Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks.

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market.

Analysis of the spatial and temporal evolution process and development trend of innovation capability of Chinese sports industry.

The paper uses spatial panel data on the number of patents in the field of sports in each province from 2017-2022 to explore the spatial and temporal evolution process of the innovation capacity of China's sports industry and analyze its development trend based on theories related to economic geography, using exploratory spatial data analysis (ESDA) and Spatial-temporal correlation analysis research methods. The study shows that: 1. there is a large spatial regional disparity in the innovation capacity of China's sports industry, showing an obvious pattern of strength in the east and weakness in the west. 2. there is an obvious phenomenon of spatial agglomeration in the innovation capacity of China's sports industry and the agglomeration phenomenon in space is gradually increasing. 3. there is a spatial spillover effect in the innovation capacity of China's sports industry, and the inward spillover continues to strengthen mainly into Zhejiang, Fujian and Jiangsu provinces, while the outward spillover mainly goes to Guangdong, Hubei, Hebei, Hunan, Sichuan. Sports industry innovation capacity development put forward three suggestions: to promote the development of sports industry in the northwest region; to play the advantages of regional characteristics of sports industry; to accelerate the integration of neighboring regions to build the process. in order to help the high-quality development of sports industry.

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm.

Learning about the regional business model is essential for the sustainable development of the regional economy. From the perspective of urban renewable energy, city A is the product of energy development. This paper analyzes the current situation and existing problems of the industrial model of city A through fuzzy k-means clustering algorithm. The results show that although the optimization of industrial structure in city A has achieved some results, the more intuitive problems mainly include low labor productivity of the primary industry, strong resource dependence, insufficient extension of industrial chain, and slow development of technology intensive industries. This paper uses fuzzy k-means clustering algorithm to select the leading industries from the perspective of the current situation of leading industries, urban development pattern, and regional policies in city A. The results show that, as a renewable resource-based city, the leading industries suitable for the current development of city A include manufacturing, power, alkali gas and water production and supply, transportation, warehousing and postal industry, leasing, and business services. The results of fuzzy k-means clustering algorithm are quite excellent, and the accuracy rate is 93.3%. This paper uses the grey dynamic linear programming model to predict the future development of the Urban A business model and combines the selection of key functions to obtain the best business model: deep and efficient technical equipment as a good goal, achieved through regional logistics, transportation, new services, etc., to enhance the output value of the tertiary industry in city A and optimize the internal structure of the secondary industry in city A.

Area efficient camouflaging technique for securing IC reverse engineering.

Reverse engineering is a burning issue in Integrated Circuit (IC) design and manufacturing. In the semiconductor industry, it results in a revenue loss of billions of dollars every year. In this work, an area efficient, high-performance IC camouflaging technique is proposed at the physical design level to combat the integrated circuit's reverse engineering. An attacker may not identify various logic gates in the layout due to similar image output. In addition, a dummy or true contact-based technique is implemented for optimum outcomes. A library of gates is proposed that contains the various camouflaged primitive gates developed by a combination of using the metal routing technique along with the dummy contact technique. This work shows the superiority of the proposed technique's performance matrix with those of existing works regarding resource burden, area, and delay. The proposed library is expected to make open source to help ASIC designers secure IC design and save colossal revenue loss.

Study on the effect of digital economy on high-quality economic development in China.

At present, the digital economy, which takes information technology and data as the key elements, is booming and has become an important force in promoting the economic growth of various countries. In order to explore the current dynamic trend of China's digital economy development and the impact of the digital economy on the high-quality economic development, this paper measures the digital economic development index of 30 cities in China from the three dimensions of digital infrastructure, digital industry, and digital integration, uses panel data of 30 cities in China from 2015 to 2019 to construct an econometric model for empirical analysis, and verifies the mediating effect of technological progress between the digital economy and high-quality economic development. The results show that (1) The development level of China's digital economy is increasing year by year, that the growth of digital infrastructure is obvious, and that the development of the digital industry is relatively slow. (2) Digital infrastructure, digital industry and digital integration all have significant positive effects on regional total factor productivity, and the influence coefficients are 0.2452, 0.0773 and 0.3458 respectively. (3) Regarding the transmission mechanism from the digital economy to the high-quality economic development, the study finds that the mediating effect of technological progress is 0.1527, of which the mediating effect of technological progress in the eastern, northeast, central and western regions is 1.70%, 9.25%, 28.89% and 21.22% respectively. (4) From the perspective of spatial distribution, the development level of the digital economy in the eastern region is much higher than that in other non-eastern regions, and the development of digital economy in the eastern region has a higher marginal contribution rate to the improvement of the total factor productivity. This study can provide a theoretical basis and practical support for the government to formulate policies for the development of the digital economy.

