RESUMEN
As it is known that the whole world is battling against the Corona Virus Disease or COVID19 and trying their level best to stop the spread of this pandemic. To avoid the spread, several countries like China, Italy, Spain, America took strict measures like nationwide lockdown or by cordoning off the areas that were suspected of having risks of community spread. Taking cues from the foreign counterparts, the government of India undertook an important decision of nationwide full lockdown on March 25th which was further extended till May 4th, 2020 (47 days-full lockdown). Looking at the current situation government of India pushed the lockdown further with eased curbs, divided the nation into green, orange and red zones, rapid testing of citizens in containment area, mandatory wearing of masks and following social distancing among others. The outbreak of the pandemic, has led to the large economic shock to the world which was never been experienced since decades. Moreover it brought a great uncertainty over the world wide electricity sector as to slow down the spread of the virus, many countries have issued restrictions, including the closure of malls, educational institutions, halting trains, suspending of flights, implemented partial or full lockdowns, insisted work from home to the employees. In this paper, the impact analysis of electricity consumption of state Haryana (India) is done using machine learning conventional algorithms and artificial neural network and electricity load forecasting is done for a week so as to aid the electricity board to know the consumption of the area pre hand and likewise can restrict the electricity production as per requirement. Thus, it will help power system to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. For this the dataset from regional electricity boards of Haryana that is, Dakshin Haryana Bijli Vitran Nigam and Uttar Haryana Bijli Vitran Nigam were analysed and electricity loads of state were predicted using python programming and as per result analysis it was observed that artificial neural network out performs conventional machine learning models.
RESUMEN
Time-restricted feeding (TRF) is a nutritional intervention wherein food intake is limited to a consistent 8- to 10-h daily window without changes in nutritional quality or quantity. TRF can prevent and treat diet-induced obesity (DIO) and associated metabolic disease in young male mice fed an obesogenic diet, the gold standard preclinical model for metabolic disease research. Because age and sex are key biological variables affecting metabolic disease pathophysiology and response to therapies, we assessed their impact on TRF benefits by subjecting young 3-month-old or middle-aged 12-month-old male and female mice to ad libitum or TRF of a Western diet. We show that most of the benefits of TRF are age-independent but are sex-dependent. TRF protects both sexes against fatty liver and glucose intolerance while body weight benefits are observed only in males. We also find that TRF imparts performance benefits and increases survival to sepsis in both sexes.