The edge devices will produce enormous quantities of data daily as the Industrial Internet of Things (IIoT) expands in scope. Still, most IIoT data is stored in data centers, making it challenging to transfer data between domains safely. Smart logistic products have dramatically changed due to the prevalence of decentralized edge computing and blockchain in the industry sector. To address the need to exchange data between logistics networks, we proposed a novel decentralized hierarchical attribute-based encryption (HABE) scheme combining edge computing and blockchain. To begin, we offer an IoT data encryption strategy in which edge devices can send data to a nearby cloud network for data processing while maintaining privacy. In addition, we developed a blockchain-integrated data-sharing scheme that makes it possible for users to share data via the use of edge and cloud storage. In particular, an IoT device incorporates an encryption-based authentication system to verify users’ access rights at the network’s periphery in a decentralized manner. Using HABE, we provide a blockchain-integrated architecture for the IoT that protects user privacy. The suggested design utilizes the edge and cloud network paradigms and HABE to maintain privacy and works well with smart logistics applications. The authentication time of the proposed model is reduced by 1.5 times compared with the centralized model. The analyses and experimental findings show that the proposed blockchain-integrated edge computing architecture is better than the existing schemes in terms of data sharing, data privacy, and security.

Click on the DOI link to access the article (may not be free).With the increasing complexity of engineering systems, ensuring high system reliability and system performance robustness throughout a product life cycle is of vital importance in practical engineering design. Dynamic reliability analysis, which is generally encountered due to time-variant system random inputs, becomes a primary challenge in reliability-based robust design optimization (RBRDO). This article presents a new approach to efficiently carry out dynamic reliability analysis for RBRDO. The key idea of the proposed approach is to convert time-variant probabilistic constraints to time-invariant ones by efficiently constructing a nested extreme response surface (NERS) and then carry out dynamic reliability analysis using NERS in an iterative RBRDO process. The NERS employs an efficient global optimization technique to identify the extreme time responses that correspond to the worst case scenario of system time-variant limit state functions. With these extreme time samples, a kriging-based time prediction model is built and used to estimate extreme responses for any given arbitrary design in the design space. An adaptive response prediction and model maturation mechanism is developed to guarantee the accuracy and efficiency of the proposed NERS approach. The NERS is integrated with RBRDO with time-variant probabilistic constraints to achieve optimum designs of engineered systems with desired reliability and performance robustness. Two case studies are used to demonstrate the efficacy of the proposed approach.National Science Foundation grant CMMI-1200597

Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.

Inventory management systems and dynamic reliability measures and controls remain challenging at every stage, especially when time variances and operating conditions are considered. An inventory management system must maintain its adeptness over time while coping with the uncertainty of inventory flow. Unexpected delays during inventory movement can harm the reliability and robustness of the entire system. This paper introduces a method of quantifying the reliability of an inventory management system. Also, a novel, reliability-based robust design optimization model has been developed to optimally allocate and schedule time while considering uncertainty associated with inventory movement. The processes involved include purchasing, shipping, receiving, tracking, warehousing, storage, and turnover. A case study of a furniture company in Saudi Arabia is presented to demonstrate the efficacy of the model.

Artificial Intelligence (AI) is increasingly recognized for its potential in improving the detection, classification, prediction, and segmentation of colon cancer. Yet, the reliability of these applications depends on the quality and completeness of the underlying studies. This systematic review evaluates the current state of AI applications in colon cancer research, focusing on their impact on diagnostic accuracy, treatment planning, and patient outcomes. A comprehensive search was conducted in PubMed, Scopus, and Web of Science for articles published between 2020 and 2024. The quality of the included studies was assessed using standardized criteria. A meta-analysis was performed where applicable, and a subgroup analysis was conducted based on the type of AI technology (e.g., deep learning, machine learning) and its application (detection, classification, etc.). Additionally, we recorded whether each study incorporated Explainable AI (XAI) techniques or Generative AI (e.g., GANs) as part of its methodology. In 80 articles, AI models showed significant improvements in diagnostic accuracy, particularly in polyp detection during colonoscopies and histopathological analysis. Deep learning approaches often outperformed traditional methods. However, clinical integration remains challenging due to data and validation gaps. AI holds great promise in colon cancer diagnosis and treatment. Future work should focus on integrating AI tools into clinical workflows through explainable models and standardized validation.