Littérature scientifique sur le sujet « And Industry Forecast 2023-2027 »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « And Industry Forecast 2023-2027 ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "And Industry Forecast 2023-2027"
1
SAVCHENKO-BELSKII, K. A., E. I. MANTAEVA et 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, no 1 (24 mai 2023) : 180–202. http://dx.doi.org/10.38197/2072-2060-2023-239-1-180-202.
Texte intégralRésumé :
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.
2
HOSSEN, Sayed Mohibul, Mohd Tahir ISMAIL et Mosab I. TABASH. « THE IMPACT OF SEASONALITY IN TEMPERATURE FORECAST ON TOURIST ARRIVALS IN BANGLADESH : AN EMPIRICAL EVIDENCE ». GeoJournal of Tourism and Geosites 34, no 1 (31 mars 2021) : 20–27. http://dx.doi.org/10.30892/gtg.34103-614.
Texte intégralRésumé :
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.
3
XU, QIAN, HUA CHENG et YABIN YU. « Analysis and forecast of textile industry technology innovation capability in China ». Industria Textila 72, no 02 (22 avril 2021) : 191–97. http://dx.doi.org/10.35530/it.072.02.1759.
Texte intégralRésumé :
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.
4
Gajdzik, Bożena. « Post-Pandemic Steel Production Scenarios for Poland Based on Forecasts of Annual Steel Production Volume ». Management Systems in Production Engineering 31, no 2 (3 mai 2023) : 172–90. http://dx.doi.org/10.2478/mspe-2023-0019.
Texte intégralRésumé :
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.
5
Duanaputri, Rohmanita, Sulistyowati Sulistyowati et Putra Aulia Insani. « Analisis peramalan kebutuhan energi listrik sektor industri di Jawa Timur dengan metode regresi linear ». JURNAL ELTEK 20, no 2 (28 octobre 2022) : 50. http://dx.doi.org/10.33795/eltek.v20i2.352.
Texte intégralRésumé :
Abstrak
Pada kehidupan sekarang maupun akan datang, energi listrik menjadi kebutuhan pokok masyarakat. Kebutuhan energi listrik selalu mengalami peningkatan, diikuti meningkatnya pertumbuhan penduduk. Permasalahan akan muncul apabila kebutuhan energi listrik tidak diperkirakan. Maka perlu dilakukan peramalan kebutuhan energi listrik untuk memprediksikan ketersediaan energi listrik di masa mendatang. Pada penelitian ini, dilakukan peramalan kebutuhan energi listrik menggunakan metode regresi linier pada sektor industri di Jawa Timur untuk tahun 2023-2027. Berdasarkan hasil perhitungan prediksi dan MAPE (2009-2021), didapatkan metode regresi linier masih baik dan layak digunakan menurut standar MAPE. Kemudian dibandingkan hasil prediksi dan MAPE (2010-2020) antara metode regresi linear dengan metode time series pada penelitian sebelumnya, didapatkan metode time series menghasilkan prediksi dan MAPE lebih baik dibanding metode regresi linier pada pelanggan listrik, sedangkan pada daya tersambung, energi listrik terjual, dan pendapatan penjualan energi listrik didapatkan metode regresi linier menghasilkan prediksi dan MAPE lebih baik dibanding metode time series. Tetapi, penulis menghitung peramalan kebutuhan energi listrik pada sektor industri di Jawa Timur (2023-2027) hanya menggunakan metode regresi linier. Sehingga dihasilkan akan terjadi kenaikan setiap tahun dengan rata-rata untuk pelanggan listrik sebesar 5.264 pelanggan, daya tersambung sebesar 328,49 MVA, energi listrik terjual sebesar 580,64 GWh, dan pendapatan penjualan energi listrik sebesar 1.065.266,21 Juta Rupiah. Menurut hasil tersebut, maka pasokan energi listrik harus tercukupi dengan merencanakan pengembangan atau penambahan kapasitas pembangkit listrik.
