Layer-by-Layer Engineering of Black Seed Oil Based SNEDDSs (BSO-SNEDDSs): Optimizing Chemical Stability and Bioavailability in Ramipril Formulations by Shahba AAW, Sherif AY, Elzayat EM, Ali S, Kazi M

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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…The evaluation criteria demonstrate that the suggested method outperforms the existing methods in terms of prediction accuracy and stability, thus confirming that a hybrid forecasting model approach combining an efficient decomposition method with a simplified but efficient parameter-optimized neural network can enhance its accuracy and stability.…”
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Effective deployment strategies for optimizing area coverage in multistatic sonar detection based on Cassini oval approximation and a virtual force algorithm by Liufang Fu, Ming Zhou, Linzhou Xu, Xiaoming Dong, Zhu Kou, Chunyu Kang

“…In this work, we study the problem of optimal deployment of receivers for one source and multiple receivers type multistatic sonar to achieve maximum area coverage. …”
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Reducing the Break-even Time by Smart Power Managing in Data Center with Renewable Energy by somayyeh taheri, Maziar Goudarzi

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A Meta-Heuristic Algorithm-Based Feature Selection Approach to Improve Prediction Success for Salmonella Occurrence in Agricultural Waters by Murat Canayaz, Murat Demir, Zeynal Topalcengiz

“…Salmonella presence was also reported with PCR-confirmed method in data set. Features were selected by using binary meta-heuristic optimization methods including differential evolution optimization (DEO), grey wolf optimization (GWO), Harris hawks optimization (HHO) and particle swarm optimization (PSO). …”
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Integrated local energy market-based optimization framework for multi-energy microgrid considering power grid AC constraints and usage price by Amirhamzeh Farajollahi, Meysam Feili

“…The word ''integrated optimization'' implies that the proposed method clears customers' energy scheduling based on the market competitive price. …”
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Sparse signal transmission under lossy wireless links based on double process of compressive sensing by Peng SUN, Gui-nan LI, Lian-tao WU, Zhi WANG

“…In resource-limited wireless sensor networks,links with poor quality hinder its large-scale applications seriously.Thanks to the inherent sparse property of signals in WSN,the framework of sparse signal transmission based on double process of compressive sensing was proposed,providing an insight into a new way of real-time,accurate and energy-efficient sparse signal transmission.Firstly,the random packet loss during transmission under lossy wireless links was modeled as a linear dimension-reduced measurement process of CS (a passive process of CS).Then,considering that a large packet was often adopted in WSN for higher transmission efficiency,a random linear dimension-reduced projection (a simple source coding operation) was employed at the sender node (an active process of CS) to prevent block data loss.Now,the raw signal could be recovered from the lossy data at the receiver node using CS reconstruction algorithms.Furtherly,according to the theory of CS reconstruction and the formula of packet reception rate in wireless communication,the minimum compression ratio and the maximum packet length allowed were obtained.Extensive simulations demonstrate that the reliability of data transmission and its accuracy,the data transmission volume,the transmission delay and energy consumption could be greatly optimized by means of proposed method.…”
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Time Synchronization Techniques in the Modern Smart Grid: A Comprehensive Survey by Yu Liu, Biao Sun, Yuru Wu, Yongxin Zhang, Jiahui Yang, Wen Wang, Naga Lakshmi Thotakura, Qian Liu, Yilu Liu

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Thermal Simulation in Wire Arc Additive Manufacturing of A 5356 Aluminium Single Track Deposited on 7108 Aluminium Substrate by Eman Shams, Noha Ramadan, Marwan Abdelwahed, Nahed ElMahallawy, Mohamed A Taha

“…To analyze these thermal effects, numerical simulation serves as a valuable investigative method. In this study, SIMUFACT-Welding software was used to model and simulate different parameters of WAAM process for producing aluminium single tracks. …”
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Response surface optimization for cellulase production from Enterococcus faecium and Stutzerimonas stutzeri isolated from Gossypium arboretum and Solanum melongena soil by Mohammedfaizan Shaikh, Naureenbanu Makarani, Krishan Kumar

