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Copyright of Omer Halisdemir Universitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Omer Halisdemir University, Faculty of Economics & Admistrative Sciene and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The importance of intelligent parking lots has increased with the smarter power grids and the addition of vehicle technology to the grid on electric cars. Intelligent parking lots have many features requiring an innovative and optimal energy management program to benefit from. Therefore, providing a comprehensive and optimal model for energy management of electric car parking lots in intelligent networks is one of the basic needs of the owners and operation of electric car parking lots. According to this issue, this paper presents a comprehensive model for more optimal use of bright parking lots. In this model, to participate more optimally in the next-day market, an artificial neural network is first trained to predict the overall demand for parking charges and the number of cars in the parking lot every hour of the next day. In continuation of the problem of parking participation planning in the day-ahead market, the real-time market and the intelligent charging/discharging of cars have been formulated simultaneously. The proposed model provides the possibility of predicting parking lot charging demand using a neural network, participation in the day-ahead market and balance market, and the possibility of using the ability to discharge electric cars for the parking lot operator. This model is formulated as a mixed correct linear programming to maximize the profit of the parking garages. In the proposed method, the algorithm performs fast calculations, and this method also has the ability to be implemented practically. The simulation results demonstrate that the suggested approach could boost intelligent parking’s profit significantly.

Copyright of Journal of Hubei Open Vocational College is the property of Journal of Hubei Open Vocational College Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the energy industry based on energy resources has achieved rapid development, but under the background of green development strategy, traditional energy companies are also in trouble during the development process. However, the accompanying development of technologies such as the Internet and artificial intelligence has opened the door to a new world of digital transformation of the energy industry. The focus of whether the energy industry can truly achieve digital transformation lies in how to improve its own level of economic resilience. In order to explore how digital transformation affects the economic resilience of the energy industry in the context of artificial intelligence, after discussing the development dilemma of the energy industry and the integration of artificial intelligence and digital transformation, this paper established a Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) algorithm. Experiments have shown that the model built in this paper has a good prediction effect on the economic resilience index, and the average prediction error after training is 0.0028. Compared with the standard least squares support vector machine and back-propagation neural network methods, this method not only has more stable prediction results, but also greatly improves the prediction accuracy.

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

INTRODUCTION: Today's world has entered the digital era, and big data is flooding people's lives, with more industry development and big data between the deep connections. Digital technology, as a representative of advanced technology globally, influences the global economic pattern. In the digital era and the background of rapid economic growth, there is a deeper integration between economics and management. Financial management work occupies an essential position in the development of enterprises and has an irreplaceable role in improving the economic efficiency of enterprises. OBJECTIVES: Relying on digital technology under the extensive data statistical analysis method combines the advantages of big data technology and statistics. Its application for enterprise economic management can explore the law of enterprise management and operation to promote the improvement of management efficiency and the healthy development of enterprises. METHODS: In the context of the digital era, the six representative methods of extensive data statistical analysis methods are introduced, and then their practical application in enterprise economic management is studied, respectively, from five aspects to discuss the application of extensive data statistical analysis methods for enterprise financial management. RESULTS: Big data statistical analysis can provide information support for improving the level of enterprise economic management, promote the benign development of enterprises, and enhance the competitiveness of enterprises in the market. CONCLUSION: To strengthen the application of extensive data statistical analysis methods in enterprise economic management, its insights and measures that can provide valuable reference and help for the healthy development of enterprises. [ABSTRACT FROM AUTHOR]

The accelerated application of emerging technologies in the financial field in the era of informationization has put forward brand-new challenges and requirements for the development of disciplines as well as the teaching mode of economics and management majors in colleges and universities. In this paper, the teaching resource base of economics and management majors is first constructed on the basis of the RNMIE model combined with cloud computing and other technologies. Then the theory of self-organizing behavior is introduced to build a three-dimensional informatization teaching platform for economics and management majors. On this basis, combined with the online learning teaching resources analysis model, the personalized recommendation method of hybrid recommendation is used to recommend matching resources for economics and management students. The learning satisfaction assessment model is constructed with the students’ learning satisfaction assessment system and structural equation model. Simulation tests and example analyses were conducted to recommend resources and ensure student satisfaction in the three-dimensional computer-based teaching platform for economics and management in colleges and universities. It is found that when the number of nearest neighbors increases from 5 to 50, the MAE of personalized recommendation of economic and management teaching resources based on hybrid recommendation decreases by 30.91%, and the direct utility path coefficient of teaching effect on students’ learning satisfaction is 0.62. The teaching resources of economics and management constructed by combining the RNMIE model can help students realize the diversified choices of teaching resources and also provide a new reference for the innovative informationized teaching mode of economics and management majors.

