Based on provincial panel data from 1998-2018, this paper estimates research and development (R&D) factors, and a stochastic frontier analysis (SFA) model is constructed to examine the effects of R&D factors on regional total factor productivity (TFP). The results show that both R&D capital stock and R&D personnel can significantly promote regional TFP, but the productivity-enhancing effect of R&D factors is different between regions. Specifically, R&D capital and R&D personnel can promote TFP in eastern and central provinces, and the promotion effect is not significant in western provinces. In addition, compared with investment-driven regions, innovation-driven regions are more likely to enhance TFP by R&D factors.

The global agricultural supply chain grapples with inherent challenges, including opacity, inefficiency, and ethical concerns, necessitating innovative solutions, particularly within Indian Agriculture Supply Chain Management. Despite these challenges, a notable research gap emerges due to the limited exploration of adoption factors specific to the Indian agricultural context. To bridge this gap, a comprehensive investigation is initiated to delve into the nuanced dynamics of blockchain technology adoption in the Indian agricultural supply chain. The primary aim of this study is to provide insights into these adoption dynamics, employing a three-stage methodology. Starting with an extensive literature review to identify key adoption factors, the study then distributed a well-crafted questionnaire to 200 respondents, yielding 150 complete and analyzable responses. The chosen methodologies, including a one-sample t-test and exploratory factor analysis, allow for a quantitative assessment of the significance of various factors. The study's findings reveal crucial dimensions influencing blockchain adoption, such as perceived benefits, ease of use, trust, efficiency, ethical considerations, data security, integrity, regulatory compliance, and collaboration and stakeholder engagement, providing a comprehensive understanding of the adoption landscape. In conclusion, this research significantly contributes to comprehending the nuanced adoption dynamics of blockchain technology in the Indian agricultural supply chain, supported by a substantial sample size and robust methodological choices. The implications extend to stakeholders navigating the evolving agricultural supply chain, offering valuable insights for future research, policy considerations, and practical strategies, with a central focus on fostering transparency, efficiency, and ethical standards to address global agricultural supply chain challenges.

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.

Even though policy coordination is one of the oldest challenges that governments have to confront, as problems evolve and "New Public Management" concepts emerge, it has become even more essential. The current literature on policy coordination among government agencies, however, shows little regarding the way coordination is managed under centralized political systems. This study, based on the science and technology (S&T) policy documents issued by China's central government agencies between 1978 and 2020, provides a quantitative and dynamic analysis of the coordination of policies in China and presents a comprehensive overview of policy coordination paths and processes in centralized political systems. As a result, it provides a way that contributes to the analytical methods available for quantitatively analyzing policy documents. On the other hand, the key findings of the study show that, first, state council-administered ministries have taken the lead in coordinating policy while other types of organizations have collaborated in more subordinate capacities. Second, national themes that are democratic and driven by demand have been the core concern of coordination activities, such as social development, high-tech industrialization, and rural S&T. Third, policy coordination has evolved continuously and has mostly contributed to interpreting macrostrategies and implementing more specific implementation measures.

Background: Hepatocellular carcinoma (HCC) stands out as one of the most lethal cancers globally, given its complexity, recurrence following surgical resection, metastatic potential, and inherent heterogeneity. In recent years, researchers have systematically elucidated the significance of long non-coding RNA (lncRNA) in the initiation and progression of HCC. The introduction of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases has significantly enhanced the prognostic assessment of HCC. However, the association between HCC and cell senescence has been infrequently explored in the literature. Method: We downloaded liver hepatocellular carcinoma (LIHC)-related messenger RNA and lncRNA expression levels from TCGA. Correlation analysis, Cox regression, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to validate the lncRNA risk model associated with cellular aging. Comparing the infiltration of diverse immune cells enabled the identification of distinct differences in the immunological microenvironments of the two risk groups. Subsequently, we conducted a real-time polymerase chain reaction (qPCR) experiment to confirm the accuracy of the selected lncRNAs. Results: A predictive framework for HCC was constructed based on the expression levels of five lncRNAs. Multivariate and univariate Cox regression analyses revealed that lncRNA signatures associated with senescence were independently correlated with an increased risk of HCC. Additionally, the nomogram also provides a more refined and sensitive model. Further investigation into the variations in immune cells and functions between the high-risk and low-risk groups was conducted. Subsequently, a qPCR experiment results revealed underexpression of AC068756.1, AC090578.1, AC145343.1, and LINC0022 in Huh7 and LM3 cells. In contrast, AP003392.4 did not exhibit a significant difference between Huh7 and control cells. Conclusion: The prognostic features and nomogram, consisting of five aging-related lncRNAs (AC068756.1, AC090578.1, AC145343.1, AP003392.4, and LINC00221), may be useful in predicting the overall survival of HCC.