“…Abstract The rapid utilization of fossil fuel-based energy sources increased demand for alternate sustainable energy sources. …”
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Optimizing Xenograft Models for Breast Cancer: A Comparative Analysis of Cell-Derived and Patient-Derived Implantation Techniques in Pre-Clinical Research by Khairani AF, Harmonia S, Chou Y, Alfarafisa NM, Ramadhanti J

“…The inclusion criteria ensured relevant English sources were available in full text, while the exclusion criteria eliminated certain types of articles and inadequately comprehensive studies.Results: Subcutaneous and orthotopic implantation are critical methods for xenograft models in cancer research. …”
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Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field by Kumar Pradeep, Narayan Satya, Ai Hanbing, Abdelrahman Kamal, Pratap Birendra, Fnais Mohammed S.

“…The WF construction approach assimilates elastic wave propagation through a heterogeneous and geologically complex Earth medium. This method involves constructing an elastic Earth model with inhomogeneous properties and the propagation of elastic wavefields through the model by strategically positioning sources and receivers in accordance with the designed survey geometry. …”
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Optimal Siting and Sizing of Multiple DG Units for the Enhancement of Voltage Profile and Loss Minimization in Transmission Systems Using Nature Inspired Algorithms by Ambika Ramamoorthy, Rajeswari Ramachandran

“…In a first step, the best size of DG is determined through PSO metaheuristics and the results obtained through PSO is tested for reverse power flow by negative load approach to find possible bus locations. Then, optimal location is found by Loss Sensitivity Factor (LSF) and weak (WK) bus methods and the results are compared. …”
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Optimization of Biodiesel Production Parameters from Cucurbita maxima Waste Oil Using Microwave Assisted via Box-Behnken Design Approach by Ketema Beyecha Hundie

“…The Cucurbita maxima wastes are massive source of oils, which are believed to indicate the possible sources of renewable energy whose biodiesel can be produced. …”
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Optimization Strategies for Student Scientific Publications in Islamic Higher Education Institutions to Enhance Academic Competence Based on Outcome-Based Education (OBE) by Abil Fida Muhammad Qois Al Hadi, Fadhilla Nangroe Anggraini Anggraini, Linda Asmara, Latifah Insani Muslimah Murni, Muhammad Fathi Salim

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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning by Muhammad Zubair Yameen, Zhigang Lu, Fayez F. M. El-Sousy, Waqar Younis, Baqar Ali Zardari, Abdul Khalique Junejo

“…Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequency stability in dynamic grid conditions. …”
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Optimizing Higher Education Performance Through Data Integration Using the Zachman Framework: A Case Study on LAM Infokom Accreditation Criteria by Abdul Jahir, Arif Mu'amar Wahid, Tri Titis Sufranto

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Hybrid Machine Learning in Hydrological Runoff Forecasting: An Exploration of Extreme Gradient-Boosting and Categorical Gradient Boosting Optimization in the Russian River Basin by Reza Seifi Majdar, Ali Rahnamaei, Vahid Babazadeh

“…This study focuses on evaluating commonly used ML methods for runoff prediction, with an emphasis on simplicity and comparability to more advanced models. …”
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Frequency-Constrained Economic Dispatch of Microgrids Considering Frequency Response Performance by Zhigang Wu, Chuyue Chen, Danyang Xu, Lin Guan

“…Furthermore, the model leverages deep neural networks (DNNs) to convexify non-convex frequency constraints and employs a distributionally robust chance-constrained approach with Wasserstein distance-based ambiguity sets to handle RES uncertainty. Additionally, a method of directly obtaining the compromise optimal solution is used to transform the multi-objective problem into a single-objective one. …”
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Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer by Osama Elnahas, Waleed M. Ead, Yushan Qiu, Jian Lu

“…We propose an ensemble machine learning-based pre-trained annotation framework that integrates gradient boosting and genetic optimization for robust feature selection. The proposed method uses ensemble learning to enhance annotation accuracy under data scarcity, addressing limitations in existing supervised methods by leveraging a combination of multiple annotated datasets and feature alignment strategies. …”
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