Epidemics and the future of coffee production.

In this perspective, we draw on recent scientific research on the coffee leaf rust (CLR) epidemic that severely impacted several countries across Latin America and the Caribbean over the last decade, to explore how the socioeconomic impacts from COVID-19 could lead to the reemergence of another rust epidemic. We describe how past CLR outbreaks have been linked to reduced crop care and investment in coffee farms, as evidenced in the years following the 2008 global financial crisis. We discuss relationships between CLR incidence, farmer-scale agricultural practices, and economic signals transferred through global and local effects. We contextualize how current COVID-19 impacts on labor, unemployment, stay-at-home orders, and international border policies could affect farmer investments in coffee plants and in turn create conditions favorable for future shocks. We conclude by arguing that COVID-19's socioeconomic disruptions are likely to drive the coffee industry into another severe production crisis. While this argument illustrates the vulnerabilities that come from a globalized coffee system, it also highlights the necessity of ensuring the well-being of all. By increasing investments in coffee institutions and paying smallholders more, we can create a fairer and healthier system that is more resilient to future social-ecological shocks.

Productivity growth, economies of scale and scope in the water and sewerage industry: The Chilean case.

Evaluating the performance and analyzing the cost drivers of water utilities is of great interest for water regulators and water sector managers. This study uses a quadratic cost function to investigate the existence of economies of scale and scope in the Chilean water and sewerage industry over the period 2010-2017. We also estimate and decompose productivity growth into technical change and scale efficiency change. Technical change is further broken into pure, non-neutral and scale-augmenting technical change. The results indicate that cost savings can be achieved by increases in the scale of production and the separation of water and sewerage services. Productivity progressed favorably throughout the whole period at an annual rate of 8.4%, which was attributed to the scale effect, the adoption of new technologies and a good allocation of resources. Some policy implications are finally discussed based on our findings.

Regional development potentials of Industry 4.0: Open data indicators of the Industry 4.0+ model.

This paper aims to identify the regional potential of Industry 4.0 (I4.0). Although the regional background of a company significantly determines how the concept of I4.0 can be introduced, the regional aspects of digital transformation are often neglected with regard to the analysis of I4.0 readiness. Based on the analysis of the I4.0 readiness models, the external regional success factors of the implementation of I4.0 solutions are determined. An I4.0+ (regional Industry 4.0) readiness model, a specific indicator system is developed to foster medium-term regional I4.0 readiness analysis and foresight planning. The indicator system is based on three types of data sources: (1) open governmental data; (2) alternative metrics like the number of I4.0-related publications and patent applications; and (3) the number of news stories related to economic and industrial development. The indicators are aggregated to the statistical regions (NUTS 2), and their relationships analyzed using the Sum of Ranking Differences (SRD) and Promethee II methods. The developed I4.0+ readiness index correlates with regional economic, innovation and competitiveness indexes, which indicates the importance of boosting regional I4.0 readiness.

Beta decoupling relationship between CO2 emissions by GDP, energy consumption, electricity production, value-added industries, and population in China.

The credible sources of fossil energy efficiently are a vital cause of economic growth and considerable influence on adequate security. Whereas radiant energy positively enhances or ostensibly promotes socio-economic stability and the controlled environment. The fossil energy sources supply has become progressively stern in China and reconnoitering the beta decoupling relationships between CO2 emissions, GDP, energy consumption, electricity consumption, value-added industries, and population. The results will be favorable for illustrative the security of the valuable resources. This study adopts the extended stochastic model (STIRPAT) with Beta Decoupling Techniques (BDT). This modern technique merely employs the decoupling situation by the alpha and beta effects from 1989 to 2018 and calculates the % change in CO2 emissions by GDP growth and energy consumption. The estimated results represent negative and economic growth depends on coal and natural gas. First, CO2 emissions annually increasing cause of rapid growth, energy consumption, and electricity production, and the structural contradiction of energy remained static. Second, the Value-added industries estimated that CO2 emissions reduce by primary industries. Third, the decoupling states of CO2 emissions and population show an inverse relationship. This paper tentatively suggests China is sustainable, naturally strengthens energy output, transmutes the energy consumption structure, and advances development policies under environmental circumstances.

The Commercial Determinants of Three Contemporary National Crises: How Corporate Practices Intersect With the COVID-19 Pandemic, Economic Downturn, and Racial Inequity.

