Scopus Research — Muamer Nafaa Mohammed AlMafrachi AlZahrani

The growing integration of renewable energy into modern power systems presents significant challenges for optimal distributed energy resource (DER) planning in interconnected microgrids due to the stochastic nature of wind and solar generation. To address this, we propose a robust multi-objective planning framework that combines Vine Copula-based spatiotemporal scenario generation with a Multi-Objective Antlion Optimizer (MOALO). The Vine Copula approach captures complex, non-Gaussian dependencies among renewable sources, generating realistic and correlated scenarios. The planning model aims to minimize both the annualized total cost and source–load mismatch, thereby enhancing economic efficiency and operational reliability. Simulation results on a three-microgrid system demonstrate that the proposed method achieves an annualized cost of 13,070 $, a source–load deviation of 3.75 × 107 kW2, and over 50 % renewable energy penetration, outperforming traditional methods such as MOPSO and NSGA-II. These findings validate the framework as an effective and resilient solution for cost-efficient microgrid planning under uncertainty. © 2025

The ability to accurately predict suspended sediment load (SSL) in a river is vital for various stakeholders. Predicting SSL can help inform efforts to reduce the negative impacts of floods and droughts and help inform mitigation efforts for extreme environmental events that have a significant impact on the availability of clean water. In this regard, this study proposes a multifunctional Genetic Algorithm-Neural Network (GA-NN) model to predict the SSL using flow discharge and SSL data at Johor River. Furthermore, a comparison study was conducted between the results obtained with the proposed model and with traditional input selection, as well as another optimization method (GHS algorithm). The findings of this study indicate that the GA-NN model is a proficient instrument for forecasting Suspended Sediment Load (SSL) utilizing river discharge and sediment load data from the Johor River. Furthermore, a superior improvement in prediction accuracy was achieved using the GA algorithm, compared to the traditional input selection and GHS algorithm. Based on several statistical matrices and graphical appraisals, the optimum results were achieved within five inputs by providing low margins of errors in terms of Mean Absolute Error (MAE) of 14.366 and Root Mean Square Error (RMSE) of 24.560 and higher correlation accuracy in terms of coefficient of determination (R2) of 0.911. Thus, the Genetic Algorithm (GA) proved its ability to select input patterns, which is considered a critical step in modeling, as it helps to simplify the process of finding the optimal solution to obtain more accurate predictions. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

While heat pump technologies provide sustainable heating and cooling, their performance depends on system design, energy storage, and economic feasibility. In cold climate regions, maintaining high heating efficiency while minimizing electricity consumption remains a critical challenge. This study presents a multi-criteria techno-economic and environmental optimization of three system configurations: CO2 heat pump (CO2 HP) alone, ground source heat pump (GSHP) alone, and a Hybrid CO2 HP + GSHP system. The system serves a multi-apartment smart building in Beijing, China, using a 50 kW CO2 heat pump, a 60 kW GSHP, and up to 700 m2 of PVT panel area. The configuration includes thermal storage with 6–9 m3 hot and 80–100 m3 cold storage capacity, delivering up to 1.92 GWh of annual energy. The optimization process is conducted using an iterative finite difference method (FDM)-based approach, solving time-dependent energy balance equations for heat pump operation, thermal storage dynamics, and PVT energy generation. The model evaluates different configurations to identify the optimal system setup that minimizes operational costs (OPEX), maximizes renewable energy utilization, and enhances CO2 emissions reduction. The results indicate that the Hybrid CO2 HP + GSHP system achieves the highest return on investment (ROI) of 13.27 % per year, with a payback period of approximately 6.8 years and a CO2 saving ratio of ∼1.05. The optimal configuration consists of 250 m2 PVT, 6 m3 hot storage, and 80 m3 cold storage, significantly reducing grid dependency and improving energy efficiency. © 2025 Elsevier Ltd

