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.

Statistical Analysis of Abilities to Give Consent to Health Data Processing

The recent pandemic crisis has highlighted the importance of the availability and management of health data to respond quickly and effectively to health emergencies, while respecting the fundamental rights of every individual. In this context, it is essential to find a balance between the protection of privacy and the safeguarding of public health, using tools that guarantee transparency and consent to the processing of data by the population. This work, starting from a pilot investigation conducted in the Polyclinic of Bari as part of the Horizon Europe Seeds project entitled “Multidisciplinary analysis of technological tracing models of contagion: the protection of rights in the management of health data”, has the objective of promoting greater patient awareness regarding the processing of their health data and the protection of privacy. The methodology used the PHICAT (Personal Health Information Competence Assessment Tool) as a tool and, through the administration of a questionnaire, the aim was to evaluate the patients’ ability to express their consent to the release and processing of health data. The results that emerged were analyzed in relation to the 4 domains in which the process is divided which allows evaluating the patients’ ability to express a conscious choice and, also, in relation to the socio-demographic and clinical characteristics of the patients themselves. This study can contribute to understanding patients’ ability to give their consent and improve information regarding the management of health data by increasing confidence in granting the use of their data for research and clinical management.

Teaching programming skills to finance students:how to design and teach a great course

A motivated finance-major student should master at least one programming language.This is especially true for students from quantitative finance,business analytics,those attending a Master of Science in Finance or other financial engineering programs.Among the preferred languages,R holds one of the first places.This paper explains seven critical factors for designing and teaching a programming course:strong motivation,a good textbook,hands-on learning environment,being data intensive,a challenging term project,multiple supporting R datasets,and an easy way to upload such R datasets.

A Contribution to a Better Understanding of Global Land Grabbing＇s Impact on Employment and Environmental Threats

In the last two decades, the global interest on farmland grew at a remarkable pace. As a consequence, million hectares of land exchanged hands. The ways the transfers happened combined with their geographic concentration in Sub-Saharian Africa, have earned the phenomenon the name of ＂land grab＂. The agricultural sector considered a ＂sunset industry＂ when commodities prices were declining, is now attractive to financial investors. These foreign investments may be good as they may improve agricultural productivity or instead bad as they may benefit only financial investors. Some results in terms of environmental and local communities＇ worsening conditions have already emerged. This paper aims to investigate what drives the big size transfers of land, to empirically estimate their effects in terms of local employment and to assess the environmental effects produced by the rapid transformation in the use of vast amount of land in terms of CO2 emissions. It is also proposed to use the estimation in terms of local employment impact as a way of distinguishing between foreign direct investment and land grabbing.

Application of a distributed verification in Islamic microfinance institutions:a sustainable model

The literature gap in microfinance paradox of double bottom line(financial performance vs.outreach)has always been an interesting area of research.This paper proposes a theoretical model most suitable for Islamic Microfinance Institutions(MFIs)which enables Islamic MFIs’to operate together with the existing financial models compliant with Islamic Shariah Law.This model is based on a distributed verification/decision-making process that might be realized(but not necessary)through block-chain.Among the available distributed verification techniques,blockchain technology is an attractive emerging computing paradigm due to its decentralized,immutable,shared,and secure data structure characteristics.This model proposes three significant propositions.First,sharing information through blockchain will allow a transparent network in MFI operations,which will raise confidence for donors resulting in a causal effect of a relatively lower profit rate to be charged by the MFIs.Second,the consensus mechanism will enable risk-sharing,a character of Islamic finance;thus,the MFIs will operate without any collateral for low-risk firms.Third,the double bottom line of MFIs’long-lasting paradox would be solved.As for practical implication of this proposed model,the causal impact of lower cost investment by the lenders would increase social welfare because of no collateral and no initial wealth requirement.The proposed model proposes a credit rationing approach where profit can be negative.No collateral will be used when calculating the creditworthiness of a borrower.

