Impacts of institutional change on industrial economy: a China's automobile industry perspective

China' s automobile industry is a perfect example of institutional change it went through a series of development stages including the shaping up of industrial system, rapid expansion, and competitiveness improvement, as the industry’s policy system and the implementation effects improve. Our main studies and conclusions include: (1) Review and analysis of institutional change and the development of China’s automobile industry. The continuous improvement of the policy system of China’s automobile industry, a s re presented by the above mentioned policies, as well as their implementation effects are the main drivers behind China’s ever improving independent R&D capability and global competitiveness. (2) Quantitative analysis of the characteristics of the impacts of institutional change on China’s automobile industry. As the research shows, the growth process of China's automobile industry and its key influencing factors can be described with the Cobb Douglas production function model containing institutional variable s. The intensity of the impact of institutional change variables on the automobile industry changes significantly over time. (3) Case study on the impacts of institutional change on China's automobile industry. As shown in the study, the automobile industry policy plays a significant role in driving the development of Chinese automobile companies who should therefore take such opportunities to enhance technological innovation, resource integration and other abilities.A indústria automobilística da China é um exemplo perfeito de mudança institucional passou por uma série de estágios de desenvolvimento, incluindo a formação do sistema industrial, a expansão rápida e a melhoria da competitividade, à medida que o sistema de políticas industriais e os efeitos da sua implementação melhoraram. Esta tese analisou as mudanças institucionais e o desenvolvimento da indústria automobilística chinesa. Em nossa opinião as mudanças institucionais que se consubstanciaram em políticas industriais para o setor automobilístico contribuíram para a crescente capacidade independente de pesquisa e desenvolvimento da indústria e da sua competitividade global . Procedemos também a uma análise quantitativa sobre as características do impacto das mudanças institucionais na indústria automobilística chinesa. Como mostra a nossa pesquisa, o processo de crescimento da indústria automobilística chinesa e seus principais fatores de influência podem ser descritos com o modelo de função de produção Cobb Douglas, introduzindo variáveis institucionais. A intensidade do impacto das variáveis de mudança institucional na indústria automobilística muda significativamen te a o longo do tempo. Procedemos também a um estudo de caso sobre os impactos das mudanças institucionais na indústria automobilística chinesa. Como mostra o estudo, as políticas industriais referentes à indústria automobilística desempenham um papel importante no desenvolvimento das empresas automobilísticas chinesas. As empresas devem estar atentas a estas políticas e aproveitar as oportunidades para aprimorar a inovação tecnológica, a integração de recursos e outras competências

Evolving integrated multi-model framework for on line multiple time series prediction

Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much less researched problem is multiple time series prediction where the objective is to simultaneously forecast the values of multiple variables which interact with each other in time varying amounts continuously over time. In this paper we describe the use of a novel Integrated Multi-Model Framework (IMMF) that combined models developed at three di erent levels of data granularity, namely the Global, Local and Transductive models to perform multiple time series prediction. The IMMF is implemented by training a neural network to assign relative weights to predictions from the models at the three di erent levels of data granularity. Our experimental results indicate that IMMF signi cantly outperforms well established methods of time series prediction when applied to the multiple time series prediction problem

Aeronautical Engineering. A continuing bibliography with indexes, supplement 156

This bibliography lists 288 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1982

A GPT-Based Approach for Scientometric Analysis: Exploring the Landscape of Artificial Intelligence Research

This study presents a comprehensive approach that addresses the challenges of scientometric analysis in the rapidly evolving field of Artificial Intelligence (AI). By combining search terms related to AI with the advanced language processing capabilities of generative pre-trained transformers (GPT), we developed a highly accurate method for identifying and analyzing AI-related articles in the Web of Science (WoS) database. Our multi-step approach included filtering articles based on WoS citation topics, category, keyword screening, and GPT classification. We evaluated the effectiveness of our method through precision and recall calculations, finding that our combined approach captured around 94% of AI-related articles in the entire WoS corpus with a precision of 90%. Following this, we analyzed the publication volume trends, revealing a continuous growth pattern from 2013 to 2022 and an increasing degree of interdisciplinarity. We conducted citation analysis on the top countries and institutions and identified common research themes using keyword analysis and GPT. This study demonstrates the potential of our approach to facilitate accurate scientometric analysis, by providing insights into the growth, interdisciplinary nature, and key players in the field.Comment: 29 pages, 10 figures, 5 table