Policy Points  The United States finds itself in the middle of an unprecedented combination of crises: a global pandemic, economic crisis, and unprecedented civic responses to structural racism.  While public sector responses to these crises have faced much justified criticism, the commercial determinants of these crises have not been sufficiently examined.  In this commentary we examine the nature of the contributions of such actors to the conditions that underpin these crises in the United States through their market and nonmarket activities.  On the basis of this analysis, we make recommendations on the role of governance and civil society in relation to such commercial actors in a post-COVID-19 world.

Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis.

In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China's energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future.

Association Between Physician Industry Payments and Cost of Anterior Cervical Discectomy and Fusion in Medicare Beneficiaries.

BACKGROUND: Neurosurgical spine specialists receive considerable amounts of industry support that may impact the cost of care. The aim of this study was to evaluate the association between industry payments received by spine surgeons and the total hospital and operating room (OR) costs of an anterior cervical discectomy and fusion (ACDF) procedure among Medicare beneficiaries. METHODS: All ACDF cases were identified among the Medicare carrier files from January 1, 2013, to December 31, 2014, and matched to the Medicare inpatient baseline file. The total hospital and OR charges were obtained for these cases. Charges were converted to cost using year-specific cost-to-charge ratios. Surgeons were identified among the Open Payments database, which is used to quantify industry support. Analyses were performed to examine the association between industry payments received and ACDF costs. RESULTS: Matching resulting in the inclusion of 2209 ACDF claims from 2013-2014. In 2013 and 2014, the mean total cost for an ACDF was $21,798 and $21,008, respectively; mean OR cost was $5878 and $6064, respectively. Mann-Whitney U test demonstrated no significant differences in the mean total or OR cost for an ACDF based on quartile of general industry payment received (P = 0.21 and P = 0.54), and linear regression found no association between industry general payments, research support, or investments on the total hospital cost (P = 0.41, P = 0.13, and P = 0.25, respectively), or OR cost for an ACDF (P = 0.35, P = 0.24, and P = 0.40, respectively). CONCLUSIONS: This study suggests that spine surgeons performing ACDF surgeries may receive industry support without impacting the cost of care.

Competitive intelligence and its impact on innovations in tourism industry of China: An empirical research.

Competitive intelligence (CI) has attracted much attention in innovation research, but most of existing literature studies CI in technological innovations in manufacturing industry, with little empirical research in context of service businesses. This paper first analyzes CI of service businesses and then uses covariance-based structural equation modeling (SEM) on a data of 333 got from the survey in tourism enterprises of east China to test the effect of customer CI, opponent CI, and supplier CI on service innovations in China's service industry. Results show that opponent CI and supplier CI have positive influence on both exploratory and exploitative service innovation. Customer CI has more obvious positive influence on exploratory service innovation than on exploitative service innovation.

Addressing Sustainable Social Change for All: Upcycled-Based Social Creative Businesses for the Transformation of Socio-Technical Regimes.

The global challenges caused by socio-economic inequalities, climate change and environmental damage caused to ecosystems, require changes in human behavior at all organizational levels, including companies, governments, communities, and individuals. In this context, it is important to analyse how social and creative companies that work in the fashion and industrial design recycling sector can address sustainable social change. In this paper, we propose an analysis in the countries of the global South. To learn how grassroots innovations can contribute to the development of sustainable strategies, we perform the framework of Technical transitions. We analyze the three main areas of activity that constitute an effective niche construction: social networks, expectations and visions, and learning. A qualitative methodology is used, a video case study with six grassroots organizations in South Africa, Sri Lanka, Malaysia, and Brazil. The results reflect the important role played by these grassroots innovations, contributing to the development of social and creative recycling companies that address socio-economic and environmental problems.

Occupational gender segregation and economic growth in U.S. local labor markets, 1980 through 2010.

The exchange of diverse ideas has been shown to be a major driver of economic growth and innovation in local labor markets across the U.S. Yet, persistently high levels of occupational gender segregation pose a barrier to such exchange between women and men workers. Consistent with this, organizational sociologists have identified multiple economic benefits to gender diversity in workplaces. Yet, it is unclear whether these trends apply to local labor markets, which constitute the ecological geographic environment for firms. In this study, I use fixed effects regression models to examine the relationship between labor market levels of segregation and economic growth from 1980 through 2010. I find that gender segregation hinders the expansion of finance and technology sectors as two industries that rely on the exchange of information and innovation. Consequently, higher levels of gender segregation are also a bane to economic productivity, as measured through hourly wages. Results from this study suggest that gender equity, manifested in lower levels of occupational segregation, is a vital ingredient in the economic development of local U.S. labor markets.

