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A new series-parallel switched capacitor converter topology capable of operating off two independent input sources and generating target output voltage in buck or boost mode is presented. Operation principle, conversion ratios, modelling considerations in different operation modes and extensive loss analysis are derived. The converter is robust and may be operated off one or two input sources, having the ability to change conversion ratio over a wide range. The proposed model assumes that the conduction losses are proportional to the average current flowing through each of the charge pump capacitor in a switching phase. Capacitor current instantaneous waveform in the complete charge, partial charge and no charge modes are captured in simulation and experiment. Model predicted equivalent resistance, output voltage and instantaneous capacitor current waveform concur excellent with simulated and experimental values, rendering the new converter as an excellent candidate for two and single input source applications.

The performance of the supervised learning algorithms such as k-nearest neighbor (k-NN) depends on the labeled data. For some applications (Target Domain), obtaining such labeled data is very expensive and labor-intensive. In a real-world scenario, the possibility of some other related application (Source Domain) is always accompanied by sufficiently labeled data. However, there is a distribution discrepancy between the source domain and the target domain application data as the background of collecting both the domains data is different. Therefore, source domain application with sufficient labeled data cannot be directly utilized for training the target domain classifier. Domain Adaptation (DA) or Transfer learning (TL) provides a way to transfer knowledge from source domain application to target domain application. Existing DA methods may not perform well when there is a much discrepancy between the source and the target domain data, and the data is non-linear separable. Therefore, in this paper, we provide a Kernelized Unified Framework for Domain Adaptation (KUFDA) that minimizes the discrepancy between both the domains on linear or non-linear data-sets and aligns them both geometrically and statistically. The substantial experiments verify that the proposed framework outperforms state-of-the-art Domain Adaptation and the primitive methods (Non-Domain Adaptation) on real-world Office-Caltech and PIE Face data-sets. Our proposed approach (KUFDA) achieved mean accuracies of 86.83% and 74.42% for all possible tasks of Office-Caltech with VGG-Net features and PIE Face data-sets.

Due to the high demand for rubble and aggregates for construction purposes, rubble quarries and aggregate crushers are very common. Out of the different quarry wastes, quarry dust is one, which is produced in abundance. About 20-25% of the total production in each crusher unit is left out as the waste material-quarry dust. Bulk utilization of this waste material is possible through geotechnical applications like embankments, back-fill material, sub-base material and the like. It becomes a useful additive to the natural soil to improve its strength characteristics. For the above applications one of the important engineering properties is the shear strength. The purpose of the present investigation is to understand the shear strength behavior of quarry dust and soil-quarry dust mixes.