Controlling the Distance Between the Robot and Target During the Tracking Process

The tracking process of dynamic target has been played a significant role in industrial environment especially in military industrial, medical and surgical applications, dinger seeking and automatic control cars.In this paper implemented mobile robot and visionary system with two parts the first one is 3D Camera ( Kinect ) fixed on the mobile robot with computer connection . the kinect Camera installation on computer have been install series of open source software respectively . This work involves constructing integrated in MATLAB program automatically. It depends on a new approach in analyzing the robotic environment by a Kinect. The approach uses colors to detect and recognize the locations object and target.By analysis and processing the image captured by 3D Camera (Kinect) in computer are detection the target in the image, find it's center and measure the depth from robot to target .The calculated depth and angle from image processing in computer is transmitting from computer to robot by using wireless unit and finally the robot go to this location. Finally by using specific algorithm can be controlling the distance between robot and target.The second part of visionary system is a WebCamera fixed in the roof of the working environment to detect the target and robot. The instantaneous distance between robot and target in each frame is finding by WebCam. Keywords: Mobile Robot, Object Tracking, Visionary System, 3D Kinec

Combining depth and intensity images to produce enhanced object detection for use in a robotic colony

Robotic colonies that can communicate with each other and interact with their ambient environments can be utilized for a wide range of research and industrial applications. However amongst the problems that these colonies face is that of the isolating objects within an environment. Robotic colonies that can isolate objects within the environment can not only map that environment in de-tail, but interact with that ambient space. Many object recognition techniques ex-ist, however these are often complex and computationally expensive, leading to overly complex implementations. In this paper a simple model is proposed to isolate objects, these can then be recognize and tagged. The model will be using 2D and 3D perspectives of the perceptual data to produce a probability map of the outline of an object, therefore addressing the defects that exist with 2D and 3D image techniques. Some of the defects that will be addressed are; low level illumination and objects at similar depths. These issues may not be completely solved, however, the model provided will provide results confident enough for use in a robotic colony

Design and Implement a Gas Pipeline Inspection System using Robotic Vehicle

Gas leak is an important safety issue in oil and gas production. During the past fifteen years, a considerable number of studies have been made into how to detect and localize gas leaks. Equipped with sensors measuring the point concentration of specific substances, a variety of mobile robots and technologies have been looking for gas sources. This paper presents a real-time system to detect abnormal events on gas pipes, by developing a data monitoring system to detect the gas levels and concentration using GAS leak detector system that is positioned on robotic vehicle (pioneer p3-dx) combined with modern communication technologies in terms of GPS to locate the robot in real-time accuracy of tracking process. Keywords: Gas Detection, Robotic (outdoor seeker), EASYPEE (zigbee) board, GP

Influence of Family on Saudi Arabian Emergency Medical Services Students

Objective: To identify influences on learning for Saudi male students studying Emergency Medical Services at a college in Riyadh, Saudi Arabia. Previous research on influences on student learning in the Kingdom of Saudi Arabia focused on the historical development of education in Saudi Arabia, English language development, and intrinsic motivations of students and excluded a focus on students studying Emergency Medical Services. Methods: Exploratory sequential mixed-methods study was deployed. Results: Family support was an exceptionally strong predictor of student confidence in both skills and post-graduate EMS employment. Concepts involving application, memorization, motivation, and English language did not present as statically significant. The discovery of the strong influences that a family can have on Saudi EMS student’s confidence is noteworthy, as this was not previously discovered in the literature. Conclusion: This discovery holds practical implications for EMS education and training programs as emphasizes the importance of developing practical ways to include a student’s family as a source of support in ensuring student success and confidence.

Surgical Approaches to Congenital Anomalies of Esophagus

With prevalence of about 1 in 3000 live births, pediatric surgeons commonly deal with esophageal abnormalities, which may provide substantial clinical complications. Surprisingly, the embryologic processes underlying esophageal atresia (EA) with or without tracheoesophageal fistula (TEF), one of the hallmark disease entities of pediatric surgery, have only lately been largely uncovered. When it comes to the treatment of congenital esophageal abnormalities, notably esophageal atresia and tracheoesophageal fistula, surgical methods are essential. In order to address the anatomical abnormalities and restore normal function, surgical correction is often necessary in the care of congenital esophageal anomalies, including esophageal atresia and tracheoesophageal fistula. In this review we are going to cover surgical approaches to repair those malformations, long-term outcomes, and latest developments in esophageal surgical approaches

