This paper develops a recent methodology based on social spider optimizer (SSO) to determine the optimal sizing of a hybrid renewable energy sources (RESs) integrated microgrid (MG). It comprises photovoltaic (PV), wind turbine (WT), battery, diesel generator (DG), and inverter. The cost of energy (COE) is proposed as fitness function. The objective of the proposed SSO is to determine three design variables which are number of PV panels, number of WT, and number of battery autonomy days such that COE is minimized. Additionally, an energy management strategy is presented. Loss of power supply probability (LPSP) is considered to confirm the reliability of operation. The selection of SSO is due to its simplicity in construction and requiring less controlling parameters. The proposed MG is installed in a remote area at Aljouf region in the northern of Kingdom of Saudi Arabia. Annual data of irradiance, wind speed, and temperature are recorded. The SSO results are compared to Harris Hawks optimizer (HHO), Grey Wolf Optimizer (GWO), Multi-Verse Optimizer (MVO), Antlion Optimizer (ALO), and Whale Optimization Algorithm (WOA). The results obtained show that the proposed approach provides the best optimal configuration of hybrid RESs compared to HHO, GWO, MVO, ALO and WOA with COE of 0.1349 $/kWh and LPSP of 0.01714. Moreover, sensitivity analysis with sizing different topologies of MG including PV/Battery/DG, WT/Battery/DG, and PV/WT/ Battery/DG is presented. The best COEs are obtained via SSO achieving 0.2180 $/kWh for the first topology and 0.2161$/kWh for the second architecture. Furthermore, sensitivity analysis is also presented to investigate the effect of design variables on COE. The experimental results confirm the superiority of the proposed approach in designing reliable and costless microgrid.

This article presents a sub-6GHz ISM-band flexible wearable MIMO antenna array for wireless body area networks (WBANs) and biomedical telemetry devices. The array is based on metasurface inspired technology. The antenna array consists of 2×2 matrix of triangular-shaped radiation elements that were realized on 0.8 mm thick Rogers RT/duroid 5880 substrate. Radiation characteristics of the array are enhanced by isolating the surface current interaction between the individual radiators in the array. This is achieved by inserting an electromagnetic bandgap (EBG) decoupling structure between the radiating elements. The radiating elements were transformed into a metasurface by etching sub-wavelength slots inside them. The periodic arrangement of slots acts like resonant scatterers that manipulate the electromagnetic response of the surface. Results confirm that by employing the decoupling structure and sub-wavelength slots the isolation between the radiators is significantly improved (>34.8 dB). Moreover, there is an improvement in the array’s fractional bandwidth, gain and the radiation efficiency. The optimized array design for operation over 5.0-6.6 GHz has an average gain and efficiency of 10 dBi and 83%, respectively. Results show that the array’s performance is not greatly affected by a certain amount of bending. In fact, the antenna maintains a gain between 8.65-10.5 dBi and the efficiency between 77-83%. The proposed MIMO antenna array is relatively compact, can be easily fabricated on one side of a dielectric material, allows easy integration with RF circuitry, is robust, and maintains its characteristics with some bending. These features make it suitable for various wearable applications and biomedical telemetry devices.

Multimodal medical image fusion is crucial for enhancing diagnostic accuracy by integrating complementary information from different imaging modalities. Current fusion techniques face challenges in effectively combining heterogeneous features while preserving critical diagnostic information. This paper presents a Temporal Decomposition Network (TDN), a novel deep learning architecture that optimizes multimodal medical image fusion through feature-level temporal analysis and adversarial learning mechanisms. The TDN architecture incorporates two key components: a salient perception model for discriminative feature extraction and a generative adversarial network for temporal feature matching. The salient perception model identifies and classifies distinct pixel distributions across different imaging modalities, while the adversarial component facilitates accurate feature mapping and fusion. This approach enables precise temporal Decomposition of heterogeneous features and robust quality assessment of fused regions. Experimental validation on diverse medical image datasets, encompassing multiple modalities and image dimensions, demonstrates the TDN’s superior performance. Compared to state-of-the-art methods, the framework achieves an 11.378% improvement in fusion accuracy and a 12.441% enhancement in precision. These results indicate significant potential for clinical applications, particularly in radiological diagnosis, surgical planning, and medical image analysis, where multimodal visualization is critical for accurate interpretation and decision-making.