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.

Metastasis is the major cause of cancer-related mortality. Metastasis is a process through which cancer spreads from its initial location to other sections of the body. Cancer cells' epithelial-mesenchymal transition (EMT), anoikis resistance, cell migration, and angiogenesis are all well-known steps in this process. Investigating the molecular processes that govern cancer metastatic progression may lead to more effective diagnostic and treatment strategies. Long non-coding RNAs (lncRNAs) have recently discovered to have a vital more than 200 nucleotides. A rising body of research indicates that lncRNAs have a role in a wide range of biological processes and diseases, including cancer. The usage of LncRNA in cancer metastasis has been widely researched. However, according to current studies, lncRNA is mostly associated with the EMT process. This review focuses on the processes behind lncRNA involvement in cancer metastasis.

Environmental concerns are increasing and business firms are inclined towards adoption of eco-friendly products and services. They are hiring employees via set of green HRM practices and struggling to become an environmentally sustainable firm. Pakistani food industry is booming day by day. However, less is known about the green HRM practices executed in the fast food industry of Pakistan. Therefore, this research is interested in examining the impact of Green recruitment and selection, green training and development and green performance and compensation management on the P-O fit, affective commitment and green recovery performance in Pakistan’s fast food industry. The moderating effects of transformational leadership will be examined between Green HRM practices and green recovery performance. A sample of 408 employees who are working at different designations were selected to accomplish the purpose of the research. These managers will be selected through convenient sampling technique. There were 21 fast food restaurants (Burger Joints) selected from all over Karachi, few of them includes; KFC, Burger Lab, Burger O Clock, Vintage, Plan B and more. The collected primary data will be tested through the application of structural equation modeling technique. Smart PLS software is used to assess the objectives. The results of the study revealed significant association between Green R&S, Green T&D and P-O fit. A non-significant association is found between Green P& C and P-O Fit. The study affirmed that Affective commitment posit a direct influence on GRP. The Green R&S and Green P&C revealed insignificant impacts on the green recovery performance within the fast food industry. The indirect association was analyzed between P&C, P-O fit, AF and GRP and revealed an insignificant result. Likewise, an indirect association is found between R/S, P-O Fit, AF and GRP. The future researchers are recommended to analysis the remaining elements of GHRM and investigate their impacts on the fast food industry of Pakistan. There is a need to create awareness regarding green HRM practices and their influence on the Green recovery performance in Pakistan. Policy makers are recommended to develop such policies that promotes GHRM in Pakistan.

Carbon emissions trading is an important part of emission reduction policy tools, and manufacturing is the foundation of a strong country. This paper explores the impact of carbon emissions trading pilot on the high-quality development of manufacturing industry by using the double difference method. It is found that carbon emissions trading can significantly promote the high-quality development of manufacturing industry, in which the government's will of ecological environment governance, technological progress and industrial structure rationalization have significant intermediary effects. Heterogeneity analysis reveals that the impact of carbon emissions trading on the high-quality development of the manufacturing industry is most significant in the western region. Accordingly, the study proposes suggestions on how to further improve China's carbon emissions trading market and realize the high-quality development of the manufacturing industry.

This study explores the cause and effect of endogenous risk aversion in land pricing, where state intervention through taxation remains a general practice. Using a consumption-based asset pricing model incorporating taxation, it is shown that high taxation, due to the indexation effect, supporting land prices and reducing individuals' risk expectations, could lead to an endogenous decrease in risk aversion, which could result in market dysfunction because risk aversion plays a key role in the market mechanism. China, with its wholly state-owned land and the general use of land sales to cover financial deficits, is a typical case for empirical tests. The tests confirm that there, the rise in land prices was driven by the increase in reserve prices set by local governments, a strong means of taxation, and not by the market, indicating the endogenous decrease in risk aversion.

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.

We employ the joint production decomposition model to conduct a full decomposition of CO2 emission among 36 industrial sectors in China from 1998 to 2011, under the framework of growth accounting. The results show that: (1) the average CO2 emission increases at an annual rate of 3.01%, and production technology progression is the main driving force, while the transformation toward clean production effectively curb the rapid growth of CO2 emissions; (2) the effect of technology changes on CO2 emission is larger during the "10th Five-Year Plan" compared with the "11th Five-Year Plan", which makes the annual growth rate of CO2 emission during the "11th Five-Year Plan" 1% lower than its counterpart; This study has important theoretical and practical significance for understanding the driving factors of CO2 emission and the corresponding emission reduction measures.

