Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.

Analysis of the effect of the 2021 Semeru eruption on water vapor content and atmospheric particles using GNSS and remote sensing

Mount Semeru,an active volcano in East Java,Indonesia,erupted on December 4,2021,following extreme rainfall that caused an avalanche of hot pyroclastic flows and lava.The tropospheric conditions and dominant particle components in the atmosphere can be monitored using Global Navigation Satellite System(GNSS)technology and remote sensing satellites.GNSS signal propagation delay in Precise Point Positioning(PPP)processing can be used to determine Zenith Tropospheric Delay(ZTD)and Precipitable Water Vapor(PWV)variables so that atmospheric conditions can be generated.In addition,by using remote sensing satellite data,it is possible to obtain rainfall data with high temporal resolution as well as the dominant particle and gas content values during eruptions.During the eruption period,the high value of PWV was dominated by the high intensity of precipitation during the rainy season.High rainfall before the eruption caused activity inside the mountain to increase,which occurred in avalanche type eruption.Apart from that,the atmosphere around Semeru was also dominated by SO_(2)content,which spreaded for tens of kilometers.SO_(2)content began to be detected significantly by remote sensing sensors on December 7,2021.In this study,deformation and atmospheric monitoring were also carried out using low-cost GNSS at the Semeru Monitoring Station on September 9-15,2022.The results of the ZTD and ZWD values show the dominance of the wet component,which is directly proportional to rainfall activity in this period.

Privacy Protection in COVID Data Tracking: Textual Analysis of the Literature

The literary review presented in the following paper aims to analyze the tracking tools used in different countries during the period of the COVID-19 pandemic. Tracking apps that have been adopted in many countries to collect data in a homogeneous and immediate way have made up for the difficulty of collecting data and standardizing evaluation criteria. However, the regulation on the protection of personal data in the health sector and the adoption of the new General Data Protection Regulation in European countries has placed a strong limitation on their use. This has not been the case in non-European countries, where monitoring methodologies have become widespread. The textual analysis presented is based on co-occurrence and multiple correspondence analysis to show the contact tracing methods adopted in different countries in the pandemic period by relating them to the issue of privacy. It also analyzed the possibility of applying Blockchain technology in applications for tracking contagions from COVID-19 and managing health data to provide a high level of security and transparency, including through anonymization, thus increasing user trust in using the apps.

The Future Trend of E-Mobility in Terms of Battery Electric Vehicles and Their Impact on Climate Change: A Case Study Applied in Hungary

The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

Indirect Water Consumption:The Scope 3 of Water?Estimation of This Indicator in Sectors Relying on Agriculture

The aim of this research project is to assess indirect water consumption,that represents the water consumed in the upstream part of the production life cycle.Estimations are provided for agriculture related companies,as agriculture represents 70%of water consumption on Earth.This consumption varies greatly according to the countries where supply chain is located.The estimation is attempted for 27 European listed companies in sectors relying on agriculture in its supply chain.A data is estimated for 22 companies,showing that indirect water consumption is much more important than direct one.Strategic questions raised through the estimation of indirect water consumption in various subsectors show the interest in this data,which represents the equivalent of Carbon Scope 3 for water issues.

A Structured Method of Teaching Epidural Block reduces the incidence of accidental dural puncture in inexperienced trainees.

