Effect of amorphous silica by rice husk ash on physical properties and microstructures of recycled aluminium chip AA7075

High strength to weight ratio of aluminium reinforced as metal matrix composites is a well known material used in automotive application. The effects of recycled aluminium chips AA7075 with amorphous silica by rice husk ash on the physical properties and microstructure were investigated. Recycled aluminium chip AA7075 was reinforced with agro waste of amorphous silica rice husk ash i. e., 2.5 %, 5 %, 7.5 %, 10 % and 12.5 %. Samples of these metal matrix composites were prepared by cold compaction method due to the lower energies consumption and operating cost compared to conventional recycling by casting. Physical testing of density, apparent porosity, water absorption and hardness tests of the metal matrix composites samples were examined in the current study. The density of metal matrix composites was increased up to 5 % of amorphous silica, and then decreased with increasing mass fraction of amorphous silica. Porosity and water absorption of metal matrix composites were significantly consistent at increasing mass fraction of amorphous silica, while the hardness of metal matrix composites was increased at increasing amorphous silica. Consequently, the microstructures of metal matrix composites were observed via optical microscope to analyze the dispersion of the reinforced composites. The microstructures of metal matrix composites were found non-homogeneous and random distribution of amorphous silica and aluminium chip AA7075 compared to 100 % recycled aluminium chip AA7075. Based on investigation to aluminium reinforced rice husk ash composites, it has good potential to improve the material behavior of metal matrix composites by appropriate composition amorphous silica to composite

Fast and Effective Bag-of-Visual-Word Model to Pornographic Images Recognition Using the FREAK Descriptor

Recently, the Bag of Visual Word (BoVW) has gained enormous popularity between researchers to object recognition. Pornographic image recognition with respect to computational complexity, appropriate accuracy, and memory consumption is a major challenge in the applications with time constraints such as the internet pornography filtering. Most of the existing researches based on the Bow, using the very popular SIFT and SURF algorithms to description and match detected keypoints in the image. The main problem of these methods is high computational complexity due to constructing the high dimensional feature vectors. This research proposed a BoVW based model by adopting very fast and simple binary descriptor FREAK to speed-up pornographic recognition process. Meanwhile, the keypoints are detected in the ROI of images which improves the recognition speed due to eliminating many noise keypoints placed in the image background. Finally, in order to find the most representational visual-vocabulary, different vocabularies are generated from size 150 to 500 for BoVW. Compared with the similar works, the experimental results show that the proposed model has gained remarkable improvement in the terms of computational complexity

Molecular Survey of Tularemia and Plague in Small Mammals From Iran

Introduction: Plague and tularemia are zoonoses and their causative bacteria are circulating in certain regions of Iran. This study was conducted to investigate potential disease reservoirs amongst small wildlife species in different regions of Iran.Methods: Rodents, insectivores and hares from 17 different provinces of the country were collected in 2014 and 2015. Samples were taken from the spleens of the animals and Real-time PCR was applied to detect nucleic acid sequences that are specific to Francisella tularensis and Yersinia pestis, respectively.Results: Among 140 collected rodents, 25 distinct species were identified out of which five were the most common: Microtus paradoxus (21% out of 140 rodents), Apodemus witherbyi (12%), Microtus irani (11%), Mus musculus (11%) and Microtus socialis (10%). Seventeen insectivores were collected and identified as Crocidura suaveolens (82%) and C. leucodon (18%). Fifty-one hares were collected and identified as Lepus europaeus (57%), Lepus tolai (14%) and Lepus sp. (29%). Three out of 140 explored rodents (1.91%) were positive for F. tularensis, an A. witherbyi, a Mus musculus domesticus, and a Chionomys nivalis collected from Golestan, Khuzestan and Razavi Khorasan provinces, respectively. Two hares (3.92%) were F. tularensis-positive, a L. europaeus from Khuzestan and a Lepus sp. from the Sistan and Baluchistan province. None of the tested animals were positive for Y. pestis.Conclusion: This is the first report of direct detection of F. tularensis in mammals of Iran and the first-time observation of the agent in a snow vole, C. nivalis worldwide. The results indicate that tularemia is more widespread in Iran than previously reported including the Northeast and Southwestern parts of the country. Future studies should address genetic characterization of F. tularensis positive DNA samples from Iran to achieve molecular subtyping and rule out assay cross-reactivity with near neighbor Francisella species

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Quantitative, Qualitative and Economic Assessment of Agricultural Land Suitability of Rokh Plains Torbat Heydaryeh for Saffron and Wheat Cultivation

Identifying of optimal use of resources and in line with the production of any land is an important step in achieving sustainable development while preserving the ecological system. The purpose of this study is determination quality, quantity and economic suitability of Torbat Heydaryeh lands (Rokh Plain) in Khorasan Razavi for wheat (Triticum Aestivum) and saffron (Crocus Sativus) cultivation. For this purpose, climate condition and soil properties in the study area compare with requirements of wheat and saffron, so qualitative suitability were determined by using FAO method and GIS according to parametric method of Kalogirou. Quantitative and economic evaluation done according to actual yield in each agricultural unit and gross profit per unit area, respectively. Results showed that in this area despite, climate index and yield potential is high but because of soil properties restriction, qualitative suitability is moderate and this restriction is higher for saffron. The results showed that quantitative suitability class is equal or higher than qualitative suitability especially for saffron. Economics results showed that saffron is higher profitable than wheat but its enlargement restricted because of soil limitation, so that its cultivation isn’t recommended. Perform corrective actions and improvement of soil properties in most units can increase production efficiency

