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An analysis of publishing trends and bbibliographic analysis data was conducted to critically analyse the research landscape regarding the utilisation of machine learning techniques in the field of energy and power production (EPP). The Elsevier Scopus database and the PRISMA methodology were utilised to locate and evaluate the published papers. The bibliometric analysis software package RStudio (Biblioshiny) was employed to examine the most significant sources, authors, and institutions with the highest productivity. The findings indicated that a total of 653 documents were published on the subject, consisting of conference proceedings (41.8%) and articles (50.8%), spanning the years 2012 to 2023. An review of publishing patterns indicated a significant increase in the number of publications, rising from 3 to 190, representing a growth of 5,000% over the same period. This surge can be attributed to the expanding scientific interest and the influence of research on the subject. The stakeholder analysis identified Boumaiza A, Sanfilippo A, and Wang X as the leading authors/researchers in the field. Additionally, it revealed that China, India, and the United States are the most actively involved nations in this area. In contrast, the primary financial organisation that actively supports study on the subject is the National Natural Science Foundation of China (NSFC). Overall, the study demonstrated that the utilisation of machine learning in evidence-based policy and practice (EPP) is a dynamic and interdisciplinary field of research that has the potential to make significant contributions to both research and society.

This study aims to discuss the effectiveness of “Demand-Supply Coordinated control” in reducing the power consumption required for conveyance by the heat transport medium in District Cooling and Heating (DHC) systems. The problem with DHC systems is that increased conveyance power is required to provide heating to consumers. As one of the measures to solve this problem, Demand-Supply Coordinated (DSC) control is introduced; however, its effectiveness and limitations have not been clarified so far. In this paper, first, the fundamental characteristics of a DHC system under DSC control are numerically examined. The results showed that the conveyance power consumption of DHC systems under DSC control can be classified into three regions, depending on the relative rate of demand change against the load-following capability of the heat source. Next, the authors compared the conveyance power of DSC control with that of Constant Supply Pressure (CSP) control adopted in conventional DHC, and showed regularity for each of the three regions mentioned above. Finally, the authors show the appearance frequency of these three regions of the practical DHC system under a real heat load in Japan. The results showed that the conveyance power required for DSC control is markedly lower than that of CSP control.

The development of active-adaptive electrical networks with an intelligent control system involves the creation of energy information complexes with the possibility of continuous monitoring and remote control of the operating modes of all its components in order to optimize network parameters. This necessitates the need to adapt educational programs and training technologies to train specialists with skills in related fields. The paper describes a developed research complex with elements of artificial intelligence for performing research and studying intelligent systems and means of controlling the modes of power supply systems based on a set of educational equipment using real and virtual objects of electric power systems with adjustable parameters. Pychram Community Edition was chosen as the integrated development environment. The software part of the complex has been developed, including a digital twin of the stand, an executive part and a neural network model. The neural network allows you to optimize the parameters of an active-adaptive electrical network losses during electricity transmission.

In recent years, in order to promote the independent development of the new energy vehicle industry, Chinese government has decided to reduce the consumption subsidies for new energy vehicles until the subsidies are completely withdrawn. The reduction of consumption subsidy has a great impact on the production and sales of new energy vehicles in the whole vehicle market. However, does the reduction of this subsidy also have an impact on other enterprises in the new energy vehicles industry chain? This paper tests this problem using data from 2016 to 2018, and finds, through empirical analysis, that during the period of subsidy decline, the profitability of component enterprises is significantly positively correlated with this subsidies, while the r&d investment of enterprises is significantly negatively correlated with this subsidies. The results show that in terms of profitability, the reduction of consumer subsidies not only has an impact on the whole vehicle industry of new energy vehicles, but also has an adverse impact on the core component companies in the industrial chain. However, in terms of r&d, the reduction of subsidies has more negatively strengthened the input and attention of R&D in component companies.

In the process of operation, integrated energy systems encounter a series of complex optimization problems such as complex conversion of multiple types of energy, coupling of multiple types of equipment, and uneven distribution of multiple demands. In recent years and in the process of achieving the goal of "double carbon" in China, more and more problems of optimal scheduling of biomass integrated energy systems have become hot topics of research. In this paper, we propose an artificial bee colony algorithmbased operation and scheduling method for a biomass fuel cell (SOFC) cooling, heating, and power triplesupply system with the objective function of maximizing the economic profit of the integrated energy system (including fuel, operation, and maintenance costs), and the constraints of energy conservation, system safety, and operation state. The equipment included are biomass boiler and corresponding steam turbine, biogas digester, SOFC, gas storage tank, etc. The results of the algorithm include the economic benefits of using the artificial bee colony algorithm for the same load scenario. The operational scheduling results show that the artificial bee colony algorithm is able to maximize the profitability of the integrated energy system while reducing carbon emissions.

Algeria heavily relies on fossil fuels, particularly oil and gas, leading to inadequate energy supply in rural areas. However, the country possesses vast agricultural land and abundant renewable energy resources like solar and wind. Due to the insufficient national electricity grid, rural farmers must resort to expensive and environmentally unfriendly diesel generators to meet their energy needs. To address this issue, decentralised energy systems are proposed as a solution to enhance energy resilience on Algerian farms. These systems can power irrigation pumps, lighting, and other electrical appliances on the farm. Furthermore, surplus energy generated by the PV systems can be sold back to the grid or used to offer paid energy services, creating a new revenue stream for farmers. This study evaluates the potential opportunities for implementing Decentralized and Distributed Renewable Energy Systems (DRES) on farms in Algeria. It will review current policies and regulations for microgrids in the country and present various scenarios for bottom-up energy transformation. This paper will identify the challenges and barriers Algerian farms face in energy, water, and agriculture practices. The findings will demonstrate how DRES can improve energy efficiency, reduce costs, increase the share of renewable energy, and enhance electricity supply reliability in Algeria. The study will provide practical guidance to policymakers and identify opportunities for integrating DRES on farms while suggesting future research directions in sustainable energy.

One of the most important tasks of engineering hydraulics is to determine the energy loss during the motion of the fluid flow. The study of the question of whether the patterns of hydraulic resistances are similar in a calm and turbulent flow is relevant in the design of hydraulic structures. In most cases, a turbulent regime of fluid motion is observed in various applications, but to date, the theory of turbulence is not considered complete. When designing hydraulic structures, inaccuracies in the existing calculation methods can lead to a decrease in the efficiency and reliability of the entire spillway structure as a whole. The need for an integrated approach to the analysis of the impact on the hydraulic resistance of various factors is noted (degree of spread (Bh)$ \left( {{B \over h}} \right) $), the degree of turbulence (Re) and the degree of flow roughness (Fr)), which is not always provided by known dependencies and methods of calculation. On the basis of our own experimental data, a new formula for calculating the hydraulic resistance of turbulent flows in smooth channels was obtained. The functional dependence of the hydraulic resistance coefficient on the parameters (Bh)$ \left( {{B \over h}} \right) $, Re and Fr is obtained.

In this work, for the first time, an in vivo system was developed for the production of exogenous double-stranded RNA (dsRNA), complementary to the potato phytoene desaturase (pds) gene. For this purpose, a recombinant plasmid was constructed based on the L4440 vector; the pds gene fragment was inserted between two late T7 promoters, which allows the production of dsRNA. The resulting plasmid was used to transform the E. coli strain HT115, which is deficient in RNase III. This strain prevents the hydrolysis of the dsRNA produced in it. The presence of the required dsRNA fragments was confirmed by electrophoretic analysis. This system can be used as a positive control in experiments on regulating the activity of potato genes using spray-induced gene silencing (SIGS).