This paper proposes a novel high voltage gain structure of DC-DC converter with soft-switching ability for photovoltaic (PV) applications. A small size coupled inductor with one magnet core is utilized to improve the voltage conversion ratio in the proposed converter. The converter has one active MOSFET with low conducting resistance ( $R_{DS}-_{ON}$ ), which in turn reduces the conduction losses and complexity of the control section. Due to the low input current ripple, the lifetime of the input PV panel is increased, and the maximum power point (MPP) of the PV panel can be easily tracked. The MOSFET’s zero-voltage and zero-current switching and diodes are the other countenance of the proposed converter, which improve its efficiency. Additionally, an improved Perturb and Observe MPP tracking (IP&O MPPT) algorithm is introduced to boost the extracted power of the input PV sources. To validate the performance of this converter, the operation modes principle, steady-state and efficiency survey, and comparison results with other same family converters are carried out. Finally, an experiential prototype is built with 20 V input, 200 V output, power rate of 200 W, and 50 kHz operating frequency to validate the mathematical analysis and effectiveness of the proposed structure. The efficiency of the proposed converter was estimated by over 95% at various power levels.

Portfolio theory underpins portfolio management, a much-researched yet uncharted field. This research suggests a collective framework combined with the essence of deep learning for stock selection through prediction and optimal portfolio formation through the mean-variance (MV) model. The CNN-LSTM model, proposed in Stage I blends the benefits of the convolutional neural network (CNN) and the long-short-term memory network (LSTM). The model combines feature extraction and sequential learning about temporal data fluctuations. The experiment considers thirteen input features, combining fundamental market data and technical indicators to capture the nuances of the wildly fluctuating stock market data. The input data sample of 21 stocks was collected from the National Stock Exchange (NSE) of India from January 2005 to December 2021, spanning two significant market crashes. Thus, the sample makes it possible to catch subtle market shifts for model execution. The shortlisted stocks with high potential returns are advanced to Stage II for optimal stock allocation using the MV model. The proposed hybrid CNN-LSTM outperformed the single models, i.e., CNN and LSTM, per the six-performance metrics and advocated by the 10-fold cross-validation technique. Furthermore, the statistical significance of the model is established using non-parametric tests followed by post hoc analysis. In addition, this method is validated by comparing the proposed model to four baseline strategies and relevant pieces of research, which it considerably outperforms in terms of cumulative return per year, Sharpe ratio, and average return to risk with and without transaction cost. These findings highlight the effectiveness of the hybrid CNN-LSTM approach in stock selection and portfolio optimization.

The recent advancement in the application of the internet of things in the smart grid has led to an industrial revolution in the power industry. The Industry 4.0 revolution has already set in, allowing computers to interact for an efficient and intelligent approach in solving smart grid issues. multilevel inverters (MLIs) are an integral part of the smart grid system for integrating the distributed generation sources and storage energy systems into the smart grid. It attracted attention in industrial applications as they can handle high power and high voltage with an inherent feature of superior output voltage waveform quality. Moreover, its variant, the switched-capacitor MLI (SCMLI), has the added benefit of lesser DC supply requirement. In this paper, a switched-capacitor multilevel inverter topology has been proposed, which can operate in symmetric and asymmetric mode. The proposed SCMLI generate thirteen and thirty-one level output voltages for symmetric and asymmetric selection of DC voltage sources, respectively. The proposed SCMLI has a smaller number of switching devices for a given output voltage level as compared to other recently proposed topologies. A thorough comparison is presented with the recently proposed topologies on several parameters, including cost function. To validate the proposed topology, symmetric and asymmetric cases were simulated using Matlab^® 2018a and the results were verified using an experimental hardware setup.

Degradation of organic dyes and their byproducts by heterogeneous photocatalysts is an essential process, as these dyes can be potentially discharged in wastewater and threaten aquatic and xerophyte life. Therefore, their complete mineralization into nontoxic components (water and salt) is necessary through the process of heterogeneous photocatalysis. In this study, Zr/CrO₂ (Zirconium-doped chromium IV oxide) nanocomposite-based photocatalysts with different compositions (1, 3, 5, 7 & 9 wt.%) were prepared by an environmentally friendly, solid-state reaction at room temperature. The as-prepared samples were calcined under air at 450 °C in a furnace for a specific period of time. The synthesis of Zr/CrO₂ photocatalysts was confirmed by various techniques, including XRD, SEM, EDX, FT-IR, UV-Vis, and BET. The photocatalytic properties of all samples were tested towards the degradation of methylene blue and methyl orange organic dyes under UV light. The results revealed a concentration-dependent photocatalytic activity of photocatalysts, which increased the amount of dopant (up to 5 wt.%). However, the degradation efficiency of the catalysts decreased upon further increasing the amount of dopant due to the recombination of holes and photoexcited electrons.

In this study, we investigate the interactions of a hybrid nanofluid on a curved surface that is being stretched.

Low-cost and washable resistive switching (RS) memory devices with stable retention and low operational voltage are important for higher speed and denser non-volatile memories. In the case of green electronics, pectin has emerged as a suitable alternative to toxic metal oxides for resistive switching applications. Herein, a pectin-based thin film was fabricated on a fluorine-doped tin oxide glass substrate for RS mechanism. The presence of sp³–C groups with low binding energy corresponds to tunable charged defects and the oxygen vacancies confirmed by the O 1s spectra that plays a decisive role in the resistive switching mechanism, as revealed by X-ray photoemission spectroscopy (XPS). The surface morphology of the pectin film shows homogeneous growth and negligible surface roughness (38.98 ± 9.09). The pectin film can dissolve in DI water (10 minutes) owing to its ionization of carboxylic groups, that meet the trends of transient electronics. The developed Ag/pectin/FTO-based memory cell exhibits stable and reproducible bipolar resistive switching behavior along with an excellent ON/OFF ratio (10⁴) and negligible electrical degradation was observed over 30 repeated cycles. Hence, it appears to be a valuable application for green electronics. Indeed, biocompatible storage devices derived from natural pectin are promising for high-density safe applications for information storage systems, flexible electronics, and green electronics.