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In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

Background: The increasing demand for electricity, coupled with an imbalanced supply and demand, population growth, and climate change, has prompted the shift from conventional to non-conventional energy systems. However, the unreliability and intermittency of the latter pose a challenge to their feasibility. To address this challenge, a proposal has been made to explore the combination of two renewable energy sources (RES) using a unique DC-DC converter topology, with the aim of meeting the load demand in a sustainable and efficient manner. Objective: The focus of this research was to explore solutions for the challenges associated with operating RES independently, including issues with intermittency, weather dependence, and meeting load demands. The proposed hybrid system features exclusively RES, offering a promising approach to reducing carbon footprint. Ultimately, we aimed to develop a CUK-SEPIC-based converter that can effectively integrate two independent RES to satisfy the load demand of a standalone application. Method: Effective hybrid power generation through RES is a complex challenge, but it has been found that combining solar and biomass energy sources is one of the best options for achieving this goal. To tap into these sources, it is essential to have a suitable power electronic converter, and the CUK-SEPIC converter has been chosen for its many benefits. The features of this converter have been described in detail. The integration of solar and biomass energy sources is achieved using this converter, which has been designed and mathematically modeled in the MATLAB/Simulink environment to ensure optimal performance. To validate the effectiveness of the proposed converter, a comparison with existing power electronic converters has been done using the MATLAB/Simulink platform. Results: The hybrid power generation system model has been comprehensively developed in this work using the sophisticated MATLAB/Simulink environment. The input and output parameters have been diligently estimated through an extensive simulation process. The research has yielded valuable insights, indicating that the CUK-SEPIC converter exhibits an impressive power conversion efficiency of 96.57%, along with an overall step-up ratio of 5.25 and significantly reduced ripple content. Conclusion: Upon conducting a comprehensive analysis of the CUK-SEPIC DC-DC converter, it has been observed that the proposed system exhibits significant promise in rectifying the reliability issues commonly associated with renewable energy power generation. Therefore, it is recommended that this system be considered for implementation in rural electrification initiatives. Furthermore, it is worth noting that this system represents one of the most recent developments in the field of renewable energy power generation technology.

Background: This paper presents a novel approach to enhance the efficiency of solar cells by employing a modified spin coating technique with a Zinc oxide (ZnO) solution. Spin coating, known for its ability to achieve uniform, thin coatings on flat to moderately curved surfaces, serves as the central method in this research. The study meticulously investigates various factors affecting the coating process, including the volume of the solution, spinning speed, and spinning duration. To optimize these factors effectively, the Taguchi approach is employed, aiming to achieve the desired ZnO layer thickness and uniformity. The experimental findings reveal that the most favorable results are obtained when implementing a 3-second spin cycle at a rapid spin speed of 2000 rpm while using a ZnO solution volume of 5 microliters. Furthermore, advanced techniques such as scanning electron microscopy (SEM) are harnessed to scrutinize the surface characteristics of the ZnO layer and its interaction with the solution. To gauge the quality of the coatings, the signal-tonoise ratio (SNR) main impact plot is thoughtfully utilized. Subsequent in-depth analysis, employing the analysis of variance (ANOVA) technique, delves into the intricate relationship between the experimental parameters and the response parameter. The research outcomes are nothing short of remarkable, showcasing that the modified spin coating technique significantly elevates the efficiency of coated solar cells, ultimately achieving an impressive efficiency rate of 5.4%. In summation, this study introduces a pioneering spin coating technique tailored for solar cell applications with ZnO solution, leading to substantial enhancements in efficiency. The thorough optimization of process parameters through the Taguchi technique, coupled with the comprehensive analysis of experimental results via ANOVA, not only advances the comprehension of the coating process but also paves the way for more efficient and sustainable solar cell applications in the future. Methods: The research systematically explored critical factors affecting the coating process for solar cells, optimizing the ZnO layer's thickness and uniformity. The ideal parameters identified were a 3-second spin cycle at 2000 rpm with a ZnO solution volume of 5 microliters. Quality assessment was done using the signal-to-noise ratio (SNR) main impact plot, and further analysis via ANOVA revealed intricate parameter relationships. These findings offer a precise and efficient method for improving solar cell coatings, promising enhanced efficiency in renewable energy production. Results: The research achieved a minimum film thickness of 4.2 micrometers and revealed a correlation between spinning speed and film thickness. Solar cell efficiency reached an impressive 5.4% post-ZnO coating. The modified spin coating device outperformed conventional methods, enhancing efficiency by 5% to 10%. These results signify a significant breakthrough in improving solar cell performance and hold promise for more efficient solar energy production. Conclusion: This research optimized the spin coating process to apply ZnO solution to solar cells, achieving the desired film thickness. Ideal parameters were found: 2000 rpm spinning speed, three seconds spinning duration, and four microliters of solution. This resulted in a minimum film thickness of 4.2 micrometers. Higher spinning speeds correlated with thinner films, as shown in a contour plot. Solar cell efficiency reached 5.4% after the ZnO coating. A redesigned spin coating device outperformed conventional methods, improving efficiency by 5% to 10%. This modified technique holds promise for more efficient solar panel production.

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

COVID-19 (Corona Virus Disease of 2019) is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) virus. This disease has significantly impacted every aspect of people's lives, including their work style, leisure activities, and use of technology. Additionally, due to psychological factors or other reasons, there has been a surge in deaths from cardiovascular failure during the pandemic. As COVID-19 is a silent killer whose symptoms only become visible after significant damage has been done, constant monitoring of heart parameters is crucial to address this issue. This paper explores the emerging trends in monitoring vital signs such as the electrocardiogram (ECG), heart rate, respiration rate (breaths), related sensors, remote sensor organization, and telemedicine innovations. Furthermore, this paper discusses the potential application of non-contact radar-based remote monitoring for vital sign monitoring of affected patients.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.