Mean Difference and Mean Deviation of Tukey Lambda Distribution

The purpose of this paper is to broaden the knowledge of mean difference and, in particular, of an important distribution model known as Tukey lambda, which is generally used to choose a model to fit data. We have obtained compact formulas, which are not yet reported in literature, of mean deviation and mean difference related to the said distribution model. These results made it possible to analyze the relationships among variability indexes, namely standard deviation, mean deviation and mean difference, regarding Tukey lambda model.

Extreme dependencies and spillovers between gold and stock markets:evidence from MENA countries

This study addresses whether gold exhibits the function of a hedge or safe haven as often referred to in academia.It contributes to the existing literature by(i)revisiting this question for the principal stock markets in the Middle East and North Africa(MENA)region and(ii)using the copula-quantile-on-quantile and conditional value at risk methods to detail the risks facing market participants provided with accurate information about various gold and stock market scenarios(i.e.,bear,normal,bull).The results provide strong evidence of quantile dependence between gold and stock returns.Positive correlations are found between MENA gold and stock markets when both are bullish.Conversely,when stock returns are bearish,gold markets show negative correlations with MENA stock markets.The risk spillover from gold to stock markets intensified during the global financial and European crises.Given the risk spillover between gold and stock markets,investors in MENA markets should be careful when considering gold as a safe haven because its effectiveness as a hedge is not the same in all MENA stock markets.Investors and portfolio managers should rebalance their portfolio compositions under various gold and stock market conditions.Overall,such precise insights about the heterogeneous linkages and spillovers between gold and MENA stock returns provide potential input for developing effective hedging strategies and optimal portfolio allocations.

Tail spillover effects between cryptocurrencies and uncertainty in the gold,oil,and stock markets

This study investigates tail dependence among five major cryptocurrencies,namely Bitcoin,Ethereum,Litecoin,Ripple,and Bitcoin Cash,and uncertainties in the gold,oil,and equity markets.Using the cross-quantilogram method and quantile connectedness approach,we identify cross-quantile interdependence between the analyzed variables.Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles,implying that diversification benefits for these assets may differ widely across normal and extreme market conditions.Under normal market conditions,the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions.Moreover,we show that under all market conditions,cryptocurrencies have a leadership influence over the volatility indices.Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges,as we show that cryptocurrency and volatility markets are insignificantly(weakly)connected under normal(extreme)market conditions.

Do farmers care about climate change? Evidence from five major grain producing areas of China

Using a logistic model,this paper empirically investigated farmers’perception of climate change and its determinants based on a field survey of 1 350 rural households across five major grain producing provinces in China.The results show:i)There is an apparent difference in perception levels for long-term temperature and precipitation changes.Specifically,57.4%of farmers perceived the long-term temperature change correctly,but only 29.7%of farmers perceived the long-term precipitation change correctly;ii)The factors influencing the farmers’perceptions are almost completely different between precipitation and temperature,the former are mostly agriculture related,while latter are mostly non-agriculture related,except for farm size;and iii)Farmers are not expected to pay more attention to long-term precipitation changes over the crop growing seasons,because less than 30%of farmers can correctly perceive long-term precipitation change.Therefore,to improve the accuracy of farmers’perceptions of climate change,the government is recommended to:i)enhance education and training programs;ii)speed up land transfer and expand household land farm size;iii)develop farmer cooperative organizations;iv)invest more in agricultural infrastructure,specifically in major grain producing regions;and v)improve the agricultural environment and increase farming income.

What determines irrigation efficiency when farmers face extreme weather events? A field survey of the major wheat producing regions in China

Water availability is a major constraint on grain production in China, therefore, improving irrigation efficiency is particularly important when agriculture faces extreme weather events. This paper first calculates irrigation efficiency with a translog stochastic frontier production function and then investigates what happens when extreme weather events occur via a Tobit model. The estimated results reveal several important features of irrigation practices: i) irrigation efficiency is lower when extreme weather events occur; ii) large variations in irrigation efficiency occur across irrigation facilities; iii) the farm plots exhibit an extreme distribution across efficiency levels; and iv) water-saving techniques, technology adoption, and the maintenance of farmers’ economic resilience are major determinants of irrigation efficiency. Based on these results we propose the following recommendations: i) farmers should balance crop yield and water use; undertake relevant training programs and adopt water-saving techniques; ii) local governments and researchers should help farmers to find the optimal level of irrigation water use based on their own circumstances and provide better water-saving techniques and training programs rather than simply encouraging farmers to invest in irrigation facilities in the most extreme weather years; and iii) the income level of farm households should be increased so as to improve their resilience to natural disasters.