Abstract
In present and future life, electrical energy becomes basic needs of community. Electrical energy needs always increased, followed by increased population growth. Problem will appear if electrical energy needs is not expected. Therefore, it is necessary to forecast electrical energy needs to predict the availability of electrical energy in future. In this study, calculation of forecasting electrical energy needs using linear regression methods in industrial sector in East Java for 2023-2027. Based on calculation results of prediction and MAPE (2009-2021), it is obtained linear regression method is still good and worthy of use according to MAPE standard. Then comparison results of prediction and MAPE (2010-2020) between linear regression method with time series method in previous study, it was obtained that time series method produced predictive and MAPE is better than linear regression methods on electricity customers, while in power connected, electric energy sold, and earnings of electrical energy sales obtained linear regression method produces predictive and MAPE better than time series method. However, authors calculation of electrical energy needs in industrial sector in East Java (2023-2027) only using linear regression methods. So there will be increase every year with average for electricity customers of 5,264 customers, power connected of 328.49 MVA, electric energy sold of 580.64 GWh, and earnings of electrical energy sales of 1,065,266.21 million rupiah. According to results, supply of electrical energy should be fulfilled by planning development or additional power plant capacity.
6
S. Milovanov, Svyatoslav. « Clinical Trials Trends of 2023 Year and Visionary to the Future ». International Journal of Clinical Investigation and Case Reports 02, no 01 (2023) : 13–19. http://dx.doi.org/10.55828/ijcicr-21-04.
Texte intégralRésumé :
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.
7
Rubtsova, Natalia. « Microfinance in the Russian Federation : Changes in Industry Indicators in the Context of Global Challenges ». Baikal Research Journal 15, no 1 (30 mars 2024) : 13–24. http://dx.doi.org/10.17150/2411-6262.2024.15(1).13-24.
Texte intégralRésumé :
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.
8
Zhang, Bowen. « Analysis of Bilibili's Competitive Strategy in the New Trends ». BCP Business & ; Management 34 (14 décembre 2022) : 849–55. http://dx.doi.org/10.54691/bcpbm.v34i.3104.
Texte intégralRésumé :
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.
9
Tahir, Saad, et Asher Ramish. « Xarasoft (Pvt) Ltd – vision 2027 to implement a digital supply chain for industry 4.0 ». Emerald Emerging Markets Case Studies 12, no 1 (15 février 2022) : 1–22. http://dx.doi.org/10.1108/eemcs-05-2021-0180.
Texte intégralRésumé :
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.
10
López, Gustavo Rosal. « SS62-02 REFLECTIONS ON THE USE OF EXOSKELETONS IN THE HEALTHCARE SECTOR ». Occupational Medicine 74, Supplement_1 (1 juillet 2024) : 0. http://dx.doi.org/10.1093/occmed/kqae023.0361.
Texte intégralRésumé :
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.
Chapitres de livres sur le sujet "And Industry Forecast 2023-2027"
1
Akanmu, Abiola, Adedeji Afolabi et Akinwale Okunola. « Predicting Mental Workload of Using Exoskeletons for Construction Work : A Deep Learning Approach ». Dans CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 691–700. Florence : Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.69.
Texte intégralRésumé :
Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using active back-support exoskeletons for construction work. Laboratory experiments were conducted wherein Electroencephalography (EEG) data were collected from participants wearing active back-support exoskeletons to perform flooring task. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory network was trained to forecast future time steps of the processed EEG data. The performance of the network was evaluated using root mean square error (RMSE) and r-squared (R2). A RMSE of 0.1527 and R2 of 0.9665 indicating good fit and strong correlation, respectively, were observed between the predicted and actual EEG data. Results of the comparison between the actual and predicted mental workload also show strong correction with an R2 of 0.8692. The findings motivate research directions into real-time monitoring of mental workload of workers during exoskeleton-use. The study has significant implications for stakeholders, enabling them to gain a deeper understanding of the impact of mental workload while using exoskeletons thereby providing opportunities for mitigation
2
Akanmu, Abiola, Adedeji Afolabi et Akinwale Okunola. « Predicting Mental Workload of Using Exoskeletons for Construction Work : A Deep Learning Approach ». Dans CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 691–700. Florence : Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.69.
