Scopus Research — Mohammed Shawkat Majid Hasnawi

The rapid growth of multi-energy systems (MESs) with electricity, natural gas, and heat generations and conversions necessitates efficient management frameworks to harness the offered flexibility by the system components. This paper proposes a mixed-integer linear programming (MILP) model to optimally manage a MES equipped with electric vehicle (EV) charging stations, combined heat and power (CHP) units, renewable energy sources (RESs), energy storage systems, and electric heat pumps. The objective function of the problem is to minimize the operation cost of MES, which includes the cost of purchased energy from the external electricity and gas networks, the cost of carbon emissions, and the penalty to compensate for the dissatisfaction of households and EV owners. The constraints of the electricity grid and natural gas network are incorporated into the proposed energy management scheme using the linearized AC power flow and gas flow equations. Moreover, the uncertainties associated with RESs and demand are modeled using the distributionally robust chance-constrained (DRCC) approach to not only guarantee the robustness of the optimal scheduling plan against uncertainties, but also incorporate the probabilistic nature of these uncertain parameters. Finally, the IEEE 33-bus electricity grid and 14-node gas network are employed to validate the effectiveness and applicability of the presented methodology from the viewpoints of the system operator and customers. © 2025 Elsevier Ltd

Fault detection has taken on critical relevance in today’s automated manufacturing processes. Defect tolerance, dependability, and safety are some of the fundamental design attributes of complex engineering systems provided by this method. Fault Diagnosis is made more difficult by a lack of performance; data-driven design and the capacity to transfer learning are also essential considerations. This paper proposes the ResNet-based deep learning multilayer fault detection model (ResNet-DLMFDM) to enrich high performance, design, and transmission-learning skills. Wavelet pyramid packet decomposition and each sub drive coefficient utilize the input of each deep research network channel for multi-kernel domain analysis. Pseudo-label networks have been developed conceptually to investigate different interval lengths of sequential functionality and to gather local database flow sequence functions to improve existing error detection processes. Experiment findings reveal that the proposed approach outperforms current algorithms regarding data correctness, storage space utilization, computational complexity, noiselessness, and transfer performance. The results are obtained by analyzing the multi-kernel and showing the domain ratio of 87.6%, increased storage space ratio of 88.6%, wavelet decomposition performance ratio of 84.5%, and the high accuracy of the data transmission ratio of 83.5%, and the noiseless diagnosis ratio of 93.8%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

The Editor-in-Chief and the publisher have retracted this article. An investigation by the publisher found a number of concerns, including but not limited to citations which do not support claims made in the text, non-standard phrasing, and image irregularities. Based on the investigation’s findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. The publisher has been unable to obtain a current email address for authors Mohammed Sh. Majid, Mustafa Mohammed Jassim and Ahmed Rashid Alkhuwaylidee was not delivered. Authors Mustafa Musa Jaber, Sura Khalil Abd, Mohammed Hasan Ali, Ahmed Alkhayyat and Shahad Alyousif have not responded to correspondence regarding this retraction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

To compensate for the lack of fossil fuel-based energy production systems, hybrid renewable energy systems (HRES) would be a useful solution. Investigating different design conditions and components would help industry professionals, engineers, and policymakers in producing and designing optimal systems. In this article, different tracker systems, including vertical, horizontal, and two-axis trackers in an off-grid HRES that includes photovoltaic (PV), wind turbine (WT), diesel generator (Gen), and battery (Bat) are considered. The goal is to find the optimum (OP) combination of an HRES in seven locations (Loc) in Saudi Arabia. The proposed load demand is 988.97 kWh/day, and the peak load is 212.34 kW. The results of the cost of energies (COEs) range between 0.108 to 0.143 USD/kWh. Secondly, the optimum size of the PV panels with different trackers is calculated. The HRES uses 100 kW PV in combination with other components. Additionally, the size of the PVs where 100% PV panels are used to reach the load demand in the selected locations is found. Finally, two sensitivity analyses (Sens) on the proposed PV and tracker costs and solar GHIs are conducted. The main goal of the article is to find the most cost-effective tracker system under different conditions while considering environmental aspects such as the CO2 social penalty. The results show an increase of 35% in power production from PV (compared to not using a tracker) when using a two-axis tracker system. However, it is not always cost-effective. The increase in power production when using vertical and horizontal trackers (HT) is also significant. The findings show that introducing a specific tracker for all locations depends on renewable resources such as wind speed and solar GHI, as well as economic inputs. Overall, for GHIs higher than 5.5 kWh/m2/day, the vertical tracker (VT) is cost-effective. © 2023 by the authors.

Purpose: Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classification and segmentation of medical images. One of the most prevalent types of brain cancers is glioma, and early, accurate diagnosis is essential for both treatment and survival. In this study, MRI scans were examined utilizing deep learning techniques to examine glioma diagnosis studies. Methods: In this systematic review, keywords were used to obtain English-language studies from the Arxiv, IEEE, Springer, ScienceDirect, and PubMed databases for the years 2010–2022. The material needed for review was then collected from the articles once they had been chosen based on the entry and exit criteria and in accordance with the research's goal. Results: Finally, 77 different academic articles were chosen. According to a study of published articles, glioma brain tumors were discovered, categorized, and segmented utilizing a coordinated approach that included image collecting, pre-processing, model design and execution, and model output evaluation. The majority of investigations have used publicly accessible photo databases and already-trained algorithms. The bulk of studies have employed Dice's classification accuracy and similarity coefficient metrics to assess model performance. Conclusion: The results of this study indicate that glioma segmentation has received more attention from researchers than glioma detection and classification. It is advised that more research be done in the areas of glioma detection and, particularly, grading in order to be included in systems that support medical diagnosis. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.