This paper aims to study and quantify the impact of agricultural exports on economic growth along with other variables in Tunisia. For that purpose, we estimate the relationship between GDP, agricultural exports, other exports, and imports by applying the ARDL approach under the period 1990-2020. The results show a negative and insignificant impact of agricultural exports on economic growth in the long and short run. Other exports negatively and significantly impact economic growth, while imports are found to have a positive impact on long-term economic growth. The findings have economic implications for sectors and activities that should be promoted to accelerate economic growth.

Access to electricity, a fundamental element of contemporary life, is essential for economic success. Its impact extends to the fundamental foundations of industrial development and has the power to improve a wide range of industries, including healthcare, transportation, utilities, and education. This article aims to analyse the effect of electricity access on primary education in Central Africa over the period 1997-2019. To this effect, we employ data mainly from the World Development indicator on 9 countries of Central Africa. Using the pooled ordinary least squares estimation technique, the results indicate that access to electricity contributes positively and significantly to primary educational attainment in Central Africa during the study period. The results are Robust to the use of alternative estimation strategy and eventual endogeneity problems in the results are account through the two stage least square estimation techniques which confirm our baseline results as well as the nature of the relationship between access to electricity and primary education in Central Africa. These results have important implications for policies in overcoming barriers to electricity access.

Although docetaxel treatment yields a high survival rate for prostate cancer (PCa), resistance eventually develops in many patients. Understanding the underlying mechanisms of docetaxel resistance is essential for improving treatment strategies. Cytokines, which play a role in cell signaling and immune responses, have been implicated in drug resistance mechanisms. The study revealed that interleukin-8 (IL-8) was consistently overexpressed in both docetaxel-resistant PCa cell lines. Thus, the expression levels of cytokines released from docetaxel-sensitive (PC-3- and DU-145) and resistant (PC-3/R-DU-145/R) PCa cells were compared. IL-8 was found to be commonly expressed in resistant cell lines. This finding led to the hypothesis that IL-8 could play a key role in mediating PCa cell resistance to docetaxel. IL-8 siRNA treatment increased docetaxel sensitivity in both resistant cells. To demonstrate the mechanism of IL-8-related resistance, MDR1 expression was evaluated. After IL-8 siRNA treatment MDR1 expression was reduced in both resistant cells suggesting that IL-8 regulates the docetaxel resistance of PCa cells via modulation of multidrug resistance 1 (MDR1). By expanding the knowledge of the cytokines and their effect mechanisms, novel approaches can be developed for the treatment of docetaxel-resistant prostate cancer. Further investigations into the role of IL-8 in docetaxel resistance could offer valuable insights into the development of effective treatment strategies for PCa patients.

This paper provides an overview of the existing literature on the use of artificial intelligence (AI) in various fields, including economics, finance, mining, manufacturing, and innovation. The paper identifies the drivers and effects of AI deployment in the context of innovation and highlights the challenges and opportunities that arise from the use of AI. The studies reviewed in this paper cover various topics related to forecasting, including the impact of AI on professional skills, hybrid forecasting techniques for predicting commodity prices, and novel deep reinforcement learning algorithms for crude oil price forecasting. The paper’s contribution lies in its systematic and comprehensive approach to reviewing the literature, which allows for a better understanding of the impact of AI on various fields and the identification of strategies to address the challenges that arise from its deployment.

Information leakage in the stock market has been widely proven. Information disclosure is sometimes uneven, and there is significant information asymmetry between ordinary investors and professional institutional investors. In this paper, Regression Discontinuity design (RDD) model is first employed to analyze the information leakage issues. Based on the daily closing stock prices of 15 capital service listed companies, we analyze the difference between the market reaction time and the disclosure time of two stamp tax policies. We found that the sample policies information may leaked to the market about two days earlier. This paper provides a new method analyzing information leakage.

