Exploiting and Enhancing the Heat Transport Rate of MoS2-Ag/EO Hybrid Nanofluid Flow via a Stretching Cylinder using Extended Yamada-Ota and Xue Models.

The current model offers valuable insights for materials science, heat exchangers, renewable energy production, nanotechnology, manufacturing, medicinal treatments, and environmental engineering. The findings of this study have the potential to improve material design, increase heat transfer efficiency across various systems, enhance energy conversion processes, and drive advancements in nanotechnology, medicinal treatments, and engineering design. The goal of the current research is to analyze the effects of thermal radiation and the volume fraction of nanoparticles in MoS2-Ag/engine oil-based hybrid nanofluid flow passing through a cylinder. After performing a substantial similarity transformation, the nonlinear dimensionless framework is recast as ODEs. The Yamada-Ota and Xue models are then applied to the dimensionless equation setup, which is numerically solved using the BVP4C approach. The resulting velocity and temperature fields, corresponding to various parameters, are examined and compared across both models. This investigation demonstrates a significant variation in heat transfer rates between the Yamada-Ota and Xue models, with the former having a larger impact. The velocity and temperature fields decrease as the magnetic field parameter increases in both nanofluids. However, as the magnetic field parameter values grow, the velocity fields in the two nanofluids behave differently. The Yamada-Ota and Xue models are used to determine the behavior of the hybrid nanofluid flow over a nonlinear extended cylinder. In all situations, the velocity and temperature fields exhibit superior decay characteristics.

Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning.

In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems.

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply.

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms.

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler's laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

Improvement of heat transfer within phase change materials using V-shaped rods.

This study numerically investigated the improvement of heat transmission to phase change material (PCM) paraffin wax in a triangular cell with and without fins. The enthalpy-porosity combination was quantitatively evaluated using the ANSYS/FLUENT 20 program. Materials with the phase shifts of paraffin wax were used in this study (RT42). According to the study findings, fins significantly accelerate the melting process and decrease the time required to finish it. The time difference between melting with and without fins is 125%. Moreover, the inclusion of v-shaped fins contributed to a 200% reduction in the melting process time. Thus, the use of v-shaped fins facilitates faster heat transfer to and from the applications wherein the phase change materials are used.

Removal of heavy metal ions from wastewater using two-dimensional transition metal carbides.

Water is an indispensable material for human life. Unfortunately, the development of industrial activities has reduced the quality of water resources in the world. Meantime, heavy metals are an important factor in water pollution due to their toxicity. This study highlights the method for the capture of heavy metal ions from wastewater using the procedure of adsorption. The adsorption of toxic heavy metal ions (Pb2+, Hg2+, and Cd2+) on Ca2C as well as Cr2C carbide-nitride MXene monolayers is investigated using the density functional theory. We have carried out the optimization of the considered MXenes by nine DFT functionals: PBE, TPSS, BP86, B3LYP, TPSSh, PBE0, CAM-B3LYP, M11, and LC-WPBE. Our results have shown a good agreement with previously measured electronic properties of the Ca2C and Cr2C MXene layers and the PBE DFT method. The calculated cohesive energy for the Ca2C and Cr2C MXene monolayers are -4.12 eV and -4.20 eV, respectively, which are in agreement with the previous studies. The results reveal that the adsorbed heavy metal ions have a substantial effect on the electronic properties of the considered MXene monolayers. Besides, our calculations show that the metal/MXene structures with higher electron transport rates display higher binding energy as well as charge transfers between the metal and Ca2C and Cr2C layers. Time-dependent density functional analysis also displayed "ligand to metal charge transfer" excitations for the metal/MXene systems. The larger Ebin for the Pb@Ca2C as well as Pb@Cr2C are according to larger redshifts which are expected (Δλ = 45 nm and 71 nm, respectively). Our results might be helpful for future research toward the application of carbide-nitride MXene materials for removing wastewater pollutants.

Technical and financial feasibility of a chemicals recovery and energy and water production from a dairy wastewater treatment plant.