The burgeoning realm of Internet technology has ushered e-commerce into a pivotal economic role. However, navigating the myriad risks inherent in e-commerce operations is vital for the sustained growth of businesses in this sector. This study melds economic management principles with a deep dive into e-commerce risk management, focusing on predictive strategies and mitigation measures. We commence by dissecting the principal risk categories within e-commerce operations. Subsequently, we employ Structural Equation Modeling (SEM) and Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for quantitatively dissection of these risk factors. Our findings pinpoint internal, technological, and operational management risks as the critical triad influencing e-commerce strategic operations. Remarkably, the PSO-GRNN model’s risk prediction accuracy stands at 93.62%, outstripping conventional models significantly. Through this research, we offer a robust framework for e-commerce entities to enhance their strategic foresight and resilience, aiding in optimizing their strategic maneuvers.

This manuscript focuses on analyzing the growth dynamics of the Central Taiwan Science Park (CTSP) and Silicon Glen in Scotland with a specific emphasis on their approaches to energy, environmental conservation, and economic management. The objective is to provide insights into their sustainable development strategies. In terms of energy, CTSP addresses Taiwan's energy security and green transformation challenges, while Silicon Glen concentrates on Scotland's wind energy generation technologies. Both regions prioritize the advancement of renewable energy sources and smart grid technologies. In the realm of environmental conservation, both CTSP and Silicon Glen prioritize environmental protection and sustainability by implementing rigorous environmental monitoring measures. Regarding economic management, CTSP and Silicon Glen serve as vital technology industry hubs in Taiwan and Scotland, respectively, attracting a multitude of high-tech and startup enterprises. This growth is facilitated through various means, including policy support, access to research resources, and robust infrastructure. This manuscript presents a comparative analysis of these two industrial parks, focusing on their environmental and economic management strategies. It aims to elucidate the principles underpinning the sustainable development and economic growth of industrial parks, offering valuable insights to decision-makers and stakeholders involved in the planning of sustainable industrial parks. [ABSTRACT FROM AUTHOR]

Copyright of Dilemas Contemporáneos: Educación, Política y Valores is the property of Dilemas Contemporaneos: Educacion, Politica y Valores and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Organizations are increasingly concerned with new strategic guidelines and ways of managing physical assets to improve their competitiveness and sustainability. In this paper, we analyze the determinants of the economic management of physical assets in the specific case of a public passenger transport company located in one of the main cities of Portugal. Based on the case under analysis, it was possible to conclude that the economic management of physical assets is oriented by relevant indicators, including, for example, expenses associated with acquisition, maintenance, and operation. This paper provides a relevant contribution to monitoring and evaluating the life cycle of equipment, enabling more efficient and effective management of these physical assets for transport companies. We are convinced that the valuable results presented in this paper can open up new research avenues in the area of physical asset management. [ABSTRACT FROM AUTHOR]

The article examines the identification of an unknown church painting related to the work of the Bulgarian artist Zlatyu Boyadzhiev in the “Dormition of the Virgin” Church in Plovdiv. The author studied documentary texts and spoke with the artist's family before approaching the Plovdiv Metropolis to obtain permission to digitize this mural. The unknown church painting of Zlatyu Boyadzhiev is a real challenge for researchers and provides an opportunity for a new perspective that would complement the biography and expand the knowledge of this great Bulgarian artist. [ABSTRACT FROM AUTHOR]

This paper constructs the state transition diagram of the retailer’s economic management risk control in the supply chain. At the same time, we construct the differential equation of the model. Based on the differential equation theory, we establish a dynamic equation model for product demand forecasting, which is suitable for the historical data of the retailer’s supply chain. At the same time, we use the two-party game model to determine the strategy choice of maximizing the income of a single enterprise in the secondary supply chain. The study finds that retailers can only maximize revenue through suboptimal order quantities when the financing limit has a substantially limiting effect.

