Assessment of Breeding Practice and Statues of Estrus Synchronization and Mass Insemination Conception Rate of Dairy Cattle in North Shewa Zone, Ethiopia.

BackgroundAssisted reproductive biotechnology like oestrus synchronization mass insemination(OSMI), and artificial insemination (AI) are the most important bio techniques for improving the reproductive and productive performance of dairy cattle including enhancing overall profit in Ethiopia. In North Shewa zone different study were conducted. However, there is no study conducted on breeding practice, and status of OSMI conception rate of dairy cattle. Therefore the aim of this study to assess breeding practice, and status of OSMI conception rate of dairy cattle in North Shewa zone.MethodsOut of 27 districts, three district and 135 respondents were selected purposive followed by random sampling techniques per each district. Data were analyzed using SPSS version 20 and Ms-Excel (2010).ResultMilk yield, High growth rate, body weight, fertility and udder size were the major traits perceived by farmers. Breed preference of the respondents were HHFC and HFC in Basonaworena and Angolelanatera ranked first. Breeding objective and rearing system of cattle were milk production with sale of calves and all cattle categories reared together except HHFC and lactating cows respectively. Most of the respondents were used AI mating system due to rapid genetic improvement. HHFC and HJERC bulls breed were not available in the study area as a result alternative strategies taken by the respondents was take cows in other kebele. Reproductive performance of dairy cattle per district and breeds were statistically significant. 86.6% of respondents were not maintained mating and pedigree records due to lack of awareness. Heat detection problem and AIT efficiency were the major factor that affect CR in OSMI program. The perception and satisfaction of farmer on CR of OSMI (34.4%) was not good and (67.1%) not satisfied respectively. The selection criteria of cows for OSMI program (58.9%) of respondents were not aware. The status of CR and NSPC per district, breed and year were vary (p<0.05) in table16 in OSMI program.ConclusionIn conclusion that the status of CR was increasing starting 2013/14-2015/16 in OSMI. In addition creation of farmer’s awareness on breeding aspects as well as OSMI is mandatory. Finally empowering the AI technician efficiency and procurement of the necessary facilities should be in place before implementing an OSMI.

Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA).

A Multiperson Pose Estimation Method Using Depthwise Separable Convolutions and Feature Pyramid Network

In the process of multiperson pose estimation, there are problems such as slow detection speed, low detection accuracy of key point targets, and inaccurate positioning of the boundaries of people with serious occlusion. A multiperson pose estimation method using depthwise separable convolutions and feature pyramid network is proposed. Firstly, the YOLOv3 target detection algorithm model based on the depthwise separable convolution is used to improve the running speed of the human body detector. Then, based on the improved feature pyramid network, a multiscale supervision module and a multiscale regression module are added to assist training and to solve the difficult key point detection problem of the human body. Finally, the improved soft-argmax method is used to further eliminate redundant attitudes and improve the accuracy of attitude boundary positioning. Experimental results show that the proposed model has a score of 73.4% in AP on the 2017 COCO test-dev dataset, and it scored 86.24% on [email protected] on the MPII dataset.

Vision Transformer-Based Tailing Detection in Videos

Tailing is defined as an event where a suspicious person follows someone closely. We define the problem of tailing detection from videos as an anomaly detection problem, where the goal is to find abnormalities in the walking pattern of the pedestrians (victim and follower). We, therefore, propose a modified Time-Series Vision Transformer (TSViT), a method for anomaly detection in video, specifically for tailing detection with a small dataset. We introduce an effective way to train TSViT with a small dataset by regularizing the prediction model. To do so, we first encode the spatial information of the pedestrians into 2D patterns and then pass them as tokens to the TSViT. Through a series of experiments, we show that the tailing detection on a small dataset using TSViT outperforms popular CNN-based architectures, as the CNN architectures tend to overfit with a small dataset of time-series images. We also show that when using time-series images, the performance of CNN-based architecture gradually drops, as the network depth is increased, to increase its capacity. On the other hand, a decreasing number of heads in Vision Transformer architecture shows good performance on time-series images, and the performance is further increased as the input resolution of the images is increased. Experimental results demonstrate that the TSViT performs better than the handcrafted rule-based method and CNN-based method for tailing detection. TSViT can be used in many applications for video anomaly detection, even with a small dataset.

Retrospective Change-Points Detection for Multidimensional Time Series of Arbitrary Nature: Model-Free Technology Based on the ϵ-Complexity Theory

We consider a retrospective change-point detection problem for multidimensional time series of arbitrary nature (in particular, panel data). Change-points are the moments at which the changes in generating mechanism occur. Our method is based on the new theory of ϵ-complexity of individual continuous vector functions and is model-free. We present simulation results confirming the effectiveness of the method.

MEA-CNDP: A Membrane Evolutionary Algorithm for Solving Biobjective Critical Node Detection Problem

The critical node detection problem (CNDP) refers to the identification of one or more nodes that have a significant impact on the entire complex network according to the importance of each node in a complex network. Most methods consider the CNDP as a single-objective optimization problem, which requires more prior knowledge to a certain extent. This paper proposes a membrane evolution algorithm MEA-CNDP to solve biobjective CNDP. MEA-CNDP includes a population initialization strategy based on the evaluation of decision variables, a strategy to transform the main objective, a strategy to update the membrane inherited pool, and four membrane evolutionary operators. The numerical experiments on 16 benchmark problems with random and logarithmic weights show that MEA-CNDP outperforms other algorithms in most cases. In particular, MEA-CNDP has unique advantages in dealing with large-scale sparse bi-CNDP.