioxidants are widely distributed in food and medicinal plants. These natural antioxidants, especially polyphenols and carotenoids, exhibit a wide range of biological effects, including anti-inflammatory, anti-aging, anti-atherosclerosis and anticancer. The effective extraction and proper assessment of antioxidants from food and medicinal plants are crucial to explore the potential antioxidant sources and promote the application in functional foods, pharmaceuticals and food additives. The present paper provides comprehensive information on the green extraction technologies of natural antioxidants, assessment of antioxidant activity at chemical and cellular based levels and their main resources from food and medicinal plants.

Shrimp processing generates substantial waste, which is rich in valuable components such as polysaccharides, proteins, carotenoids, and fatty acids. This review provides a comprehensive overview of the valorization of shrimp waste, mainly shrimp shells, focusing on extraction methods, bioactivities, and potential applications of these bioactive compounds. Various extraction techniques, including chemical extraction, microbial fermentation, enzyme-assisted extraction, microwave-assisted extraction, ultrasound-assisted extraction, and pressurized techniques are discussed, highlighting their efficacy in isolating polysaccharides, proteins, carotenoids, and fatty acids from shrimp waste. Additionally, the bioactivities associated with these compounds, such as antioxidant, antimicrobial, anti-inflammatory, and antitumor properties, among others, are elucidated, underscoring their potential in pharmaceutical, nutraceutical, and cosmeceutical applications. Furthermore, the review explores current and potential utilization avenues for these bioactive compounds, emphasizing the importance of sustainable resource management and circular economy principles in maximizing the value of shrimp waste. Overall, this review paper aims to provide insights into the multifaceted aspects of shrimp waste valorization, offering valuable information for researchers, industries, and policymakers interested in sustainable resource utilization and waste-management strategies.