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The development of society and the economy depends on the wise use of renewable energy sources and reduced reliance on fossil fuels. In both industrialized and developing nations, solar energy, which is a significant source of renewable energy, is emerging as a dependable and cost-effective replacement for fossil fuels. In several industries, including residential, commercial, and agricultural, there is an increasing demand for solar photovoltaic (PV) modules. However, dust buildup on solar panels can limit energy transmission and result in power loss in regions with significant amounts of sunlight. The solar cells are shaded by dirt, debris, and soil carried by the wind, which reduces the energy they receive and raises cell temperature, which further reduces solar cell efficiency. The natural accumulation of dust and heat significantly lowers the panel’s ability to produce electricity. This article is intended to develop an automatic self-cleaning mechanism to solve this problem, which seeks to increase panel efficiency, monitor and control cell temperature, and provide a more affordable solution than the current alternatives. The experimental evaluation of cleaning system performance shows a 14.81% increase in output efficiency, demonstrating its effectiveness in preventing solar degradation. For PV modules, the suggested technique provides an accessible and low-cost automatic self-cleaning alternative.

One of the most critical operations aspects is power management strategies for hybrid AC/DC microgrids. This work presented power management in hybrid AC–DC microgrids with a droop control strategy. At first, photovoltaic, Wind, and battery are used as the power sources, which supply the power with uncertainties. The AC and DC microgrids are controlled by an Adaptive neuro-fuzzy inference system (ANFIS) controller and Proportional Integral Derivative (PID) controller. Simultaneously we calculate the running cost for photovoltaic, Wind, and Battery. Moreover, an optimizer based on the elephant herding optimization algorithm is formulated to reduce the cost price. This method utilizes two stages like clan updating operator and separating operator. This cost value is used to calculate the Droop Coefficients in Droop Control Strategy. The autonomous droop control strategy is utilized in the interlinking converter to share the load between AC and DC. This proposed concept is implemented in the MATLAB tool, and the performance is taken in terms of voltage, power, and current for PV and wind, DC link voltage and load current. The bidirectional ac/dc interlinking converter power flow was subsequently changed from 2.2k W to −2k W. The effectiveness of the power dispatch mode under uniform control has been verified.

This paper presents the design, optimization, and analysis of a tri-band monopole antenna tailored for wireless communication, constructed with a composite FR4 substrate and incorporating split ring resonators (SRRs). The antenna is fabricated on a 19-mm-wide, 31-mm-long FR4 substrate featuring a ground structure integrated with SRRs. Through parameter analysis and optimization, the antenna achieves resonance at 2.3, 4.8, and 7 GHz, catering to diverse wireless communication standards. The use of composite FR4 material ensures enhanced performance. Post-fabrication measurements reveal that the antenna's s11, gain, directivity, and E-plane and H-plane patterns are consistent with simulations. Its compact size, stable radiation patterns, dual-band functionality, excellent impedance matching, and wide bandwidth make it ideal for the industrial, scientific, and medical sectors, as well as other wireless communication applications. This study marks a significant step forward in multi-band antenna technology using composite materials, offering promising solutions for future wireless communication needs. [ABSTRACT FROM AUTHOR]

A novel non-isolated high-gain DC–DC converter for green energy employment is presented and analyzed. The proposed converter comprises a switched capacitor cell, passive clamp circuit, coupled inductors, and voltage multiplier unit. An interleaved boost converter (IBC) is placed on the input side of the proposed design. The voltage multiplier unit (VMU) with the secondary windings of the coupled inductors is located on the load side. It is used to accomplish interleaved power storage. The leakage energy of coupled inductor is recirculated to the load side, and the reverse recovery problem of diodes is effectively suppressed. As a result, a low-on-state resistance power switch with a low voltage rating is employed and minimizing conduction losses. The presented topology is suitable for sustainable energy applications due to its low operating duty cycle, high voltage conversion ratio, and higher efficiency. The output voltage causes substantially low voltage stress on semiconductor switches and passive elements. This proposed paper deals with simulation, parameter selection, experimental design, results and discussion. The simulation is verified using MATLAB/Simulink with a DC voltage of 30–517 V. To validate the design analysis, a 1.5-kW experimental prototype is designed with a switching frequency of 10 kHz and attained an efficiency of 96.07%. [ABSTRACT FROM AUTHOR]

