The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

Unmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4Dcapture software

<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in

This study was aimed to produce bacteriocin from Bacillus. licheniformis isolated from local soil of corn and sunflower fields and using as antimicrobial agent . Fourteen of local isolates of Bacillus sp. were obtained and ability of these isolates for growth on Brain heart infusion agar (BHI) at 550C were tested. Isolate C4 was revealed high growth density in comparison with other isolates. Isolate C4 was identified as Bacillus licheniformis according to morphological, cultural and biochemical tests, Moreover genetic analysis for 16S rRNA gene and given accession number MT192715.1 in GenBank of NCBI . Production of bacteriocin from this isolate was carried out in Luria Broth (LB) and partially purified by precipitation with 30-70 % saturat

    Lipase was extracted by Sodium acetate buffer (pH=6; 0.05M) Containing 0.1M NaCl. Enzyme content of crude extract was concentrated by gradual addition of ammonium sulfate at 30-60% saturation. The dialyzed extract was purified on ion-exchange chromatography through DEAE–Cellulose and gel-filtration chromatography through sephacryl S-200 column. The specific activity, enzyme yield and fold purification were 54.06 unit/mg, 42.6% and 10.88 respectively. The molecular weight of the Lipase was 43.651 KDa as determined by gel-filtration chromatography through sephacryl S-200 column. Partial purified lipase used for the improvement of the flavor of butter fat after 12 hours for storage.

This research aims at building a proposed training program according to the self-regulated strategies for the mathematics teachers and to identify the effect of this program on relational Mathematics of teachers. The sample of the research was (60) Math teachers; (30) teachers as experimental group and (30) teachers as control group. The results of the current research reacheded that the proposed training program according to some self-managed learning strategies, meets the needs of trainees with remarkable effectiveness to improve the level of their teaching performance to achieve the desired goals. Training teacher according to self-managed learning strategies is effective in bringing about the transition of training to their students

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff