Sankalchand Patel University

The shift towards Electric Vehicles (EVs) is transforming the transportation sector, with Lithium-ion batteries playing a crucial role due to their high power-to-weight ratio, energy efficiency, thermal stability, and minimal self-discharge. This research addresses the critical challenge of accurately estimating the state of charge (SOC) and state of health (SOH) of Li-Ion batteries, a key factor in optimizing battery management systems and enhancing overall performance. We assess various SOC estimation techniques and finds that the Adaptive Double Kalman Filter Method excels in delivering accurate combined SOC and SOH measurements. Our findings reveal a notable enhancement in estimation accuracy, achieving a 25

Eltrombopag is an orally active thrombopoietin receptor agonist whose clinical performance is limited by poor aqueous solubility and variable gastrointestinal absorption caused by chelation with dietary cations. The present study was undertaken to develop and optimize β-cyclodextrin-based nanosponge carriers to improve the solubility, dissolution behavior, and oral delivery of Eltrombopag using a systematic Quality by Design (QbD) approach. β-Cyclodextrin nanosponges loaded with Eltrombopag were prepared using different cross-linking agents and optimized through a Taguchi L9 experimental design. The effects of formulation and process variables on critical quality attributes, including particle size, drug entrapment efficiency, and in vitro drug release, were systematically investigated. The optimized formulation was characterized using Fourier-transform infrared spectroscopy, X-ray diffraction, differential scanning calorimetry, dynamic light scattering, scanning electron microscopy, phase-solubility analysis, and in vitro dissolution studies. The optimized nanosponge formulation (F7) demonstrated a mean particle size of 195 ± 8 nm with a polydispersity index of 0.18 ± 0.02, a drug entrapment efficiency of 78.2 ± 1.4

A new simple stability indicating reverse phase liquid chromatography method was developed by employing Quality by Design (QbD) approach for the simultaneous determination of Dapagliflozin and Linagliptin. Within QbD paradigm, the present study aimed to establish the optimization of the RP-HPLC (Reverse phase high performance liquid chromatography) by means of design of experiments and response surface mythology like, Centre composite design (CCD) in order to achieve a good separation and resolution. The developed method is effective to separate Dapagliflozin and Linagliptin with a good chromatographic resolution of 5.272. Chromatographic separation was acquired with column C8 Inertsil column (250 × 4.6 mm, 5 µm) at flow rate 1.0 mL/min with the mobile phase consists of methanol and water (85:15 % v/v). The detection of Dapagliflozin and Linagliptin was carried out at 225 nm. The proposed method was validated according to ICH guidelines. The method was linear in range of 2.5-15 μg/mL and 1.25-7.5 μg/mL of Dapagliflozin and Linagliptin respectively and recovery were in the range of 99% to 100%. The degradation product found in stress patterns were well separated among the drug compounds. The method was validated to be specific, rapid, precise and robust for routine analysis in its pharmaceutical dosage form.

Transformers are critical assets in electrical power systems, and their early fault detection is vital for operational reliability. This study presents an intelligent diagnostic framework based on dissolved gas analysis (DGA), using over 5800 validated transformer cases. Key dissolved gases namely H2, CH4, C2H6, C2H4, C2H2, CO, and CO2 along with gas ratios such as Rogers and International Electrotechnical Commission (IEC), are employed to detect insulation degradation and incipient faults. A novel parallel convolutional neural network (CNN) and Support Vector Machine (SVM) named as Parallel Support Vector Machine (PSVM) model is proposed, in which a CNN extracts nonlinear gas features and SVM performs fault classification. The model is optimized using hyperparameters and trained to distinguish thermal faults, arcing, and partial discharge events. For comparative analysis, the PSVM model is benchmarked against traditional diagnostic methods like the Rogers Ratio Method (RRM), Duval Triangle Method (DTM), and modern classifiers including artificial neural networks (ANN), and standalone SVM. The proposed PSVM framework achieves a classification accuracy of 90.9

The caregivers of mentally ill patients are facing a lot of burden, Strain, unhealthy life style and not able to cope with their mentally ill patient and needs support for the family. Therefore, it is of interest to assess perceived burden, coping strategies and psychological well-being among caregivers of mentally ill patients in selected psychiatric hospitals of Gujarat. Hence, a total of 100 caregivers were selected using purposive sampling. The findings showed that 63% of caregivers experienced high burden, while 37% reported very high burden. Nearly 75% of caregivers had inadequate coping strategies. Data shows the substantial caregiver burden and the need for targeted interventions to improve coping and psychological well-being. This study advances knowledge by documenting high caregiver burden, inadequate coping and compromised psychological well-being among caregivers of mentally ill patients in Gujarat. Thus, demographic associations, supporting the need for structured psychosocial and nursing interventions.