 In the past, researchers investigated the immune response acting against mycobacterial antigens in tuberculosis (TB). Several ELISA tests have been conducted for the diagnosis of tuberculosis. Other researchers manipulated 16 kDa antigen from Mycobacterium Tuberculosis to develop a tuberculosis diagnostic test while some others studied the subspecies of M. tuberculosis. In the present study, we explored the possibilities of detecting 16 kDa protein derived from M. tuberculosis, on an interdigitated electrode (IDE). Measurements with Electrochemical impedance spectroscopy and current-volt systems were demonstrated using antibody as the probe.  Further, the involvement of gold nanoparticle for the signal-enhancement and high-performance are evidenced. The specificity in the serum containing samples is used to show the clinical relevancy. 16 kDa antigen used in this experiment is highly reliable for TB diagnosis as it largely expresses and can be implemented in other sensing system.

Skateparks, often called 'wheel parks,' are becoming increasingly common within communities worldwide. Despite this growth in parks, estimates show that the development of parks needs to catch up to users. Using a count data travel cost model, we estimate the adult user benefits associated with the Lauridsen Skatepark in Des Moines, Iowa – the largest in the United States. We estimate adult user benefits to be $61 per user per day and roughly $488,000 annually. This work contributes to the literature by being the first study to use econometric techniques to estimate skatepark user benefits. Second, we develop a simple and easy-to-apply method to assist municipalities in determining community skatepark needs. Third, our results support the literature showing that skateboarders are increasingly diverse.

We study analytically the conflict of goals between stabilizing economic activity and public debt sustainability, for the fiscal authorities. In the short run, an active and expansionary fiscal policy, increasing public investment or reducing the labor taxation rate, is growth enhancing. However, as these short term fiscal policies also decrease government revenue and increase the public debt, budgetary and fiscal multipliers are reduced in the long run. In the framework of a potential ZLB constraint for monetary policy, an expansionary fiscal policy is thus appropriate only if long term labor and consumption taxation rates are small, and if the share of public expenditure in GDP is high. On the contrary, fiscal policy should be contractionary, in order to insure the sustainability of the long term public debt, if long term labor and consumption taxation rates are high. A contractionary fiscal policy is then also all the more appropriate as the sensitivity of government expenditure to the public debt is high, and as the labor share in the production function is high.

This study investigates the likelihood of time to bank failures in the US between 2001 and April 2023, based on data collected from the Federal Deposit Insurance Corporation's report on "Bank Failures in Brief - Summary 2001 through 2023". The dataset includes 564 instances of bank failures and several variables that may be related to the likelihood of such events, such as asset amount, deposit amount, ADR, deposit level, asset level, inflation rate, short-term interest rates, bank reserves, and GDP growth rate. We explore the efficacy of machine learning survival models in predicting bank failures and compare the performance of different models. Our findings shed light on the factors that may influence the probability of bank failures with a time perspective and provide insights for improving risk management practices in the banking industry.

This paper uses the questionnaire data of the World Bank’s China Enterprise Survey 2012 to establish a Logit model to empirically study the relationship between knowledge sources and technological innovation in Chinese manufacturing enterprises. The results show that although internal and external knowledge has a positive effect on enterprise technological innovation in general, there are significant differences when taking the types of innovation, subject characteristic and the types of cooperation into consideration. From the current development stage of China's manufacturing enterprises, external knowledge sources, external enterprise knowledge subjects, and employee knowledge have a more positive effect on technological innovation, which also shows that the role of technical employees on technological innovation has a significant threshold effect, and only after a certain degree of accumulation can internal technical employees have a more significant positive effect on technological innovation.

This paper describes two novel approaches to cost estimation of manufactured products where a data set of similar products have known manufactured costs. The methods use the notion of piecewise functions and are (1) clustering and (2) splines. Cost drivers are typically a mixture of categorical and numeric data which complicates cost estimation. Both clustering and splines approaches can accommodate this. Through four case studies, we compare our approaches with the often-used regression models. Our results show that clustering especially offers promise in improving the accuracy of cost estimation. While clustering and splines are slightly more complex to develop from both a user and a computational perspective, our approaches are packaged in an open-source software. This paper is the first known to adapt and apply these two well-known mathematical approaches to manufacturing cost estimation.