Texte intégralRésumé :
Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using active back-support exoskeletons for construction work. Laboratory experiments were conducted wherein Electroencephalography (EEG) data were collected from participants wearing active back-support exoskeletons to perform flooring task. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory network was trained to forecast future time steps of the processed EEG data. The performance of the network was evaluated using root mean square error (RMSE) and r-squared (R2). A RMSE of 0.1527 and R2 of 0.9665 indicating good fit and strong correlation, respectively, were observed between the predicted and actual EEG data. Results of the comparison between the actual and predicted mental workload also show strong correction with an R2 of 0.8692. The findings motivate research directions into real-time monitoring of mental workload of workers during exoskeleton-use. The study has significant implications for stakeholders, enabling them to gain a deeper understanding of the impact of mental workload while using exoskeletons thereby providing opportunities for mitigation
3
Heineken-van Dooren, Marierose M. M., et Roy Lindelauf. « Leveraging Data Science for Defence in the Digital Age : Defence AI in the Netherlands ». Dans Contributions to Security and Defence Studies, 217–35. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58649-1_10.
Texte intégralRésumé :
AbstractData science and AI play vital roles in realizing the Dutch Ministry of Defence’s (MoD) vision to work in a “data-driven” manner by 2035. Regarding these technologies, the Dutch MoD prioritizes responsible AI and data science, aiming for technological advancement, information-driven operations, while at the same time becoming a reliable player and advocate in the field of responsible AI. The Dutch MoD holds a human-centric view on AI as a capability multiplier. The Data Science and AI Strategy 2023–2027 emphasizes the importance of high-quality IT, data governance, and ethical decision-making using state of the art AI and data science methodologies. To gain new insights and support decision-making with the use of AI and data science, the Dutch MoD invests in enhancing knowledge and collaboration with public and private partners, while also experimenting internally with AI and data science on five key themes: autonomous systems, military decision-making and intelligence, predictive maintenance, safety, and business operations. The Dutch MoD commits to invest at least 2% of expenses of the defence budget in research and technology development and focuses on integrating AI into unmanned systems, decision support, logistics, and security. Collaboration and human oversight are emphasized through partnerships with EU and NATO partners, knowledge institutions, and industry. Educating personnel at all levels within the MoD on the use of data (scientific tools) and AI’s implications, including their ethical aspects, is crucial, with the Data Science Centre of Excellence leading in the field of academic knowledge enhancement. Overall, the Dutch MoD is dedicated to advance research, development, collaboration, and ethical principles in AI and data science to position the Netherlands as a leader in the responsible use of AI in the military domain.
4
Kathirvel, A. « Applications of Serverless Computing ». Dans Advances in Systems Analysis, Software Engineering, and High Performance Computing, 221–33. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1682-5.ch014.
Texte intégralRésumé :
Applications that are serverless can be distributed (many services are connected for smooth operation), elastic (resources can be scaled up and down without limit), stateless (interactions and data aren't stored), event-driven (resources are allocated only when triggered by an event), and hostless (apps aren't hosted on a server). Serverless computing is becoming more and more popular as cloud adoption rises. In many respects, serverless computing unleashes the entire potential of cloud computing. we pay only for the resources consumed, and resources are allocated, increased, or decreased dynamically based on user requirements in real-time. It makes sure that when there are no user requests and the application is effectively dormant, resources are immediately scaled to zero. More scalability and significant cost reductions are the outcomes of this. According to research by Global industry Insights, the serverless industry is expected to reach $30 billion in market value by the end of the forecast period, growing at an above-average rate of 25% between 2021 and 2027.
5
Chen, Rurong, Cai Jing, Fu Yingjie et Wei Ziji. « Comprehensive analysis and forecast of Chinese NEV industry development from 2012 to 2025 ». Dans Foresight in Research : Case studies on future issues and methods, 105–28. Budapesti Gazdasági Egyetem, 2023. http://dx.doi.org/10.29180/9786156342560_6.
