Employment and development levels in rural areas of the Russian Federation

The development of agro-industrial complex is important for ensuring national food security and national health.The development of rural areas is subject to the development of agriculture and local infrastructure,as well as the availability of various services.This study selected 15 indicators in 2021 to analyze the employment and development levels in rural areas of 71 regions of the Russian Federation using the analytical grouping method.The results indicated that 20 regions(Group 1)had the highest percentage of rural population(33.10%).The percentage of population engaged in agriculture had the highest value(12.40%)in 31 regions(Group 2).Moreover,20 regions(Group 3)had the highest investments in fixed assets at the expense of municipal budget(11.80 USD/person).Increasing the investments in fixed assets carried out from the budget of the municipality can improve the employment level in rural areas.Then,we used cluster analysis to divide 14 regions of the Volga Federal District in the Russian Federation into 3 clusters.Cluster 1 covered Kirov Region and Republic of Mari El;Cluster 2 included Ulyanovsk Region,Saratov Region,Nizhny Novgorod Region,Perm Territory,Orenburg Region,Chuvash Region,and Republic of Mordovia;and Cluster 3 contained Republic of Tatarstan,Samara Region,Udmurtian Republic,Penza Region,and Republic of Bashkortostan.Results indicated that the 2 regions of Cluster 1 need to increase the availability of resources and natural gas and improve the investment attractiveness of rural areas.The 7 regions of Cluster 2 needed to develop infrastructure,public services,and agricultural production.We found the highest employment level in rural areas,the largest investments in fixed assets at the expense of municipal budget,the largest residential building area per 10,000 persons,and the largest individual residential building area in the 5 regions of Cluster 3.This study makes it possible to draw up a comprehensive regional development program and proves the need for the development of rural areas,which is especially important for the sustainable development of the Russian Federation.

Market Positioning Analysis of CJ Brand Fitness Equipment

CJ Health Technology Co.has been an export-oriented supplier of fitness equipment,boasting leading technology innovation and manufacturing capabilities.However,in the process of expanding into the domestic market,the challenges of unclear market positioning and lack of brand awareness have emerged as major obstacles.To address these issues,a SWOT analysis was conducted to explore a suitable market positioning strategy for the CJ brand.The analysis reveals that the market demand for commercial fitness equipment is relatively saturated,with fierce competition.In contrast,the home fitness market represents a new blue ocean with promising development opportunities for the CJ brand.Leveraging excellent product quality,stringent quality control measures,and innovation-driven cutting-edge technology,it is recommended that CJ brand fitness equipment target young customers aged between 25 and 35 years old.Additionally,vigorously developing the home fitness product line is proposed to capitalize on this burgeoning market segment.

Architecting the Metaverse: Blockchain and the Financial and Legal Regulatory Challenges of Virtual Real Estate

There has been disagreement over the value of purchasing space in the metaverse, but many businesses including Nike, The Wendy’s Company, and McDonald’s have jumped in headfirst. While the metaverse land rush has been called an “illusion” given underdeveloped infrastructure, including inadequate software and servers, and the potential opportunities for economic and legal abuse, the “real estate of the future” shows no signs of slowing. While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the end of 2028. But the long-term legal and regulatory considerations of capitalizing on the investment, as well as the manner in which blockchain technology can secure users’ data and digital assets, has yet to be properly investigated. With the metaverse still in a conceptual phase, building a new 3D social environment capable of digital transactions will represent most of the initial investment in time in human capital. Digital twin technologies, already well-established in industry, will be ported to support the need to architect and furnish the new digital world. The return on and viability of investing in the “real estate of the future” raises questions fundamental to the success or failure of the enterprise. As such this paper proposes a novel framing of the issue and looks at the intersection where finance, technology, and law are converging to prevent another Dot-com bubble of the late 1990s in metaverse-based virtual real estate transactions. Furthermore, the paper will argue that these domains are technologically feasible, but the main challenges for commercial users remain in the legal and regulatory arenas. As has been the case with the emergence of online commerce, a legal assessment of the metaverse indicates that courts will look to traditional and established legal principles when addressing issues until the enactment of federal and/or state statutes and accompanying regulations. Lastly, whereas traditional regulation of real estate would involve property law, the current legal framing of ownership of metaverse assets is governed by contract law.