This paper critically analyzes the influence of non-terrestrial networks (NTN) on the NR random access mechanism for 5G New Radio (NR). The use of NTN in 5G enables widespread connection but presents technological issues like heightened propagation delays, differential delays, and Doppler shifts. This work investigates the effect of NTN on Physical Random Access Channels (PRACH) preamble configurations, random access response window lengths, and uplink timing advance techniques. We present a novel method that maximizes these values to improve the NR random access efficiency in NTN environments. One thing that needs to be thought about is switching from stationary to adaptive timing advance models, as well as Doppler-resilient PRACH preamble designs and adaptive response window approaches. These improvements lower latency and increase synchronizing accuracy; hence, they enhance NTN-supported 5G NR implementations. The results of the research are vital for increasing the dependability and user experience of next-generation wireless communication systems coupled with NTN. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

Developing environmentally friendly solutions that optimize existing power plants while considering economic factors is crucial for ensuring sustainable energy production and mitigating environmental impact. This study promotes a gas turbine-based power plant design that prioritizes environmental concerns and underscores its versatility. The proposed setup is composed of various components, including a gas turbine, a Rankine cycle (RC), an organic Rankine cycle (ORC), a reverse osmosis (RO) desalination unit, a proton exchange membrane electrolyzer (PEME), and a hydrogen blending module. This system engenders the simultaneous generation of hydrogen, electrical power, and desalinated water. Additionally, the direct utilization of produced hydrogen in the proposed system offers improved technical performance and serves as an effective means to mitigate greenhouse gas emissions. A resilient programming code is formulated to systematically evaluate the system through the lenses of techno-economic and environmental considerations. Incorporating innovative modifications into the existing system resulted in an 8% cost reduction, a 1.2% decrease in carbon dioxide emissions, and a 5% enhancement in exergy efficiency. To attain optimal performance of the system, a data-driven and machine learning methodology is employed, wherein two distinct optimization scenarios are used to define the conditions that yield the utmost system performance. In the first scenario, the optimal values for cost, exergy efficiency, and normalized CO2 emissions have been computed as 39%, 5963 $/h, and 367.4 kg/MWh, respectively. In the subsequent scenario, the optimized system demonstrates the capability to generate a fresh water flow rate of 840.4 kg/s. Concurrently, the cost rate for the system equates to 7054.5 $/h. © 2024 Elsevier Ltd

A combined electric vehicles (EVs) and controllable loads scheduling framework is presented in this paper for a microgrid aimed at minimizing the operating cost and emissions. The microgrid is equipped with renewable power generation by using wind turbines and solar photovoltaic panels. In this respect, EVs would be used for load profile flattening and controllable loads would be used to address the reserve requirements of the system mainly due to intermittent renewable power generation. The problem is formulated as a two-stage scheduling program to specify the expected operating cost and reserve. The first stage aims to minimize the total costs including the generation and reserve costs. The second stage seeks to minimize the redispatch costs due to volatile renewable power generation. The resulting optimization problem is then solved by using the modified manta ray foraging optimization algorithm known as "MMRFO". This algorithm is an efficacious one being capable of handling various types of optimization problems. The findings obtained from a 24-h analysis of an MG model demonstrate the superior performance of the MMRFO algorithm when compared to other established methodologies. The obtained results by applying the MMRFO method indicate high efficiency of this algorithm in comparison with some other well-known algorithms when tackling the combined EV and controllable loads scheduling problem in the presence of wind and solar power generation. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Renewable energy sources have been widely installed and operated in power systems, particularly in microgrids in the form of distributed generation units. This issue requires efficient energy management tools which take into account the inherent uncertainties of such energy resources. Thus, this paper presents a stochastic framework aimed at scheduling the renewable energy-based and thermal units in a coordinated way. The generation units comprise fuel cell units with proton exchange membrane known as PEMFC-CHP producing heat and power, concurrently. Moreover, the uncertainties arising from wind and solar power as well as market prices are characterized by deploying scenario-based optimization. The mentioned framework considers storing hydrogen and the model is presented within a stochastic mixed-integer nonlinear programming (MINLP) framework. The resulting problem is simulated on a modified 33-bus distribution network and tackled using the modified marine predators algorithm (MMPA)algorithm. The obtained results indicate that the revenue increases by more than 5% compared to other optimization algorithms. Furthermore, taking into account CHP will increase the total profit of the system by more than 15%. © 2024 Hydrogen Energy Publications LLC