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.

Liquidity can be a real phenomenon for execution of the financial holding. Its risk falls in debate to impose a conditional cost on the counterparty. The time-varying liquidity is often linked to the expected fundamental value of an investment. In this work, the microblogging-based informed transaction is examined as a determinant of the liquidity-facilitating cost. Most importantly, this study investigates the economic blockade era and post-pandemic uncertainty. The sentiment indicators were found to be determinants of liquidity. These findings were consistent in the post-pandemic period. However, the investor pessimistic sentiment was a priced risk factor in liquidity during the economic blockade period. Based on the Bayesian theorem, a relativeness was reported between sentiment indicators and the liquidity-facilitating cost. The same findings were depicted in environments of the pandemic era. Nevertheless, the posterior probability indicated an occurrence of the liquidity-associated cost in response to the pessimistic sentiments during the economic blockade period. This quantification may have potential implications in terms of exploring liquidity from the microblogging perceptive.

With the development of information technology and its application in environmental governance, the role of the internet in improving energy efficiency and reducing energy-saving potential (ESP) has attracted more attention. In this study, the slack-based model (SBM) and the unexpected model, along with the entropy method, were applied to measure China's energy-saving potential and internet development. Further, we empirically analyzed the direct effect, mediating effect, threshold effect, and regional heterogeneity of the internet on ESP. Our conclusion shows that there is a significant spatial correlation between internet penetration and ESP. Internet penetration has become an important tool for reducing ESP, but this effect shows regional heterogeneity. Human capital accumulation, financial development, and industrial upgrading are important influencing mechanisms, but indirect effects are weaker than direct effects. The impact of internet penetration on ESP is non-linear, and for improving human capital accumulation, financial development, and industrial upgrading, the role of internet popularization in energy conservation is more obvious.

We present two approaches to forecasting parameters in the SABR model. The first approach is the vector autoregressive moving-average model (VARMA) for the time series of the in-sample calibrated parameters, and the second is based on machine learning techniques called epsilon-support vector regression (ε-SVR). Using daily data of S&P 500 ETF option prices from January 1, 2014, to December 31, 2018, we first calibrate the daily values of the model parameters from the training sample, then conduct out-of-sample forecasting of parameters and pricing of options. Both approaches produce good fits between the forecasted and calibrated parameters for out-of-sample dates. A comparison study shows that using forecasted parameters as inputs, the SABR model generates better pricing results than assuming constant parameters or using lag parameters. We also discuss the market conditions under which one approach outperforms the other.

This study examines the role of the Corporate Social Responsibility Committee (CSRC) in the adoption of strategic Corporate Social Responsibility (CSR) projects that surpass regulatory requirements and basic stakeholder expectations. Our results demonstrate that the establishment of CSRCs enhances all strategic CSR mechanisms, particularly business reputation, stakeholder interaction, risk mitigation, and innovation. Additionally, strategic CSR practices exhibit a strong correlation with CSRC attributes and organizational characteristics. Specifically, the size of the CSRC is positively associated with all dimensions of strategic CSR. Furthermore, CEO membership is likely to impede risk mitigation and innovation capacity, whereas board chair membership enhances business reputation, stakeholder communication, and innovation potential. In terms of gender diversity, female CSRC members show a greater concern for business reputation and associated risks. Regarding committee functioning, increased meeting frequency has the potential to enhance various aspects of strategic CSR.

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.

Triggered by the rising demand, a complex aircraft production network has been evolving during the past half century. In order to analysis the spatial-temporal evolution of this network, this paper establishes a novel and complex data by collecting 1774 major global aircraft manufacturers' production relationship from Jane's All the World' s Aircraft Development & Production (1965-2021), and visualizes the spatial temporal characteristics of the global aircraft production network by using Social Net Analysis method. The characteristics are summarized as follows: (1) the main aircraft firms from 48 counties form a complex production network centered on the United States, Europe and China; (2) The production network remains diffusion during 1965-2005, but it retracts after 2005, which documents evidences of de-globalization trend in the aircraft industry; (3)AVIC, Airbus, Boeing, Textron, Lockheed, Kawasaki, Mitsubishi, Sikorsky, Bell, and Embraer are all ranked high in the study period and play the role of leading firms in the production network and drive the cooperative connections of other companies.