Research on interaction of innovation spillovers in the AI,Fin‑Tech,and IoT industries:considering structural changes accelerated by COVID‑19

This paper aims to probe the influence of innovation spillovers in the artificial intelligence(AI)and financial technology(Fin-tech)industries on the value of the internet of things(IoT)companies.Python was utilized to download public information from Yahoo Finance,and then the GARCH model was used to extract the fluctuations of cross-industry innovation spillovers.Next,the Fama–French three-factor model was used to explore the interactive changes between variables.The panel data regression analysis indicates that the more firms accept innovation spillovers from other industries,the better the excess return;however,this effect differs because of industrial attributes and the environmental changes induced by COVID-19.Additionally,this study finds that investing in large-cap growth stocks of IoT firms is more likely to yield excess returns.Finally,the study yields lessons for policy leverage to accelerate the upgrading and transformation of innovation-interactive industries by referring to the practices of Singapore and South Korea.

Exploring the asymmetric effect of COVID‑19 pandemic news on the cryptocurrency market:evidence from nonlinear autoregressive distributed lag approach and frequency domain causality

This paper explores the asymmetric effect of COVID-19 pandemic news,as measured by the coronavirus indices(Panic,Hype,Fake News,Sentiment,Infodemic,and Media Coverage),on the cryptocurrency market.Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heter-oskedasticity model,the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns,indicating fear of missing out behavior does not prevail.Furthermore,when the nonlinear autoregressive distributed lag model is esti-mated,both positive and negative shocks in pandemic indices promote Bitcoin’s daily changes;thus,Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil.The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns,whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index.These findings may have significant policy implications for investors and governments because they highlight the impor-tance of news during turbulent times.The empirical results indicate that pandemic news could significantly influence Bitcoin’s price.

Design of the contingent royalty rate as related to the type of investment

This study investigates the design of the royalty rate in a first-price auction across three types of investments:incremental and lumpy with or without an exogenously given intensity.A bidder’s investment cost comprises private information.This,together with the stochastic evolution of the price of the output generated from the auctioned project,precludes the seller from setting the exact dates of investment with the winner.However,the seller can set the royalty rate to equate the winner’s royalty payment with the winner’s information rent so that the winner acts as if to maximize the seller’s revenue.We derive two main conclusions.First,compared with the case in which investment is lumpy with an exogenously given intensity,the seller can set a lower royalty rate on incremental investment because she can collect additional royalty payments from the winner,who has the option to later expand capacity.Second,the impact of output price uncertainty on the optimal royalty rate for the three types of investments exhibits two different patterns.When investment is either incremental or lumpy with an exogenously given intensity,greater output price uncertainty reduces the royalty rate.When investment is lumpy with variable intensity,greater output uncertainty raises the royalty rate.Our results imply that auctioneers may charge differential royalty rates for different types of investments.

Probability of informed trading during the COVID‑19 pandemic:the case of the Romanian stock market

Using data from the Bucharest Stock Exchange,we examine the factors influencing the probability of informed trading(PIN)during February—October 2020,a COVID-19 pandemic period.Based on an unconditional quantile regression approach,we show that PIN exhibit asymmetric dependency with liquidity and trading costs.Furthermore,building a customized database that contains all insider transactions on the Bucharest Stock Exchange,we reveal that these types of orders monotonically increase the infor-mation asymmetry from the 50th to the 90th quantile throughout the PIN distribution.Finally,we bring strong empirical evidence associating the level of information asym-metry to the level of fake news related to the COVID-19 pandemic.This novel result suggests that during episodes when the level of PIN is medium to high(between 15 and 50%),any COVID-19 related news classified as misinformation released during the lockdown period,is discouraging informed traders to place buy or sell orders condi-tioned by their private information.

Roles of Corporate Reputation and Service Recovery in Food Safety Crises:A Case Study of Food Hygiene Incidents in Hot Pot Restaurants

Food hygiene incidents in hot pot restaurants were studied,and a relationship model between corporate reputation and consumers behavioral intention was established.Hot pot consumers of hot pot restaurants were surveyed through questionnaires.The results show that corporate reputation positively affects service recovery and repurchase intention,and service recovery positively influences repurchase intention,while corporate reputation affects repurchase intention through service recovery.In other words,under the situation of enterprise crises,the service recovery of an enterprise can restore its image and reputation.Therefore,when an enterprise has a crisis,it should positively respond to the crisis incident,and take timely crisis recovery to maintain its positive image.

