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The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model’s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

In Wireless Sensor Networks (WSNs), achieving optimal coverage in dynamic environments remains a significant challenge. Traditional optimization techniques, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have demonstrated adaptability in node placement but struggle with real-time self-learning capabilities, requiring frequent retraining to handle continuously changing conditions. To address these limitations, this research introduces a novel hybrid model that integrates Deep Reinforcement Learning (DRL) with Graph Neural Networks (GNN). The DRL component enables adaptive decision-making, allowing real-time sensor node adjustments based on network performance feedback. Simultaneously, the GNN model enhances spatial awareness by capturing relational dependencies among sensor nodes, optimizing coverage efficiency. This integration significantly improves network adaptability and operational efficiency. Extensive simulations demonstrate that the proposed DRL-GNN model achieves a coverage ratio of up to 96.4%, energy efficiency of 95.8%, and minimizes overlap to 5.2%, outperforming traditional methods. These results validate the effectiveness of the proposed approach in enhancing WSN coverage while maintaining energy efficiency and minimal redundancy.

Free Space Optical Orthogonal Frequency Division Multiplexing (FSO- OFDM) has emerged as a powerful distinct transmission framework for next-generation wireless links; here the performance of FSO system using OFDM in Low Earth Orbit/Medium Earth Orbit (LEO/MEO) inter-satellite communication is investigated under the combined effects of thermal, background noise and solar storm disturbances over a Gamma-Gamma (GG) channel model. An impact of channel impairments are analysed for BPSK, QPSK and M-ary (M = 64,128,256) QAM transmission. A bit error rate in the received signal is analysed for different refractive index with weak and strong turbulence conditions. To increase the transmission link distance and reduce the bit error rate, we employed zero forcing equalization techniques at the receiver. We examine the different transmission channel length from 1000 to 11000 m. Lower order PSK modulation schemes such as BPSK and QPSK indorse reliable performance for link ranges up to 7000 m due to their robustness to turbulence and noise. The simulation results substantiate that in weak turbulence OFDM-FSO system, 256-QAM achieves high spectral efficiency with a BERvalue of 10 at 23 dB SNR. Under severe turbulence, BPSK is optimal up to 7000 m with a BERof 10 at 22 dB SNRin weak, and 27 dB in strong turbulence, whereas for long distance up to 11000 m, 256-QAM becomes more efficient, reaching a BERof 10 at 27.5 dB SNR. The analysis further demonstrate that for an identical symbol rate, the BER achieved at 11000 m in weak turbulence closely bouts the BER obtained at 9000 m when the channel experiences strong turbulence like solar storm. Furthermore, the developed equalization framework exhibits superior performance compared to traditional attitudes, demonstrating its capability to sustain stable long-range OFDM-FSO communication for LEO/MEO inter-satellite links.

The Energy Efficient Regional Area Metropolitan Optical Access Network (MOAN) is a modern optical communication system specifically designed for metropolitan areas. It addresses the increasing demand for high-speed data transmission while optimizing energy consumption. In this paper, energy-efficient traffic data aggregation and energy-aware routing are presented to increase the network lifetime of the system. The traffic data aggregation reduces redundant transmissions, while energy-aware routing minimizes energy consumption by selecting energy-efficient paths. Initially, the wavelength utility-based dynamic wavelength allocation approach (WU-DWA) was developed to facilitate efficient resource utilization. Then, the data aggregation is performed in the context of traffic grooming using the adaptive principal component analysis (APCA)technique. APCA combines or grooms multiple low-bandwidth data streams into higher-capacity data channels to optimize the use of available network resources, such as wavelengths in optical networks or channels in general communication systems. The aggregated data is routed with the proposed energy efficient adaptive Tuna slap Swarm Optimization strategy (ATSSO). By using the proposed approach, the performance obtained in terms of energy consumption is 88, throughput is 131.63, average packet delay is 3.551, and energy savings are 29.99, respectively. The proposed approach is implemented, and the performance is evaluated in terms of standard performance metrics and analyzed using traditional approaches. The better performance indicates that the proposed approach is more efficient than existing approaches.

Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.

Activation of microglia by classical inflammatory mediators can convert astrocytes into a neurotoxic A1 phenotype in a variety of neurological diseases 1 , 2 . Development of agents that could inhibit the formation of A1 reactive astrocytes could be used to treat these diseases for which there are no disease-modifying therapies. Glucagon-like peptide-1 receptor (GLP1R) agonists have been indicated as potential neuroprotective agents for neurologic disorders such as Alzheimer’s disease and Parkinson’s disease 3 – 13 . The mechanisms by which GLP1R agonists are neuroprotective are not known. Here we show that a potent, brain-penetrant long-acting GLP1R agonist, NLY01, protects against the loss of dopaminergic neurons and behavioral deficits in the α-synuclein preformed fibril (α-syn PFF) mouse model of sporadic Parkinson’s disease 14 , 15 . NLY01 also prolongs the life and reduces the behavioral deficits and neuropathological abnormalities in the human A53T α-synuclein (hA53T) transgenic mouse model of α-synucleinopathy-induced neurodegeneration 16 . We found that NLY01 is a potent GLP1R agonist with favorable properties that is neuroprotective through the direct prevention of microglial-mediated conversion of astrocytes to an A1 neurotoxic phenotype. In light of its favorable properties, NLY01 should be evaluated in the treatment of Parkinson’s disease and related neurologic disorders characterized by microglial activation. Agonism of microglial glucagon-like peptide-1 receptor (GLP1R) using a brain-penetrant peptide prevents the generation of neurotoxic astrocytes and ameliorates disease progression in two rodent models of Parkinson’s disease.