Texte intégralRésumé :
More and more countries are turning to renewable energy to reduce their dependence on traditional fossil fuels as climate change increases. Electric cars have also become a new trend in energy transformation that is strongly supported by many governments. To answer the research questions, we employ a literature review approach and the TOPSIS method based on entropy. This paper begins with a systematic review of the relevant literature to identify key characteristics of the Chinese market for new energy vehicles (NEVs), providing clear theoretical support. Then, the author chose data from 2012 through 2022 as the primary research object for analysis and chose a total of five first-level indicators and fifteen second-level indicators as the main observation indicators. Using the TOPSIS method, the authors evaluate the entire NEV market in China and make predictions for three years into the future. Based on the results, the NEV composite score ranking is continuing to increase, which indicates a very promising future. However, when a black swan event occurs, such as a car safety incident, it can seriously impede its development. One of the most significant contributions is that based on the evaluation scores from the indicators, the positive and negative future state of NEV is indicated. Since the data has not yet been updated to 2023, it is necessary to continue to verify the new data to make up for the lack of data.
6
Kraus, Kateryna, et Nataliia Kraus. « FORECAST OF CAPITAL INVESTMENTS IN THE INDUSTRY OF UKRAINE AND THE PROSPECTS OF POST-WAR RECOVERY OF THE NATIONAL ECONOMY ON THE BASIS OF DIGITAL DEVELOPMENT OF ENTREPRENEURSHIP ». Dans GLOBAL DIGITAL TRENDS AND THEIR IMPACT ON NATIONAL ECONOMIC PROGRESS. OKTAN PRINT, 2024. http://dx.doi.org/10.46489/gdtatione-05-24-29.
Texte intégralRésumé :
The 21st century was marked by a number of financial, political, socio-economic crises in a number of countries of the world. But despite these challenges and some global turbulence and uncertainty, the innovative development of various sectors of the economy in combination with the digital ‘virus’ is observed in most countries of the world. Ukraine is no exception. Despite the state of war, most sectors of the economy took a course for recovery from the middle of 2023. It is clear to both society and the government that financial and economic improvement in the country, development of digital and innovative entrepreneurship, reduction of unemployment by creating new jobs due to the expansion of production, starting new industries, returning relocated businesses from abroad, is possible under the conditions of available investment funds. injections into the national economy. We are convinced that despite the factor of martial law, it is still worth developing effective mechanisms and tools for attracting investment resources to Ukraine, at least in regions where the situation is more or less good in terms of safety for people’s lives and probable destruction of production and industrial infrastructure.
7
Chowdhury, Sonali. « LIVING IN THE WORLD OF INTELLIGENT VIRTUAL ASSISTANTS – SKY IS THE LIMIT ». Dans Futuristic Trends in Management Volume 3 Book 17, 50–54. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bhma17p1ch7.
Texte intégralRésumé :
The international market for Virtual assistants in growing at a gigantic pace, and it is forecasted that this market will be on the peak from 2023-2030. The growth of virtual assistant will lead to the revolutionizing the workforce, and this is the result of digitization and the technological advancement where every task can be completed quickly from the comfort zone of our houses. This chapter showcases some points as to how the HVA (Human Virtual Assistant) and the IVA (Intelligent Virtual Assistant) have clear benefits in saving time and improving efficiency and productivity across all the organizational departments. The rapid adoption of IVA has helped lots of businesses to be in sync with the technological innovation happening in the world which in turn is a primary driver of the global virtual assistant market. Virtual assistants are widely being used across industry verticals due to ML, deep neural networks and other artificial intelligence technologies.
8
Gnylianska, Lesia, et Lesia Sai. « THE ROLE AND FUNCTIONS OF THE SALES DEPARTMENT IN THE DEVELOPMENT OF SALES ACTIVITIES OF IT ENTERPRISES IN EXTREME CONDITIONS ». Dans GLOBAL DIGITAL TRENDS AND THEIR IMPACT ON NATIONAL ECONOMIC PROGRESS. OKTAN PRINT, 2024. http://dx.doi.org/10.46489/gdtatione-05-24-09.