Background: The rate of accidental dural puncture is particularly high during the period of training, especially in novices. The structured epidural teaching model (SETM) includes three standardized video lessons, the construction of a 3D epidural module by trainees and practical training by using an epidural simulator with and without the CompuFlo™ Epidural instrument. In this study we report the retrospective analysis of the accidental dural puncture rate of inexperienced trainees during their 6 months clinical practice rotation in obstetrics before and after the introduction of the SETM in our Institution. Method: We evaluated the incidence of accidental dural puncture before the introduction of the SETM methodology and afterwards by analyzing our departmental database from February 2019 to January 2023. All epidural blocks were executed by trainees who had never previously performed an epidural block and were about to begin their obstetrics rotation. Results: We analyzed 7415 epidurals: 3703 were performed before the introduction of the SETM methodology (control group) and 3712 afterwards (study group). The incidence of accidental dural puncture was 0.37% for the control group and 0.13% for the study group (p<.05). The probability of making an accidental dural puncture was 64% (OR: 0.36) lower for trainees who had the training than for those who did not. Conclusions: After the introduction of the structured teaching method, we observed a significant reduction of accidental dural puncture during the training period. We hope that our observation will encourage a constructive discussion among experts about the need to use standardized and validated tools as a valuable aid in teaching epidural anesthesia.

Hourly Wage and the Likelihood of Stealing an Item

Researchers have extensively explored the impact of wages on individuals’ decisions to engage in property crimes. While most of these studies in the past have relied on macro-level data to investigate the relationship between crime rates and hourly wages, this paper takes a novel approach by utilizing micro-level data to examine the influence of hourly wages on the likelihood of stealing an item valued at least $50. The results obtained from the estimations reveal that an increase in hourly wage leads to a decrease in the probability of theft, all other factors being held constant. Further estimation by gender revealed that hourly wages given to both male and female have no bearing on the decision to steal. Additionally, the analysis of the differences in theft probabilities across gender and race demonstrates that males consistently exhibit a higher likelihood of engaging in theft when compared to females across various racial groups.

How signaling and search costs affect information asymmetry in P2P lending:the economics of big data

In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.

Exploring the moderating role of financial development in environmental Kuznets curve for South Africa:fresh evidence from the novel dynamic ARDL simulations approach

The extant literature has produced mixed evidence on the relationship between finan-cial development and ecological sustainability.This work addresses this conundrum by investigating financial development’s direct and indirect consequences on ecologi-cal quality utilizing the environmental Kuznets curve(EKC)methodological approach.Our empirical analysis is based on the novel dynamic autoregressive distributed lag simulations approach for South Africa between 1960 and 2020.The results,which used five distinct financial development measures,demonstrate that financial develop-ment boosts ecological integrity and environmental sustainability over the long and short terms.In the instance of South Africa,we additionally confirm the validity of the EKC theory.More importantly,the outcomes of the indirect channels demonstrate that financial development increases energy usage’s role in causing pollution while attenuating the detrimental impacts of economic growth,trade openness,and foreign direct investment on ecological quality.Moreover,the presence of an inadequate financial system is a requirement for the basis of the pollution haven hypothesis(PHH),which we examine using trade openness and foreign direct investment variables.PHH for both of these variables disappears when financial development crosses specified thresholds.Finally,industrial value addition destroys ecological quality while tech-nological innovation enhances it.This research provides some crucial policy recom-mendations and fresh perspectives for South Africa as it develops national initiatives to support ecological sustainability and reach its net zero emissions goal.

Construct Validation by Hierarchical Bayesian Concept Maps: An Application to the Transaction Cost Economics Theory of the Firm

A concept map is a diagram depicting relationships among concepts which is used as a knowledge representation tool in many knowledge domains. In this paper, we build on the modeling framework of Hui et al. (2008) in order to develop a concept map suitable for testing the empirical evidence of theories. We identify a theory by a set of core tenets each asserting that one set of independent variables affects one dependent variable, moreover every variable can have several operational definitions. Data consist of a selected sample of scientific articles from the empirical literature on the theory under investigation. Our “tenet map” features a number of complexities more than the original version. First the links are two-layer: first-layer links connect variables which are related in the test of the theory at issue;second-layer links represent connections which are found statistically significant. Besides, either layer matrix of link-formation probabilities is block-symmetric. In addition to a form of censoring which resembles the Hui et al. pruning step, observed maps are subject to a further censoring related to second-layer links. Still, we perform a full Bayesian analysis instead of adopting the empirical Bayes approach. Lastly, we develop a three-stage model which accounts for dependence either of data or of parameters. The investigation of the empirical support and consensus degree of new economic theories of the firm motivated the proposed methodology. In this paper, the Transaction Cost Economics view is tested by a tenet map analysis. Both the two-stage and the multilevel models identify the same tenets as the most corroborated by empirical evidence though the latter provides a more comprehensive and complex insight of relationships between constructs.

