The integrated Electronic Control Unit (ECU) plays a pivotal role in optimizing energy efficiency within electric vehicles (EVs) by coordinating various subsystems, including the Vehicle Control Unit (VCU), Electrical Power Steering (EPS), Electronic Stability Control (ESC), and Body Control Unit (BCU). Our comprehensive review deeply explores the various aspects of integrated ECUs and their sub-disciplines, emphasizing development approaches and algorithms specifically designed for mobility energy efficiency. The study shows the effect of various control units on energy efficiency by carefully examining case studies and real-world applications. Employing a critical assessment, the paper examines the advantages and disadvantages of various control systems utilized in electric vehicles (EVs), providing insight into their effectiveness in various situations. The presented study is typically related to the relationship between ECUs and their sub-branches through an integrated approach. By comparing current control approaches, the study offers a deep knowledge of the role of these units in improving vehicle performance, stability, and overall control. Furthermore, we evaluate the strengths and limitations of integrated ECU algorithms in the context of EV control by comparing them in a fair amount of detail. We identify important research gaps by integrating knowledge of multiple control areas in EV. The research effort will give researchers a path forward and make it possible to pinpoint the prospective dimensions that need further exploration in advancing the area of integrated ECUs in EVs.

Breast cancer, a widespread malignancy predominantly affecting women aged 40 and above, presents a significant global health challenge with high mortality rates. The scarcity of medical data underscores the need for collaborative efforts among hospitals to enhance automated breast cancer detection. This research employs decentralized Federated Learning (FL) to facilitate cooperative learning across an interconnected smart hospital network, addressing data privacy, regulatory compliance, voluminous medical image data, and the necessity for distributed machine learning. Our innovative approach integrates Ant Colony Optimization (ACO) for hyperparameter fine-tuning and Neural Architecture Search (NAS) in a collaborative framework for smart hospitals linked with decentralized edge intelligent networks. This optimization strategy significantly improves the performance of our breast cancer detection system. Through a comprehensive experimental study (including diverse datasets), we classify Normal vs. Mass and Benign vs. Malignant regions in mammograms within a decentralized, federated collaborative learning environment. Empirical results consistently highlight the superiority of models trained using our method over individual hospital client-level training. Our method yielded significant improvements across evaluation measures: for Normal vs. Mass, achieving 92.6% sensitivity, 93.0% specificity, and 93.0% accuracy; for Benign vs. Malignant, achieving 89.6% sensitivity, 91.6% specificity, and 89.7% accuracy. Moreover, it has obtained a 6% and 5% increase in accuracy for Normal vs. Mass and Benign vs. Malignant cases, respectively, compared to the PSO-based HPO method. This evidence underscores the potential of collaborative approaches, emphasizing decentralized FL as a robust paradigm in medical research. The incorporation of ACO optimization reinforces the effectiveness of the proposed computer-aided diagnosis (CAD) system, marking a noteworthy advancement in the ongoing fight against breast cancer.

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis by integrating blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. Combining blockchain with intelligent power management systems makes the efficient utilization of hydrogen resources feasible. Using smart contracts and distributed ledger technology facilitates secure data analysis (SDA), real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus advancing clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, specifically on secure hydrogen data analysis and intelligent power management systems.

DevOps is the bundle of applications and culture activity that focuses to separate the challenges between evaluation and performance associates to boost participation and communication. Several companies clutch DevOps fundamentals due to tremendous perspectives, like small tempo for production, rise accuracy, and durability. In contempt of the global assumption of DevOps and its framework, there is an absence of interpretation studies and essential ideas, implementation, and issues related to executing DevOps procedures. The major objective of this research study is to investigate and study issues affiliated with the DevOps culture (DVC) and applications. The challenges that are challenging in adopting DevOps culture in software development (SD) are inspected by conducting the systematic literature review (SLR). Furthermore, it explains the working of DevOps in industries, gives full clarification of DevOps, and probes the cultural issues faced by the industries while executing DevOps. This study discloses ten barriers that should be declamation in accepting DevOps culture. In accordance with the discoveries, these ten barriers contemplate during the execution of DevOps culture i.e. lack of collaboration and coordination, Lack of skill and knowledge, complicated infrastructure, Lack of management, Lack of DevOps approach, and trust confidence problems.