Power and freshwater are two energy-intensive products, which consume a huge amount of fossil fuels. It is important to supply the aforementioned products using renewable energy sources due to the depletion of fossil fuel resources and environmental issues. This paper investigates the exergy and exergy-economic analysis of water and power production using a small-scale combined cycle encompasses the concentrated photovoltaic thermal (CPVT) solar collectors, a Kalina cycle (KC), and a humidification-dehumidification (HDH) desalination unit. An exergo-economic parametric analysis was first investigated to determine the influence of some pertinent parameters on the exergy efficiency, and specific unit cost of the products. In the second stage, two intelligent forecasting approaches based on the artificial neural network (ANN) and improved particle swarm optimization (PSO) algorithms were utilized for predicting the performance metrics of the studied system. The system was supposed to work at half of the year's hour. The results demonstrated that the shares of CPVT, generator, humidifier, dehumidifier, and condenser in exergy destruction are 84 %, 6 %, 3 %, 2.5 %, and 2 %, respectively. Moreover, the exergy efficiency, and specific unit cost of the products, unit cost of electricity, and unit cost of the fresh water at the design condition were obtained as 23.23 %, 0.0806 $/kWh, 5.44 $/m3, and 31.15 $/GJ, respectively. Besides, the most effective parameter on the exergy efficiency and the specific unit cost of the products was the solar beam radiation, the increment in which from 300 W/m2 to 1000 W/m2 improved the exergy efficiency by 15.21 % and reduced the specific unit cost of the products by 63.16 %. In addition, the increase in the condenser pressure from 15 bar to 22 bar and the generator pinch point temperature difference from 5 °C to 15 °C reduced the exergy efficiency by 8.13 % and 4.03 %, respectively, leading to increasing the specific unit cost of the products by 1.30 % and 4.30 %. The results of modeling showed that hybrid ANN-IPSO models provide the most accurate prediction, highest tendency, and agreement to observation as compared to ANN in terms of (R2| exergy efficiency = 0.9903 and R2| specific unit cost of products = 0.9948) and (RMSE| exergy efficiency = 0.0010 and RMSE| specific unit cost of products = 0.9684). © 2024 Elsevier Ltd

Fundamentally, a system is inherently connected to the presence of uncertainty, which consequently leads to the development of uncertain design and scheduling. In the contemporary power system, the prevailing conditions can primarily be attributed to the uncertain efficiency of several variables, such as pricing. Consequently, the utilization of uncertainty modeling turns into imperative. This study utilizes an optimization technique that employs a hybrid whale optimization algorithm and pattern search (HWOA-PS) to achieve the optimum efficiency in the smart parking of electric vehicles under uncertain conditions arising from fluctuations in the pricing of the main grid within the demand response program (DRP). The proposed method can effectively reduce daily costs by shifting the load between peak and light-load conditions. The suggested scheme has several features such as a non-dominated arrangement model, variable discovery, memory-based approach assortment and fuzzy theory to select the greatest Pareto. In addition to all the advantages mentioned above, the proposed algorithm has a high response speed in achieving the final and high possibility to reach the global point. There exist several important limitations for Hydrogen Storage Systems (HSS) that needs to be considered in modeling. The most important limitations include the restrictions of the electrolyzer and the boundaries of Fuel cell (FC) as well as a storage tank. The performance of the suggested algorithm is confirmed in a system with parking and several resources under uncertainty. The results confirm that the proposed method has a great ability to cope with uncertainty. Hence, there has been a reduction of as much as 41 % in the expense of SPL variation. Conversely, by taking into account the DPR, the mean expense of the SPL increases by 4.92 %, resulting in a corresponding decrease of 47.01 % in the variation of SPL costs. When evaluating the impact of DPR, it is seen that an increase in the mean expense of SPL leads to a decrease in its overall magnitude. © 2024 Elsevier Ltd

