Abstract We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify positive and negative bubbles in the short-, medium, and long-term for the Indian stock market, using weekly data from November 2003 to December 2020. We use a nonparametric causality-in-quantiles approach to analyze the predictive impact of monetary policy shocks on bubble indicators. We find, in general, strong evidence of predictability across the entire conditional distribution for the two monetary policy shock factors, with stronger impacts for negative bubbles. Our findings have critical implications for the Reserve Bank of India, academics, and investors.
The Malabo Declaration commits African Union member states to eliminating hunger by 2025. Progress toward this target has been uneven and poorly understood. While some countries have recorded gains in non-hunger thematic areas such as finance, trade, resilience to climate variability, and governance and accountability mechanisms, the extent to which these improvements contribute to hunger reduction remains unclear. This study investigates whether performance in non-hunger areas, as measured through the Comprehensive Africa Agriculture Development Programme Biennial Review C-scores, is statistically associated with outcomes under Commitment 3, which focuses on hunger reduction. We used random effects panel regression model covering 55 African countries from 2017 to 2023, the analysis identifies five significant predictors: agricultural GDP and poverty reduction (PC 4.1), foreign private investment (PC 2.3), multi stakeholder coordination (PC 1.2), inclusive public–private partnerships (PC 4.2), and trade policies (PC 5.2). Investment in resilience (PC 6.2) and capacity for planning and monitoring (PC 7.1) showed marginal associations. Our findings suggest that institutional presence alone does not drive hunger outcomes. We reflect that what matters is the structure, inclusiveness, and functionality of these mechanisms, including whether investments reach food-insecure populations, coordination platforms influence decisions, and policies adapt to local conditions. This study concludes that some high-performing categories fail to deliver tangible hunger reduction benefits when implementation is fragmented or disconnected from context. These findings challenge how progress is currently measured and interpreted at the regional level. Finally, we reiterate that as the region prepares for the post-2025 agenda, future strategies must directly link agricultural transformation to hunger reduction through targeted interventions and accountable institutions.
As new progress has been made in urban–rural integration in recent years, the gap between urban and rural development in China has gradually narrowed. However, balancing urban–rural relations, especially accelerating rural development, remains a great challenge. Quantitative research on the level of urban–rural integrated development (URIDL) and its drivers is essential for establishing harmonious urban–rural relations. By utilizing a URIDL indicator system, vertical and horizontal scatter degree method and Geodetector methods, this study evaluates the URIDL in the three agglomerations in China’s Yangtze River Economic Belt from 2010 to 2019, and provides an in-depth exploration of their spatiotemporal evolution and its influencing factors. The results show that: (1) Overall, the URIDL has been on the rise from 2010 to 2019, but the Chengdu-Chongqing urban agglomeration (CYUA) is significantly lower than the Yangtze River Delta urban agglomeration (YRDUA) and the middle reaches of the Yangtze River urban agglomeration (MRYRUA). (2) In terms of spatial distribution, the URIDL exhibits a clear clustering effect, with high value areas concentrated in the east (north) and low value areas concentrated in the west (south). (3) The study found that the interaction effects of factors such as the proportion of non-agricultural population to total population (X1), per capita arable land area (X3), and industrial structure (X6) are significant, surpassing the impact of single factors. In addition, the volatility of industrial structure (X6) reflects the impact of China’s economic transformation on URID. This study provides scientific guidance for the high-quality development of urban–rural integration in urban agglomerations. It also provides important theoretical and practical references for optimizing regional development layouts and promoting the coordinated development of urban and rural areas.
ObjectiveThe main objective of this study is to explore the relationships among job stress, job satisfaction, and turnover intentions among bank employees during the COVID-19 pandemic, with a focus on variations across different demographic characteristics.MethodsA cross-sectional design was employed, and data were collected from 501 bank employees using the Job Stress Questionnaire (JSQ), the Simplified Minnesota Satisfaction Questionnaire (MSQ), and the Intention to Leave Scale (ILS). Descriptive statistics, t-tests, ANOVA, Pearson correlation, and multiple regression analyses were used to test the research hypotheses.ResultsThe findings show that job stress is significantly negatively correlated with job satisfaction and positively correlated with turnover intention. Interpersonal relationship stress emerged as the strongest predictor of turnover intention, while job autonomy stress significantly influenced job satisfaction. Demographic factors, including age, income, and education level, moderated these relationships, with younger, higher-income, and more educated employees reporting lower stress and higher satisfaction. Employees with dependents reported higher stress levels, lower job satisfaction, and greater turnover intentions compared to those without dependents.ConclusionThis study underscores the importance of managing workplace stress and enhancing job satisfaction to reduce turnover intention, particularly during the COVID-19 pandemic. Interventions focused on improving interpersonal relationships and providing targeted support for older and lower-income employees are recommended to mitigate stress and improve retention rates.