Quantum Model of Decision-Making in Economics

The paper designs a quantum model of decision-making (QMDM) that utilizes neuroscientific evidence. The new model provides both normative and positive implications to economics. First, it enhances the study of decision-making which is an extension of the expected utility theory (EUT) in mathematical economics. Second, we demonstrate how the quantum model mitigates drawbacks of the expected utility theory of today.

Dynamicity of Land Use/Land Cover(LULC):An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation(DMC)in India

The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.

Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

Effects of financial development and capital accumulation on labor productivity in sub‑Saharan Africa:new insight from cross sectional autoregressive lag approach

This study aims to shed light on the effects of financial development and accumula-tion of capital on the productivity of labor in the sub-Sahara African region within the period of 1990–2018.In this work,we used the(dynamic)common correlated effects estimator-mean group and additional techniques such as cross-section autoregressive distributed lag to calibrate the sample into the African subregion to ensure robustness.The findings reveal that financial progress in the region over time leads to an increase in productivity of labor and also the accumulation of capital.Furthermore,financial markets have a progressive impact on the productivity of labor within sub-Saharan African regions.We extend the very limited literature on the nexus between financial development and labor productivity by incorporating capital accumulation into our model which has not been previously studied.

Cybersecurity: Integrating Information into the Microeconomics of the Consumer and the Firm

The connectivity of information has changed many things but not the way economists model consumers, firms and government. Information is here newly modeled as a fundamental element of microeconomic choices and utility, cost and tax functions. The results are more clearly defined metrics for losses due to cyber breaches or productivity gains from cyber investments. The integration of information into standard microeconomics also allows use of econometric and other tools to analyze the empirics of the consumer and the firm. In particular, the results identify ways in which losses in the Gordon and Loeb [1] model can be specified in more detail.

Students' sustainability consciousness with the three dimensions of sustainability:Does the locus of control play a role?

The persistent environmental problems and the growing difficulty to achieve sustainable development require a re-evaluation of person’ sustainable knowledge, attitude, and behaviour. To go to where we want to go, we must first assess where we are now. The study investigated the level of sustainability consciousness of students in India, and sustainability consciousness was expressed in sustainability knowledge, attitude, and behaviour. Sustainability consciousness was further examined using the three dimensions, such as environment, society, and economy. By drawing the perception map of students and studying the role of the locus of control, the reasons why sustainability knowledge does not appear in sustainability behaviour were analyzed. Quantitative analysis was conducted for a sample of 205 students from higher educational institutions across central India(Madhya Pradesh) from January to March in 2022. The data were analyzed using the SPSS 26 and Smart Partial Least Square(PLS) 3.0. The research revealed that sustainability attitude impacts sustainability behaviour more than sustainability knowledge. The results showed that respondents’ sustainability knowledge is higher than their sustainability attitude and behaviour. Moreover, it was discovered that economic concerns can lead to sustainability behaviour. In order to study respondents’ behaviour regarding sustainability consciousness, the study divided respondents into two groups according to the locus of control: the internal and external locus of control. It was found that most respondents thought whatever happened around them was beyond their control. Further, the locus of control influenced sustainability behaviour in groups with the internal locus of control. The study helps to explain why sustainability knowledge and attitude cannot translate into sustainability behaviour and provides a basis for achieving sustainable development. The conclusions of this study are crucial for governments, educational institutions, and organizations that empower youth to change their behaviour.