Prediction of cell painting facilitates highthroughput screening of cellular biology and disease mechanisms, accelerating the discovery of novel therapeutic targets or drugs. This study explores the prediction of cell painted Endoplasmic Reticulum (ER) images from transmitted light brightfield images employing deep learning techniques. A conditional Generative Adversarial Network (cGAN) based framework incorporating Wasserstein loss and Gradient Penalty with a modified UNet++ based generator and a patch discriminator is used to predict cell-painting images from brightfield images captured at varying focal planes. Evaluation of the GAN network reveals promising results with a Mean Absolute Error (MAE) of 0.20, Multi-Scale Structural Similarity Index (MS-SSIM) of 0.80, and Peak Signal-to- Noise Ratio (PSNR) of 56.52. The results showcase the effectiveness of the proposed approach in accurately predicting the ER cell painting channel from transmitted light microscopy. Consequently, the study proposes the ER-WGAN network for predicting cell painting using brightfield images.

Distortions in laparoscopic videos affect surgeon visibility and surgical precision, underscoring the need for sustained high video quality. This study presents a real-time laparoscopic video quality assessment algorithm independent of reference content availability. Statistical parameters derived from luminance, local binary pattern and motion-vector maps of video frames are observed to effectively discern distortion types and severities. These parameters are used to train an evolutionary adaptive neuro-fuzzy inference system (ANFIS) end-to-end with subjective score labels. Training and validation loss curves saturate at the 85th epoch, demonstrating the model’s efficient data fitting capability. Performance comparison with other state-of-the-art methods reveals superior results, with high correlation scores of 0.9989 and 0.9446 for experts and 0.9956 and 0.9847 for non-experts, alongside low root mean square errors of 0.0828 and 0.1685 for expert and non-experts, respectively. The model accurately replicates the expert and non-expert perceptual opinions, encouraging future research in stereoscopic, augmented, and virtual reality data.

The analysis of spontaneous mind wandering is crucial for comprehending an individual mental state and holds the potential to enhance performance and productivity. This paper proposes a framework using Continuous Wavelet Transform (CWT) based ResNet model to analyze Electrodermal Activity (EDA) signals for mind wandering detection. In this analysis, EDA signals are sourced from an openly accessible database and preprocessed for artifact and noise removal. Time-frequency analysis generates CWT spectrogram images, which are classified using a modified ResNet50 model that is custom built to classify the spectrogram images corresponding the mind wandering and awareness. Hyperparameter tuning is carried to obtain the optimal network parameters that provides the best accuracy. Results indicate that batch size of 32, learning rate 1e-5 provides better results. This hyperparameter tuned model achieved an accuracy of 64% in differentiating between the two classes. This paper proposes an adapted ResNet50 model that could be employed in wearable devices as a potential application of knowing the mind awareness of an individual.

The increasing concerns about greenhouse gas emissions have made it necessary to incorporate environmental constraints in the operation of power systems. The CO2 emission-constrained short-term unit commitment problem (CSCUCP) is a multiobjective optimization problem that involves minimizing both the cost of operation and the CO2 emissions. This paper proposes an integer-coded shuffled frog-leaping algorithm (SFLA) to minimize both total CO2 emissions and operating costs for the unit commitment problem (UCP) over a one-day scheduling period. The SFLA is inspired by the natural food-searching behavior of frogs. The proposed method aims to determine the optimal start-up and shut-down times for generating units to meet fluctuating loads while minimizing operating costs and CO2 emissions. The method takes into account fuel costs, start-up and shut-down costs, and maintenance costs while satisfying various constraints. The study uses the IEEE 39 bus with a 10-unit test system, and the results are related to conventional methods. The proposed method consistently produces lower CO2 emissions and total operating costs compared to the existing methods. [ABSTRACT FROM AUTHOR]