Since the Industrial Revolution, the emission of carbon dioxide and other greenhouse gases by human production activities has increased year by year, and the greenhouse effect has also increased. A series of problems, such as global warming, melting of the Antarctic continent, sea level rise and frequent occurrence of extreme climate, have attracted the attention of countries all over the world. As a developing country with the fastest economic growth rate, China's rapid economic growth is inseparable from the contribution of manufacturing industry. As an important engine of China's economic development, manufacturing industry inevitably brings environmental problems and a large number of greenhouse gas emissions while promoting rapid economic development. Therefore, manufacturing has become a major area for China to achieve its carbon emission reduction targets. With the "Made in China" to the world, the export trade of manufacturing industry plays a decisive role in China's total export trade and even the global trade. But whether the huge export demands will affect carbon emissions. Through the empirical study on carbon emissions of China's manufacturing export trade, the paper found that the increase of export demand of manufacturing industry will indeed bring a significant increase in carbon emissions, but also affected by some other factors. Finally, it provides reference for the high-quality development and export of China's manufacturing industry under the background of the target of “dual-carbon”.

The study analyses the long growth rate period contribution to human development in West African Economic and Monetary Union (WAEMU) from 1996 to 2019 introducing time and institutional indicators effects analyses. Time and institutional effects both greatly improve model's diagnostics statistics. Time effects reduce growth variable coefficient and its significance, but not institutional effects. Combined effects inhibit growth contribution to human development. It appears essential to increase the resiliency of growth and the efficiency of government institutions.

This paper intends to analyze efficiency, in its weak form, in the stock markets of Austria (ATX), Poland (WIG), the Czech Republic (PX Prague), Hungary (BUX), Croatia (CROBEX), Serbia (BELEX 15), Romania (BET), and Slovenia (SBI TOP), from February 16, 2018, to February 15, 2023. To achieve the research aim, we intend to answer the following research question: i) Have events in 2020 and 2022 heightened the persistence of Central European stock markets? Results suggest that persistence in returns has increased significantly during the first wave of Covid-19 and the Russian invasion in the year 2023, but we also saw that most stock markets already exhibit long memories, implying that the research question has been partially validated. This research can provide valuable insights to investors, policymakers, and others interested in financial risk management.

The dawn of the Artificial Intelligence (AI) era presents a plethora of new possibilities for analyzing regional economic development. The present article provides an in-depth exploration of the methods employed in this field, highlighting the immense opportunities that AI offers while also addressing potential challenges. The role of AI is crucial in complex data handling, enabling efficient analyses of intricate regional economic patterns. This capacity is paramount in shaping economic policies and strategies that are reflective of each region's unique needs and potential. The article firstly explores various AI methods used in economic analysis, including but not limited to machine learning, deep learning, and natural language processing. It delves into the application of these methods in discerning development trends, predicting economic shifts, and identifying strategic economic drivers unique to various regions. Subsequently, the potential of AI to transform regional economic analysis is discussed, encompassing its capability to process large and complex datasets, its power to predict future trends based on past and present data, and its ability to aid in strategic decision-making. However, this new era of AI-driven economic analysis is not without challenges. The latter part of this article thus confronts the issues related to data privacy, ethical use of AI, and the necessity of interdisciplinary skills in AI and economics. This exploration contributes to a broader understanding of how AI is transforming the landscape of regional economic development analysis, illuminating both its present use and future implications. By understanding these dynamics, we can better harness the potential of AI to advance economic prosperity in various regions around the globe.

This paper evaluates the use of macro-level insurance to finance post-disaster recovery and reinstatement by investigating the case of the National Disaster Insurance policy implemented by Sri Lanka. The national scheme provided cover for all properties in the country against hazards such as flooding and provided compensation for those affected. Analysis of secondary data obtained from the organisations that managed the scheme showed that the scheme has delivered a much higher return compared to the total insurance premium paid, suggesting that the scheme has delivered a net positive benefit compared to the cost of the premium and can be considered a viable option. However, the existing secondary data did not reveal the values of other costs and indirect benefits associated with the scheme to compute a meaningful benefit-to-cost ratio. Further rigorous, evidence-based cost-benefit analysis is required to assess the cost-effectiveness of a scheme of this nature. A cost-benefit analysis approach based on an analytical hierarchy process is proposed as a possible solution to assess the cost-effectiveness of the scheme, which could be used by government organisations as an alternative, where analytical and research resources may be limited, to evaluate a range of disaster recovery financing options using stakeholder opinion.

