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SAVCHENKO-BELSKII, K. A., E. I. MANTAEVA i A. A. MANTSAEVA. "ESTABLISHMENT OF A TOURIST AND RECREATIONAL CLUSTER IN THE REGION: REASONABILITY AND FORECAST". Scientific Works of the Free Economic Society of Russia 239, nr 1 (24.05.2023): 180–202. http://dx.doi.org/10.38197/2072-2060-2023-239-1-180-202.
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The article assesses the feasibility of establishing a regional economic cluster. The assessment is tested for the tourism industry. It is based on a two-level classification system of Russian regions and simulation modeling. The classification made it possible to single out typological groups of regions with different industry orientations and to identify groups of different industry development levels. Simulation modeling required studying a number of indicators of the tourism industry and identifying patterns and processes occurring in it in a formalized form. Using built models, the results of the tourism industry between 2018–2027 were predicted. Along with that, the investments were provided for the establishment and development of a tourist and recreational cluster.
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López, Gustavo Rosal. "SS62-02 REFLECTIONS ON THE USE OF EXOSKELETONS IN THE HEALTHCARE SECTOR". Occupational Medicine 74, Supplement_1 (1.07.2024): 0. http://dx.doi.org/10.1093/occmed/kqae023.0361.
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Abstract For a few years now, the concept of Human 2.O has been very present in industry. Among the lines of work of Human 2.0, perhaps the best known is that of supporting the capabilities that humans have. Thus, we can talk about increasing human cognitive capabilities (example - augmented reality) and also physical capabilities (example -exoskeletons). And this last case is the one that we are going to evaluate in this study. The exoskeleton market was valued at USD 354.22 million in 2021, and it is expected to reach USD 1620.04 million in 2027, registering a CAGR of 12.5% during the forecast period (2022-2027). The development and production of exoskeletons requires the collaboration of experts from different fields, including engineers, medical professionals and designers. It is a task undertaken by specialized companies that focus on developing advanced exoskeletons that meet the needs of users. And finally, with all this analysis we have to think about the future of exoskeletons in the healthcare sector. Are they really going to satisfy the current needs of workers in the sector? Can their costs be assumed by health organizations? What will happen to the possible rejection of their use by patients? This and other questions must be answered in a very short period of time.
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XU, QIAN, HUA CHENG i YABIN YU. "Analysis and forecast of textile industry technology innovation capability in China". Industria Textila 72, nr 02 (22.04.2021): 191–97. http://dx.doi.org/10.35530/it.072.02.1759.
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The textile industry of China has been facing with fierce competition and transformational pressures. It is of great significance to study the evolution of textile industry’s technological progress and to predict the trends. The study analyses the technological innovation ability of China’s textile industry based on the data of 270,145 patent applications from 1987 to 2016. At the same time, the Logistic model is used to forecast the technology innovation capability of China’s textile industry. The study found out: the number of Chinese textile patent applications is on a upward trend; enterprises and universities are the most important patentee; the regional distribution of textile technology innovation is uneven; the number of patent applications in the southeast coastal areas is the largest; the distribution of the IPC is also uneven, D06 (fabric treatment) having the largest number of patent applications and the fastest growth rate; China’s textile industry technology innovation has entered a maturity stage in 2018, and will enter the recession stage after 2027 based on the Logistic model.
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Gajdzik, Bożena. "Post-Pandemic Steel Production Scenarios for Poland Based on Forecasts of Annual Steel Production Volume". Management Systems in Production Engineering 31, nr 2 (3.05.2023): 172–90. http://dx.doi.org/10.2478/mspe-2023-0019.
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Abstract The paper presents the results of forecasts made for the volume of steel production in Poland based on actual data for the period from 2006 to 2021 with forecasting until 2026. The actual data used for the forecasts included annual steel production volumes in Poland (crude steel) in millions of tons. Basic adaptive methods were used to forecast the volume of steel production for the next five years. When selecting the methods, the course of the trend of the studied phenomenon was taken into account. In order to estimate the level of admissibility of the adopted forecasting methods, as well as to select the best forecasts, the errors of apparent forecasts (ex post) were calculated. Errors were calculated in the work: RMSE Root Mean Square Error being the square root of the mean square error of the ex-post forecasts yt for the period 2006-2021; ? as the mean value of the relative error of expired forecasts y*t (2006-2021) – this error informs about the part of the absolute error per unit of the real value of the variable yt. Optimization of the forecast values was based on the search for the minimum value of one of the above-mentioned errors, treated as an optimization criterion. In addition, the value of the point forecast (for 2022) obtained on the basis of the models used was compared with the steel production volume obtained for 3 quarters of 2022 in Poland with the forecast for the last quarter. Forecasting results obtained on the basis of the forecasting methods used, taking into account the permissible forecast errors, were considered as the basis for determining steel production scenarios for Poland until 2026. To determine the scenarios, forecast aggregation was used, and so the central forecasts were determined separately for decreasing trends and for increasing trends, based on the average values of the forecasts obtained for the period 2022-2026. The central forecasts were considered the baseline scenarios for steel production in Poland in 2022-2026 and the projected production volumes above the baseline forecasts with upward trends were considered an optimistic scenario, while the forecasted production volumes below the central scenario for downward trends were considered a pessimistic scenario for the Polish steel industry.
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Zhang, Bowen. "Analysis of Bilibili's Competitive Strategy in the New Trends". BCP Business & Management 34 (14.12.2022): 849–55. http://dx.doi.org/10.54691/bcpbm.v34i.3104.
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According to the "2022-2027 China Internet Video Industry Market Depth Research and Investment Strategy Forecast Report" published by the China Research Institute of Industry, as of the end of June 2021, the size of China's Internet users broke one billion, reaching 1.011 billion people, an increase of 0.22 billion people compared to the end of December 2020, the massive size of Internet users to promote the development of China's online video industry. The size of the short video market will increase more quickly between 2020 and 2022, with a compound annual growth rate of about 44%. The market size will grow at a slower rate during 2023-2025, but will still maintain a CAGR of 16%. China's short video market is expected to reach nearly 600 billion yuan in 2025 [1]. More than a quarter of a day is spent watching short videos on mobile devices in China. Along with visuals and audio, short video has emerged as the "third language" of the mobile Internet. Short-form video has rapidly increased in the new Internet economy. Bilibili's future development has attracted much attention. With the development of the Internet economy and the increase in significant video websites, whether Bilibili can continue its competitive advantage and successfully achieve business transformation has become controversial. This research will analyse Bilibili's business model through a SWOT analysis and make feasible suggestions for its future development.
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Dynkin, A., V. Baranovsky, I. Kobrinskaya, G. Machavariani, Y. Adno, S. Afontsev, O. Bogaevskaya i in. "Russia and the World: 2022 IMEMO Forecast". Analysis and Forecasting. IMEMO Journal, nr 1 (2022): 13–39. http://dx.doi.org/10.20542/afij-2022-1-13-39.
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Abridged English-language version of the annual IMEMO forecast ‘Russia and the World: 2022’ is dedicated to the analysis and the forecast of key trends in the world economy and international relations in 2022. The primary focus is on the issues that are of fundamental importance for ensuring stable economic and political development of Russia in the near (2022) and more distant future. In the preparation of the paper, the long-term experience of predictive research carried out by IMEMO RAS was used. The paper consists of three parts: global trends, economics, and foreign policy. Among the global trends accelerated digitalization and all related social, political, and economic changes; focus on technological competition; climate/environmental and energy agendas are coming to the fore; increasing geopolitical and geo-economic tensions and the raging global pandemic of COVID-19 are highlighted. In the world economy, the authors of the paper expect an increase in inflation, acceleration of digitalization amid intensifying technology competition and the pursuit of digital sovereignty, acceleration of decarbonization, launch of demethanization process, and shifts in food systems. The paper further assesses the prospects of the leading industry drivers of world economic growth in 2022: production of semiconductors; pharmaceuticals and biomedicine; rare earth, non-ferrous, and ferrous metals. In the next section of the paper, political and social trends are analyzed in the countries of North America, Europe, Pacific and South Asia, Post-Soviet space. The final section of the paper gives the forecast of the relations of the Russian Federation with its key foreign partners. Against the backdrop of the renewal of the global economy, which includes energy transition and accelerated digitalization, it is becoming vitally important for Russia to conduct a comprehensive balanced restructuring of the national economy and social sphere. Translation: Artamonova U.Z., Samarskaya L.M., Sokolova P.S., Urumov T.R., translation editors Mamedyarov Z.A., Moiseeva D.E.