This paper presents an innovative solution to the key exchange problem in the Industrial Internet of Things (IIoT) implementation. Communication between connected devices must be safe and effective if the IIoT is to continue its fast growth. A crucial part of safeguarding the security and integrity of data shared across the IIoT ecosystem is played by cryptographic key exchange approaches. To provide reliable cryptographic key exchange in the IIoT context, this paper offers a unique approach that makes use of hyperchaotic with complex values, variable parameters, and vector-valued neural synchronization. The method addresses the continued demand for effective cryptographic key exchange across IIoT devices by using drive–response techniques to speed up important applications. Long assessment times in conventional algorithms make it difficult to hide neuronal coordination. As a result, this paper offers a condensed evaluation of ANNs’ (Artificial Neural Networks) synchronization by coordinating ANNs for session key exchange using a hyperchaotic setting. The recommended approach has several benefits, including the following: (1) introducing a hyperchaotic system to generate synchronized input vectors for ANN synchronization; (2) The adaptive rules of parameters-based control procedures are constructed mathematically; (3) reciprocal alignment of vector-valued ANNs to form a neural network for establishing session keys throughout the IIoT network; and (4) relevant numerical simulations are carried out to assess the scheme's consistency. The recommended approach performs better than other methods that have been published in the literature, widening up opportunities for more effective and reliable industrial applications. © 2023 Elsevier Ltd

Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions. © The Author(s) 2024.

Protecting patient privacy has become a top priority with the introduction of Healthcare 5.0 and the growth of the Internet of Things. This study provides a revolutionary strategy that makes use of blockchain technology, information fusion, and federated illness prediction and deep extreme machine learning to meet the difficulties with regard to healthcare privacy. The suggested framework integrates several innovative technologies to make healthcare systems safe and privacy-preserving. The framework leverages the blockchain system, a distributed and unchangeable ledger, to secure the integrity, traceability and openness of private medical information. Patient privacy is better protected as a result, and there is less chance of data breaches or unauthorized access. The system makes use of the Linear Discriminant Analysis (LDA), Decision Tree, Extra Tree Classifier, AdaBoost, and Federated Deep Extreme Machine Learning algorithms to increase the accuracy and efficacy of illness prediction. This method allows for collaborative learning across many healthcare organizations without disclosing raw data, protecting privacy. The system obtains a thorough awareness of patient health, allowing for the early diagnosis of diseases and the development of individualized treatment suggestions. To further detect and reduce possible security risks in the IoMT environment, the framework also includes intrusion detection methods. Protecting patient data and infrastructure, the system can quickly identify and react to unauthorized actions or threats. High accuracy and privacy protection are shown by the results, making it appropriate for Healthcare 5.0 applications. The findings have important ramifications for researchers, politicians, and healthcare professionals who are seeking to develop safe and privacy-conscious healthcare systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

In the ever-evolving landscape of technology and industry, there's a growing awareness of the need for environmentally friendly solutions. This survey marks a pioneering investigation into utilizing eco-environmentally friendly organic mixtures within indirectly integrated heat pump-driven humidification-dehumidification desalination systems. Its primary objective is to determine the most effective approach for enhancing the performance of the integrated cycles: employing organic blends or implementing structural modifications. The proposed layouts for distilled water production are structured around two distinct scenarios: the first scenario involves a basic heat pump, and the second incorporates a vapor injection heat pump. To scrutinize the devised systems' performance, an exergoeconomic analysis is performed, taking into account a meticulous heat exchanger modeling approach. In addition, extensive sensitivity analysis and NSGA.II optimization are conducted based on the most efficient mixture in terms of electricity consumption at a consistent feed-water flow rate. The findings highlight a notable enhancement in the efficiency of the two developed systems when organic blends are employed instead of pure working fluids, resulting in a substantial reduction in electricity consumption. Specifically, replacing R134a with R22/R142b increased the Gain-Output-Ratio (GOR) by 42.26% for the first scenario and 29.06% for the second scenario. As a result of the cost assessment, distilled water unit costs have decreased by 12.87% and 14.32%, respectively, for the first and second scenarios. Notably, using a ternary blend slightly enhances the performance of the proposed systems compared to when a binary blend is employed. Specifically, the highest Gain-Output-Ratio (GOR) of 12.28 was achieved for the second scenario when using R142b/R22/R236fa, representing a modest 3.2% increase compared to the case where R22/R142b was utilized. Furthermore, optimizing the first and second scenarios and utilizing R22/R142 leads to an improvement of 7.31% and 12.55% in exergetic efficiency and 7.36% and 16.21% in UCDW, respectively. Ultimately, it is evident that incorporating eco-environmentally friendly organic blends in the simple heat pump, alongside their minimal ecological footprint and their role in promoting sustainability and adhering to environmental mandates, stands out as a remarkably efficient choice. In fact, the GOR of the first scenario is approximately 27.66% higher when using R142b/R22/R236fa compared to the second scenario employing a pure working fluid. Also, PP of the second scenario increases from 7.76 years to 9.66 years. © 2024 The Institution of Chemical Engineers