Analysis of AY Field Brand Planning Based on Raw Milk Traceability

Mengniu and Yili have virtually monopolized the traditional Chinese dairy market,while new media marketing and digital strategies have opened up substantial business opportunities for emerging brands.Leveraging a differentiation strategy,the Adopt A Cow brand has successfully carved out its niche in the market,serving as an exemplary example.However,AY Field,as a self-owned brand by a supplier,faces the challenge of low product awareness among end consumers.This paper primarily concentrates on brand planning for the AY Field milk brand,with a focus on positioning the product for mid-range consumers,including students and office workers.The strategy involves the development of a new logo and packaging design,the selection of sports stars as brand ambassadors,and the implementation of innovative online sales promotions to bolster brand awareness and competitiveness.Research indicates that incorporating raw milk traceability and digital technology to establish a product brand with transparent ranches can significantly enhance AY Field’s competitive edge,facilitating the execution of a differentiated marketing strategy.

The Reform Process of English Medium Instruction in Korean Higher Education

In the context of the internationalization of higher education,many non-English speaking countries,from Europe to Asia,are promoting English as a medium of instruction(EMI).The EMI in South Korea started earlier and developed rapidly under the promotion.From the initial“Study Abroad in Korea Plan”to the World Class University(WCU)and Brain Korea 21st century(BK21)PLUS programs,with the support and guidance of the government,South Korea’s higher education has made significant progress,with the global top 100 universities in QS increasing from 1 to 6 in South Korea.

Research on the Impact of Low Carbon Economy on China’s Foreign Trade

Considering the growing prominence of global environmental issues,a low-carbon economy has emerged as a crucial direction for economic development across various countries.As the world’s second-largest economy,China has also witnessed the influence of a low-carbon economy on its international trade development.This article aims to commence with an exploration of the development background,meaning,and significance of a low-carbon economy.Subsequently,it delves into an in-depth analysis of the impact that a low-carbon economy has on China’s international trade.The article concludes by proposing pertinent countermeasures and suggestions.

Positioning Analysis of AY Field Brand

For a considerable duration,the traditional dairy product market in China has been dominated by a dual oligopoly,primarily led by Mengniu and Yili.Regional brands such as Guangming,Junlebao,and Sanyuan have engaged in staggered competition,contributing to a pattern of feudal separatism among local brands.AY Group,positioned as a significant independent player in the China dairy industry,has strategically extended its reach downstream in the supply chain,establishing the consumer brand AY Field.Despite possessing a robust advantage in self-owned ranches and delivering high-quality products,AY Field grapples with challenges related to unclear market positioning and low brand awareness among final consumers.This study employs a SWOT analysis to delve into identifying a market positioning strategy that best suits the AY Field brand.The research reveals that the current dynamics of competition in the dairy industry market are predominantly concentrated in the high-end and low-end segments,leaving a noticeable gap in the middle market.Leveraging the natural,high-quality,and competitively priced attributes of AY Field milk,the recommended market position involves supplying ambient liquid milk that aligns with the consumption habits of young individuals and office workers.This strategic positioning aims to offer high-quality products at moderate prices,addressing the specific needs of the identified consumer demographic.

Exploring Bilingual Instruction for Intermediate Microeconomics in the Context of Sino-Foreign Collaborative Education

With the establishment of intermediate microeconomics courses in an increasing number of economics majors at universities,its importance is becoming more prominent.This article analyzes the current situation and existing problems of bilingual teaching in intermediate microeconomics.It also proposes improvement suggestions for bilingual teaching in intermediate microeconomics under Sino-foreign collaborative education,addressing aspects such as teaching mode innovation,teaching team building,bilingual teaching differentiation,and diverse process assessments.

Spatio-Temporal Agricultural Drought Quantification in a Rainfed Agriculture, Athi-Galana-Sabaki River Basin

This study employs a quantitative approach to comprehensively investigate the full propagation process of agricultural drought, focusing on pigeon peas (the most grown crop in the AGS Basin) planting seasonal variations. The study modelled seasonal variabilities in the seasonal Standardized Precipitation Index (SPI) and Standardized Agricultural Drought Index (SADI). To necessitate comparison, SADI and SPI were Normalized (from −1 to 1) as they had different ranges and hence could not be compared. From the seasonal indices, the pigeon peas planting season (July to September) was singled out as the most important season to study agricultural droughts. The planting season analysis selected all years with severe conditions (2008, 2009, 2010, 2011, 2017 and 2022) for spatial analysis. Spatial analysis revealed that most areas in the upstream part of the Basin and Coastal region in the lowlands experienced severe to extreme agricultural droughts in highlighted drought years. The modelled agricultural drought results were validated using yield data from two stations in the Basin. The results show that the model performed well with a Pearson Coefficient of 0.87 and a Root Mean Square Error of 0.29. This proactive approach aims to ensure food security, especially in scenarios where the Basin anticipates significantly reduced precipitation affecting water available for agriculture, enabling policymakers, water resource managers and agricultural sector stakeholders to equitably allocate resources and mitigate the effects of droughts in the most affected areas to significantly reduce the socioeconomic drought that is amplified by agricultural drought in rainfed agriculture river basins.