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Dong, Yanan, Zheng Ren i Lian Hui Li. "Forecast of Water Structure Based on GM (1, 1) of the Gray System". Scientific Programming 2022 (23.05.2022): 1–7. http://dx.doi.org/10.1155/2022/8583959.
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A forecast approach of water structure based on GM (1, 1) of the gray system is proposed. Based on economic and water information of Hebei Province from 2000 to 2018, the water use structure of Hebei’s industrial sector form 2019 to 2030 is forecasted according to the composition data and gray system GM (1, 1) model. The forecasting results by the proposed approach shows that the water structure of the tertiary industry has changed from 62.8 : 10.3 : 26.9 in 2018 to 60.5 : 10.2 : 29.3 in 2030. The proportion of water used in the primary and secondary industries has decreased slightly, the proportion of water used in the tertiary industry has increased, and the proportion of water used in the tertiary industry has not changed significantly.
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Tahir, Saad, i Asher Ramish. "Xarasoft (Pvt) Ltd – vision 2027 to implement a digital supply chain for industry 4.0". Emerald Emerging Markets Case Studies 12, nr 1 (15.02.2022): 1–22. http://dx.doi.org/10.1108/eemcs-05-2021-0180.
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Learning outcomes This case study aims to be taught at an MBA level. Specifically, those students who are majoring in supply chain would benefit the most from this case study. This case study has elements of supply chain management, supply chain strategy, warehousing and logistics, and a digital supply chain for Industry 4.0. The learning outcome of this case study could be seen if the students are able to identify the challenges and opportunities of a digital supply chain for Industry 4.0 and how it could be implemented methodically. Teaching Objective 1: Students should be able to identify what challenges organizations face if they implement a digital supply chain for Industry 4.0. Teaching Objective 2: Students should be able to identify what opportunities can be tapped if Big Data Analytics are used in a supply chain teaching. Objective 3: Students should layout a methodical plan of how an analogue company can gradually achieve the objective of implementing a digital supply chain for Industry 4.0 in procurement function. Case overview/Synopsis Based in the Lahore region of Pakistan, Xarasoft is a footwear manufacturing company which has undertaken a decision to transcend to a digital supply chain for Industry 4.0 by 2027. Asif, who is the Head of the Department of Supply Chain, has to come up with a plan to present in the next meeting with the CEO. Xarasoft is a company that preferred to work in an analogue routine. The company set production targets and sold goods through marketing. With no forecast or exact demand, the company had decided to procure 140 million units of raw material and carrying a huge inventory, a percentage of which had to be thrown away as it started to degrade. While the company did have machinery on the production floor, they were operated manually and were a generation behind. Asif faced the question of what challenges he would face and exactly how would a digital supply chain for Industry 4.0 be implemented in the company. Complexity academic level Masters level supply chain courses Supplementary materials Teaching notes are available for educators only. Subject code CSS 9: Operations and Logistics.
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Widjanarko, Bambang, Awan Panjinata, Agus Sukoco i Joko Suyono. "Analyzing the Financial Performance of PT. Steel Pipe Industry of Indonesia Tbk". International Journal of Industrial Engineering, Technology & Operations Management 1, nr 2 (31.12.2023): 86–92. http://dx.doi.org/10.62157/ijietom.v1i2.32.
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The financial report is a vital tool for acquiring insights into a company's financial position and business performance. Through financial statement analysis, crucial indicators pertaining to the company's financial health are unveiled, rendering it a valuable resource for guiding financial decision-making processes and offering a comprehensive portrayal of the company's performance. This study evaluates the financial performance of PT Steel Pipe Industry of Indonesia Tbk. and forecasts the company's sales turnover over the next five years. This research adopts a quantitative descriptive approach, utilizing secondary data spanning from 2018 to 2022 from the PT Steel Pipe Industry of Indonesia Tbk. The data analysis process encompasses several stages, including (i) Ratio Analysis of Financial Reports from 2018 to 2022, (ii) Compilation of sales data, (iii) Projections of sales figures using the least squares method, and (iv) Forecasting profits for the period from 2023 to 2027. The findings of this study indicate that the PT Steel Pipe Industry of Indonesia Tbk. is facing challenges in its financial performance, as the ratio values consistently fall below industry-standard financial metrics. However, the company has demonstrated resilience in maintaining its profitability levels, evidenced by a 6% increase in profit percentage in 2021 compared to 2020. This can be attributed to the company's consistent profit generation efforts, resulting in year-on-year profit growth.
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Wang, Liangliang. "Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence". Computational Intelligence and Neuroscience 2022 (18.01.2022): 1–14. http://dx.doi.org/10.1155/2022/3376296.
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In order to improve the effect of tourism demand forecast, the commercial development of the tourism industry, and the actual experience of users, this paper uses adaptive neural network technology to conduct tourism demand forecast analysis. Moreover, this paper improves the adaptive neural network algorithm so that it can handle multiple data for tourism demand forecast. After improving the algorithm, this paper employs the actual process of tourism demand forecast to construct a tourism demand forecast model based on adaptive neural network technology. After that, this paper combines travel time and space data analysis to determine the system’s functional structure and network topology. Through experimental research, it can be seen that the tourism demand forecast model based on adaptive neural network technology proposed in this paper performs well in tourism demand forecast and meets the actual demand of modern tourism forecast.
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Zhang, Boyuan. "Financial Analysis and Forecast of Nike, Inc." Advances in Economics, Management and Political Sciences 43, nr 1 (10.11.2023): 51–59. http://dx.doi.org/10.54254/2754-1169/43/20232124.
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This article aims to analyze the overall performance of Nike Inc. over the past three years and make predictions about its future. Firstly, the paper will discuss the three most significant accounting policies in Nike's 2022 annual report. This will help determine the company's asset management and utilization, profitability, and long-term development trends. Furthermore, the article will evaluate the company's performance in the previous year by comparing various financial ratios with its competitors in the Nike industry. This assessment will enable an evaluation of the company's financial position, and operational achievements, and showcase Nike's position within the industry. Lastly, based on Nike's overall performance in 2022, the paper will provide forecasts for the next two years regarding Nike's market value and performance. As a leading brand in the world of sports goods, Nike has proven through practice to be one of the most successful companies to date. It has adopted a strategically significant product portfolio strategy, resulting in significant market advancements. The conclusion of this article will contribute to other sports goods brands learning from Nike's relevant marketing strategies, thereby driving the overall development of the industry.
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Zheng, Runnan. "Financial Analysis and Strategic Forecast of Volkswagen". Advances in Economics, Management and Political Sciences 42, nr 1 (10.11.2023): 185–91. http://dx.doi.org/10.54254/2754-1169/42/20232107.
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As one of the important components of world economic development, the automobile industry has broad prospects. The automobile industry is the future direction of development, with the improvement of living standards, the demand for buying cars is also increasing. Therefore, the market demand for automobiles remains high, which also drives the development of the later service market. Moreover, even though the automobile industry is a highly competitive industry, it is also an industry with rapid development. As one of the leaders in the automotive industry, Volkswagen has attracted the attention of many investors. This paper mainly analyzes the 2022 fiscal year of Volkswagen, including whether the accounting policy conforms to the operational background, compares the profitability, solvency, and operational ability of Porsche, Honda, BMW, and Mercedes-Benz, and pays attention to the future development prospects of Volkswagen, and draws the conclusion that Volkswagen is a company worth investing in for the investors.