Work-related asthma surveillance in Washington State: time trends, industry rates, and workers' compensation costs, 2002-2016.

Objective: Washington State's work-related asthma (WRA) surveillance program utilizes workers' compensation (WC) data as its primary data source and has spanned a 15-year time period. This study analyses trends for WRA claim incidence rates compared to all WC claim incidence rates. WRA claim incidence rates and WC costs are analyzed by industry. Methods: Potential WRA cases were identified through the WC system and through direct provider report and classified by industry, age, and year of illness onset. WRA claim rates by industry and year were calculated using total work hours reported by employers covered by the WC system. Claim costs for accepted claims were compared by industry and year. Results: WRA claim incidence rates decreased 8.9% (95% CI: -10.6, -7.2) annually for the time period 2002-2016. The decline in WRA claim incidence rate is slightly faster than the incidence rate for all WC claims which had its steepest decrease from 2007 to 2010 at an estimated annual 8.4% decrease (95% CI: -11.8, -5.0). WRA claim rates were highest for workers in Public Administration, Manufacturing, and the Agricultural, Forestry, Fishing and Hunting industries. Median claim costs for WRA did not change significantly by year (p = 0.2, range $595-$1442) and the distribution of WRA WC claim costs by industry were highest in Manufacturing (21.3%) and Construction (16.4%) industries. Conclusion: WRA claim incidence rates are declining in Washington State. The cause for the decline is unclear. Workers across all industries in Washington remain at risk for WRA.

Dynamic eco-efficiency evaluation of the semiconductor industry: a sustainable development perspective.

Serious environmental problems have accompanied remarkable global economic growth for decades. To assist managers in the semiconductor industry with economic and environmental management, this study executes DuPont analysis to examine economic impacts from the effective implementation of sustainability initiatives. We propose a two-stage process including economic development efficiency and environmental protection efficiency through the dynamic data envelopment analysis (DDEA) to reflect the characteristics of eco-efficiency. Through DuPont analysis, the main finding shows the potential improvement in firms' return on equity (ROE) by efficiently utilizing assets to generate sales quickly.Relative to economic development efficiency, the firms show lower scores and higher standard deviations in the environmental protection ability, thus denoting a large gap in the level of environmental protection production technology. The findings in this study reveal that the financial foundations and sustainable development of industries should be improved simultaneously even though specific levels of semiconductor industrial eco-efficiency improvement vary among companies in Taiwan.

How Industrial Transfer Processes Impact on Haze Pollution in China: An Analysis from the Perspective of Spatial Effects.

Industrial transfer from advanced regions is a good way to foment economic development in less advanced regions. Nevertheless, does industrial transfer intensify or alleviate haze pollution? To answer this question, this study employed the shift-share method and spatial panel models to explore how industrial transfer processes impact haze pollution in the case of China. The main results are as follows: (1) With the advances made in industrial transfer and upgrading, China has entered the stage of decoupling between the economic development level and haze pollution. (2) Industrial transfer could effectively alleviate the degree of haze pollution in the transferred-out areas, but it would have a significant accelerating effect on haze pollution in the transferred-in areas. Compared with non-polluting industries, polluting industries would be responsible for a large deterioration in the local air quality. (3) Environmental regulations, as the main factor mitigating environmental pollution, do not achieve the desired effects and significantly reduce the regional pollution levels that led to haze. Therefore, the effects of industrial transfer should also be comprehensively considered in government of undertake regions. There would likely be great economic costs if the old path of "pollution first and treatment later" is followed. This study not only advances the existing literature, but also is of considerable interest to policy makers.

Consolidation and maturation of the orthopaedic medical device market between 1999 and 2015.

Orthopaedic surgeons often require highly specialized medical devices, implants, and equipment, which are usually offered by several vendors/companies. This study assesses long-term market trends for orthopaedic medical device companies and examines various implications for healthcare cost. Using S&P Capital IQ, a Wall Street database, financial data were gathered on orthopaedic device companies, ranked by worldwide sales, from 1999 to 2015. Annual sales were aggregated to calculate market share and compounded annual growth rates (CAGRs). Overall, the global orthopaedic device market grew at 12.0% CAGR from 1999 to 2008, before slowing to 2.8% from 2009 to 2015. Between 1999 and 2015, the top 5 companies increased total market share from 52.8 to 62.2%. The orthopaedic device market is not only consolidating under a few dominant players, but also growing at a decreasing rate, both of which signal a maturing industry. These trends are likely to shape patient care and healthcare costs in orthopaedic surgery in years to come.