Analog and digital precoding are used in distributed massive multiple-input multiple-output (MIMO) at millimeter wave (mmWave) frequencies to efficiently manage data transfer across several antennas and base stations (BSs) situated at different locations. This method enhances spectral efficiency(SE) in spite of having a smaller amount complexity and cost compared fully digital systems. This paper presents a fully connected hybrid precoding design for a downlink mmWave dispensed or distributed massive multi-user MIMO. The objective function for the optimization problem is the SE of the proposed system, subject to constraints on analog radio frequency (RF) precoding and power budget. The main aim is to maximize SE. Due to the nonconvex nature of the problem, a two-stage iterative algorithm is proposed to conclude the optimal analog and digital beamforming matrices and sum rate. The 1st stage obtains the optimal digital matrix assuming the analog RF precoder matrix is known, followed by acquiring the optimal analog RF precoder matrix in the next step. The Karush–Kuhn–Tucker (KKT) condition for each maximization problem are compute and examine to derive the solving algorithms for each stage. The simulation results display that the proposed design outperforms current methods in sum rate and approaches the performance of fully digital systems with reduced complexity compared to other alternatives.

Disease detection plays an important role in shrimp aquaculture to ensure the health and sustainability of farming operations. Specifically, detecting viral infections at early stages can prevent significant losses. Image processing applications have been developed to detect different types of diseases in shrimp. However, theaccuracy of detection models needs improvement to detect various diseases through a single model. Therefore, this research presents a novel disease detection model using an Enhanced Recurrent Capsule Network (ERCN) with a hybrid optimization model for enhanced detection performance. The proposed ERCN utilizes dynamic routing of capsules to extract spatial hierarchies and patterns in shrimp images, while the recurrent layer extracts temporal dependencies. Performance is further improved by incorporating spatial and channel attention models to select optimal regions and features in the images for the fusion process. The dual-level feature fusion procedure combines local and global features, providing a final fused data to classify different types of diseases. Additionally, the proposed work incorporates a hybrid optimization that combines Harris Hawks Optimization (HHO) with the Marine Predator Algorithm (MPA) to fine-tune the classifier model parameters. Experiments evaluate the performance of the proposed disease detection model through various metrics such as accuracy, precision, recall, specificity, Matthews correlation coefficient, and F1-score. The resutls confirms that the performance of the proposed model is superior with precision of 94.9%, recall of 93.5%, F1-score of 94.6% and detection accuracy of 95.2% over conventional Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Long Short Term Memory (LSTM) Networks.

The majority of daily networks and communications rely heavily on network security. Researchers in cybersecurity emphasize the necessity of developing effective intrusion detection systems (IDS) to safeguard networks. The importance of efficient IDS escalates as attackers devise new types of attacks and network volumes expand. Furthermore, IDS aims to ensure the integrity, confidentiality, and availability of data transmitted across networked systems by preventing unauthorized access. Following numerous studies utilizing machine learning (ML) to develop effective IDS, the focus has shifted towards deep learning (DL) techniques as artificial neural networks (ANNs) and DL systems have become prevalent. ANNs are capable of generating features autonomously, eliminating the need for manual intervention. This paper introduces an innovative adaptive recurrent neural network-based fox optimizer (ARNN-FOX) method. The primary objective of the ARNN-FOX system is to efficiently detect and classify network intrusions, thereby enhancing network security. Data normalization is conducted to scale the incoming data into a usable format. The gray level co-occurrence matrix (GLCM) method is proposed for selecting the optimal subset of features for the ARNN-FOX method. In the proposed approach, the fox algorithm (FOX) is utilized for the adjustment of hyperparameters in the ARNN model. The efficacy of the ARNN-FOX approach is assessed using benchmark datasets. Based on comparative results, the ARNN-FOX method demonstrates superior performance in parameters such as accuracy, specificity, sensitivity, F 1 Score, recall value, and precision values over existing models . The proposed ARNN-FOX-based IDS model for the network security in terms of accuracy is 15.12%, 8.79%, 6.45%, and 4.21% better than RNN, CNN-LSTM, DASO-RNN, and ChCSO-LSTM, respectively. Similarly, with respect to specificity, the suggested ARNN-FOX-based IDS model for network security outperforms RNN, CNN-LSTM, DASO-RNN, and ChCSO-LSTM by 32.43%, 8.89%, 3.16%, and 2.08%, respectively.

Alzheimer’s is a serious neurodegenerative disease that requires early detection for effective intervention. Traditional methods often struggle with accurately identifying the early stages, such as mild cognitive impairment (MCI), due to limitations in feature extraction and classification. To address these challenges, we present an optimized hybrid deep learning model for Alzheimer’s disease detection. Our model employs the Inception v3 algorithm for initial feature extraction and the ResNet 50 algorithm for classification. Additionally, we optimize the network parameters using the Adaptive Rider Optimization (ARO) algorithm to enhance detection performance. Experimental analysis using a benchmark dementia dataset demonstrates that our model achieves superior accuracy of 96.6%, precision of 98%, recall of 97%, and F1-score of 98%, outperforming state-of-the-art techniques.