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HOSSEN, Sayed Mohibul, Mohd Tahir ISMAIL i Mosab I. TABASH. "THE IMPACT OF SEASONALITY IN TEMPERATURE FORECAST ON TOURIST ARRIVALS IN BANGLADESH: AN EMPIRICAL EVIDENCE". GeoJournal of Tourism and Geosites 34, nr 1 (31.03.2021): 20–27. http://dx.doi.org/10.30892/gtg.34103-614.
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In the present study, we aim to investigate how seasonality influences the climate changes on the outdoor thermal comfort for traveling to visit Bangladesh. Wherein, the effect of temperature on tourist arrival is assessed using SANCOVA and SARIMA model at seven attractive sightseeing diverse places in Bangladesh. The highest temperature has appeared in Khulna and Rajshahi with 35.53 °C and 35.85 °C and the lowest temperature was appeared in Rajshahi and Rangamati with 10.40 °C and 11.72 °C, respectively. This result also revealed that the temperature for Dhaka, Chittagong, Cox’s Bazar, Khulna, and Sylhet has extreme values of decreasing, in Dhaka the temperature will be 25.140 °C on January 2023, in Chittagong 260 °C on January 2027, Cox’s Bazar 26.490 °C on January 2030, in Khulna 25.610 °C on January 2023, and in Sylhet 26.560 °C on January 2020. Our findings also indicate that the tourism industry of Bangladesh is more vulnerable to seasonal variation and this seasonality has a 74% effect on tourist’s arrival as well as a 98% effect on overall temperature in Bangladesh.
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Cai, Hongbiao. "Promoting Regional Economic Transformation Forecast Based on Intelligent Computing Technology". Computational Intelligence and Neuroscience 2022 (4.03.2022): 1–12. http://dx.doi.org/10.1155/2022/1835376.
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The core of the value of artificial intelligence is to integrate with the real economy and become more dependent on local industries. In the entire artificial intelligence + industry development process, many new changes have appeared. This article mainly studies how to promote regional economy based on smart technology. In the context of rapid economic development, the significance of artificial intelligence and the necessity of regional economic transformation are put forward. The model has designed the national science and technology mechanism framework from the four directions of technology guidance, technology service, technology innovation, and technology balance; following the cultivating ideas from products to innovative industries to innovative intelligent environments, due to the low income elasticity of product demand, technological innovation is mainly based on the dissemination of emerging technologies outside the industry and the lack of coordination between companies under the fiercely competitive market structure; the focus is on promoting the continuous improvement of labor productivity. The experimental results prove that the emerging technology industries of enterprises can promote economic transformation in the era of artificial intelligence, provide a reference for better optimization of industrial innovation and development, and provide methodological support for the government to establish sound emerging technology business models and optimized management methods.
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Zhang, Youyun, i Jing Yang. "Forecast Analysis of the Overall Structure Characteristics and Development Potential of the Sports Industry Based on Wireless Communication Networks". Wireless Communications and Mobile Computing 2022 (14.03.2022): 1–10. http://dx.doi.org/10.1155/2022/5487681.
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With the continuous development of China’s social economy, China’s sports industry has also continued to develop nowadays and has been increasingly noticed. Although the sports industry has achieved great improvement in recent years, compared with the sports industry in developed countries, the development of China’s sports industry is still in a relatively weak state. The consumption of China’s sports industry does not account for a high proportion of GDP. However, with the improvement of people’s income level and quality of life, as well as the improvement of the quality of the national group, people consume increasingly sports products. The sports market continues to expand and becomes more diversified, providing good conditions for the development of China’s sports industry. Forecasting the improvement potential of China’s sports industry has also become a hot research topic in recent years. Based on the characteristics of the structure of China’s sports industry and the status quo of the industry’s development, this paper was aimed at researching the prediction of the improvement potential of the sports industry based on radio communication networks. The article combines the time series prediction algorithm and SVM regression algorithm under the radio spectrum prediction technology to forecast and analyze the development potential of China’s sports industry. The conclusion is that the prediction accuracy rate of China’s sports industry development potential based on radio communication spectrum prediction technology is 90%, which shows that the prediction of the development potential of sports industry based on radio communication network is relatively accurate and effective.
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Barač Miftarević, Sandra. "Medical Tourism in Croatia". Journal of applied health sciences 8, nr 1 (3.02.2022): 121–31. http://dx.doi.org/10.24141/1/8/1/11.
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Recently, medical tourism became one of the rapidly growing industries globally with 25% growth yearly with the value of over 200 billion euros. North America, Asia and Europe hold the most significant share of this value. According to The Medical Tourism Market – Global Industry Analysis Report, the forecast by 2027 will be a value of 272.70 billion US dollars. Croatia has strong potential for developing the medical tourism industry as an integral and essential part of the whole tourism industry in Croatia. But, lack of political will and public sector efforts decrease these opportunities. Fundamental healthcare reform is needed and improves outdated infrastructure with low service quality, including accommodation and accompanying catering and recreational facilities. Health care tourism is not competitive in this exceptionally demanding market. Singapore, India and Turkey can be excellent examples of doing thing rights, showing the path to success to the Croatian medical tourism industry. Where is Croatia right now, and what can be done to move forward is a big question. Several authors offer possible solutions that can lead to achieving objectives and goals stated in the National Strategy for Development of Healthcare and Action Plan until 2028. The future development of the medical tourism industry is an exciting area both in applicative and scientific fields, which can encourage further scientific efforts to explore more deeply the subject.
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Savinkov, Sergey V., Vladimir M. Kiselev i Boris D. Loshkov. "FORESIGHT FOR THE DEVELOPMENT OF THE RUSSIAN FEDERATION’S CHEMICAL COMPLEX: PROSPECTS UNTIL 2035". EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 12/2, nr 132 (2022): 40–46. http://dx.doi.org/10.36871/ek.up.p.r.2022.12.02.005.
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The authors, based on previously made forecasts, consider the dynamics of the development of the scientific and practical level of the production systems of the Russian chemical industry on the world market after half the implementation period of the chemical industry development program in 2022. It is shown that, taking into account the positive dynamics of most integral indicators, the development forecast, based on the actual values achieved by the end of 2021, the estimated values of indicators for 2022 and the current situation in the world and Russian economy, assumes a gradual increase in exports of domestic chemical products, the implementation of major investments in the modernization of the industry until 2030, followed by an increase in gross the added value of the chemical complex. The share of imports of chemical products will grow slightly, while the existing trend towards the innovative nature of production and the creation of qualified human resources will continue, which will lead to a twofold effectiveness of research and development work with a corresponding increase in the share of organizations investing in technological innovations.
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Ņikitenko, Agris, Signe Bāliņa, Andrejs Dubrovskis, Ilze Andersone i Ilze Birzniece. "Precision Livestock Farming IT Support Model for the Poultry Industry". Complex Systems Informatics and Modeling Quarterly, nr 32 (28.10.2022): 44–54. http://dx.doi.org/10.7250/csimq.2022-32.03.
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The presented work proposes a practical approach to bird weight data processing and augmentation to enable production outcome forecast model training, which contributes to higher productivity. We suggest using the parametrized model, where parameter values are found through genetic optimization and thus are closely corresponding to broiler body weight factual measurements. The proposed approach is implemented as a stand-alone software system, exposing the models through containerized web services enabling different use scenarios.
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Borisova, V., i D. Kuznetsova. "CURRENT STATE AND DIRECTIONS OF DEVELOPMENT OF THE GAS INDUSTRY OF THE RUSSIAN FEDERATION". National Association of Scientists 3, nr 66 (14.05.2021): 41–44. http://dx.doi.org/10.31618/nas.2413-5291.2021.3.66.413.
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The article describes the role of the gas industry in the fuel and energy complex of Russia. The dynamics and structure of gas production are considered. A forecast is given for gas production volumes for 2021 and 2022. Particular attention is paid to the key challenges of the industry, as well as the measures required to address them.