The article is devoted to the study of legal entities created by state property legal regimes. Philosophical, general scientific, special; dogmatic methods, and methods of interpretation of legal regulations were used in the course of the research. As a result of research, it has been established that the term "property legal regime" applies only to the property of an economic entity, both transferred to it and acquired by it as a result of economic activity. The author concludes that the only function performed by the authorized capital is to determine the share of the participant in the share capital of the company. The author analyzes scientific approaches to reforming proprietary rights to other's property and legal regimes of property of legal entities from the standpoint of socio-economic conditions in which the state of Ukraine finds itself. The legal regime of property acquired by state joint-stock companies beyond the limits defined in the charter remains undefined. All the above indicates the need for legislative regulation of legal entities created by the state property legal regimes. [ABSTRACT FROM AUTHOR]

The purpose of the article is to analyze the influence of the information environment factor on the assessment of a country's economic security in the digital economy. This work is devoted to the actual problem of managing a country's economic security. The methodology proposed in the paper for assessing the level of economic security is based on a comprehensive analysis and assessment of the main factors affecting economic security. In this context, proposals are developed for the use of digital methods for assessing the impact of various factors on a country's economic security, which will allow for obtaining various simulated data on an automated basis. Based on the data obtained, artificial intelligence develops solutions to improve the efficiency of economic security. The results of the study show that there is a strong correlation between the set of selected factors of economic security and the factor of the information environment. As a result, an increase in the influence of factors causes an increase in the level of security of the information environment, and a decrease in threats to the information environment of a country has a positive effect on its development. [ABSTRACT FROM AUTHOR]

Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squares (OLS) regression as a base-learner, and we re-update the parameters of our base-learner by using neural networks, which is a meta-learner in our proposed model. The meta-learner updates the regression coefficients using the confidence interval formula. We extensively compared our proposed model to other benchmark approaches on public datasets for regression task. The results showed that our proposed neural network-based interpretable model showed outperformed results compared to the benchmark models. We also applied our proposed model to the synthetic data to measure model interpretability, and we showed that our proposed model can explain the correlation between input and output variables by approximating the local linear function for each point. In addition, we trained our model on the economic data to discover the correlation between the central bank policy rate and inflation over time. As a result, it is drawn that the effect of central bank policy rates on inflation tends to strengthen during a recession and weaken during an expansion. We also performed the analysis on CO2 emission data, and our model discovered some interesting explanations between input and target variables, such as a parabolic relationship between CO2 emissions and gross national product (GNP). Finally, these experiments showed that our proposed neural network-based interpretable model could be applicable for many real-world applications where data type is tabular and explainable models are required. [ABSTRACT FROM AUTHOR]

This manuscript focuses on analyzing the growth dynamics of the Central Taiwan Science Park (CTSP) and Silicon Glen in Scotland with a specific emphasis on their approaches to energy, environmental conservation, and economic management. The objective is to provide insights into their sustainable development strategies. In terms of energy, CTSP addresses Taiwan’s energy security and green transformation challenges, while Silicon Glen concentrates on Scotland’s wind energy generation technologies. Both regions prioritize the advancement of renewable energy sources and smart grid technologies. In the realm of environmental conservation, both CTSP and Silicon Glen prioritize environmental protection and sustainability by implementing rigorous environmental monitoring measures. Regarding economic management, CTSP and Silicon Glen serve as vital technology industry hubs in Taiwan and Scotland, respectively, attracting a multitude of high-tech and startup enterprises. This growth is facilitated through various means, including policy support, access to research resources, and robust infrastructure. This manuscript presents a comparative analysis of these two industrial parks, focusing on their environmental and economic management strategies. It aims to elucidate the principles underpinning the sustainable development and economic growth of industrial parks, offering valuable insights to decision-makers and stakeholders involved in the planning of sustainable industrial parks.