Effect of nanoclay particles content on the mechanical properties of wood flour-polypropylene composites using dynamic mechanic thermal analysis

In this study, the effect of nanoclay particles content on the mechanical properties of wood flour-polypropylene composites was investigated using Dynamic Mechanic Thermal Analysis (DMTA). To meet this objective, wood flour was mixed with polypropylene at 60 % by weight fiber loading. The concentration was varied as 0, 3 and 5 per hundred compounds (phc) for nanoclay. The amount of coupling agent (PP-g-MA) was fixed at 2 phc for all formulations. The samples were made by melt compounding and injection molding. Static mechanical tests including bending and tensile were performed. DMTA test in the range of -60 to 120 0C with 5 0C/min temperature rate and 1 Hz frequency was done. The morphology of the nanocomposites has been examined by using x-ray diffraction and transmission electron microscopy. Results indicated that the mechanical strength and storage modulus of samples increases with increase of nanoclay up to 3 phc and then decreases with 5 phc nanoclay addition. Also, the alpha and beta transition in samples transmitted to higher temperatures with addition of nanoclay. The morphological studies with XRD and TEM revealed that nanoclay distributed as intercalation structure in polymer matrix

Probiotics as functional foods: How probiotics can alleviate the symptoms of neurological disabilities

Neurological disorders are diseases of the central nervous system with progressive loss of nervous tissue. One of the most difficult problems associated with neurological disorders is that there is no clear treatment for these diseases. In this review, the physiopathology of some neurodegenerative diseases, etiological causes, drugs used and their side effects, and finally the role of probiotics in controlling the symptoms of these neurodegenerative diseases are presented. Recently, researchers have focused more on the microbiome and the gut-brain axis, which may play a critical role in maintaining brain health. Probiotics are among the most important bacteria that have positive effects on the balance of homeostasis via influencing the microbiome. Other important functions of probiotics in alleviating symptoms of neurological disorders include anti-inflammatory properties, short-chain fatty acid production, and the production of various neurotransmitters. The effects of probiotics on the control of abnormalities seen in neurological disorders led to probiotics being referred to as ''psychobiotic. Given the important role of the gut-brain axis and the imbalance of the gut microbiome in the etiology and symptoms of neurological disorders, probiotics could be considered safe agents that positively affect the balance of the microbiome as complementary treatment options for neurological disorders

Biodegradation behaviors of cellulose nanocrystals -PVA nanocomposites

In this research, biodegradation behaviors of cellulose nanocrystals-poly vinyl alcohol nanocomposites were investigated. Nanocomposite films with different filler loading levels (3, 6, 9 and 12% by wt) were developed by solvent casting method. The effect of cellulose nanocrystals on the biodegradation behaviors of nanocomposite films was studied. Water absorption and water solubility tests were performed by immersing specimens into distilled water. The characteristic parameter of diffusion coefficient and maximum moisture content were determined from the obtained water absorption curves. The water absorption behavior of the nanocomposites was found to follow a Fickian behavior. The maximum water absorption and diffusion coefficients were decreased by increasing the cellulose nanocrystals contents, however the water solubility decrease. The biodegradability of the films was investigated by immersing specimens into cellulase enzymatic solution as well as by burial in soil. The results showed that adding cellulose nanocrystals increase the weight loss of specimens in enzymatic solution but decrease it in soil media. The limited biodegradability of specimens in soil media attributed to development of strong interactions with solid substrates that inhibit the accessibility of functional groups. Specimens with the low degree of hydrolysis underwent extensive biodegradation in both enzymatic and soil media, whilst specimens with the high degree of hydrolysis showed recalcitrance to biodegradation under those conditions

Developing a Radial Basis Function Neural Networks to Predict the Working Days for Tillage Operation in Crop Production

The aim of this study was to determine the probability of working days (PWD) for tillage operation using weather data with Multiple Linear Regression (MLR) and Radial Basis Function (RBF) artificial networks. In both models, seven variables were considered as input parameters, namely minimum, average and maximum temperature, relative humidity, rainfall, wind speed, and evaporation on a daily basis. The PWD was considered to be the output of the developed models. Performance criteria were RMSE, MAPE, and R2. Results showed that the R2-value was 0.78 and 0.99 for MLR and RBF models, respectively. Both models had acceptable performance, but the RBF model was more accurate than the MLR model. The RMSE and MAPE values for the RBF model were lower than those for the MLR model. Thus, the RBF model was selected as the suitable model for predicting PWD. Moreover, the results of these models were compared to the prior soil moisture model. It was indicated that the results of the studied models had a good agreement with the results of the soil moisture model. However, the RBF model had the highest R2 (99%). In conclusion, the developed RBF model could be used to predict the probability of working days in terms of agricultural management policies