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Gajdzik, Bożena. "The importance of prediction methods in industry 4.0 on the example of steel industry". Multidisciplinary Aspects of Production Engineering 2, nr 1 (1.09.2019): 283–95. http://dx.doi.org/10.2478/mape-2019-0028.
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Abstract This paper presents the importance of the prediction of steel production in industry 4.0 along with forecasts for steel production in the world until 2022. In the last two decades, the virtual world has been increasingly entering production. Today’s manufacturing systems are becoming faster and more flexible – easily adaptable to new products. Steel is the basic structural material (base material) for many industrial sectors. Industries such as automotive, mechanical engineering, construction and transport use steel in their production processes. Prediction methods in cyber-physical production systems are gaining in importance. The task of prediction is to reduce risk in the decision-making process. In autonomous manufacturing systems in industry 4.0 the role of prediction is more active than passive. Forecasts have the following functions: warning, reaction, prevention, normative, etc. The growing number of customized solutions in industry 4.0 translates into new challenges in the production process. Manufacturers must respond to individual customer needs more quickly, be able to personalize products while reducing energy and resource costs (saving energy and resources can increase the product competitiveness). The modern market becomes increasingly unpredictable. Production prediction under such conditions should be carried out continuously, which is possible because there is more empirical data and access to data. Information from the ongoing monitoring of the company’s production is directly transferred to the prospective evaluation. In view of the contemporary reciprocal use of automation, data processing, data exchange and manufacturing techniques, there is greater access to external data, e.g. on production in different target markets and with global, international, national, regional coverage. Companies can forecast in real time, and the forecasts obtained give the possibility to quickly change their production. Industry 4.0 (from the business objective point of view) aims to provide companies with concrete economic benefits – primarily by reducing manufacturing costs, standardizing and stabilizing quality, increasing productivity. Industry 4.0 aims to create a given autonomous smart factory system in which machines, factory components and services communicate and cooperate with each other, producing a personalized product. The aim of this paper is to present new challenges in the production processes in relation to steel production, as well as to prepare and present forecasts of (quantitative) steel production of territorial, global and temporary range until 2022, taking into account the applied production technologies (BOF and EAF). For forecasting purposes, classic trend models and adaptive trend models were used. This methodology was used to build separate forecasts for: total steel production, BOF steel and EAF steel. Empirical data is world steel production in 2000-2017 (annual production volume in Mt).
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Lu, Yichun, Junyin Luo, Yiwen Cui, Zhengbin He i Fengchun Xia. "Improved CEEMDAN, GA, and SVR Model for Oil Price Forecasting". Journal of Environmental and Public Health 2022 (27.06.2022): 1–11. http://dx.doi.org/10.1155/2022/3741370.
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Accurate prediction of crude oil prices (COPs) is a challenge for academia and industry. Therefore, the present research developed a new CEEMDAN-GA-SVR hybrid model to predict COPs, incorporating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a genetic algorithm (GA), and support vector regression machine (SVR). First, our team utilized CEEMDAN to realize the decomposition of a raw series of COPs into a group of comparatively simpler subseries. Second, SVR was utilized to predict values for every decomposed subseries separately. Owing to the intricate parametric settings of SVR, GA was employed to achieve the parametric optimisation of SVR during forecast. Then, our team assembled the forecasted values of the entire subseries as the forecasted values of the CEEMDAN-GA-SVR model. After a series of experiments and comparison of the results, we discovered that the CEEMDAN-GA-SVR model remarkably outperformed single and ensemble benchmark models, as displayed by a case study finished based on a time series of weekly Brent COPs.
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Keliji, Peyman Barzegar, Hassan Ali Aghajani, Mohammad Mehdi Movahedi i Seyed Ahmad Shayannia. "The Analysis of the Role of Bullwhip Effects on the Four-Level Supply Chain in Industry Using Statistical Methods". Discrete Dynamics in Nature and Society 2022 (6.06.2022): 1–16. http://dx.doi.org/10.1155/2022/2720244.
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Nowadays, regarding the technology development and communication means, supply chain management has gained special significance among different industries. The impact of bullwhip is one of the factors that could lessn the supply chain efficiency and increase the cost and delivery time of products and services. In this study, we explored the demand forecasting in supply chain, a four-level chain of retailers, wholesalers, manufacturers, and suppliers. Each level of the chain forecasted demand by moving average method, exponential smoothing, multilayer perceptron artificial neural network, and regression. Also, we provide a hybrid model based on statistics and mathematics to reduce the effect of bullwhip. For this purpose, at first, the supply chain simulation was performed. The results were then evaluated applying analysis of variance and the best combined model to reduce the amount of bullwhip effect was introduced. The model of this research could be useful for other studies. Finally, forecast for retail demand using the regression model; wholesale demand using the exponential smoothing model; manufacture demand using the neural network; and supplier demand using the moving average method have been done.
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Rena, Ravinder, i Albert V. Kamuinjo. "An Empirical Analysis of the Relationship Between Capital, Market Risks, and Liquidity Shocks in the Banking Industry". Studia Universitatis Babes-Bolyai Oeconomica 67, nr 2 (1.08.2022): 67–83. http://dx.doi.org/10.2478/subboec-2022-0010.
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Abstract This study explores the relation between capital, market risks and banks’ liquidity conditions. In estimating the SVAR regression model, Granger causality, impulse-response functions and forecast error variance decomposition were employed and used for estimation of the results. The data sample comprised of commercial banks over the 2009 to 2018 period. The empirical results showed that liquidity shocks are caused by a combination of structural shocks. The Granger causality, impulse-response functions and forecast error variance decomposition documented that sensitivity to market risk is the key factor affecting liquidity conditions in the banking sector in the long run. In addition, the empirical results showed that capital adequacy has minimal impact on liquidity conditions in the short run. The reforming rate to sensitivity to market risk policies, capital adequacy policies and liquidity policy measures can be valuable policy tools to minimize liquidity shortages and avoid insolvent banks.
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S. Milovanov, Svyatoslav. "Clinical Trials Trends of 2023 Year and Visionary to the Future". International Journal of Clinical Investigation and Case Reports 02, nr 01 (2023): 13–19. http://dx.doi.org/10.55828/ijcicr-21-04.
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Introduction: The importance of studying historical changes in the development of human activity is substantiated by the need to systematize such changes and the possibility of predicting them. Historical changes are extended in time and do not have clear boundaries, requiring greater involvement in their study and the prerequisites for their appearance. Clinical research is more than just the practical application of medical changes and discoveries. They make changes in medical practice but are subject to change. Changes in the clinical research industry are tendentious and develop gradually, requiring study and forecasting. According to the generally accepted temporal gradation of the forecast, there is an operational forecast of up to one month, a short-term forecast of up to one year, a medium-term forecast of up to five years, a long-term forecast of up to 20 years and a long-term forecast over long-term, and a short-term forecast is common in the clinical research industry. We analyzed publications in open sources from 1930 to 2023 by keywords in the Russian-language literature trends in the clinical trial industry and the English-language literature trends in the clinical trial industry. Discussion and Conclusion: Trends in the development of clinical trials until the end of 2023 can be divided into two groups, those related to changes in the conduct of clinical trials and changes in the products of clinical trials in nosologies. If in the first group, the trends remain similar to 2022, the ongoing digitalization of operations, the shift of centralized research towards decentralization, and the shift in protocol design towards patient-centricity, then in the second group, the number of expected drugs has decreased, and there is a shift of drugs towards biologics and gene therapy drugs.
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Rubtsova, Natalia. "Microfinance in the Russian Federation: Changes in Industry Indicators in the Context of Global Challenges". Baikal Research Journal 15, nr 1 (30.03.2024): 13–24. http://dx.doi.org/10.17150/2411-6262.2024.15(1).13-24.