Texte intégralRésumé :
. If we analyse the significance and trends of the IT sector over the past few decades, we can see that it has evolved from being an ordinary and sometimes secondary sector to one of the main drivers of the global economy and a catalyst for structural changes and transformations in many other industries. Every year, the range of industries and sectors affected by the implementation of IT results is expanding, including education, manufacturing, real estate, financial services, etc. Gartner, Inc, the world's leading information technology research and advisory firm, forecasts that total global IT spending will reach $4.6 trillion in 2023, up 5.5% year-on-year. Despite the fact that the information technology industry is quite young, for Ukraine compared to others, it is safe to say that Ukraine occupies a leading global position in this context. This is evidenced by the constant increase in budget revenues due to the inflow of foreign capital from IT activities, the growing number of specialists, the increasing number of educational programs in the relevant specializations, the expanding range of global brands and companies that are ready to work with these companies (a huge number of Fortune 500 companies are already familiar with the quality of Ukrainian specialists), and the growing number of Ukrainians holding key positions in enterprise-level IT companies that are known around the world.
Actes de conférences sur le sujet "And Industry Forecast 2023-2027"
1
Kamble, Torana, Madhuri Ghuge, Ronit Rana, Harsh Vardhan, Yash Shelar et Tushar Machale. « Ensemble Machine Learning Models to Forecast Sales ». Dans 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2023. http://dx.doi.org/10.1109/icimia60377.2023.10426565.
Texte intégral
2
Olonichev, Vasiliy, Boris Staroverov et Sergey Tarasov. « The Holographic and Perceptron Neuron Networks Joint Application for the Dynamic Systems Behavior Forecast ». Dans 2023 International Russian Smart Industry Conference (SmartIndustryCon). IEEE, 2023. http://dx.doi.org/10.1109/smartindustrycon57312.2023.10110729.
Texte intégral
3
Zhou, Rong, et JiangXue Di. « Energy Demand Forecast of Hubei Logistics Industry Based on RBF Neural Network ». Dans Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China. EAI, 2023. http://dx.doi.org/10.4108/eai.26-5-2023.2334201.
Texte intégral
4
DiMarco, Steven Francis, Scott M. Glenn, Benjamin Jaimes de la Cruz, Rosalinda Monreal Jiménez, Anthony Hayden Knap, Yonggang Liu, Bruce Magnell et al. « Applications of Adaptive Sampling Strategies of Autonomous Vehicles, Drifters, Floats, and HF-Radar, to Improve Loop Current System Dynamics Forecasts in the Deepwater Gulf of Mexico ». Dans Offshore Technology Conference. OTC, 2023. http://dx.doi.org/10.4043/32459-ms.
Texte intégralRésumé :
Abstract The Gulf of Mexico holds vital natural, commercial, and societal resources. A diverse array of stakeholders (which includes the offshore energy sector, climate scientists, living resources managers, recreational and commercial fishing industry, tourism, navigation, homeland security, the National Weather Service, oil spill, tropical weather forecasters) rely on accurate and timely prediction of the deepwater dynamics to perform safe operations and to understand the complex interactions of the earth climate and weather system. A strategy to improve predictive skill of numerical ocean circulation models of the deepwater Gulf of Mexico using adaptive sampling of in situ oceanographic observational platforms, which includes autonomous vehicles, buoyancy gliders, floats, drifters, and high-frequency radar is described. Profiling platforms, i.e., gliders and floats, will collect co-located estimates of temperature, salinity, and current velocity, to provide estimates of the total kinematic vertical water-column structure. The observations will be made available to numerical circulation modelers for injection into data assimilation routines and for model skill assessment, validation, and data denial experiments. The activities, to take place in 2023 to 2027, are focused on the Mini Adaptive Sampling Test Run, i.e., MASTR, (summer 2023) and the Grand Adaptive Sampling Experiment, GrASE (2024-2025).
5
Brodetsky, G. L., V. D. Gerami, D. A. Gusev et A. Yu Zimin. « PROBLEMS OF BALANCE OF EFFICIENCY METRICS OF IMPLEMENTING STRATEGIES FOR DIGITAL TRANSFORMATION OF TRANSPORT AND MANUFACTURING INDUSTRY ». Dans Digital transformation in industry : trends, management, strategies. Institute of Economics of the Ural Brach of Russian Academy of Sciences, 2023. http://dx.doi.org/10.17059/dti-2023-2.
