Bank efficiency in Middle East and North African countries:Does political connection type matter?

This study examines the effects of the political connections of chief executive officers(CEOs)or directors on technical,allocative,and cost bank efficiencies examining a panel of 144 banks operating in 12 Middle Eastern and North African(MENA)countries observed over the 2008–2021 period.Using random effect tobit regressions,we find that the three types of political connections explored(aggregate,CEO,and board of directors)have negative effects on banks’technical and cost efficiencies.In addition,CEO political connections exhibit superior explanatory power.These findings remain robust when we consider the sample in terms of monarchist and republican countries.Further evidence reveals that the effect of political connections is observed more strongly during the pandemic period(2020–2021)than during the 2008–2009 financial crisis period.Our results indicate that banks in MENA countries must strategically regulate bank political connections during crises and consistently thereafter.Our findings have implications for regulators investors and authorities in MENA countries.

Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water shortages.This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers.The proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and bean.The proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real time.This helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop failure.Our dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research centers.The experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,respectively.The new smart greenhouse automation system was also evaluated on four crops with a high turnover rate.The system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

Effectiveness and time-course adaptation of resistance training vs. plyometric training in prepubertal soccer players

Purpose:This study aimed to assess the effectiveness and time course for improvements in explosive actions through resistance training(RT)vs.plyometric training(PT)in prepubertal soccer players.Methods:Thirty-four male subjects were assigned to:a control group(n=11);an RT group(5 regular soccer training sessions per week,n=12);a PT group(3 soccer training sessions and 2 RT sessions per week,n=11).The outcome measures included tests for the assessment of muscle strength(e.g.,1 repetition maximum half-squat test),jump ability(e.g.,countermovement jump,squat jump,standing long jump,and multiple 5 bounds test),linear speed(e.g.,20m sprint test),and change of direction(e.g.,Illinois change of direction test).Results:The RTG showed an improvement in the half-squat(△=13.2%;d=1.3,p<0.001)and countermovement jump(△=9.4%;d=2.4,p<0.001)at Week 4,whereas improvements in the 20-m sprint(△=4.2%;d=1.1,p<0.01);change of direction(CoD)(△=3.8%;d=2.1,p<0.01);multiple 5 bounds(△=5.1%;d=1.5,p<0.05);standing long jump(△=7.2%;d=1.2,p<0.01);squat jump(△=19.6%;d=1.5,p<0.01);were evident at Week 8.The PTG showed improvements in CoD(△=2.1%;d=1.3,p<0.05);standing long jump(△=9.3%;d=1.1,p<0.01);countermovement jump(△=16.1%;d=1.2,p<0.01);and squat jump(△=16.7%;d=1.4,p<0.01);at Week 8 whereas improvements in the 20-m sprint(△=4.1%;d=1.3,p<0.01);and multiple 5 bounds(△=7.4%;d=2.4,p<0.001);were evident only after Week.The RT and PT groups showed improvements in all sprint,CoD,and jump tests(p<0.05)and in half-squat performance,for which improvement was only shown within the RTG(p<0.001).Conclusion:RT and PT conducted in combination with regular soccer training are safe and feasible interventions for prepubertal soccer players.In addition,these interventions were shown to be effective training tools to improve explosive actions with different time courses of improvements,which manifested earlier in the RTG than in the PTG.These outcomes may help coaches and fitness trainers set out clear and concise goals of training according to the specific time course of improvement difference between RT and PT on proxies of athletic performance of prepubertal soccer players.

Dynamic connectedness between stock markets in the presence of the COVID‑19 pandemic:does economic policy uncertainty matter?

This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.

Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.