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The purpose of the study was to predict the state of microfinance organizations in the Russian Federation, verify the trends that determine their development in the context of global geopolitical challenges. The research was carried out using logical and empirical methods. The article analyzes changes in the main indicators of the functioning of microfinance organizations (MFOs) in the Russian Federation over a ten-year period (2014–2023). Based on an analysis of changes in key indicators characterizing domestic microfinance organizations (the number of microfinance organizations, the volume of microloans issued and their structure in the context of online and offline formats, the main segments of microfinance), the author comes to the conclusion that microfinance activities in the Russian Federation are highly resistant to negative impacts environmental factors. The scientific novelty of the article lies in the verification of the main trends in the development of domestic microfinance, which include tightening regulation of microfinance organizations by the Central Bank of Russia, further consolidation, industry concentration, development of non-core activities, BNPL services, dominance of online microcredit, deterioration in the quality of debt servicing, reducing the investment attractiveness of the industry. In conclusion, the author identified the forecast values of the main performance indicators of MFOs for the period 2024–2027, and possible restrictions on the future development of this.
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Şeker, Ferhat. "Combining the power of artificial intelligence and mathematical modelling: A hybrid technique for enhanced forecast of tourism receipts". European Journal of Tourism Research 36 (1.11.2023): 3614. http://dx.doi.org/10.54055/ejtr.v36i.3246.
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Despite being one of the most visited countries in the world, Türkiye's share of tourism revenue does not rank among the top ten. Therefore, it would be worth researching tourist expenditures and analysing this data could provide valuable insights. This research develops a novel approach to estimating and modelling tourism receipts by analysing expenditure types. Artificial intelligence-based methods, such as machine learning, have been increasingly used in the tourism literature to improve various aspects of the industry. However, little research has been conducted using a hybrid method to model and estimate tourist expenditure. This paper is the first to combine conventional mathematical analysis, specifically first-order two-variable polynomial equations, with artificial intelligence-based machine learning algorithms in a tourism setting. The research results indicate that expenditure types such as accommodation and food & beverage significantly impact Türkiye's tourism revenue and Türkiye's total tourism revenue will not exceed 45 billion dollars by 2027. This study provides a valuable and practical contribution to improving the accuracy and efficiency of methods for managing tourism economics, particularly in European countries where the economy heavily relies on income generated by tourism. Additionally, it fills a gap in studies focused on tourists' expenditure types by combining artificial intelligence and traditional analysis, making it a unique piece of research.
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Mierzwiak, Michał, Krzysztof Kroszczyński i Andrzej Araszkiewicz. "WRF Parameterizations of Short-Term Solar Radiation Forecasts for Cold Fronts in Central and Eastern Europe". Energies 16, nr 13 (3.07.2023): 5136. http://dx.doi.org/10.3390/en16135136.
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The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations.
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Peng, Hua, Luxiao Dong, Yi Sun i Yanfang Jiang. "Analysis of Marketing Prediction Model Based on Genetic Neural Network: Taking Clothing Marketing as an Example". Journal of Mathematics 2022 (30.03.2022): 1–14. http://dx.doi.org/10.1155/2022/8743568.
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With the economic and social development and the improvement of people’s living standards, consumers have put forward higher requirements for clothing from quantity to quality. The clothing industry has ushered in vigorous vitality and broad development space. China’s clothing industry has achieved great results after long-term development. With the gradual abolition of world textile and apparel trade quotas, China’s apparel and textile industry is facing greater opportunities and challenges. In today’s increasingly developing market economy, many production companies are making marketing forecasts. A good forecast result can be used to guide the company’s decision-making. The results of the model help decision-makers to reasonably arrange production and formulate marketing strategies. With the development of genetic neural network technology, this technology has been more and more widely used in signal processing, pattern recognition and other application fields. This article discusses a marketing forecasting model based on genetic neural network, predicting model parameters based on historical data of actual sales, and then carrying out experimental analysis. First of all, a series of analysis and preprocessing must be performed on the collected data. In the process of estimating and calculating the parameters of the prediction model, an error criterion is selected to determine a set of relatively optimal prediction parameters, and finally the model results A verification analysis was carried out. The experimental results show that the genetic neural network method can be used to establish a marketing forecasting model, and the established forecasting model has certain practical application value.
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Zhou, Na, Juan Tan, Xiangyang Ren i Xinxin Jiang. "Measurement of Coordination Degree between Economy and Logistics in Hebei Province, China, Based on Fractional Grey Model (1, 1)". Discrete Dynamics in Nature and Society 2022 (14.03.2022): 1–12. http://dx.doi.org/10.1155/2022/9539940.
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Modern logistics is the supporting industry of the national economy. The synergy between logistics and economy has been highlighted in northern China’s Hebei Province. This study measures the coordination degree between economy and logistics in Hebei, drawing on the grey system theory. Specifically, the entropy weight-grey correlation method was introduced to evaluate the interplay between economic and logistics factors in Hebei between 2011 and 2020. The evaluation suggests that private car ownership has the most significant correlation with the economy and that the total retail sales of social consumer goods are the leading impactor of logistics. Next, the fractional grey model (FGM) (1, 1) was employed to forecast the economic and logistics indices of Hebei in the next five years. The forecast results show that FGM (1, 1) achieved a higher prediction precision than the conventional GM (1, 1) and discrete grey model (DGM) (1, 1). Based on the original data and forecasted results, the coupling coordination degree (CCD) model was adopted to compute the CCD between the economy and logistics in Hebei during 2011–2025. It was calculated that the coupling coordination exhibited a continuous upward trend. From 2011 to 2025, the CCD between economy and logistics in Hebei evolves from moderate incoordination, mild incoordination, weak incoordination, and weak coordination, all the way to moderate coordination. In the light of the analysis results, several suggestions were presented to promote the coordinated development between economy and logistics in Hebei.
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Kiseleva, Е. А. "Forecast of development of vegetable growing industry in the North-West of Russia". Voprosy regionalnoj ekonomiki 35, nr 3 (30.10.2018): 24–34. http://dx.doi.org/10.21499/2078-4023-2018--3-24-34.
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The article discusses the development of the vegetable industry in the North-West of Russia. The analysis reveals the main trends in the production of vegetables, the assessment of their provision to the population of the region. The system of indicators for innovative forecasting of development of branch on the medium-term and long-term prospect allowing to reach further indicators of food safety is offered. The main directions of investment for the development of the industry are described. The forecast of vegetable production in the North-West Federal district is based on the introduction of innovations until 2022.
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Gong, Yuqian. "Traffic Flow Prediction and Application of Smart City Based on Industry 4.0 and Big Data Analysis". Mathematical Problems in Engineering 2022 (1.08.2022): 1–11. http://dx.doi.org/10.1155/2022/5397861.
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For smart city traffic flow prediction in the period of big data and industry 4.0, the prediction accuracy is low, the prediction is difficult, and the prediction effect is different in different geographical locations. This paper proposes a smart city traffic communication forecast based on Industry 4.0 and big data analysis application. Firstly, this paper theoretically explains the application scenario of urban traffic fault text big data and analyzes the characteristics of related problems, especially the fault problems. Secondly, the AC traffic prediction algorithm is studied, and the application analysis of PVHH, IDT, and Ford–Fulkerson algorithms is applied, respectively. Finally, the above three algorithms are used to predict and analyze traffic flow.
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Li, Fang, i Tao Li. "Tourism Consumer Demand Forecasting under the Background of Big Data". Mathematical Problems in Engineering 2022 (31.07.2022): 1–6. http://dx.doi.org/10.1155/2022/4335718.
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In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world’s leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country’s total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists’ willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans.
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Kos’yanov, Vadim Aleksandrovich, Aleksandr Nikolaevich Lun’kin i Dmitriy Aleksandrovich Lun’kin. "Forecast of environmental response to mining in the KMA basin". NEWS of the Ural State Mining University 4 (15.12.2022): 64–74. http://dx.doi.org/10.21440/2307-2091-2022-4-64-74.