Texte intégralRésumé :
The article analyzes the effectiveness of the implementation of the Transport Strategy of the Russian Federation until 2030 with a forecast for the period until 2035 and the Strategy for the digital transformation of manufacturing industries in order to achieve their “digital maturity” until 2024 and for the period until 2030. The inconsistency of a number of metrics for the development and digital transformation of these strategies has been revealed, taking into account the differences in the structure of their beneficiaries, as well as the uncertainty that can manifest itself in difficult-to-predict disproportions in the development of industries, taking into account the dynamics of changes in the preferences of the parties. To harmonize multidirectional metrics and model the preferences of stakeholders, it is proposed to use modern methods and models of multi-criteria choice, including under conditions of uncertainty. The prospects for increasing the efficiency of sales of manufacturing industries, together with their transport support, correlate with the possibilities of a multidimensional search for a balance of development metrics and preferences when making decisions.
6
Netzell, Pontus, Hussain Kazmi et Konstantinos Kyprianidis. « Applied Machine Learning for Short-Term Electric Load Forecasting in Cities - A Case Study of Eskilstuna, Sweden ». Dans 64th International Conference of Scandinavian Simulation Society, SIMS 2023 Västerås, Sweden, September 25-28, 2023. Linköping University Electronic Press, 2023. http://dx.doi.org/10.3384/ecp200005.
Texte intégralRésumé :
With the growing demand, electrification, and renewable proliferation, the necessity of being able to forecast future demand in combination with flexible energy usage is tangible. Distribution network operators often have a power capacity limit agreed with the regional grid, and economic penalties await if crossed. This paper investigates how cities could deal with these issues using data-driven approaches. Hierarchical electric load data is analyzed and modeled using Multiple Linear Regression. Key calendar variables holidays, industry vacation, ”Hour of day” and ”Day of week” are identified alongside the meteorological heating-, and cooling degree hours, global irradiance, and wind speed. This inexpensive algorithm outperforms the benchmark ”weekly Naïve” with a relative Root Mean Squared Error of 35% for the year-long rolling origin evaluation. Learnings from the data exploration and modeling are then used to evaluate the AI-based model Light Gradient Boosting Machine. Using similar explanatory variables for this expensive algorithm results in a relative error of 45%, although it outperforms the previous one during the summer. The models have varying strengths and weaknesses and could advantageously be combined into an ensemble model for improving accuracy. Incorporating detailed knowledge of local renewable electricity production in combination with hierarchical forecasting could further increase accuracy. With domain knowledge and statistical analysis, it is possible to create robust load forecasts with acceptable accuracy using easily available machine-learning libraries. Both models have good potential to be used as input to economic optimization and load shifting.
7
Mayer, Jan, et Roland Jochem. « Quality forecasts in manufacturing using autoregressive models ». Dans Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002848.
Texte intégralRésumé :
Companies in the manufacturing industry are facing a variety of challenges such as increasing product complexity and variety and the accompanying complexity of production processes. The developments for more sustainability and optimized use of resources are additional societal requirements. Consequently, the demands of efficient solutions in quality management are also increasing. Innovative processes are needed to meet industrial challenges. Therefore, enhanced availability of data in production offers an opportunity. Hence, the combination of associated process and manufacturing knowledge and data availability creates the possibility to improve product- and process-related quality as well as the use of resources. As a consequence, machine learning methods are used to utilize and evaluate the collected data volumes. Their application in quality control enables the operation of smart solutions like the detection of anomalies in both product and process quality. However, there is no standardized algorithm to implement in any desired production environment. Conclusively, the application of specific algorithms is highly dependent on the desired project output, human factors and the underlying infrastructure. As main manufacturing branch, mass production combines the potential benefits of machine learning applications and their occurring challenges for product and process and monitoring. Existing reporting tools like the statistical process control (SPC) enhance process owners to continuously monitor manufactured products and processes. Nonetheless, the execution of the SPC is naturally reactive, once the monitored products have been already produced. Thus, process owners require a proactive, user friendly and interactive forecast application regarding their product and process quality.Predictive quality control is one way of improving product- and process-related quality while taking advantage of greater data availability. It represents an implementation of quality control in conjunction with data-driven quality forecasting. This application enables companies to conduct data-driven forecasts of product- and process-related quality. The aim is to use machine predictions as a basis for decision-making for action measures to be derived by the user. On the basis of the large amounts of data and algorithmic evaluation, measures can be derived by process and utilization investigations. Among other things, future events with influence on the quality can be controlled in an improved way. In quality management, decision-making processes are based on extensive data collection and analysis. Predictive quality should be seen as a supplement to conventional methods, e.g. SPC.Convenient implementation methods are key to achieve effective quality monitoring in terms of product and process control. For this reason, automated machine learning can be used to ease the realization of forecasting methods. Specifically, autoregressive models are robust and optimized statistical methods which fit to both forecasts of product and process quality. An observed evaluation metric like the mean absolute error for the next ten forecast items has been decreased by more than 50% from 0.141 to 0.66 with an underlying data range from 0.38 to 1.998. Since this calculation was processed including a univariate feature vector, improvements can be achieved by adding connected features, i.e. sensor data, for a higher accuracy in the forecasting results.