Impact of trading hours extensions on foreign exchange volatility:intraday evidence from the Moscow exchange

Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze the impact of trading hours extensions on volatility.During the sample period,the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate.To analyze the effect of the implementations,various measures of historical and realized volatility are calculated for 5-and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions.Besides historical volatility measures,we also examine volume and spread.We apply an autoregressive moving average-autoregressive conditional heteroscedasticity(ARMA-GARCH)model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading.The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes.Volatility changes mostly occur after the opening of the market.The length of the extension has a significant positive effect on realized volatility.The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement,whereas this is not observed for next-day settlement.Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets,they may also lead to higher volatility in the market.Furthermore,this distortion is more significant at opening and midday.A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.

Socio-economic development of countries based on the Composite Country Development Index(CCDI)

World experience indicates the existence of significant imbalances in the development of countries.The problem of assessing the rational development of the regional and national economy is becoming urgent,since such assessments can prevent development imbalances across countries.The aim of this study is to elaborate a methodology to assess the countries’socio-economic development by integraring 12 modern indices of socio-economic development into the Composite Country Development Index(CCDI).The methodology of this research was based on a set of key indices that described socio-economic development level in four fields(social development,digital development,economic development,and environmental security)and then these indices were integrated into the CCDI.The study further applied factor analysis and R-Studio software to define the gaps of social and economic development in 59 selected countries using the trigonometric function of the angle sine.The correlation analysis confirmed the existence of a close interrelation among the studied countries.This paper noted that due to the emergence of new priorities,it is necessary to revise the assessment methodology of socio-economic development level and expand them to cover the decisive factors.This was confirmed by the results obtained,demonstrating various combinations of the development level in the four fields and their impact on the CCDI.The scientific contribution of this research is to form a methodology(e.g.,the CCDI)for evaluating the socio-economic development level of countries in the world.

The influence of CEO’s financial literacy on SMEs technological innovation:the mediating effects of MCS and risk‑taking

Previous literature showed mixed results about the impact of CEOs’financial literacy(CFL)on small and medium-sized enterprises’(SMEs)innovation.This relationship can be motivated by relevant variables,which are missing in the previous literature and make a difference as mediators.In this sense,based on the theoretical framework related to upper echelon theory and resource-based view,this study focuses on the mediating effect of risk-taking attitude and management control systems(MCS)varia-bles.Empirical data from 310 SMEs gathered using a qualitative research questionnaire are analyzed using structural equation modeling methodology.Specifically,estimations are carried out considering the partial least square method.Findings show that MCS and managers’risk attitudes fully mediate the relationship between financial literacy(FL)and innovation.Between these two mediating variables,the implementation of MCS stands out because it also enables the mediating effect of CEOs’risk-taking in the CFL–technological innovation relationship.As the results do not support the significant direct relationship between FL and risk attitude,they confirm an indirect effect through MCS.Furthermore,based on the study findings,SMEs’directors and owners,business associations,and public authorities can improve SMEs’technological innovation by implementing training programs and policies to foster CFL.They can also acknowledge the interdependency between organizational factors and individual characteristics to enhance SMEs’technological innovation.

X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and Ravaz index

Annual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions.Viticultural traits measuring vegetative growth,yield,berry quality,and nutrient uptake were collected at the beginning(1995 to 1999)and end(2017 to 2020)of the lifetime of a vineyard initially planted in 1991 and removed in 2021.X-ray Computed Tomography(CT)was used to measure ring widths in 103 vines.Ring width was modeled as a function of ring number using a negative exponential model.Early and late wood ring widths,cambium width,and scion trunk radius were correlated with 27 traits.Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay of later rings,consistently shortening ring width throughout the lifetime of the vine.Ravaz index,juice pH,photosynthetic assimilation and transpiration rates,and instantaneous water use efficiency are correlated with scion trunk radius.Ultimately,our research indicates that rootstocks modulate secondary growth over years,altering physiology and agronomic traits.Rootstocks act in similar but distinct ways from climate to modulate ring width,which borrowing techniques from dendrochronology,can be used to monitor both genetic and environmental effects in woody perennial crop species.