Under the China’s increasing attention to the technological innovation of agricultural production, all kinds of agricultural information have exploded on the internet, and agricultural informatization has developed rapidly. The related information has spread all over the whole network, which makes it gradually difficult to extract useful information from the network. To solve the deficiency of information classification ability of traditional agricultural information collection methods, the classification method of agricultural information is optimized, to realize searching the required agricultural information quickly. At first, the study introduces Deep Learning (DL) technology and the Internet of Things (IoT) and their advantages. Then, based on Bayesian Networks (BN) and Decision Tree (DT) algorithm, the agricultural information classification model is implemented and trained. Using various agricultural economic development theories, analyzation is made on the present situation of domestic agricultural informatization development. Finally, the advantages are put forward of agricultural production informatization development and economic management development based on IoT technology. The research results show that, the agricultural information classification model based on DL and IoT technology can accurately select the required effective information from the network, and the application of IoT technology in agricultural production big data plays an important role in production and economic management. Therefore, the agricultural information classification model based on DL and IoT technology can make an effective and accurate judgment on the classification of agricultural information, and then provide a focus for agricultural production and economic development. A new idea is provided for the application of new technologies in agricultural production and management.

This study aims to explore the application of agricultural information classification combined with the Internet of Things technology in agricultural production and economic management in the context of deep learning (DL). The agricultural information classification system is built based on DL. In terms of experimental methods, qualitative and quantitative methods are used to compare the extractive forward and the generative reverse generation algorithm in the abstract generation method. Typical extraction methods that directly extract important sentences are relatively ineffective. The model parameter tuning and training of generative (regenerating new sentences) are difficult, and the effect does not reach the level of agricultural scientific research. Qualitative and quantitative evaluation practice proves that the effect is better. In the data training effect of Rice Digest, after nine days of iterative training for about 1400 steps, the loss value is 1.33, which is equal to the standard data set. In the data training effect of Wheat Digest, after nine days of iterative training for about 1400 steps, the loss value is 1.496, which is equal to the standard data set. Later, the existing original model is adjusted to fit the model of the agricultural dataset scale. Among them, the minimum learning rate is reduced from 0.01 to 0.003 to expand the learning rate drop rate. In the visual training comparison, when training to about 1400 steps, the cross-entropy loss function values are about 1.43 and 1.29, respectively. The curve smoothing factor is set to 0.97 to observe the overall change in target loss value. The results show that when the parameter settings remain unchanged, the loss value of the model starts to increase linearly at about 1400 steps, and the effect reaches the expected value. This study improves the relevance, completeness, and accuracy of information acquisition in the field of agricultural science and technology information and improves the utilization of agricultural information.

This article proposes a distribution function based on normal distribution to express the distribution of criterion values in the interval. This method considers that the economic evaluation target of investment projects is multi-attribute. The economic management risk control environment is divided into information environment, time and space environment, and subject environment. The paper gives the corresponding multi-attribute economic evaluation method for the six possible combinations of project risk environments under the possibility of mutual comparison between interval numbers under the normal distribution. In the end, an example is used to illustrate the practicability and simplicity of the decision-making method.

Copyright of International Journal of Social Sciences (IJSS) / Uluslararasi Sosyal Bilimler Dergisi is the property of International Journal of Social Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Globally, energy consumption has been considered the significant factor that affects economic management and needs the focus of recent studies and regulators. Hence, the present article examines the impact of renewable energy consumption (REC), fossil fuel energy consumption (FFEC), energy use, electric power consumption (EPC) and energy import on the economic management of the Indonesian economy. The present research has taken the data from world development indicators (WDI) from 1991 to 2020. The present article used the autoregressive distributed lag (ARDL) model to test the linkage among the variables and executed the Granger causality test to examine the causality association between variables. The results revealed that REC, FFEC, energy use, EPC and energy import have significant and positive linkage with the economic management of the Indonesian economy. The current article has guided the regulators that they should develop the regulators related to the economic management using effective energy consumption.

• Economic management of hybrid electric/hydrogen vehicles charging station is studied. • Green and blue hydrogen production and storage technologies are considered. • Innovative method based on conditional value-at-risk is proposed to risk measure. • Investigating a risk-averse and risk-neutral strategies. This paper proposes a stochastic programming for the economic operation of an electric/hydrogen refueling station. To reduce the carbon footprint, the charging station is connected to solar power generation, electrolyzer, and fuel-cell device to interlink the electric and hydrogen energies. Extra hydrogen is procured through an innovative steam methane reform process, which charges additional gas prices and emission penalties in return. The optimal scheduling minimizes the operation cost of the charging station and the emissions produced. The decision-maker faces uncertainties of electricity price, solar power generation, and vehicle demand that are modeled using stochastic programming. The innovative method based on conditional value-at-risk method is proposed to develop risk-hedging strategies addressing financial risks using economic management. With this respect, the risk-aversion strategies increased the expected cost from $ 322.97 to $ 388.71 (about 17 %) and decreased the difference between the expected cost and the cost of the worst scenario from $ 78.04 to $ 12.30 (about 84.23 %). Moreover, expected risk reduced from $ 24.63 to $ 1.23. [ABSTRACT FROM AUTHOR]

