Detection of Noble Gas Scintillation Light with Large Area Avalanche Photodiodes (LAAPDs)

Large Area Avalanche Photodiodes (LAAPDs) were used for a series of systematic measurements of the scintillation light in Ar, Kr, and Xe gas. Absolute quantum efficiencies are derived. Values for Xe and Kr are consistent with those given by the manufacturer. For the first time we show that argon scintillation (128 nm) can be detected at a quantum efficiency above 40%. Low-pressure argon gas is shown to emit significant amounts of non-UV radiation. The average energy expenditure for the creation of non-UV photons in argon gas at this pressure is measured to be below 378 eV.Comment: 16 pages, 7 figure

IoT and Neural Network-Based Water Pumping Control System For Smart Irrigation

This article aims at saving the wasted water in the process of irrigation using the Internet of Things (IoT) based on a set of sensors and Multi-Layer Perceptron (MLP) neural network. The developed system handles the sensor data using the Arduino board to control the water pump automatically. The sensors measure the environmental factors; namely temperature, humidity, and soil moisture to estimate the required time for the operation of water irrigation. The water pump control system consists of software and hardware tools such as Arduino Remote XY interface and electronic sensors in the framework of IoT technology. The machine learning algorithm such as the MLP neural network plays an important role to support the decision of automatic control of IoT-based irrigation system, managing the water consumption effectively.Comment: 6 pages, 5 figures, 1 tabl

iRegNet: Non-rigid Registration of MRI to Interventional US for Brain-Shift Compensation using Convolutional Neural Networks

Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance

Building and codification of the emotional equilibrium measure and its relationship with some of the offensive skills of basketball players for the season (2016 – 2017)

The study aimed to: to build the emotional equilibrium measure for basketball youth players to identify the relationship between the emotional equilibrium measure and some of the offensive skills for the basketball youth players in Maysan province. The researchers proposed that there is a statistical relationship between the emotional equilibrium measure and some of the offensive skills for the basketball youth players in Maysan province. The researchers concluded that the effectiveness of the emotional equilibrium measure for the Theoretical and practical aspect of the players, the results of the emotional equilibrium measure for the Chest Pass was moral from the results that the players got, the researchers reached the following recommendation The need to take care of the general psychological preparation of players through the training process with special attention to the development of the skill levels of the basketball players in the Psychological traits especially the emotional equilibrium trait, because the distinguished players with high level emotional equilibrium are more capable to take advantage of their physical ability, skills and following plans which contribute to winning superior then their peers. The exploratory experiment was conducted on 7/7/2016 and up to 72/7/2016 to demonstrate the validity of the scale to a sample of 15 players representing the Ali al-Gharbi Sports Basketball Club in Maysan Governorate. The researchers used the internal consistency coefficient with sincerity of construction and it measures the relationship between the scale total degree and the grades of each paragraph, because the coefficient of differentiation does not determine

Explainability of deep neural networks for MRI analysis of brain tumors

Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. Methods In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. Results NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. Conclusion Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI

Production and use of estimates for monitoring progress in the health sector: the case of Bangladesh

Background: In order to support the progress towards the post-2015 development agenda for the health sector, the importance of high-quality and timely estimates has become evident both globally and at the country level