Recently, the interest of researchers in the use of hybrid models in the process of analyzing model time series with fluctuations and forecasting fluctuations in financial time series has increased significantly. Hybrid ARMA-GARCH models were created for medium- and long-term forecasts of time series of financial market index prices: ARMA models are used to analyze their linear component, which is a combination of autoregressive models and moving average models, and GARCH models are used to analyze the nonlinear component. which are generalized autoregressive models that depend on the nonconstancy of variance models. Hybrid ARMA-GARCH models eliminate the weaknesses and gaps that exist in each group of models (ARMA and GARCH) separately, which increases their forecasting accuracy and reliability, so they have already been successfully applied to model and forecast daily stock returns for three standard indices in the USA. The purpose of this article is to investigate which of the hybrid ARMA-GARCH models is optimal for forecasting the return of the DAX index, which is the most important stock index of the German securities market. It is the German equivalent of the American Dow Jones Index, has been calculated since 1988 by Deutsche Börse AG and reflects the total return on capital of the largest stock companies listed on the Frankfurt Stock Exchange (currently 40; by 2021 – 30): calculated as a weighted average of capitalization of the value of Free Float share prices on the Xetra electronic exchange, and also takes into account dividends on shares, assuming that the dividend is reinvested in the share on which it was accrued. The database of this study consisted of the daily closing prices of the DAX index presented on the official website of the Frankfurt Stock Exchange during the period from 01.01.2018 to 09.30.2023 (altogether about 1,500 observations), the stability of the time series was assessed using Expanded Dickey Fuller Liquidity (ADF). The article proposes 7 hybrid models, from which the one that is best suited for modeling the volatility of the DAX index is selected. It is an ARMA (2,3)-EGARCH (1,1) model because it captures volatility and leverage effects on DAX returns and its expected returns more than other models. The selection of the best alternative from the developed array of hybrid models was carried out according to the following criteria: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), H-QIC (Hannan-Quinn Information Criterion).
Improving food security in contemporary conditions directly depends on solving numerous issues. It is crucial to consider that the processes of globalization significantly impact food security. In countries where the influence of import channels on the food market is high, this often leads to a deterioration in food security. Therefore, there is an urgent need for state regulation measures. The regulatory measures implemented by the government to improve food security, especially in the context of globalization and market economies, involve the application of guaranteed and intervention prices. The implementation of both guaranteed and intervention prices is considered one of the indirect tools for regulating the food market and does not contradict the fundamental principles of market economies. The application of these price controls particularly involves the government's procurement processes, which stabilize market prices and the incomes of agricultural producers. Such interventions create favorable conditions for stabilizing food prices and improving food security. In this environment, it becomes possible to improve food security by ensuring equal access to the food market for producers. Imbalances in the food market, particularly among low-income populations, can lead to food security issues.
Marketing. Distribution of products, Office management
The outflow of the rural labor force has a great impact on the location distribution of agricultural production, which has not attracted enough attention in existing studies. This article describes the mechanism of agricultural production location adjustment and further studies the influence of a regional difference in labor endowment on agricultural production location adjustment. Based on commodity and province-level panel data calculation, the results show that the agricultural production location in China has been adjusted from the East to the Central and then to the West with accelerating transfer speed. Furthermore, it is found that the regional differences in labor endowment are the main driving force for the shift of low alternative crop production. The more labor-intensive the crop varieties are, the more obvious the influence of the regional differences in labor endowment is on the crop production location adjustment. Moreover, regional differences in labor endowment have little effect on the location adjustment of vegetable production, as expanding the vegetable market demand may offset the effect of labor supply shortage on the adjustment of vegetable production location. Therefore, it is necessary to formulate a regional industrial development plan consistent with the trend of agricultural production location adjustment, as well as promote the construction of agricultural circulation facilities and socialized services in less developed areas.
Elmer V. Bernstam, Paula K. Shireman, Funda Meric‐Bernstam
et al.
Abstract Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011–2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.
Therapeutics. Pharmacology, Public aspects of medicine
This paper aims to investigate the impact of corporate debt on firm growth in Malaysia post Global Financial Crisis 2007-2008. Using a sample of 334 non-financial public listed companies in Bursa Malaysia from 2009 to 2018, this study finds that corporate debt is positively associated with firm growth. The possible reasons for this are; 1) the underdeveloped equity market in Malaysia that forced the firms to take up more debt as a financing resource and 2) the highly associated cost of issuing shares caused the firms to choose debt over equity, to finance the firms’ growth. The result is robust using the random effects panel regression model which mitigates unobserved heterogeneity. The finding supports the Pecking Order theory. The practical contribution of the study lies in the need for firms to deliberately design the application of debt in order to mitigate the associated cost of financial distress that arises from debt.