Teaching quality evaluation is an important means of colleges to control teaching quality, and expedite teachers to keep improving teaching levels. Based on industry-university-research interaction (IURI) model, this paper surveys the current state of teaching quality evaluation of college teachers, establishes the indices for teaching quality evaluation of economic management courses, and presents a few countermeasures for improving teaching quality evaluation. The results show that: teaching quality evaluation covers three types of data: student evaluation, supervisor evaluation, and network-assisted teaching evaluation. Our student evaluation index system covers four dimensions: teaching attitude, teaching content, teaching method, and teaching effect. Our expert evaluation index system also covers four dimensions: teaching preparation, teaching ability, teaching management, and teaching effect. Our multi-subject teaching quality evaluation system for economic management courses covers four modules: student module, teacher module, supervisor module, and administrator module. This research lays a theoretical basis for applying the IURI model in teaching reform. [ABSTRACT FROM AUTHOR]

Abstract As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong nonlinear mapping capacity, artificial neural network has already been applied in many fields, time series analysis, and trend prediction. Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Simulation results show that the proposed method does not rely on gradient information and has strong optimization calculation ability. At the same time, it can analyze and predict economic management, so as to provide strong decision support for decision makers.

The continuous development of the IOT technology is rapidly penetrating into all walks of life, which promotes the mobile Internet into a new service system, and mobile teaching methods with the characteristics of flexibility and interactivity are also becoming popular. This kind of learning mode enables students to get rid of the limitation of time and space, and can carry out targeted learning, so that different types of students can reasonably arrange their learning objectives according to their own learning quality. For students majoring in economic management, more and more enterprises require them to have practical ability and cooperation ability. Therefore, it is necessary to focus on cultivating students' cooperation ability and theoretical practice ability in education and teaching. The traditional teaching mode does not meet the needs of the times. It is necessary to study the economic management teaching mode in the new era of Internet of things to promote the reform of education industry. Therefore, it is of great significance to study the economic management teaching mode based on mobile learning and collaborative learning. This paper first introduces the principle and technical support of the IOT, analyzes the reform of economic management teaching mode under the environment of the IOT and the experimental teaching process using the IOT technology and virtual technology. This paper analyzes the current situation of experimental education and the characteristics of the teaching mode based on mobile learning and collaborative learning through the method of literature research, and uses the method of questionnaire survey to investigate the students in the experimental teaching center of economic management in Colleges and universities. The survey results show that in the IOT environment, the experimental teaching mode based on mobile learning and collaborative learning can effectively mobilize students' learning enthusiasm, and can improve students' theoretical and practical ability, cooperative ability and autonomous learning ability to a certain extent.

The organizational structure of the national economy, its internal levels (macro, meso and micro) has been analyzed in the article. The problems of the content of the concept of “economy”, segmented it into material and social components, have been identified. Attention has been focused on the negative aspects of the globalization of the world economy, based on the main provisions of the neoclassical paradigm. It has been noted, that to ensure the socio-economic integrity of the national economy, it is necessary to transform the generic state of society into a centrally-organized.According to the authors, to implement the transformation of the socio-economic space into an integrated socioeconomic system, it is necessary to coordinate three hierarchically subordinate levels: macro, meso, and microeconomic agents. The macroeconomic agent forms subject relations between all hierarchical levels, which require a high level of government: it cannot be a command economy, since this strategy of economic development paralyzes competition and free enterprise, but there can be no purely market economy, because the “invisible hand of the market” unable to ensure sustainable harmonious development of the economy without government intervention.It has been concluded about the need for an integrated nature of management of the Russian economy and the tasks for improving the system of management of the Russian economy have been determined. It is emphasized, that at the present stage, the greatest attention should be paid to the issues of improving the management of the mesostructure of the Russian economy – the development of industries and regions, since at the moment this level of the economy is paid fragmentary attention. The analysis has been carried out using general scientific methods: the method of abstraction, historical research methods; formalization method, reconstruction method; based on a systematic approach using the method of forecasting.