Government Engineering College, Arwal

Climate change driven by global warming has emerged as a critical global issue in recent years, particularly due to its adverse effects on water resources. Some examples of climate change include the drying of small rivers, increased temperatures resulting from heatwaves, more frequent and intense flooding, and prolonged dry seasons that, with time, highlighted the necessity of reinforcing hydrometeorological data analysis and establishing early warning systems to mitigate the associated risks. In this context, this study applies the Innovative Trend Analysis (ITA) and Innovative Polygonal Trend Analysis (IPTA) methods to assess 32 years of monthly precipitation data across nine sectors of the Krishna Basin in India. The Krishna Basin was selected due to its vulnerability to climate variability, characterized by irregular rainfall distribution, a warm climate, high population density, and the presence of extensive water infrastructure including dams, reservoirs, and hydropower facilities. The objectives of this study are twofold: (1) to evaluate the variability of monthly precipitation, and (2) to determine the mean and standard deviation for the trend analysis. The findings were evaluated against results from conventional time series methods to assess the effectiveness and reliability of the applied approach. As a result, the ITA and the IPTA methods effectively identified temporal shifts and trend behaviors in rainfall patterns across different districts. In fact, the results indicate spatial variability in precipitation trends, with certain districts exhibiting increasing trends and others showing a decline. Such variability reflects the localized impacts of climate change. The application of ITA and IPTA offers a practical approach for trend detection and risk assessment, contributing to informed water resource management under changing climatic conditions.

This study proposed a hybrid machine learning framework integrated with reliability analysis to predict the factor of safety against liquefaction and quantify the associated uncertainties. Three optimized Extreme Gradient Boosting models, XGB-AOA, XGB-ESMA, and XGB-MPA, were developed and evaluated using comprehensive statistical, graphical, and probabilistic analyses. Model performance was assessed through actual and predicted comparisons, multi-criteria score evaluation, Regression Error Characteristic curves, Anderson-Darling testing, internal k-fold cross-validation, and external validation. Reliability-based methods, including the first-order reliability method, second-order reliability method, and higher-order approximations, were applied to estimate the probability of failure and reliability index. Model interpretability was investigated using SHapley Additive explanations (SHAP) to identify the most influential variables governing liquefaction resistance. Results indicated that the XGB-ESMA model achieved superior prediction accuracy, robustness, and generalisation capability compared with XGB-MPA and XGB-AOA. Reliability analysis revealed a high probability of failure (approximately 60–64

Multilevel inverters (MLIs) are widely employed in medium- and high-power applications due to their ability to generate high-quality output voltage with reduced switching losses and electromagnetic interference. However, conventional MLIs topologies often suffer from increased circuit complexity, higher switch count and large total standing voltage (TSV), leading to increased cost and reduced reliability. To address these challenges, this paper proposes a novel reduced switch single source (RSSS) MLI topology capable of generating multiple voltage levels with a significantly lower number of power switches. For a nine-level RSSS MLI, a genetic algorithm selective harmonic elimination (GA-SHE) technique is employed to determine optimal switching angles, enabling effective elimination of dominant lower-order harmonics, particularly the fifth and seventh harmonics, while maintaining a high fundamental voltage component at low switching frequency. A detailed comparative analysis is carried out to demonstrate the advantages of the proposed topology in terms of switch count and TSV. The performance of the proposed RSSS MLI is validated through MATLAB/Simulink (MathWorks, Inc.) and real-time experimental implementation using a dSPACE CP1104 controller under no-load, resistive and resistive-inductive load (RL-load) conditions. Both simulation and experimental results confirm that the proposed inverter produces high-quality output voltage and current waveforms with harmonic distortion levels compliant with IEEE-519 standards.

Grid connected Photovoltaic (PV) systems are establishing worldwide for numerous applications. Transformerless PV inverters play a crucial role in the solar power industry, primarily due to their cost-effectiveness, lightweight design, and high efficiency. However, these systems face significant challenges, particularly in managing ground leakage currents, which can endanger human safety due to the additional ground currents that flow through the resonant circuit parameters. This work presents a novel configuration that produces a multilevel voltage at output terminals by utilizing a modified sinusoidal multi-carrier PWM technique for controlling the inverter. Furthermore, super junction MOSFETs are employed as the primary switches. The key benefits of this proposed configuration include the elimination of reverse recovery issues, operation at high efficiency, and the reduction of ground current and current distortion. This work incorporates various modes of operation to enhance the understanding of the proposed methodology. A closed loop control system is developed for single phase grid connected transformerless PV system. Ultimately, the proposed topology and its performance evaluation are validated through Simulink supported OPAL-RT based results. The Hardware - in the Loop (HIL) configuration on OPAL-RT platform is utilized to present various responses in the results section.

In the case of communicable diseases, such as COVID-19, effective and quick testing techniques make it easier to identify a contaminated person, so that he or she can be easily isolated. To predict COVID-19-infected individuals through chest computed tomography scans, this study suggests an effective feature selection technique incorporated in clinical decision support system that may be used for testing. After pre-processing, we retrieved 213 features from the chest computed tomography images of a public data set with 2482 images. Then, in a two-step process, the most significant features for recognizing the difference between COVID patients and healthy individuals are selected. Initially, the Chi-square test selects 75