8
Musora, T., Z. Chazuka, A. Jaison, J. Mapurisa et J. Kamusha. « Sales Forecasting of Perishable Products : A Case Study of a Perishable Orange Drink ». Dans 10th International Conference on Computer Networks & Communications (CCNET 2023). Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130408.
Texte intégralRésumé :
The primary goal of any organization involved in trading business is to maximize profits while keeping costs to a bare minimum. Sales forecasting is an inexpensive way to achieve the aforementioned goal. Sales forecasting frequently leads to improved customer service, lower product returns, lower deadstock, and efficient production planning. Because of short shelf life of food products and importance of product quality, which is of concern to human health, successful sales forecasting systems are critical for the food industry. The ARIMA model is used to forecast sales of a perishable orange drink in this paper. The methodology is applied successfully. ARIMA (0,1,1)(0,1,1)12 was concluded as the appropriate model. Model diagnostics were done; results showed that no model assumption was violated. Fitted values were regressed against observed values. A very strong linear relationship was evident with an R 2 value of over 90% which is very plausible.
9
Candido, Sílvio, Mohammad-Reza Pendar et José Carlos Páscoa. « Improving Efficiency of Automotive Coating and Curing Processes Through Deep Learning Algorithms and High-Fidelity CFD Modeling ». Dans ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-112373.
Texte intégralRésumé :
Abstract The automobile industry has relied on computational fluid dynamics (CFD) simulations to analyze and optimize the coating and curing processes, speed up product development, and lower the cost of product development. However, CFD modelings of these processes are computationally expensive due to the complexity of the models and the large number of simulations needed, especially when its used complex sprays such as the nithrothermal electrospray. As a result, more efficient methods must be developed to reduce computing time without compromising accuracy. In this article, we analyze how deep learning techniques can be used to predict coating and curing processes using electrospray CFD simulation. A dataset of 3D Eulerian-Lagrangian CFD simulations of coating and curing processes employing electro-spray for the automotive industry has been used to train different deep-learning models. We investigated how hyperparameters such as batch size and layer count affected deep learning model performance compared to conventional CFD simulations. For this, we evaluated the deep learning models’ efficiency and accuracy in terms of computing time. We also investigated how hyperparameters such as batch size and layer count affected deep learning model performance. Also, we’ve looked at the target’s final droplet deposition, and distribution that is required to accurately estimate the distribution. Furthermore, we studied the percentage of snapshots of the droplet distribution electrospray necessary to predict the target’s final deposition from the Lagrangian distribution. According to our findings, deep learning models can drastically reduce the amount of time needed to run CFD simulations. Depending on the model and hyperparameters applied, we can forecast the whole CFD simulation by utilizing somewhere between 10% and 15% of the initial spray development. Also, we discovered that the use of recurrent cells as an LSTM model outperformed the other models in terms of accuracy and computational efficiency, where the LSTM layers can extract better the features of the input snapshots. Overall, our research demonstrates the potential of deep learning techniques to significantly shorten the computing time of CFD simulations of coating and curing processes for the automotive sector. The results of this study have significant implications for coating and curing process design and optimization in the automobile industry as well as in other industries where CFD simulations are frequently employed.