Capital mobility in Latin American and Caribbean countries: new evidence from dynamic common correlated effects panel data modeling

This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel data techniques.This is the first study on capital mobility in Latin American and Caribbean countries to employ the recently developed panel data procedure of the dynamic common correlated effects modeling technique of Chudik and Pesaran(J Econ 188:393–420,2015)and the error-correction testing of Gengenbach,Urbain,and Westerlund(Panel error correction testing with global stochastic trends,2008,J Appl Econ 31:982–1004,2016).These approaches address the serious panel data econometric issues of crosssection dependence,slope heterogeneity,nonstationarity,and endogeneity in a multifactor error-structure framework.The empirical findings of this study reveal a low average(mean)savings–retention coefficient for the panel as a whole and for most individual countries,as well as indicating a cointegration relationship between saving and investment ratios.The results indicate that there is a relatively high degree of capital mobility in the Latin American and Caribbean countries in the short run,while the long-run solvency condition is maintained,which is due to reduced frictions in goods and services markets causing increase competition.Increased capital mobility in these countries can promote economic growth and hasten the process of globalization by creating a conducive economic environment for FDI in these countries.

Detecting conflicts of interest in credit rating changes:a distribution dynamics approach

In this study,we compare the adjustments of credit ratings by an investor-paid credit rating agency(CRA),represented by Egan-Jones Ratings Company,and an issuer-paid CRA,represented by Moody’s Investors Service,vis-à-vis conflict of interest and reputation.A novel distribution dynamics approach is employed to compute the probability distribution and,hence,the downgrade and upgrade probabilities of a credit rating assigned by these two CRAs of different compensation systems based on the dataset of 750 U.S.issuers between 2011 and 2018,that is,after the passage of the Dodd–Frank Act.It is found that investor-paid ratings are more likely to be downgraded than issuerpaid ratings only in the lower rating grades,which is consistent with the argument that investor-paid agencies have harsher attitudes toward potentially defaulting issuers to protect their reputation.We do not find evidence that issuer-paid CRAs provide overly favorable treatments to issuers with threshold ratings,implying that reputation concerns and the Dodd–Frank regulation mitigate the conflict of interests,while issuerpaid CRAs are more concerned about providing accurate ratings.

Long-Term Projections of Global Food Requirements: Why Were We Wrong?

This article reports on and analyzes long-term projections of world food requirements compared with observed 2000 data reported by the United Nations’ Food and Agriculture Organization. The importance of this “post-mortem” is to strengthen the case for carrying out long-term projections of essential resources—food, energy, and non-fuel minerals— because of the long-lead times needed to insure that adequate global output levels of these basic ingredients of living standards will be met. This study should prove useful to those preparing today’s long-term projections, with world population projected to rise to over 9bn by mid-century.

The Economic Impacts of Restrictions on the Transportation of Petroleum Coke

Petroleum coke is the?third?leading refined petroleum product export from the US behind distillate fuel oil. Legal challenges and proposals could either increase the cost or restrict the transportation of petroleum coke. This paper develops an econometric model of world markets for refined petroleum markets to estimate the effects of such restrictions. The model is used to estimate how supply, demand, trade flows, and prices would adjust under a shutdown of US petroleum coke production. The market impacts are significant, withsubstantially higher prices for jet fuel and petroleum coke, significantly higher prices for gasoline and other products, and sharply lower prices for residual fuel oil. Over a four-year simulation of the model, the US petroleum trade balance deteriorates by $85 billion and consumers pay over $187 and $376 billion more for refined petroleum products in the US and the rest of the world respectively.

Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment

Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance.To solve the task scheduling problem for remote sensing data processing in cloud computing,this paper proposes a workflow task scheduling algorithm—Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning(WDRL).The remote sensing data process modeling is transformed into a directed acyclic graph scheduling problem.Then,the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation method.Finally,combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes from experience.The experiment is based on the development of CloudSim and Python and compares the change of completion time in the process of remote sensing data.The results showthat compared with several traditionalmeta-heuristic scheduling algorithms,WDRL can effectively achieve the goal of optimizing task scheduling efficiency.

Youth voting and institutional change in the post-Arab Spring MENA region

Using a unique and novel dataset on the youth,the SAHWA Youth Survey(2016),we apply probit and ordered probit models to study the determinants of voting behaviour change among the youth in the Middle East and North Africa(MENA)region in the post-Arab Spring era.We find that drivers of voting vary depending on whether we consider the voting behaviour in the last elections or the likelihood of voting in the next elections.Specifically,socioeconomic variables and some Arab Spring factors are significant for both types of elections.However,institutional variables and personal beliefs only affect the likelihood of voting in the next elections.We also document heterogeneous effects for the last and next votes by gender.

Geographic Spread of Currency Trading:The Renminbi and Other Emerging Market Currencies

This paper studies the ongoing diffusion of renminbi (RMB) trading across the globe, the first of such research of an international currency. It analyses the distribution in offshore RMB trading in 2013 and 2016 using comprehensive data from the Triennial Central Bank Survey of foreign exchange markets. In 2013, Asian centers favored by the policy of RMB internationalization had disproportionate shares in global RMB trading. Over the following three years, RMB trading seemed to converge to the spatial pattern of all currencies, with a half-life of seven to eight years. The previously most traded emerging market currency, the Mexican peso, shows a similar pattern, although it is converging to the global norm more slowly. Three other emerging market currencies show a qualitatively similar evolution in the geography of their offshore trading. Overall, the RMB's internationalization is tracing an arc from the influence of administrative measures to the working of market forces.

Consumer lending efficiency:commercial banks versus a fintech lender

Fintechs are believed to help expand credit access to underserved consumers without taking on additional risk.We compare the performance efficiency of LendingClub’s unsecured personal loans with similar loans originated by banks.Using stochastic frontier estimation,we decompose the observed nonperforming loan(NPL)ratio into three components:the best-practice minimum NPL ratio,the excess NPL ratio,and a statistical noise,the former two of which reflect the lender’s inherent credit risk and lending inefficiency,respectively.As of 2013 and 2016,we find that the higher NPL ratios at the largest banks are driven by inherent credit risk,rather than lending inefficiency.Smaller banks are less efficient.In addition,as of 2013,LendingClub’s observed NPL ratio and lending efficiency were in line with banks with similar lending volume.However,its lending efficiency improved significantly from 2013 to 2016.As of 2016,LendingClub’s performance resembled the largest banks–consistent with an argument that its increased use of alternative data and AI/ML may have improved its credit risk assessment capacity above and beyond its peers using traditional approaches.Furthermore,we also investigate capital market incentives for lenders to take credit risk.Market value regression using the NPL ratio suggests that market discipline provides incentives to make less risky consumer loans.However,the regression using two decomposed components(inherent credit risk and lending inefficiency)tells a deeper underlying story:market value is significantly positively related to inherent credit risk at most banks,whereas it is significantly negatively related to lending inefficiency at most banks.Market discipline appears to reward exposure to inherent credit risk and punish inefficient lending.

Mu-Net:Multi-Path Upsampling Convolution Network for Medical Image Segmentation

Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.

High frequency multiscale relationships among major cryptocurrencies:portfolio management implications

This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin,Ethereum,Monero,Dash,Ripple,and Litecoin.We apply nonlinear Granger causality and rolling window wavelet correlation(RWCC)to 15 min-data.Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies,especially between Bitcoin,Ethereum,and Monero.The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs.We advance evidence to improve portfolio risk assessment,and hedging strategies.