Hydrogen production via catalyst of green laser, molybdenum and ethanol

Electrolysis is an electrochemical process which is known as a green technology. Laser irradiation and the presence of catalyst in water electrolysis are identified as ways of improving the efficiency and increment of hydrogen production. The enhancement of hydrogen production through water electrolysis is obtained by adding molybdenum to increase the current in electrochemical cell and ethanol as an agent in photochemical reaction. In addition, diode pumped solid-state laser green laser at 532 nm is employed with the purpose to compensate the residual electrical field effect. The combination of the three catalysts is found more powerful to cause water splitting, thus produced 5 times greater H2 production in comparison to the action of individual catalyst

Nano silica dispersion in epoxy: the investigation of heat, milling speed and duration effect

Nano composites are a promising development but the challenge of homogenous and discrete dispersion of the nano fillers are barriers that must be overcome before they can be effectively implemented. Although the common dispersion methods such as particle surface modification, comprehensive milling metrologies and the usage of solvents bear results, these are time consuming and not cost effective. In this paper, we explore the efficiency of coupling the usage of ball-media and heat on the dispersion of nano silica in epoxy. No solvents are involved. The effects of milling speed and duration are also studied albeit under a fixed ball media : silica-epoxy volume ratio of 3:5. The experiment set-up involves a simple 3-blade mixer, round bottom flask and 60 ? m zirconia ball. At nano silica loading of 10 wt % the nano silica clusters are systematically reduced from 1.5 - 2 ? m to 100 - 200 nm with the usage of ball media and application of heat. At the optimum milling speed and duration of 500 rpm for 5 hours, the aggregate sizes were further reduced to 30 - 70 nm, which is almost a discrete dispersion

Family Archives from Elephantine. The Evidence from the Ostraka

The majority of Greek ostraka from Egypt, and from Elephantine in particular, are tax receipts. The huge number of names which come up in these texts provide the opportunity to reconstruct family archives enhancing our knowledge of various aspects of the prosopography and sociology of Elephantine. Two small families are documented here through one already known, and three newly published ostraka from the Egyptian Museum in Cairo

RGB-D and corrupted images in assistive blind systems in smart cities

Assistive blind systems or assistive systems for visually impaired in smart cities help visually impaired to perform their daily tasks faced two problems when using you only look once version 3 (YOLOv3) object detection. Object recognition is a significant technique used to recognize objects with different technologies, algorithms, and structures. Object detection is a computer vision technique that identifies and locates instances of objects in images or videos. YOLOv3 is the most recent object detection technique that introduces promising results. YOLOv3 object detection task is to determine all objects, their location, and their type of objects in the scene at once so it is faster than another object detection technique. This paper solved these two problems red green blue depth (RGB-D) and corrupted images. This paper introduces two novel ways in object detection that improves YOLOv3 technique to deal with corrupted images and RGB-D images. The first phase introduces a new prepossessing model for automatically handling RGB-D on YOLOv3 with an accuracy of 61.50% in detection and 57.02% in recognition. The second phase presents a preprocessing phase to handle corrupted images to use YOLOv3 architecture with high accuracy 77.39% in detection and 71.96% in recognition

A new model for early diagnosis of alzheimer's disease based on BAT-SVM classifier

Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due to its ability to measure regional changes in the brain that reflect disease progression to detect early stages of the disease. In this paper, we propose a new model that adopts Bat for parameter optimization problem of Support vector machine (SVM) to diagnose Alzheimer’s disease via MRI biomedical image. The proposed model uses MRI for biomedical image classification to diagnose three classes; normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The proposed model based on segmentation for the most involved areas in the disease hippocampus, the features of MRI brain images are extracted to build feature vector of the brain, then extracting the most significant features in neuroimaging to reduce the high dimensional space of MRI images to lower dimensional subspace, and submitted to machine learning classification technique. Moreover, the model is applied on different datasets to validate the efficiency which show that the new Bat-SVM model can yield promising acceptable level of accuracy reached to 95.36 % using maximum number of bats equal to 50 and number of generation equal to 10

An accurate traffic flow prediction using long-short term memory and gated recurrent unit networks

Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes

Surveillance on A/H5N1 virus in domestic poultry and wild birds in Egypt

The endemic H5N1 high pathogenicity avian influenza virus (A/H5N1) in poultry in Egypt continues to cause heavy losses in poultry and poses a significant threat to human health. Here we describe results of A/H5N1 surveillance in domestic poultry in 2009 and wild birds in 2009-2010. Tracheal and cloacal swabs were collected from domestic poultry from 22024 commercial farms, 1435 backyards and 944 live bird markets (LBMs) as well as from 1297 wild birds representing 28 different types of migratory birds. Viral RNA was extracted from a mix of tracheal and cloacal swabs media. Matrix gene of avian influenza type A virus was detected using specific real-time reverse-transcription polymerase chain reaction (RT-qPCR) and positive samples were tested by RT- qPCR for simultaneous detection of the H5 and N1 genes. In this surveillance, A/H5N1 was detected from 0.1% (n = 23/) of examined commercial poultry farms, 10.5% (n = 151) of backyard birds and 11.4% (n = 108) of LBMs but no wild bird tested positive for A/H5N1. The virus was detected from domestic poultry year- round with higher incidence in the warmer months of summer and spring particularly in backyard birds. Outbreaks were recorded mostly in Lower Egypt where 95.7% (n = 22), 68.9% (n = 104) and 52.8% (n = 57) of positive commercial farms, backyards and LBMs were detected, respectively. Higher prevalence (56%, n = 85) was reported in backyards that had mixed chickens and waterfowl together in the same vicinity and LBMs that had waterfowl (76%, n = 82). Our findings indicated broad circulation of the endemic A/H5N1 among poultry in 2009 in Egypt. In addition, the epidemiology of A/H5N1 has changed over time with outbreaks occurring in the warmer months of the year. Backyard waterfowl may play a role as a reservoir and/or source of A/H5N1 particularly in LBMs. The virus has been established in poultry in the Nile Delta where major metropolitan areas, dense human population and poultry stocks are concentrated. Continuous surveillance, tracing the source of live birds in the markets and integration of multifaceted strategies and global collaboration are needed to control the spread of the virus in Egypt