Does Financial Globalization Discipline Macroeconomic Policies

Using the unbalanced panel data of 160 countries from 1970 to 2007,we employ inflation and the budget deficit as proxies for monetary policy and fiscal policy,respectively,and study whether financial globalization has discipline effects on these macroeconomic policies.The empirical results in our study suggest a significant discipline effect of financial globalization on monetary policy during the entire sample period,which is robust both to de jure and to de facto measures of financial openness.Our sub-sample investigations demonstrate that financial globalization reduces inflation only in higher-middle-income and high-income countries,and when financial globalization is scaled by the proportion of a country’s foreign assets and liabilities to its GDP,the discipline is evident only after 1988.Nevertheless,we do not demonstrate any evidence of financial globalization’s discipline effect on fiscal policy.The empirical results indicate that financial globalization even increases the budget deficit in certain countries and periods.

Achieving Fine-Grained and Flexible Access Control on Blockchain-Based Data Sharing for the Internet of Things

The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node.As a distributed and collaborative alternative,approaches based upon blockchain have been explored recently for Internet of Things(IoTs).However,the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the owners.In this paper,we first address these issues by incorporating the Accountable Subgroup Multi-Signature(ASM)algorithm into the Attribute-based Access Control(ABAC)method with Policy Smart Contract,to provide a finegrained and flexible solution.Next,we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized users.Finally,we evaluate our work by comparing its performance with the benchmarks.The results demonstrate significant improvement on the effectiveness and efficiency.

LEARNING RATES OF KERNEL-BASED ROBUST CLASSIFICATION

This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers.A comparison relationship between the excess misclassification error and the excess generalization error is provided;from this,along with the convex analysis theory,a kind of learning rate is derived.The results show that the performance of the classifier is effected by the outliers,and the extent of impact can be controlled by choosing the homotopy parameters properly.

Effects of investor sentiment on stock volatility:new evidences from multi-source data in China’s green stock markets

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry.With the proposal of China’s"dual carbon"target,green stocks have gradually become an essential branch of Chinese stock markets.Focusing on 106 stocks from the new energy,environmental protection,and carbon–neutral sectors,we construct two investor sentiment proxies using Internet text and stock trading data,respectively.The Internet sentiment is based on posts from Eastmoney Guba,and the trading sentiment comes from a variety of trading indicators.In addition,we divide the realized volatility into continuous and jump parts,and then investigate the effects of investor sentiment on different types of volatilities.Our empirical findings show that both sentiment indices impose significant positive impacts on realized,continuous,and jump volatilities,where trading sentiment is the main factor.We further explore the mediating effect of information asymmetry,measured by the volume-synchronized probability of informed trading(VPIN),on the path of investor sentiment affecting stock volatility.It is evidenced that investor sentiments are positively correlated with the VPIN,and they can affect volatilities through the VPIN.We then divide the total sample around the coronavirus disease 2019(COVID-19)pandemic.The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments,especially to Internet sentiment.Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.

Numerical Methods for a Class of Hybrid Weakly Singular Integro-Differential Equations

This paper proposes numerical methods for solving hybrid weakly singular integro-differential equations of the second kind. The terms in these equations are in the following order: derivative term of a state, integro-differential term of a state with a weakly singular kernel, a state, integral term of a state with a smooth kernel, and force. The original class of weakly singular integro-differential equations of the first kind is derived from aeroelasticity mathematical models. Among the proposed methods, the method for solving linear cases is fully based on previously reported approximation scheme for equations of the first kind. For nonlinear cases, a revised method is proposed. Examples are presented to demonstrate the effectiveness of the proposed methods, and the results indicate that the proposed methods facilitate achieving satisfactory and accurate approximations.

Numerical Algorithms for Solving One Type of Singular Integro-Differential Equation Containing Derivatives of the Time Delay States

This study presents numerical algorithms for solving a class of equations that partly consists of derivatives of the unknown state at previous certain times, as well as an integro-differential term containing a weakly singular kernel. These equations are types of integro-differential equation of the second kind and were originally obtained from an aeroelasticity problem. One of the main contributions of this study is to propose numerical algorithms that do not involve transforming the original equation into the corresponding Volterra equation, but still enable the numerical solution of the original equation to be determined. The feasibility of the proposed numerical algorithm is demonstrated by applying examples in measuring the maximum errors with exact solutions at every computed nodes and calculating the corresponding numerical rates of convergence thereafter.