A general stochastic model for sporophytic self-incompatibility

Disentangling the processes leading populations to extinction is a major topic in ecology and conservation biology. The difficulty to find a mate in many species is one of these processes. Here, we investigate the impact of self-incompatibility in flowering plants, where several inter-compatible classes of individuals exist but individuals of the same class cannot mate. We model pollen limitation through different relationships between mate availability and fertilization success. After deriving a general stochastic model, we focus on the simple case of distylous plant species where only two classes of individuals exist. We first study the dynamics of such a species in a large population limit and then, we look for an approximation of the extinction probability in small populations. This leads us to consider inhomogeneous random walks on the positive quadrant. We compare the dynamics of distylous species to self-fertile species with and without inbreeding depression, to obtain the conditions under which self-incompatible species could be less sensitive to extinction while they can suffer more pollen limitation

Branching Feller diffusion for cell division with parasite infection

We describe the evolution of the quantity of parasites in a population of cells which divide in continuous-time. The quantity of parasites in a cell follows a Feller diffusion, which is splitted randomly between the two daughter cells when a division occurs. The cell division rate may depend on the quantity of parasites inside the cell and we are interested in the cases of constant or monotone division rate. We first determine the asymptotic behavior of the quantity of parasites in a cell line, which follows a Feller diffusion with multiplicative jumps. We then consider the evolution of the infection of the cell population and give criteria to determine whether the proportion of infected cells goes to zero (recovery) or if a positive proportion of cells becomes largely infected (proliferation of parasites inside the cells)

Assessing the Software Control Autonomy of System Functions in Safety-Critical Systems

Software Control Category (SCC) denotes the degree of control autonomy, command and control authority, and redundant fault tolerance software has over hazardous system functions of safety-critical systems. The use of SCC for determining the software contribution to system risks is a unique feature of the MIL-STD-882E System Safety Standard. A lower SCC designation means that the software system has a greater control autonomy over hazardous system functions, whereas SCC 1 means complete autonomous control. Software with greater control autonomy over hazardous system functions require greater effort to assure reliability and safety. Correct assessment of the SCC level of hazardous system functions is crucial for optimizing the safety property of a system developed under budget, schedule, and resource constraints. Beyond the categorical definitions provided by the MIL-STD-882E Standard, there is little information on conducting an SCC assessment. To close this knowledge gap, we present an SCC assessment method. Our paper will describe in detail the process and rules for assessing SCC. For illustration, we apply our method to assess the SCC of several safety-significant functions of an automobile’s brake-assist system

Deep convolutional neural network-based transfer learning method for health condition identification of cable in cable-stayed bridge

The cables are extremely important and vulnerable components in the cable-stayed bridges. Because cable tension is one of the most crucial structural health indicators, therefore, assessing the cable condition based on the cable tension is a major interest in the structural health monitoring (SHM) of the cable-stayed bridges. This paper aims to develop a deep convolutional neural network (DCNN)-based transfer learning method that is integrated with a continuous wavelet transform (CWT) for the health condition identification of the cables in a cable-stayed bridge using the one-dimensional time series cable tension data. For this purpose, the CWT is adopted to convert the cable tension to the images of a time-frequency representation. The last three new layers emerged in the pre-trained DCNN model, which is called AlexNet, as a new learning framework to use for the identification of the cable condition. The performance of the proposed DCNN model is examined using several statistical measures that include accuracy, sensitivity, specificity, precision, recall, and the F-measure. The results show that the proposed DCNN model gives superior accuracy (100%) for the identification of the undamaged cables and the damaged cables based on the cable tension data

Deep convolutional neural network-based transfer learning method for health condition identification of cable in cable-stayed bridge

The cables are extremely important and vulnerable components in the cable-stayed bridges. Because cable tension is one of the most crucial structural health indicators, therefore, assessing the cable condition based on the cable tension is a major interest in the structural health monitoring (SHM) of the cable-stayed bridges. This paper aims to develop a deep convolutional neural network (DCNN)-based transfer learning method that is integrated with a continuous wavelet transform (CWT) for the health condition identification of the cables in a cable-stayed bridge using the one-dimensional time series cable tension data. For this purpose, the CWT is adopted to convert the cable tension to the images of a time-frequency representation. The last three new layers emerged in the pre-trained DCNN model, which is called AlexNet, as a new learning framework to use for the identification of the cable condition. The performance of the proposed DCNN model is examined using several statistical measures that include accuracy, sensitivity, specificity, precision, recall, and the F-measure. The results show that the proposed DCNN model gives superior accuracy (100%) for the identification of the undamaged cables and the damaged cables based on the cable tension data

Some adaptive estimators for slope parameter

summary:An adaptive estimator (of a slope parameter) based on rank statistics is constructed and its asymptotic optimality is studied. A complete orthonormal system is incorporated in the adaptive determination of the score generating function. The proposed sequential procedure is based on a suitable stopping rule. Various properties of the sequential adaptive procedure and the stopping rule are studied. Asymptotic linearity results of linear rank statistics are also studied and some rates of the convergence are established