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The relevance of the research is due to the importance of the mining industry in the country’s economy. In addition to the fact that the Kursk magnetic anomaly is a powerful iron ore basin, it is also one of the largest in the world in terms of iron ore reserves. It plays an important economic and strategic role for the country in terms of the resource base for the development of the primary (mining industry; development of mine deposits) and secondary (manufacturing industry; industrial and civil construction) sectors of the economy. At the same time, one needs to take into account the level of environmental pollution associated with the activities of mining enterprises and the risks of a negative impact on the state and health of the population directly residing in the region. The purpose of the research is to assess the expected reaction of the natural environment to the direct or indirect impact of the planned activity, to solve the problems of rational nature management in accordance with the expected state of the natural environment. Research method. The predictive design assessment of the environmental impact was determined as the total of the forecast baseline assessment and the impact assessment of the planned activity. Results. The authors made a number of calculations, including the impact of the mining enterprise on the air environment around the deposit; the main pollutants in comparison with MPC in the basin of the Kursk magnetic anomaly (KMA); the impact of the enterprise’s activities on the water environment in the KMA basin; the volume of waste from vehicles and quarry equipment in the KMA basin. Conclusions. The obtained data testify to significant changes in the state of the natural environment in the region of activity of subsoil use enterprises using the example of the KMA basin.
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Zhou, Kai, Zihao Li i Zhaofeng Liu. "Trajectory Of Development in China's New Energy Vehicle Industry Through Data Analysis and Expectation". Highlights in Science, Engineering and Technology 96 (5.05.2024): 132–38. http://dx.doi.org/10.54097/06ad5c42.
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Amidst a global shift towards sustainable transportation, this study conducts an in-depth analysis of data about China's new energy vehicle (NEV) sector from 2013 to 2022. This research primarily evaluates the standards and key factors influencing the NEV industry's evolution. Spearman correlation and decision tree models indicate that the average price of new energy vehicles and the government subsidies for them have the most significant impact on the development of the new energy industry in China. Expanding on these insights, a robust LASSO linear regression model was developed to further explore these dynamics. Additionally, an ARIMA time series model was employed, leveraging historical data to forecast the factors likely to influence the NEV industry in the coming decade. Integrating these forecasts into the initial evaluation model, the study anticipates a positive growth trajectory for China's NEV development, especially between 2023 and 2025. This research not only sheds light on the current state of the NEV industry in China but also provides valuable predictions for its future direction, contributing to the broader understanding of sustainable vehicle evolution in the global context.
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Kiselev, Vladimir M., Vladimir V. Velikorossov, Sergey V. Savinkov, Alexander K. Zakharov, Sergey L. Ozerov i Anton D. Bezdelov. "The Russian chemical complex: Analysis of the trade balance in 2022". E3S Web of Conferences 535 (2024): 06002. http://dx.doi.org/10.1051/e3sconf/202453506002.
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The article provides an analysis of the trade balance of industrial products of the Russian Federation, including the results of 2022. It is concluded that the reason for the lag is the lack of sufficient capacity in Russia for processing base polymers. Almost a quarter of Russian raw materials are exported, in the future, plastic products and plastics in primary forms are purchased abroad at a more expensive price. In the field of other economically important positions for the country, although there is a dependence on foreign countries, nevertheless, the level of redistribution is much higher. Therefore, it is concluded that there is a positive trend in the development of the chemical complex, confirming the forecast made by the authors at the previous stages of the study of the dynamics of the development of the chemical industry of the Russian Federation about the successful implementation of the Program for the Development of the chemical industry until 2030.
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Zhou, Zhenghan. "A Review of the Global Economic Shock in 2022 and a Prediction of the Global Economic Development in 2023". Advances in Economics, Management and Political Sciences 21, nr 1 (13.09.2023): 142–52. http://dx.doi.org/10.54254/2754-1169/21/20230246.
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In 2022, affected by global macroeconomic fluctuations, the economies of some countries around the world have experienced obvious fluctuations, thereby promoting inflation in various countries and limiting the rapid development of some industries around the world. This paper reviews the global economic shock in 2022 and analyzes the impact of China's economy on global economic development from the perspective of the new energy sector. As an important part of new energy, the photovoltaic industry can be analyzed to obtain a development forecast of the global economy in 2023. By analyzing the development of the photovoltaic industry, it is concluded that China's economy and the global economy will show a recovery trend in 2023.
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Beisembekova, Sabina, Muratbay Sikhimbayev, Dinar Sikhimbayeva i Gulnara Srailova. "The Innovative Ways of Development in the Oil and Gas Industry of Kazakhstan". International Journal of Energy Economics and Policy 12, nr 1 (19.01.2022): 9–16. http://dx.doi.org/10.32479/ijeep.11505.
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The article considers theoretical and methodological approaches to research of sustainable development of the oil and gas industry of Kazakhstan in modern conditions. Oil and gas industry is viewed as one of the priority directions of government economic policy, which is of particular importance in providing sustainable development of the country. It is noted, that increasing of stability of Kazakhstani oil and gas industry development is presented itself one of the most important issue of state economics. Authors identify main problems affecting on innovative development of the oil and gas industry. It was emphasized, taking into account the current trends, stability of oil and gas complex is affected by the number of conjuncture market factors, including price factors, bank interest rates, market conjuncture for this product, or value of its supply and demand in domestic and foreign markets. Main principles and criteria of innovative development of the oil and gas industry were developed. It is proved, that innovative development of the oil and gas industry should be carried out on the basis of the principles. The forecast of innovative development of the oil and gas industry was built on the basis of designed formula. The forecast of revenue from innovations is on the perspective period in the extraction and processing of high-sulfur, light and heavy oil for 2022-2026 that presented as table and forecast diagram. The innovative development of the oil and gas industry should be carried out on the basis of the researched principles. As a result of conducted research priorities of innovative development of the oil and gas industry were identified.
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Chen, Yuanyuan, Mohammad Affendy Arip i Nor Afiza Abu Bakar. "Cold Chain Logistics Demand Forecasting for Fresh Agricultural Foods in Fujian Province, China". International Journal of Religion 5, nr 5 (5.04.2024): 78–84. http://dx.doi.org/10.61707/e1m9vh53.
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China's current cold chain infrastructure for fresh agricultural products faces numerous challenges, particularly within the context of Fujian Province. The cold chain logistics sector in the region is characterized by limited development and requires immediate improvements in its foundational supporting infrastructure. The establishment of a comprehensive cold chain logistics system tailored for fresh agricultural goods remains incomplete, resulting in inefficiencies within the supply network. An in-depth examination of the necessity for refrigerated transportation networks for fresh agricultural products through scientific inquiry reveals the potential for strategic investments in the industry. To address this gap, a study employing the GM (1,1) model is conducted to forecast the future demand for cold chain logistics in fresh agricultural items specifically within Fujian Province, China, over the next five years. The findings of the study indicate that by 2027, the demand for cold chain logistics services for fresh produce in Fujian Province is projected to reach 4765.6 million tons. These insights furnish valuable information for optimizing investment planning in cold chain logistics infrastructure and formulating pertinent legislative measures to stimulate industry growth. In summary, the integration of these findings into the context of Fujian Province underscores the significance of enhancing cold chain logistics capabilities to address existing challenges and capitalize on future opportunities within the region's fresh agricultural sector.
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Savchenko, Maryna, i Natalia Boichenko. "MODELING AND FORECASTING OF THE EFFICIENCY OF ELECTRICITY INDUSTRY ENTERPRISES". Actual Problems of Economics 1, nr 256-257 (październik 2022): 43–53. http://dx.doi.org/10.32752/1993-6788-2022-1-256-257-43-53.