White Matter Microstructural Alteration in Type 2 Diabetes: A Combined UK Biobank Study of Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging

Background: Type 2 diabetes mellitus impacts the brain microstructural environment. Diffusion tensor imaging (DTI) has been widely used to characterize white matter microstructural abnormalities in type 2 diabetes but fails to fully characterise disease effects on complex white matter tracts. Neurite orientation dispersion and density imaging (NODDI) has been proposed as an alternative to DTI with higher specificity to characterize white matter microstructures. Although NODDI has not been widely applied in diabetes, this biophysical model has the potential to investigate microstructural changes in white matter pathology.Aims and objectives: (1) To investigate brain white matter alterations in people with type 2 diabetes using DTI and NODDI; (2) To assess the association between white matter changes in type 2 diabetes with disease duration and diabetes control as reflected by glycated haemoglobin (HbA1c) levels.Methods: We examined white matter microstructure in 48 white matter tracts using data from the UK Biobank in 3,338 participants with type 2 diabetes (36% women, mean age 66 years) and 30,329 participants without type 2 diabetes (53% women, mean age 64 years). The participants had undergone 3.0T multiparametric brain imaging, including T1 weighted imaging and diffusion imaging for DTI and NODDI. Region of interest analysis of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), orientation dispersion index (ODI), intracellular volume fraction (ICVF), and isotropic water fraction (IsoVF) were conducted to assess white matter abnormalities. A general linear model was applied to evaluate intergroup white matter differences and their association with the metabolic profile.Result: Reduced FA and ICVF and increased MD, AD, RD, ODI, and IsoVF values were observed in participants with type 2 diabetes compared to non-type 2 diabetes participants (P<0.05). Reduced FA and ICVF in most white matter tracts were associated with longer disease duration and higher levels of HbA1c (0< r ≤0.2, P<0.05). Increased MD, AD, RD, ODI and IsoVF also correlated with longer disease duration and higher HbA1c (0< r ≤0.2, P<0.05).Discussion: NODDI detected microstructural changes in brain white matter in participants with type 2 diabetes. The revealed abnormalities are proxies for lower neurite density and loss of fibre orientation coherence, which correlated with longer disease duration and an index of poorly controlled blood sugar. NODDI contributed to DTI in capturing white matter differences in participants with type 2 diabetes, suggesting the feasibility of NODDI in detecting white matter alterations in type 2 diabetes.Conclusion: Type 2 diabetes can cause white matter microstructural abnormalities that have associations with glucose control. The NODDI diffusion model allows the characterisation of white matter neuroaxonal pathology in type 2 diabetes, giving biophysical information for understanding the impact of type 2 diabetes on brain microstructure

Environmental effects of ozone depletion, UV radiation and interactions with climate change : UNEP Environmental Effects Assessment Panel, update 2017

Peer reviewe

Analyzing the communicative strategies of Egyptian political influencers: content and discourse analyses of Twitter accounts

The advantages of social media platforms as interactive information sources raise the importance of examining how they are used by political digital opinion leaders to influence public perceptions. Twitter, especially, played a major role in Egypt’s January 25 revolution facilitating news dissemination, public discussions and debates. Analyzing the communicative strategies of two Egyptian political influencers –Ammar Ali Hassan and Ezzedine Fishere– ten years after Egypt’s political change, and the role they play in public discourse through their Twitter accounts, this research offers an overview of the current role played by Egyptian digital political influencers in influencing public opinion. Focusing on the content and discourse of their tweets for two months, October and November 2019, the digital political influencers were selected based on the number of followers divided by the amount of interactivity on their tweets, such as retweets and favorites. The unit of analysis is the tweet that received the largest amount of interactivity. Results showed that both influencers had a unidirectional opinion strategy. While Hassan’s tweets, @ammaralihassan, seemed purposeless, not yielding any clear and valuable content to the reader, Fishere, @FishereEzzedine, was more outspoken and clearer in his communicative strategy, using evidence in defending human rights in Egypt and the Arab World. The analysis indicated more fact-based tweets by Fishere, who seems to play a more significant role in his communication network, despite minimal interaction