Advancing the sustainable energy transition is a major need in nations that are constantly evolving and developing in terms of their energy economy. India has been chosen for the purpose of analysis due to the heterogenous nature of its polity, topographies, infrastructural capabilities and diverse framework. In accordance with the sustainable development goals proposed by the UN, a metamorphosis is observed within the renewable energy sector of the nation. Blockchain technology that facilitates a transparent transition is incorporated on various upcoming platforms. This is backed up by peer-to-peer trading of energy providing a prosumer with an autonomous environment. The goal of this paper is to highlight the struggles and challenges faced by the energy sector as it takes up unconventional and non-traditional approaches within the country. It also aims to discover potential ways that would help a nation like India facilitate such a transition by studying its ongoing trends. The need is eminent for a practical study that is specific to a developing nation like India in terms of P2P energy trading enabled by blockchain technology to promote the use of open-sourced electricity and achieve a decentralized system.

Improvement of the agricultural crops seeds quality with their pre-sowing treatment in the electromagnetic field and the subsequent increase in grain yield and studying the mechanisms of stimulating effects – all those are the important problems of science and practice of the crop-growing sector. The purpose of the presented research was to study the mechanism for increasing the germinating ability of spring barley seeds as a result of pre-sowing treatment with an alternating electromagnetic field of an industrial frequency (50 Hz) for one second. After four days of storing, treated seeds were germinated under optimal conditions for this process and the number of germinated seeds of two spring barley varieties was counted each day during seven days period. Also we studied the onset, duration and passage of Microphenological Phases of Seeds Germination (MPPSG), based on the observation and the time registration of each seed morphological changes during germination with two-hour periodicity for seven twenty-four hours. It was established that seeds treatment in an electromagnetic field leads to an acceleration of their germination for one day due to the appearance of rapidly germinating seeds fraction and disappearing of the slowest germinating seeds fraction. This is due to the acceleration of the onset of all MPPSGs and reducing of their duration. Despite the reduction in the absolute values of the duration (in hours) of all MPPSGs, two phases (

This chapter describes how electric current is the main electrotechnological working tool in crop farming. It can initiate the plant growth and development or it can suppress, damage and destroy them. The stimulating electrotechnology example involves the electrical stimulation of ligneous plants allowing grafting survivability and growth. Alternatively, the suppressing and damaging current influence is vividly illustrated by the electro-impulse of sunflower and tobacco plants for pre-harvest processing. The demonstrative example of plants' electric destruction is weeds control using electro-impulse technology. The electrotechnologies effect on nature with key points' of usage and issues, alongside their technological and economic efficiency benefits are considered in the chapter.

The use of a low-power solar network and standalone power plants is the most promising for the needs of the housing and utilities sector, small industrial enterprises, social and public health...

Workload prediction is essential in cloud data centers (CDCs) for establishing scalability and resource elasticity. However, the workload prediction accuracy in the cloud data center could be better due to noise, redundancy, and low performance for workload prediction. This paper designs a hierarchical tree-based deep convolutional neural network (T-CNN) model with sheep flock optimization (SFO) to enhance CDCs’ power efficiency and workload prediction. The kernel method is used to preprocess historical information from the CDCs. Additionally, T-CNN model weight parameters are optimized using SFO. The suggested TCNN-SFO technology has successfully reduced excessive power consumption while correctly forecasting the incoming demand. Further, the proposed model is assessed using two benchmark datasets: Saskatchewan HTTP traces and NASA. The developed model is executed in a Java tool. Therefore, associated with existing methods, the developed technique has achieved higher accuracy of 20.75%, 19.06%, 29.09%, 23.8%, and 20.5%, as well as lower energy consumption of 20.84%, 18.03%, 28.64%, 30.72%, and 33.74% when validating the Saskatchewan HTTP traces dataset. It has also achieved higher accuracy of 32.95%, 12.05%, 32.65%, and 26.54%.

To substantiate the quantitative values of the process of destruction of weeds by means of electric impulse and to elucidate the mechanism of damage and the sequence of disturbances that occur in the...