Design of solar cooking models to enhance energy utilization and environment protection with their cost-benefit analysis: a case study of an institutional area in the state of Rajasthan, India.

A systematic approach and methodology for financial appraisal of the Scheffler dish solar cooker has been presented in this study. An approach is applied to meet the useful cooking energy demand at a location with a high availability of DNI by a Scheffler type of 16 m2 area and the effective aperture area of 11.65 m2, weight of 400 kg, an efficiency of 40% with 980 (kg/year/ m2) steam output at 120 °C. For this analysis, 5 Scheffler dish solar cookers have been selected for preparing boiling type food like rice and pulses for 750 beneficiaries at Vivekananda Global University, Jaipur, Rajasthan, India. Presently, the cooking energy demand is fulfilled by conventional method such as LPG fuel and it makes the system costly and hazardous in certain cases. By using solar cooking at the location, it is analyzed that the cost is significantly reduced along with other socio parameters also which shows the novelty of this study. The serving time of food is taken from 12:30 to 1:30 pm. The total number of cooked rice and pulses is calculated as 586.5 and 838.38 in 325 operating days. The performance of the Scheffler dish solar cooker is found to be higher in terms of the total number of meals cooked per annum by each cooker is 1424, and the saving of LPG per annum by the Scheffler dish cooker is estimated as 679 kg per cooker. This also contributes to improving the health of women, reducing child mortality rate and CO2 emissions. The net monetary benefit is estimated at INR 30,179.9. The payback and discounted payback periods are estimated as 5.3 years and 7.9 years, respectively. The estimated positive value of net present worth also clearly indicates the profitable application of solar cooker.

Prompt-enhanced hierarchical transformer elevating cardiopulmonary resuscitation instruction via temporal action segmentation.

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a specialized CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, promoting an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which experimentally facilitates the model excavation on the CPR dataset. In general, we probe into a feasible pipeline that elevates the CPR instruction qualification via action segmentation equipped with novel deep learning techniques. Associated experiments on the CPR dataset advocate our resolution with surpassing 91.0% on Accuracy, Edit score, and F1 score.