Often‎, ‎reliability systems suffer shocks from external stress factors‎, ‎stressing the system at random‎. ‎These random shocks may have non-ignorable effects on the reliability of the system‎. ‎In this paper‎, ‎we provide sufficient and necessary conditions on components' lifetimes and their survival probabilities from random shocks for comparing the lifetimes of two fail-safe systems in two cases‎: ‎(i) when components are independent‎, ‎and then (ii) when components are dependent‎. ‎We then apply the results for some distribution-free random variables with possibly different parameters to illustrate the established results.

Often, reliability systems suffer shocks from external stress factors, stressing the system at random. These random shocks may have non-ignorable effects on the reliability of the system. In this paper, we provide sufficient and necessary conditions on components' lifetimes and their survival probabilities from random shocks for comparing the lifetimes of two $(n-1)$-out-of-$n$ systems in two cases: (i) when components are independent, and then (ii) when components are dependent.

The accuracy and reliance increase and consequently reduction of uncertainty of spatial prediction maps of environmental hazards including landslides is one of the challenges facing with in such studies Therefore, the objective of this research is to introduce a hybrid model of data mining algorithm named Random Forest (RF) -Random Subspace (RF-RS) in order to enhance the accuracy of spatial prediction map of landslide prone areas around the city of Bijar, Kurdistan province, Iran. Firstly, 19 affecting factors on shallow landslides in the study area including slope degree, slope aspect, elevation, curvature, profile curvature, plan curvature, solar radiation, stream power index (SPI), topographic wetness index (TWI), length-angle of slope (LS), land use, normalized difference vegetation index (NDVI), litho logy, distance to fault, fault density, rainfall, distance to stream, stream density and distance to road were identified. Then based on Information Gain Ratio (IGR), twelve factors among them were selected to be used in modeling. The elative importance of each factor was assessed by Random Forest (RF) model as well as Random Forest-Random Subspace (RF-RS) hybrid model. Kappa, Precision, Recall, and AUROC indices were used to evaluate the models not only for training dataset but also for testing dataset. Shallow landslide susceptibility maps of the study area were prepared using both models. The results showed that slope aspect in the RF model and slope degree in the RF-RS hybrid model is the most important affecting factor on landslide occurrence in the area. The model evaluation results indicated that both models are reasonable in application for shallow landslide susceptibility mapping. The findings also indicated that the percentage of area under the curve of ROC (AUROC) was 0.729 and 0.784 for training dataset by RF and RF-RS hybrid model, respectively, while these values were 0.717 and 0.771 for testing dataset. In conclusion, it can be claimed that the new technique (RF-RS hybrid model) is able to increase the accuracy of spatial prediction map of shallow landslides in the study area. This accurate map will help decision-makers to protect infrastructures of an area, to develop better land-use planning programs and to more effectively control sediments.

The electrical behavior of neurons can be more complex in the presence of autapse. In the presence of an autaptic connection, the Izhikevich neuron model can show a variety of dynamic behaviors, such as chaotic behavior. This paper presents a novel, high speed and robust pseudo random number generator based on the autaptic Izhikevich neuron oscillator and its FPGA implementation. The autaptic Izhikevich neuron model is simulated and dynamically analyzed. Then, the proposed pseudo-random number generator is modeled and simulated using the Xilinx system generator platform, synthesized using Xilinx Synthesis Tool, and implemented on the XILINX SPARTAN-6 XC6SLX9 FPGA evaluation board. As a post processing operation, the XOR function is used to increase the randomness of the output bit sequences. The FPGA implementation results show that the implementation cost of the proposed pseudo-random number generator is lower than similar works, and the proposed generator achieves a maximum frequency of 63.2 MHz. The NIST test suite is used for testing the quality of the generated bit sequences. The NIST test results indicates the high quality of the generated random bit sequence.

This study aimed to estimate the genetic parameters of body weight traits in Markhoz goats, using B-spline random regression models. The data used in this study included 19549 records collected during 29 years (1992-2021) in Markhoz goat Breeding Research Station, located in Sanandaj, Iran. The model used to analyze data included fixed effects (year of birth, sex, type of birth and age of dam) and random effects including direct additive genetic, maternal additive genetic, permanent environmental and maternal permanent environmental assuming homogeneous and heterogeneous residual variance during the time. Akaike (BIC) and Bayesian (BIC) information criteria were used to compare the models and bspq.4.4.4.4 was selected as the best model. The direct heritability values for birth, 3-month, 6-month, 9-month and 12-month weights were estimated to be 0.14, 0.16, 0.08, 0.28 and 0.26, respectively. Genetic correlation between body weights at birth and 3-month, birth and 6-month, birth and 9-month, birth and 12-month, 3-month and 6-month, 3-month and 9-month, 3-month and 12-month, 6-months and 9-month and 9-month and 12-month were 0.22, 0.38, 0.21, 0.56, -0.26, 0.30, 0.62, 0.86 and 0.77, respectively. The highest phenotypic correlation was between the weight of 9-month and 12-month (0.82) and the lowest correlation was between birth weight and 3-month and 6-month (0.12). The results showed that the 9-month weight is a good criterion for selection in Markhoz goats.