Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.

The increasing epileptic electricity supply, mainly in the residential areas of Nigerian cities, has been linked to the incorrect knowledge of the numerous socio-economic and physical indices that influence household electricity usage. Most of the seemingly identified explanatory factors were done at macro level which does not give a clear estimate of this electricity demand. The thrust of the study is to analyse empirically the household electricity determinants in Nigerian cities with a view to evolving a more informed and sustainable energy policy decision. Multistage area cluster sampling method was adopted in the study where 769 copies of structured questionnaire were distributed to electricity users of prepaid meters in five major Nigerian cities. The research hypothesis was tested using the multiple linear regression statistical tool. The result revealed that nine variables which include age (r = 0.05, p-value: 0.05), household income (r = 0.00, p-value: 0.05), number of hours that people stay outside the house (r = 0.043, p-value: 0.05), number of teenagers at home, (r = 0.006, p-value: 0.01) number of electrical appliances (r = 0.016, p-value: 0.01), type of house (r = 0.012, p-value: 0.01), hours that the electrical appliances are used (r = 0.043, p-value: 0.05), weather condition, (r = 0.011, p-value: 0.05) and the location of the building (r = 0.045, p-value: 0.05) were significant in determining the household electricity consumption. Policies based on the findings will give energy and urban planners an empirical basis for accurate and robust forecasting of the determinants that influence household electricity consumption in Nigeria that is devoid of any speculation or unfounded predictions.

The need to expand the range of banking services in Ukraine is stipulated with technological progress, the European integration processes and the legal regime of martial law introduced in the country. Under the conditions of war, the need to strengthen the security of banking activities and protect the banking system from the influence of any internal and external factors gains meaning. The topical direction of economic and legal research of scientists today is the possibility to introduce digital technologies with elements of artificial intelligence (AI) into the banking activity in Ukraine to improve its protection. The AI law as an independent branch of the Ukrainian law has not been developed so far. The sources of AI law, its functions, tasks, scope, risks and limits of legal responsibility for prohibited practices of artificial intelligence have not been defined. The purpose of the article is to analyze the theoretical and legal provisions that underpin the regulation of AI application in Ukrainian banking. The comparative legal method made it possible, considering the provisions of the draft law on AI of the European Union, to determine the trends in the development of the legal regulation of AI in Ukraine. Following the study, proposals to the legislation of Ukraine were formulated, which will contribute to the legal regulation of banking activities using digital technologies with elements of AI.

With the development and progress of the era, digital construction has become an important topic for enterprise development in the new era. Practice has shown that by actively carrying out corresponding digital construction work, enterprises can more comprehensively and systematically analyze the industry development and market prospects, which helps to promote the reasonable adjustment
of internal and external management work modes and the improvement of management efficiency, and has a positive guiding role for the healthy development cycle of enterprises. In this article, the author combines a large amount of research cases to conduct research on the effect of digital construction on enterprise development in the new era and proposes corresponding optimization measures, hoping to further promote the full play of information technology value, in order to safeguard the development of enterprises.

With the acceleration of economic development and urban construction, urban security accidents have occurred around the world with alarming frequency, causing serious casualties and economic losses. Urban security planning and management as emerging areas of research have drawn widespread attention. For city development plans, urban security planning and management have become one of major topics. This paper first outlines the principles of urban security planning and management, combined with the construction of a digital and intelligent platform for urban emergency management. This research then analyzes the core technology and equipment support system of urban security management and its practical application. It also presents a new model based on urban security planning and management, followed by examples of its application in some mega infrastructure development for security planning and design (for example, Singapore Changi Airport and Shanghai Hongqiao Airport Transportation Hub). Additionally, a blast protection concept of urban security planning and management is provided.

There are several factors that generate postharvest losses of Citrus sinensis, but none have been focused on the central jungle of the Junín region of Peru. The objective of this research was to evaluate postharvest losses of Citrus sinensis in the province of Satipo, Junín region of Peru, considering the stages of the production chain. The methodology was applied to descriptive and cross-sectional design. A sample of 10 orange trees, 3 transport intermediaries and 5 traders selected for compliance with minimum volume and quality requirements were used. The °Brix, pH and acidity characteristics of the fruit were determined. Subsequently, absolute and percentage losses were quantified through direct observation, surveys and interviews. The main postharvest losses of Citrus sinensis were 1.50% in harvesting and detaching, 1.75% in transport to the collection center, 2.23% in storage and transport by intermediaries, and 2.90% in storage and sale by retailers. The overall loss was 8.12% throughout the production chain and US$5.75 per MT of C. sinensis harvested. The main damages found were mechanical and biological, caused by poor harvesting and packaging techniques, precarious storage and careless transport of the merchandise.