Recommender systems in health-conscious recipe suggestions have evolved rapidly, particularly with the integration of both homogeneous and heterogeneous graphs. However, incorporating IoT devices into healthcare, such as wearable fitness trackers and smart nutrition scales, presents new challenges. These devices generate vast amounts of dynamic, personalized data, which traditional Graph Neural Network (GNN) models — limited to simple pairwise connections — fail to capture effectively. For example, IoT sensors tracking daily nutrient intake require complex, multi-faceted analysis that traditional methods struggle to handle. To overcome these limitations, researchers have employed hypergraphs, which capture higher-order relationships among nodes, such as user–food and ingredient interactions. Traditional methods using static weights in the Laplacian hypergraph, inspired by homogeneous graph techniques, often fail to account for users’ evolving health interests. Our study introduces a novel approach for recommending healthy foods by leveraging user–food and food-ingredient hyperedges, integrating both convolution and attention-based hypergraph mechanisms to dynamically adjust weights based on user similarities. Unlike previous methods, we convert the heterogeneous hypergraph into a homogeneous space, using a unified loss function to generate recommendations that adapt to individual users’ changing dietary preferences. The model is evaluated on five metrics — AUC, NDCG, Precision, Recall, and F1-score — and shows superior performance compared to existing models on two real-world food datasets, Allrecipes and Food.com. Our results demonstrate significant improvements in recommendation accuracy and personalization, showcasing the system's effectiveness in integrating IoT data for more responsive, health-focused food suggestions. © 2024 The Authors

The lack of data security and the hazardous nature of the Internet of Vehicles (IoV), in the absence of networking settings, have prevented the openness and self-organization of the vehicle networks of IoV cars. The lapses originating in the areas of Confidentiality, Integrity, and Authenticity (CIA) have also increased the possibility of malicious attacks. To overcome these challenges, this paper proposes an updated Games-based CIA security mechanism to secure IoVs using Blockchain and Artificial Intelligence (AI) technology. The proposed framework consists of a trustworthy authorization solution three layers, including the authentication of vehicles using Physical Unclonable Functions (PUFs), a flexible Proof-of-Work (dPOW) consensus framework, and AI-enhanced duel gaming. The credibility of the framework is validated by different security analyses, showcasing its superiority over existing systems in terms of security, functionality, computation, and transaction overhead. Additionally, the proposed solution effectively handles challenges like side channel and physical cloning attacks, which many existing frameworks fail to address. The implementation of this mechanism involves the use of a reduced encumbered blockchain, coupled with AI-based authentication through duel gaming, showcasing its efficiency and physical-level support, a feature not present in most existing blockchain-based IoV verification frameworks. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation. © 2024 THE AUTHORS

This study examines the impact of integrating a Tesla valve on the functionality of a photovoltaic/thermal (PVT) unit under laminar and turbulent flow conditions. Improving the geometry of PVT enhances its thermal and electrical efficiency while reducing its size. Here, a three-dimensional numerical analysis was performed for eight Reynolds numbers (Re) ranging from 500 to 20,000. The objective was to investigate the effects of reverse and forward flow patterns (RFP and FFP) on key hydrothermal and entropy generation characteristics and to determine the best geometry and flow pattern of the studied PVT. The results indicated that in the laminar and turbulent regimes, the PV panel temperature in the RFP was 0.045–0.017 % and 0.126 %–0.074 % lower than that in the FFP, respectively. Additionally, transitioning from Re of 500 to 20,000 led to a 5.09 % decrease in the PV panel temperature. Moreover, the overall efficiency in the RFP was 1.62 %–4.21 % greater than that in the FFP, and the Re rise decreased the difference between the overall efficiency in the two flow patterns. Furthermore, the frictional entropy generation rate significantly exceeded the thermal term, increasing by 514 folds and 407 folds at Re = 10,000 and 20,000 compared to the values at Re = 500 in the RFP and FFP, respectively. © 2024