10
Luniya, Tanmay, et Geetha Chimata. « Extending the Life of Classic Cars, the Additive Manufacturing Way ». Dans ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70355.
Texte intégralRésumé :
Abstract There has been a steadily increasing global market for Additively Manufactured (AM) products, with a growth forecast of USD 23.75 billion by 2027. Of the various industrial sectors applying AM, the automotive/motor vehicles market takes up approximately 18% share. Saying AM is being widely used in the automotive sector with rapidly growing application avenues is not an overstatement. One such section of the automotive industry is the classic cars. Classic cars are 20 years or more older cars no longer in regular production, preserved and restored for their historical value. Classic cars face a huge problem of spare parts. The non-availability of the spare part leads to the break-down of the car, leaving them as display pieces or eventual scrapping. It is not economically viable to manufacture the spare parts in small volume due to challenges such as high cost of tooling, and indefinite storage time. Additive manufacturing offers attractive solutions to problems precisely such as these as it requires no additional tooling and can produce functional parts in small batches on-demand, provided accurate three-dimensional model data is available. This 3D model data is converted to one of the AM compatible file formats such as STL, AMF, 3MF etc. and then is processed using a Slicer Software. The slicer software converts three-dimensional (3-D) model data to two-dimensional (2-D) layer information that will be printed by the AM machine. Obtaining drawings or 3-D model information for classic car parts is a daunting challenge in itself, often deemed impossible. However, with the advances in imaging and scanning combined with computer aided design technologies, it is shown to be possible to generate the 3-D model data from even partial or broken parts. Now, producing spare parts using AM is not just feasible but has been successfully applied. Few notable examples include restoration of Elvis Presley’s BMW 507, originally released in 1957, which took two years to complete, Jaguar’s XK120 SE restored in 2017, 2019 restorations of Volkswagens iconic 1962 minivan, Bentley’s 1929 Blowers and Bugatti’s 1926 Bugatti Baby. Not just car manufacturers, but hobbyist collectors also found success in producing spare parts for their classic cars. This paper discusses various types of additive manufacturing technologies used to manufacture classic car parts and the strategic impact after implementing them using the examples of famous restored classic cars. The discussion further includes commercialization of these technologies, challenges, material selection and availability. Additionally, the economic implications and, the future are explored.
Rapports d'organisations sur le sujet "And Industry Forecast 2023-2027"
1
O'Connor, Jack, Magdalena Mirwald, Christina Widjaja, Architesh Panda, Jessica Pinheiro et Soenke Kreft. Technical Report : Uninsurable future. United Nations University - Institute for Environment and Human Security (UNU-EHS), octobre 2023. http://dx.doi.org/10.53324/yodt6712.
Texte intégralRésumé :
Insurance is a tool for financial risk management used by individuals, organizations, governments and businesses to safeguard themselves against the risk of uncertain financial losses, such as those occurring as a result of damage during an unexpected disaster. However, providing insurance in areas prone to natural hazard events (for example wildfires, droughts, storms, floods) has long been a challenge, and as extreme weather events around the world become more frequent and severe, the increasing cost of the damage they inflict is pushing the industry to breaking point in certain areas. Since the 1970s, damages from weather-related disasters have increased seven-fold, with 2022 alone seeing $313 billion in global economic losses. Climate change is dramatically shifting the landscape of risks, with the number of severe and frequent disasters forecast to double globally by 2040, causing insurance prices to rise and threatening the viability of insurance as an option for many. As we see areas around the world being hit with increasingly expensive damages and being pushed towards a tipping point of “uninsurability”, this report delves into the various underlying drivers of the problem, and the actions we can take to avoid it. This technical background report for the 2023 edition of the Interconnected Disaster Risks report analyses the root causes, drivers, impacts and potential solutions for the space debris risk tipping point our world is facing through an analysis of academic literature, media articles and expert interviews.