This paper explores how digital technology reduces trade costs using bilateral trade data from RCEP member countries and a panel fixed effects model. The findings show that digital technology significantly lowers trade costs, a conclusion that holds even after accounting for lag effects and various robustness checks. The impact of digital technology on trade costs follows an inverted U-shape: the effect is most significant in the current period, especially with a one-period lag, and diminishes over time. Larger economies and higher export levels strengthen this negative impact due to their reliance on exports and continuous improvements in domestic digital technology. The study recommends investing in digital infrastructure, formulating reasonable internet access policies, supporting digital skills development, and enhancing digital connectivity to bridge the digital divide, thereby promoting trade facilitation and efficiency.

Trust beneficiary protection constitutes an important legal system in the trust industry. This paper, based upon the study approach of the “Theory of Law and Finance”, establishes the trust beneficiary protection index of China. We also test the influence of trust beneficiary protection on the performance of trust companies. As indicated by our study’s results, an “inverted U-shaped” relationship exists between the degree of trust beneficiary protection and the performance of trust companies. In a period when the trust law system has not yet been well established, strengthening trust beneficiary protection could help facilitate the growth of trust company performance. However, this facilitation effect gradually diminishes as the trust law system becomes more perfect. This in turn means that, after reaching a certain peak, conferring the beneficiary with more rights of supervision and participation would cause the performance of trust companies to decline. Improving regional legal environments could also help facilitate the growth of trust company performance. In addition, this facilitation effect is subject to the influence of the nature of trust company controllers. In trust companies controlled by local state-owned capitals, we find that the facilitation effect of the improvement of regional legal environments on trust company performance is inhibited. In trust companies controlled by non-state-owned capitals, however, the facilitation effect of the improvement of regional legal environments on trust company performance is enhanced.

The No.1 document of the Central Government of China has been on the theme of "agriculture, rural areas and farmers" for nearly ten years, placing the solution of "agriculture, rural areas and farmers" at the forefront of the Chinese government's work. Located in the Central Plains, Henan Province in China is one of the major agricultural provinces in China and plays an extremely important role in the field of agricultural production in China. This paper uses an empirical approach and the DEA-Malquist model to study the efficiency of rural public financial inputs to rural revitalisation in Henan Province. The results show that due to different levels of regional economic development, the efficiency of rural public finance investment in Henan Province in promoting rural revitalisation varies, and there is a phenomenon of unreasonable allocation of funds. At the same time, the region's agricultural base also has a greater impact on the efficiency of financial investment. Therefore, Henan Province should adjust measures according to the different regional economic levels and agricultural bases to improve the efficiency of the use of fiscal funds.

Cryptocurrencies have gained popularity over the past five to six years. Most recently, events like the FTX bankruptcy fueled the interest in regulation. Moreover, it is possible that the FTX event disrupting the cryptocurrency market was a factor in Silicon Valley Bank's failure. While several countries consider regulation, from soft regulation, like Japan, to more rigid standards, like the total ban in China, we study the effect of other news or events on cryptocurrency prices. This paper looks at historical closing prices for Bitcoin, the largest of the cryptocurrencies, and how prices react to various events. Then we focus on modeling the time series considering an 'event,' China's ban on cryptocurrency exchanges, using intervention analysis. We find that intervention analysis provides a reliable approach to quantifying the impact regulation may have on cryptocurrency pricing.

A differences-in-differences (DID) model and financial data from Chinese listed firms from 2011 to 2019 are used to empirically investigate the effects of the digital economy on corporate financial asset holdings using the "Broadband China" policy as a quasi-natural experiment. After robustness testing and heterogeneity effects are disposed of, the digital economy dramatically boosts corporate financial asset holdings. According to the channel analysis, the digital economy can reduce corporate financing constraints and boost corporate financial asset holdings, notably based on speculative demand. Heterogeneity analysis shows that the digital economy has a greater impact on corporate financial asset holdings for firms with higher market competition, small-scale firms with a short-listed age, and firms in the western region. This paper provides policy guidance for enterprises returning to the real economy.