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In the article, based on the content analysis of theoretical approaches to determining the efficiency category of the enterprise, the directions of the development of the theory of efficiency are visualized according to the time vector of scientific evolution. Taking into account the scientific works and theoretical views of various researchers, it is justified that for the formation of the author's definition of the efficiency of the enterprise, it is advisable to integrate the existing approaches to the essence of efficiency. On the basis of a comparative analysis of literary sources, the essential characteristics of "management of the efficiency of the enterprise" were determined. Conceptual principles were formed and a management system for the efficiency of the electric power industry enterprise was developed. One of the blocks of the company's efficiency management system is a model toolkit that will allow forecasting the level of efficiency. It is recommended to implement the model toolkit of integrated assessment of the enterprise's performance through the algorithm of the classic version of the taxonomic indicator construction in the following stages: standardization of indicator values, formation of a benchmark and calculation of a general indicator. The algorithm of the model toolkit was implemented according to the data of the enterprise of the electric power industry – JSC Khmelnytskoblenergo in the MS Excel software environment. A comparative analysis of the initial trend line of the enterprise's activity efficiency and the line of its forecast was carried out using the model toolkit. The results of the calculation of the forecast values of the performance indicator of the electric power industry enterprise and the upper and lower limits of the confidence intervals for the forecast period are presented.
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Zhu, Lin. "Methodology and Application of Fiscal and Tax Forecasting Analysis Based on Multi-Source Big Data Fusion". Mathematical Problems in Engineering 2022 (24.06.2022): 1–12. http://dx.doi.org/10.1155/2022/8028754.
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With the advent of the big data era, the use of computers has spread to all walks of life, and the finance and taxation industry is also in the middle of it. The current taxation system is huge and complex, and different tax types are inevitably linked to different economic indicators at a deep level, so tax forecasting requires personalised forecasting analysis for different tax types. This paper selects several tax types that account for a large proportion of tax revenue for prediction analysis, respectively, and conducts fusion research on multi-source big data, including business tax, corporate income tax, and personal income tax. Based on the multi-source big data fusion method, the prediction research on fiscal taxation tax types is conducted, and experiments are conducted with the taxation data of Beijing from 1995 to 2020 to predict the three tax types from 2017 to 2020. The results show that the deviation of the forecast data from the real tax data is small, controlling the forecast deviation to within 14%, indicating the effectiveness of the proposed method.
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Shi, Kaihe, Danning Du i Xiaoxuan Zhang. "Performance Prediction of the Ferrous Metal Smelting and Rolling Processing Industry in Supply-Side Structural Reform in China". Journal of Mathematics 2021 (30.11.2021): 1–9. http://dx.doi.org/10.1155/2021/2383473.
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Supply-side structural reforms and environmental protection policies have a great impact on the ferrous metal smelting and rolling processing industry. This paper uses a grey model that introduces a fractional-order cumulative generating operator to study the development of ferrous metal smelting and rolling processing enterprises under the influence of supply-side structural reform in order to derive the future development trend of the industry. The forecast results show that from 2018 to 2022, the number of enterprises and substitute enterprises, inventory, finished products, and assets and liabilities decreases; the scale of income of metal smelting and rolling processing industry increases. The results can serve as a reference for policy makers and industry investors.
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Hunt, Kieran M. R., Gwyneth R. Matthews, Florian Pappenberger i Christel Prudhomme. "Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States". Hydrology and Earth System Sciences 26, nr 21 (1.11.2022): 5449–72. http://dx.doi.org/10.5194/hess-26-5449-2022.
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Abstract. Accurate river streamflow forecasts are a vital tool in the fields of water security, flood preparation and agriculture, as well as in industry more generally. Traditional physics-based models used to produce streamflow forecasts have become increasingly sophisticated, with forecasts improving accordingly. However, the development of such models is often bound by two soft limits: empiricism – many physical relationships are represented empirical formulae; and data sparsity – long time series of observational data are often required for the calibration of these models. Artificial neural networks have previously been shown to be highly effective at simulating non-linear systems where knowledge of the underlying physical relationships is incomplete. However, they also suffer from issues related to data sparsity. Recently, hybrid forecasting systems, which combine the traditional physics-based approach with statistical forecasting techniques, have been investigated for use in hydrological applications. In this study, we test the efficacy of a type of neural network, the long short-term memory (LSTM), at predicting streamflow at 10 river gauge stations across various climatic regions of the western United States. The LSTM is trained on the catchment-mean meteorological and hydrological variables from the ERA5 and Global Flood Awareness System (GloFAS)–ERA5 reanalyses as well as historical streamflow observations. The performance of these hybrid forecasts is evaluated and compared with the performance of both raw and bias-corrected output from the Copernicus Emergency Management Service (CEMS) physics-based GloFAS. Two periods are considered, a testing phase (June 2019 to June 2020), during which the models were fed with ERA5 data to investigate how well they simulated streamflow at the 10 stations, and an operational phase (September 2020 to October 2021), during which the models were fed forecast variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to investigate how well they could predict streamflow at lead times of up to 10 d. Implications and potential improvements to this work are discussed. In summary, this is the first time an LSTM has been used in a hybrid system to create a medium-range streamflow forecast, and in beating established physics-based models, shows promise for the future of neural networks in hydrological forecasting.
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Wang, Chia-Nan, Thi-Duong Nguyen i Minh-Duyet Le. "Assessing Performance Efficiency of Information and Communication Technology Industry-Forecasting and Evaluating: The Case in Vietnam". Applied Sciences 9, nr 19 (24.09.2019): 3996. http://dx.doi.org/10.3390/app9193996.
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The Information and Communication Industry (ICT) plays a very important role in the growth of any nation. Therefore, the ITC sector attracts the great attention of the researcher. Despite many breakthroughs and favorable conditions to develop, the Information and Communication Industry (ICT) of Vietnam stands in a very modest position on the world’s ICT map. Therefore, understanding and having an outlook on the performance of companies in this field contribute to the development of the ICT industry in Vietnam. For this reason, the current study is conducted with the main purpose of assessing the performance of 24 Vietnamese ITC companies over past and future periods by applying a hybrid model, including Data Envelopment Analysis (DEA) and Grey model (GM). The author used GM to forecast the future value of inputs and output over period 2018–2022, and then forecasted data is used together with the data of previous years to evaluate the performance of these companies by two models of DEA—Malmquist productivity index (MPI) model and super-SBM-model. This study proposed an approach that can be used by policymakers and decision-makers to develop policies and strategies to sustain the development of the Vietnam ICT industry.
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Xie, Hanqi. "Research on the Models for Forecast of Tax Revenue of Wenzhou City". Highlights in Science, Engineering and Technology 88 (29.03.2024): 1043–49. http://dx.doi.org/10.54097/dg7x7t56.
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Tax revenue is a vital economic indicator that reflects the level of economic development, and tax revenue forecasting plays an important role in financial budgeting. Previous studies have demonstrated various influencing factors of tax revenue and proposed many feasible ways to forecast tax revenue. However, it is acknowledged that tax revenue of different region might bear different relation to influencing factors. In this research, tax revenue forecast of Wenzhou City is studied based on multiple linear regression model and MLP neural network model. The data for this research are collected from the website of Bureau of Statistics of Wenzhou, compiled in the 2022 statistical yearbook of Wenzhou. With multiple linear regression model, it is discovered that Value-added of the primary industry, Value-added of the tertiary industry, Investment in fixed assets, Total retail sales of consumer goods are significant for prediction. Comparing the forecasting outcomes of the two methods, the MLP neural network model appears to have better goodness of fit.
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Rasul, Azad, Amanj Ahmad Hamdamin Dewana i Saadaldeen Muhammad Nuri Saed. "Multi-model tourist forecasting: case study of Kurdistan Region of Iraq". Tourism and Travelling 2, nr 1 (2.08.2019): 24–34. http://dx.doi.org/10.21511/tt.2(1).2019.04.