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

Nowadays, in terms of trading on the world scale, to foresee a natural gas consumption represents an essential activity. In the first part, the paper examines the current state of the Serbian natural gas sector and methodology applied for prediction and capacity planning. In addition, the study intends to give a comprehensive assessment of predictive algorithms for natural gas needs involved in the last decade with projections and suggestions for future applications. The primary task is to evaluate used predictive models with an emphasis on the accuracy of the predictions obtained. Additionally, the paper will analyse used parameters, consumption scale, prediction scope, forecast algorithms, and other related information. The main objective of this study is to review the new-fangled information related analyses data from peer-reviewed journals, international conferences, and books

The Evolution of Efficiency in the Chinese Stock Market

This dissertation examines the weak-form efficiency of the Chinese stock market and provides evidence on how the market efficiency evolved throughout the last three decades. The Shanghai Composite Index (SSEC) and the Shenzhen Component Index (SZSE) are the primary indicators of the Chinese stock market in this study. Both traditional economics and the complex systems’ methods are employed to evaluate market efficiency, with an additional focus on the effect of two parameter inputs (embedded dimension and noise filter) on entropy methods to improve their ability to detect phase transitions in stock market data. The traditional efficiency tests indicate that the Chinese stock market during the full sample period of 1990-2021 is inefficient, but some of the sub-sample periods indicate the weak-form efficiency, except for the ADF test. Meanwhile, the complex systems’ methods suggest that the level of randomness in returns increases over time. Additionally, I find that the bull periods of the Chinese market are less efficient than the bust periods, which may indicate that investors tend to commit more errors during the bull period. Generally, the study concludes that the complex systems’ methods provide a more comprehensive evaluation of the changes in the market efficiency than traditional methods. The empirical results suggest that the Chinese stock market is not completely efficient based on the traditional efficiency tests but the level of efficiency has improved over time based on the evidence of the complex systems’ analysis

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

一帯一路に基づく観光予測ハイブリッドモデルの研究

富山大学・富理工博甲第180号・鄭舒心・2020/9/28富山大学202

Aeronautical engineering: A continuing bibliography with indexes (supplement 276)

This bibliography lists 705 reports, articles, and other documents introduced into the NASA scientific and technical information system in Feb. 1992. Subject coverage includes: design, construction, and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

IntroductionAccurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed.MethodsThe TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating self-attention learning and enhancing the connection of global feature information. To fuse feature information at different scales, the TBC-YOLOv7 algorithm employs a bidirectional feature pyramid network. In addition, coordinate attention is embedded into the critical positions of the network to suppress useless background details while paying more attention to the prominent features of tea buds. The SIOU loss function is applied as the bounding box loss function to improve the convergence speed of the network.ResultThe results of the experiments indicate that the TBC-YOLOv7 is effective in all grades of samples in the test set. Specifically, the model achieves a precision of 88.2% and 86.9%, with corresponding recall of 81% and 75.9%. The mean average precision of the model reaches 87.5%, 3.4% higher than the original YOLOv7, with average precision values of up to 90% for one bud with one leaf. Furthermore, the F1 score reaches 0.83. The model’s performance outperforms the YOLOv7 model in terms of the number of parameters. Finally, the results of the model detection exhibit a high degree of correlation with the actual manual annotation results ( R2 =0.89), with the root mean square error of 1.54.DiscussionThe TBC-YOLOv7 model proposed in this paper exhibits superior performance in vision recognition, indicating that the improved YOLOv7 model fused with transformer-style module can achieve higher grading accuracy on densely growing tea buds, thereby enables the grade detection of tea buds in practical scenarios, providing solution and technical support for automated collection of tea buds and the judging of grades