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Abstract Background The mechanisms enabling dynamic shifts between drug-resistant and drug-sensitive states in cancer cells are still underexplored. This study investigated the role of targeted autophagic protein degradation in regulating ovarian cancer stem cell (CSC) fate decisions and chemo-resistance. Methods Autophagy levels were compared between CSC-enriched side population (SP) and non-SP cells (NSP) in multiple ovarian cancer cell lines using immunoblotting, immunofluorescence, and transmission electron microscopy. The impact of autophagy modulation on CSC markers and differentiation was assessed by flow cytometry, immunoblotting and qRT-PCR. In silico modeling and co-immunoprecipitation identified ID1 interacting proteins. Pharmacological and genetic approaches along with Annexin-PI assay, ChIP assay, western blotting, qRT-PCR and ICP-MS were used to evaluate effects on cisplatin sensitivity, apoptosis, SLC31A1 expression, promoter binding, and intracellular platinum accumulation in ID1 depleted backdrop. Patient-derived tumor spheroids were analyzed for autophagy and SLC31A1 levels. Results Ovarian CSCs exhibited increased basal autophagy compared to non-CSCs. Further autophagy stimulation by serum-starvation and chemical modes triggered proteolysis of the stemness regulator ID1, driving the differentiation of chemo-resistant CSCs into chemo-sensitive non-CSCs. In silico modeling predicted TCF12 as a potent ID1 interactor, which was validated by co-immunoprecipitation. ID1 depletion freed TCF12 to transactivate the cisplatin influx transporter SLC31A1, increasing intracellular cisplatin levels and cytotoxicity. Patient-derived tumor spheroids exhibited a functional association between autophagy, ID1, SLC31A1, and platinum sensitivity. Conclusions This study reveals a novel autophagy-ID1-TCF12-SLC31A1 axis where targeted autophagic degradation of ID1 enables rapid remodeling of CSCs to reverse chemo-resistance. Modulating this pathway could counter drug resistance in ovarian cancer.

The purpose of the research is to address the underutilization of electric machine-based propulsion in transportation despite its numerous advantages over conventional internal combustion engines (ICE), such as reduced emissions, lower fuel costs, and improved control and operation. To achieve this goal, the study reviews state-of-the-art energy sources, storage devices, power converters, and control strategies used in electric vehicles (EVs). It particularly focuses on the implementation of the five-level charging scheme for Lithium-ion (Li-ion) batteries, which are considered a promising solution for electric vehicle power storage. The important results of this work include the advantages of a five-level charging scheme for a 1Ah, 3.7V Li-ion battery compared with conventional charging methods, i.e., superior efficiency (97.16%), lower temperature rise (1.5 degrees Celsius), faster charging times (around 40-43 minutes), and extended battery lifespan. The significance of these results lies in their potential to address key drawbacks hindering the widespread adoption of plug-in hybrid electric vehicles (PH EVs) by offering a practical solution for faster, more efficient, and safer battery charging. By isolating the battery during charging and optimizing the charging process, the proposed system not only enhances the performance of electric vehicles but also contributes to prolonging the battery life, thus promoting sustainability in transportation. Additionally, the experimental validation using MATLAB Simulink underscores the practical feasibility of the proposed charging system, providing a valuable contribution to the field of electric vehicle technology.

In recent years, additive manufacturing has increased in prominence as a primary method of manufacturing around the globe. Modern metal additive manufacturing presents an innovative approach to manufacturing complex structures for the aerospace, energy, and construction sectors, due to technological advancements. In the present study, the austenitic stainless steel 309L (SS-309L) thick wall component was manufactured employing pulsed current gas tungsten arc welding (PC-GTAW) WAAM technology. The metallurgical characteristics of the as-deposited SS-309L thick wall part were evaluated. The investigation of material characteristics in the manufactured areas encompasses a study of cross-sectional (CS) and transverse-sectional (TS) regional portions, specifically the upper, middle, and lower regions. The microstructures of the CS and TS planes showed austenite, columnar, and delta ferrite dendrites in the upper, middle, and lower regions, respectively. The CS sample reveals the greatest grain size at 159.4 μm, following the EBSD investigation. The average size of the grains across the TS is 127.09 μm. The thermal cycles in multi-layer production result in changes in grain size. The microhardness in the transverse sectional regions is higher than in the CS regions, measuring average values of 257 HV, 253 HV, and 251 HV for the upper, middle, and lower sections, respectively. The ultimate tensile strengths (UTS) in the cross-sectional parts are 656 MPa, whereas in the transverse regions it was 661 MPa for the topmost regions. This study investigates the correlation among the mechanical and metallurgical properties of wire arc additively fabricated SS-309L austenite steel material.

In a hybrid renewable system, a conventional boost converter produces more losses at the time of the energy conversion process due to this, the performance of the hybrid system is reduced total harmonic distortion is increased, and the hybrid microgrid outcome is reduced. The main objective of the work enhancing the low DC voltage produced by the PV panel, a high gain Boost converter is utilized. The objectives of the work were achieved by a High Gain Modified Z-source Boost converter along with Modified Particle Swarm Optimized- Proportional Integral (MPSO-PI) controller employed in the energy conversion stage at Grid. It reduced power conversion stages and decreases the losses compared to existing Hybrid Grid-connected systems. A new 13-bus system is developed in this work for regulating the output voltage in distribution networks. The significance of our work lies in the design of an efficient microgrid system for grid-tied applications. High Gain Modified Z-source Boost converter along with Modified Particle Swarm Optimized- Proportional Integral (MPSO-PI) controller is employed to boost the voltage obtained from the PV system. A battery converter along with a bi-directional battery is connected to the DC link, to store energy generated by Hybrid Renewable Energy System (HRES) in excess amounts. The obtained DC link voltage is transferred to Three Phase VSI for the conversion of DC to AC voltage. Effective harmonic reduction is attained with the aid of an LC filter coupled to Three Phase grid, and the PI controller connected to Voltage Source Inverter(VSI) supports achieving effective grid synchronization. The proposed work was tested with 13 bus system through MATLAB Simulink.

Solar energy is a promising renewable energy source, but its intermittent and variable nature poses significant challenges for accurate forecasting. Over the recent years, there has been a remarkable surge in research dedicated to improving the precision of solar energy forecasting models. This review article delves into the state-of-the-art in solar energy forecasting. Beginning with an exploration of the hurdles faced in forecasting solar radiation, we proceed to provide an extensive survey of various forecasting models that have been developed to tackle this complex problem. Factors influencing the accuracy of solar energy forecasts are discussed, and an insight into the future trends in solar energy forecasting is provided. Key areas of focus include machine learning techniques, artificial neural networks (ANNs), and support vector regression.

The wear behavior of squeeze cast Al5456alloy with TiC/Flyash nanoparticles is examined. L27 orthogonal array design is preferred to perform the wear experiments to study the influence of various applied loads, sliding distances and sliding speed. ANOVA is carried out to detect the significant and non-significant factors. The maximum and minimum amount of wear is attained as 0.012957mm3 /Nm and 0.002387mm3 /Nm in the 21th and 5th experiments respectively and The optimal factors are recognized to be a sliding distance of 700 m, load of 70 N, and sliding speed of 9 m/s. A model of the regression with three operational factors and three levels was developed for this study. The equations for WR were created by decreasing the sum of the square residuals using the conventional least square method. The regression output and experimental results are compared to estimate the predicted error.