The purpose of the article is to present the essence and significance of social participation in the revitalization process as well as the implementation of its objectives by territorial self-government units.
A hypothesis was advanced for the purpose of the article that without social engagement the achievement of the desired goal which consists in restoring the area subjected to regeneration will be very difficult.
Methodology – The study was based on the survey of the literature on the subject, legal acts as well the information obtained by the author of the article on the activities with regard to social participation carried out in territorial self-government units.
Results of the research – The concentration of social problems in the inner city of Łódź is of a multifaceted character (it refers to various aspects of life concerning inter alia education, financial stabilisation, the level of affluence, the occurrence of pathological phenomena) and at the same time progressive (the social problems which emerged were not systematically solved and thus there was an escalation of negative phenomena over the course of time). The conducted study indicates that the current level of involvement of the inhabitants of Łodź in the affairs connected with revitalization is relatively low. From year to year the awareness regarding the important role of society in the revitalization process is rising. It is reflected in the increasing number of inhabitants who participate in the social activities offered by the self-government. The City inter alia organises meetings, workshops, and consultations. The global Covid-19 pandemic has not resulted in giving up the said activities.
Pavel Semerád, Milena Otavová, Lucie Semerádová
et al.
The paper is focused on the area of introducing new technologies and the consequent impact on a more efficient functioning of a company, with an emphasis on reducing labour costs. A case study has been created for a company that replaced its employees with technology to reduce the dependence of its performance on the staff. Our aim was to quantify the impact of this managerial decision on the company, as well as to quantify the negative impact on the state budget. With this decision, the company will not only reduce its dependence on its staff, but also its labour costs by 88.14% while the performance will remain the same. It can be assumed that this will also have a significant impact on the sustainability of public finances, as the state will suffer a loss of income in the case of personal income tax as well as social/health insurance; the mandatory expenditure may also increase, such as in the case of paying social benefits to the unemployed. In view of the sustainability of public finances, the paper proposes recommendations that could address this negative impact.
Artykuł dotyczy pandemii koronawirusa COVID-19 i jego potencjalnego wpływu na gospodarkę, system finansów publicznych oraz finanse osobiste. Szok związany z wybuchem i ekspansją wirusa w skali globalnej stawia przez gospodarkami, finansami publicznymi i systemami zdrowotnymi ogromne wyzwania związane z groźbą recesji, drastycznego zmniejszenia dochodów osobistych w gospodarstwach domowych, konieczność opracowania programów rządowych, łagodzących skutki pandemii oraz włączenia się Banku Centralnego w postaci obniżki stóp procentowych i stopy rezerwy obowiązkowej, a także uruchomienia niestandardowych instrumentów polityki monetarnej tzw. luzowania ilościowego (QE). Celem opracowania jest zaproponowanie języka nanofinansów do opisania pandemii jako destrukcji mechanizmu solidarności międzypokoleniowej i wewnątrzpokoleniowej. Główną hipotezą badania jest, że jakość zarządzania ryzykiem finansowym codzienności przesądzi o skali strat finansowych oraz czasie niezbędnym do wyjścia z załamania społeczno-gospodarczego wywołanego pandemią.
Abstract Background Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data. Methods A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China. Results The results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections. Conclusions By involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.
Infectious and parasitic diseases, Public aspects of medicine
The relevance of the research topic is related to the growth of publishing activity in the field of foreign trade policy and is based on the analysis of such publications in the world's largest abstract database Scopus. The aim is to characterize the areas of research in the field of public administration of foreign trade, which is implemented on the basis of the method of bibliometric analysis of research in the international scientometric database Scopus. The analysis allowed to outline the subject-object sphere of foreign trade policy in different periods during 1974–2020. The positive trend of increasing the number of publications in the Scopus database on the subject is confirmed by the fact that research in public administration intensified in the 80s of the twentieth century. In general, during the study period, most articles were published in such fields as economics, econometrics, finance, social sciences, which accounts for almost half of research in the field of foreign trade policy. Research in 1974–1979 is devoted to the disclosure of the problems of foreign trade and the formation of foreign policy of the world in the field of environmental sciences and earth and planetary sciences. Instead, modern scientific research (2016–2020) deals with such issues as: supply chain management, participation of countries in global value chains, digitalization of trade, trade wars, accompanied by risks of foreign trade. The connections between the main key terms encountered in the research topic are analyzed and 6 clusters are formed (world trade policy; foreign trade of the EU and the USA; consequences of foreign trade on the socio-ecological and economic world system; strategic directions of foreign trade development; WTO principles and the policy of the participating countries; the policy of protectionism (tariff and non-tariff methods). It is determined that the task of the public sector is to form effective mechanisms of state management of foreign trade activities on the basis of identifying problematic issues in the field of foreign trade and applying the results of foreign policy.