Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.
In this research, the results of x-ray diffraction method were used to determine the uniform stress deformation and microstructure parameters of CuO nanoparticles to determine the lattice strain obtained and crystallite size and then to compare the results obtained by two model Halder Wagner and Size Strain Plot with the results of these methods of the same powder using equations during which the calculation of the size of the crystallite size and lattice strain, It was found that the results obtained the values of the crystallite size (19.81nm) and the lattice strain (0.004065) of the Halder-wagner model respectively and for the ssp method were the results of the crystallite size (17.20nm) and lattice strain (0.000305) respectively. The sa
... Show MoreThe purpose of this study is the activation of natural Iraqi bentonite that has been obtained from Wadi Bashira region, in the Western Desert of Iraq, to obtain the Nano particle sized then Nano-Quartz was extracted. This method included bentonite nano particles preparation by purification with HCl solution, calcination, the planetary ball mill to get bentonite in nanometer size and centrifugation to obtain the Nano-quartz. Results of quartz purification process were characterized by Fourier transforms infrared spectroscopy (FTIR), energy dispersive X-ray spectroscopy (EDS) and scanning electron microscope (SEM), particle size analyzer (PSA) and the X-rays diffraction (XRD). All tests have shown almost a clear decline in the proportion o
... Show MoreIn this work copper nanopowder was created at different liquid
medias like DDDW, ethylene glycol and Polyvinylpyrrolidone
(PVP). Copper nanopowder prepared using explosion wire process
and investigated the effects of the exploding energy, wire diameter,
the type of liquid on the particle size, and the particles size
distribution. The nanoparticles are characterized by x-ray diffraction,
UV-visible absorption spectroscopy and transmission electron
microscopy (TEM). The x-ray diffraction results reveal that the
nanoparticles continue to routine lattice periodicity at reduced
particle size. The UV-Visible absorption spectrum of liquid solution
for copper nanoparticles shows sharp and single surface Plasmon
r
A 3D geological model for Mishrif Reservoir in Nasiriyah oil field had been invented "designed" "built". Twenty Five wells namely have been selected lying in Nasiriyah Governorate in order to build Structural and petrophysical (porosity and water saturation) models represented by a 3D static geological model in three directions .Structural model showed that Nasiriyah oil field represents anticlinal fold its length about 30 km and the width about 10 km, its axis extends toward NW–SE with structural closure about 65 km . After making zones for Mishrif reservoir, which was divided into 5 zones i.e. (MA zone, UmB 1zone,MmB1 zone ,L.mB1 zone and mB2zone) .Layers were built for each zone depending on petrophysical propertie
... Show MoreBuilding a 3D geological model from field and subsurface data is a typical task in
geological studies involving natural resource evaluation and hazard assessment. In
this paper a 3D geological model for Asmari Reservoir in Fauqi oil field has been
built using petrel software. Asmari Reservoir belongs to (Oligocene- Lower
Miocene), it represents the second reservoir products after Mishrif Reservoir in Fauqi
field. Five wells namely FQ6, FQ7, FQ15, FQ20, FQ21 have been selected lying in
Missan governorate in order to build Structural and petrophysical (porosity and water
saturation) models represented by a 3D static geological model in three directions
.Structural model shows that Fauqi oil field represents un cylin
In this paper, mixed spinel Co0.4Zn0.6Fe2O4 ferrite was synthesized by microwave-assisted combustion method. Photocatalytic activity of the as-synthesized sample was investigated against methylene blue dye at room temperature at different exposure times (60-360 min.) under visible light. Phase impurity and surface morphology which are investigated with XRD analysis and field emission- scanning electron microscopy, indicate that a cubic spinel unit cell structure with a crystilite size and lattice constant are 22.5048nm and 8.37Å, respectively. The saturation magnetization exhibited directly from the hysteresis loop is (70.20emu/g). Optical properties for the investigated ferrite
... Show MoreA hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m
... Show MoreIn this paper, a Modified Weighted Low Energy Adaptive Clustering Hierarchy (MW-LEACH) protocol is implemented to improve the Quality of Service (QoS) in Wireless Sensor Network (WSN) with mobile sink node. The Quality of Service is measured in terms of Throughput Ratio (TR), Packet Loss Ratio (PLR) and Energy Consumption (EC). The protocol is implemented based on Python simulation. Simulation Results showed that the proposed protocol provides better Quality of Service in comparison with Weighted Low Energy Cluster Hierarchy (W-LEACH) protocol by 63%.
<p>Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In
... Show MoreThe transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the
... Show More