Due to the high volume of wastewater produced from dairy factories, it is necessary to integrate a water recovery process with the treatment plant. Today, bipolar membrane electrodialysis units (BMEUs) are increasingly developed for wastewater treatment and reutilizing. This article aims to develop and evaluate (technical and cost analyses) a combined BMEU/batch reverse osmosis unit (BROU) process for the recovery of chemicals and water from the dairy wastewater plant. The combined BROU/BMEU process is able to simultaneously produce water and strong base-acid, and reduce power consumption due to the injection of concentrated feed flow into the BMEU. A comprehensive comparative analysis on the performances of two combined and stand-alone BMEU configurations are developed. The proposed combined technology for dairy factory wastewater treatment is designed on a new structure and configuration that can address superior cost analysis compared to similar technologies. Further, the optimal values of permeate flux and current density as two vital and influencing parameters on the performance of the studied dairy wastewater treatment process were calculated and discussed. From the outcomes, the total cost of production in the combined configuration has been reduced by approximately 26% compared to the stand-alone configuration. Increasing the feed concentration rate using the batch reverse osmosis process for the dairy wastewater treatment process can be an ideal solution from an economic point of view. Moreover, point (current density, feed concentration rate, total unit cost) = 328.9 , 7 , 14.37 can be considered as an optimal point for the economic performance of the studied wastewater treatment process.

Optimizing charge transport in hybrid GaN-PEDOT:PSS/PMMADevice for advanced application.

Organic-inorganic hybrid light-emitting devices have garnered significant attention in the last few years due to their potential. These devices integrate the superior electron mobility of inorganic semiconductors with the remarkable optoelectronic characteristics of organic semiconductors. The inquiry focused on analyzing the optical and electrical properties of a light-emitting heterojunction that combines p-type GaN with organic materials (PEDOT, PSS, and PMMA). This heterojunction is an organic-inorganic hybrid. The procedure entailed utilizing a spin-coating technique to apply a layer of either poly(methyl methacrylate) (PMMA) or a mixture of PMMA and poly(3,4ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT: PSS) onto an indium tin oxide (ITO) substrate. Subsequently, different Nd:YAG laser pulses (200, 250, and 300 pulses) were used to administer a GaN inorganic layer onto the prepared organic layer using a pulsed laser deposition approach. Subsequently, the thermal evaporation technique was employed to deposit an aluminum electrode on the top of the organic and inorganic layers, while laser pulses were fine-tuned for optimal performance. The Hall effect investigation verifies the p-type conductivity of the GaN material. The electroluminescence studies confirmed the production of blue light by the GaN-based devices throughout a range of voltage situations, spanning from 45 to 72 V.

Red Cell Alloimmunisation Among Sickle Cell Disease and Thalassemia Patients Following Rh- and K-Matched Red Cell Transfusion in Southwestern Saudi Arabia: A Multicenter Study.

Background: Alloimmunisation remains a major consequence of blood transfusion among sickle cell disease (SCD) and thalassemia patients due to the exposure to non-self-red blood cell (RBC) antigen. The complication is associated with transfusion reactions and delayed transfusion procedure because of the difficulty of finding compatible blood. This study aims to determine the prevalence of alloimmunisation to RBC and alloantibody specificities among SCD and thalassemia patients in, an endemic area of SCD and thalassemia, Jazan province of Saudi Arabia, from three major hospitals. Methods: This is a retrospective, multicenter cross-sectional study conducted on 1027 patients with SCD and thalassemia, which received Rh/K matched transfusions in 2019 in the three centers. Demographic data and medical records of participants from three transfusion institutions were collected and analysed. Results: A total of 1027 were enrolled in the cohort; 906 (88.2%) and 121 (11.8%) patients with SCD and thalassemia, respectively. There were 483 (47%) males and 544 (53%) females with median age of 15 (range 1-48). Among the studied population, 78 were alloimmunised with an overall alloimmunisation rate of 7.6%. These patients developed a total of 108 alloantibodies, and anti-E was the most detected antibody (25.9%) followed by anti-K (24.1%). Conclusion: The overall rate of alloimmunisation to RBC antigen among the studied population in Jazan was low compared to other areas in the country. Most alloantibodies detected were against E and K antigens. The knowledge of most encountered alloantibodies in our population will aid in selecting the most appropriate antigen-negative red cells. Further research, however, is needed to explore factors associated with residual risk of alloimmunisation in these patients.

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis.

Biomedical engineering stands at the forefront of medical innovation, with electroencephalography (EEG) signal analysis providing critical insights into neural functions. This paper delves into the utilization of EEG signals within the MILimbEEG dataset to explore their potential for machine learning-based task recognition and diagnosis. Capturing the brain's electrical activity through electrodes 1 to 16, the signals are recorded in the time-domain in microvolts. An advanced feature extraction methodology harnessing Hjorth Parameters-namely Activity, Mobility, and Complexity-is employed to analyze the acquired signals. Through correlation analysis and examination of clustering behaviors, the study presents a comprehensive discussion on the emergent patterns within the data. The findings underscore the potential of integrating these features into machine learning algorithms for enhanced diagnostic precision and task recognition in biomedical applications. This exploration paves the way for future research where such signal processing techniques could revolutionize the efficiency and accuracy of biomedical engineering diagnostics.