Suchergebnisse

Early warning indicators (EWIs) of banking crises should ideally be judged on how well they function in relation to the choice issue faced by macroprudential policymakers. Several practical features of this challenge are translated into statistical evaluation criteria, including difficulties measuring the costs and advantages of various policy interventions, as well as requirements for the timeliness and stability of EWIs. We analyze the balance panel of possible EWIs for six countries that have experienced currency crisis and banking crisis in recent times. Using possible early warning indicators, we evaluate the suitability of these EWIs in view of their predictive power and stability of signals. The paper observes that credit disbursements to non-financial sectors and central government provides stable signal about systemic risks. Further debt service ratio, inter bank rates and total reserves are also found to be useful in predicting these crisis. Lastly, the paper observes that linear combination of these indicators improves the predictive power of EWIs further.

This study aimed to predict the culturally responsive teacher roles based on cultural intelligence and self-efficacy using machine learning classification algorithms. The research group consists of 415 teachers from different branches. The Bayes classifier (NaiveBayes), logistic-regression (SMO), lazy-classifier (KStar), meta-classifier (LogitBoost), rule-learner (JRip), and decision-tree (J48) were employed in the assessment of the predictive model. The results indicated that JRip rule-learner had a better performance than other classifiers in predicting the culturally responsive teachers based on six attributes used in the study. The JRip rule-learner classified the culturally responsive teachers as low, medium, or high with an accuracy of 99.76% (CCI: 414/415) [Kappa statistic: 0.996, Mean Absolute Error (MAE): 0.003, Root Mean Square Error (RMSE): 0.043, Relative Absolute Error (RAE): 0.663, Relative Squared Error (RRSE): 9.244]. The results indicated that all classifiers had an acceptable performance but JRip rule-learner had a better performance than the other classifiers in predicting the culturally responsive teachers.

In this study, a test device has been developed that can be used to test various vehicle cables and cable harnesses in the automotive sector. The test device can perform basic measurements such as conductivity, short circuit, voltage, resistance, capacity, and specific measurements such as diodes and active relays among the number of test points varying between 128-1024. It performs the programmed tests step by step by processing the Test Source Code (*.tkk) files prepared with the designed Interface Program, loaded from the SD memory card in the device. The tester system consists of a selection of test source codes to be run and a general-purpose USB barcode reader, a general-purpose barcode printer, external LED units, an Optional External Unit (OHB) that offer a larger screen and various features if desired, and monitor. The OHB unit can be a Raspberry PI based mini-computer that can be mounted inside or outside the device, or any Windows based computer or Industrial computer can be used if desired. The test device realized has been operated stably with all its functions and has been successfully tested.