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The tourism industry has been one of the leading service industries in the global economy in recent years and the number of international tourism in 2018 reached 1.4 billion. The goal of the research is to evaluate the performance of various methods for forecasting tourism data and predict the number of tourists during 2019 and 2022. Performance of 15 prediction models (i.e. Local linear structural, Naïve, Holt, Random walk, ARIMA) was compared. Based on error measurements matrix (i.e. RMSE, MAE, MAPE, MASE), the most accurate method was selected to forecast the total number of tourists from 2019 to 2022 to Kurdistan Region (KR), then forecasts were performed for each governorate in KR. The results show that among 15 examined models of tourist forecasting in KR, Local linear structural and ARIMA (7,3,0) model performed best. The number of tourists to KR and each governorate in KR is predicted to increase by most experimented models, especially those which demonstrated higher accuracy. Generally, the number of tourist to KR predicted by ARIMA (7,3,0) is a lot bigger than Local linear structure. Linear structural predicted the number increase to 3,137,618 and 3,462,348 in 2020 and 2022, respectively, while ARIMA (7,3,0) predicted the number of tourists to KR to increase rapidly to 3,748,416 and 8,681,398 in 2020 and 2022.
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Świątkowski, Andrzej. "SHORT-TERM, ECONOMIC AND SOCIAL, FORECAST OF THE FUTURE OF WORK". Roczniki Administracji i Prawa 1, nr XIX (30.06.2019): 313–31. http://dx.doi.org/10.5604/01.3001.0013.3604.
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The fourth industrial revolution (Industry 4.0) dynamically shift the line between the work performed by humans and those performed by machines, technologies, algorithms and artificial intelligence. The author examines The Future of Jobs Report 2018 published by Centre for the New Economy Society of the World Economic Forum. He tries to argue that the current technological transformation in the next five years, 2018-2022, managed wisely may improved the quality and productivity of work performed by human employees. The problem is that many of employees afraid that robots, computers, modern technologies an AI will eliminate jobs performed by human beings. The Author argues that technology eliminates jobs, not work
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Gulyaeva, Tamara, Manuel Hernández-Pajares i Iwona Stanislawska. "Ionospheric Weather at Two Starlink Launches during Two-Phase Geomagnetic Storms". Sensors 23, nr 15 (7.08.2023): 7005. http://dx.doi.org/10.3390/s23157005.
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The launch of a series of Starlink internet satellites on 3 February 2022 (S-36), and 7 July 2022 (S-49), coincided with the development of two-phase geomagnetic storms. The first launch S-36 took place in the middle of the moderate two-phase space weather storm, which induced significant technological consequences. After liftoff on 3 February at 18:13 UT, all Starlink satellites reached an initial altitude of 350 km in perigee and had to reach an altitude of ~550 km after the maneuver. However, 38 of 49 launched spacecrafts did not reach the planned altitude, left orbit due to increased drag and reentered the atmosphere on 8 February. A geomagnetic storm on 3–4 February 2022 has increased the density of the neutral atmosphere up to 50%, increasing drag of the satellites and dooming most of them. The second launch of S-49 at 13:11 UT on 7 July 2022 was successful at the peak of the two-phase geomagnetic storm. The global ionospheric maps of the total electron content (GIM-TEC) have been used to produce the ionospheric weather GIM-W index maps and Global Electron Content (GEC). We observed a GEC increment from 10 to 24% for the storm peak after the Starlink launch at both storms, accompanying the neutral density increase identified earlier. GIM-TEC maps are available with a lag (delay) of 1–2 days (real-time GIMs have a lag less than 15 min), so the GIMs forecast is required by the time of the launch. Comparisons of different GIMs forecast techniques are provided including the Center for Orbit Determination in Europe (CODE), Beijing (BADG and CASG) and IZMIRAN (JPRG) 1- and 2-day forecasts, and the Universitat Politecnica de Catalunya (UPC-ionSAT) forecast for 6, 12, 18, 24 and 48 h in advance. We present the results of the analysis of evolution of the ionospheric parameters during both events. The poor correspondence between observed and predicted GIM-TEC and GEC confirms an urgent need for the industry–science awareness of now-casting/forecasting/accessibility of GIM-TECs during the space weather events.
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Wang, Shiyuan. "Analysis and Prediction of Online Beer Sales Based on SARIMA Model". BCP Business & Management 36 (13.01.2023): 359–66. http://dx.doi.org/10.54691/bcpbm.v36i.3454.
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With the boom of e-commerce in China, online shopping has become the mainstream way of shopping in Chinese. To explore the impact of online shopping on beer sales, this paper uses a time series SARIMA model to analyze online beer sales data from January 2020 to September 2022 obtained from Internet platforms and predicts online beer sales from October 2022 to September 2023. This paper first introduces the current research on beer sales in China, and then briefly analyzes the current situation of the beer industry. Thirdly, based on the real data of beer online sales on the Internet platform, SARIMA model is used to forecast the sales volume of next year. The result shows that beer online sales are expected to show an upward trend, with the industry being the most competitive in June 2023, and a small sales peak both in November 2022 and January 2023 due to the e-commerce carnival. Therefore, beer online sales are significantly affected by seasonality and platform promotions.
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Çelikdin, Alperen Ekrem. "Optimizing seasonal grain intakes with non-linear programming: An application in the feed industry". An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 12, nr 2 (12.06.2022): 79–89. http://dx.doi.org/10.11121/ijocta.2022.1158.
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In the feed sector, 95% of the input costs arise from the supply of raw materials used in feed production. The selling price is determined by competition in free market conditions. Due to the use of similar technologies and the very small share of production costs in total costs, it is unlikely that a competitive advantage will be gained through innovations in production. Between 30% and 50% of grain products are used in feed ration analysis. Cereals can only be harvested at a certain time of the year. Due to this limited time frame, feed production enterprises have to balance their financial burdens with their operational needs while making their annual stocks. The study was carried out to cover all the relevant businesses of the company, which has feed factories in four regions of Turkey. Based on the season data of the year 2020-2021, the grain purchase planning for the year 2021-2022 was tried to be optimized with non-linear programming. While creating the mathematical model, grain prices, interest rates, production needs according to production planning, sales according to sales forecasts, factory stocking capacities, licensed warehouse rental, transportation, handling and transshipment costs were taken into account. With this unique paper, in the cattle feed production sector, storage, transportation and handling costs will be minimized. Cost advantage will be provided with optimum purchase planning in the season. According to the grain pricing forecast and market data for the 2021-2022 season, model can provide a cost advantage of 0.7%. Model will also provide insight to the managers for additional storage space investments.
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Isaeva, Ol'ga. "The modern state and forecast model of the development of the agrarian structure of the domestic agro-industrial complex". Agrarian Bulletin of the 221, nr 06 (30.06.2022): 78–87. http://dx.doi.org/10.32417/1997-4868-2022-221-06-78-87.
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Abstract. Purpose. The purpose of the research is to analyze the existing and build a forecast model for the development of various forms of agricultural management and their combination, which determine the level of production potential of the country's multicultural agriculture. Methods. When conducting scientific research, such methods as: monographic, economic comparisons, graphic techniques, monitoring studies, institutional and system analysis, econometric research methods were used. Statistical data of Rosstat, the Ministry of Agriculture of Russia and their regional divisions were used as an empirical basis for carrying out forecast calculations. Scientific novelty. The author proposes a trend model for forecasting the development of the agrarian structure of Russia for 2030. The most important factors of the modern development of the industry that determine the likely change in the agrarian structure of Russia in the medium term: instability of geopolitical and trade and economic cooperation, as well as the strengthening of the scale of the policy of economic sanctions are highlighted. Results. The article discusses the main current trends in the development of a multi-layered agricultural structure, as well as the forecast parameters of its development. The analysis showed that the modern agrarian structure of Russia is characterized by trends of consolidation and the predominance of agricultural organizations and agroholding structures, which account for almost 60% of agricultural production. The forecast calculations carried out allow us to talk about the strengthening of the positions of large agribusiness and the development of the farming sector with a simultaneous reduction in the role of households for the next 10 years. The developed forecast picture of the development of the agrarian structure can serve as a vector determining the main directions of improvement and adjustment of mechanisms and instruments of state regulation and support of the agricultural sector, ensuring the sustainable development of the industry on an innovative basis.