The research on Mechanical and Tribological properties using nylon66 composites with Tio2 at various volume fractions are taken into consideration. The tensile strength, compressive strength, Modulus ...and Flexural strength tests were performed on specimen prepared by twin screw extruder. It was observed that there is increase in heat deflection value, wear arte along with increase in the coefficient of friction. For effectiveness in the Tribological properties of nylon66 + Tio2 it was combined with glass fiber as filler material. Some of the tests were conducted on the pin-on-disc apparatus and analysis through SEM to study about the wear behaviour of the material. This resulted in the improvement of the Tribological, thermal and mechanical properties of the composites with reduction in wear rate and coefficient of friction up to 4 wt% of Tio2 filled with glass fiber. It was also analysed that at 2 wt% of Tio2 filled with 2 wt% glass fiber there is effective enhancement in Tribological properties along with decrease in coefficient of friction and wear rate.
In machineries, to increase or decrease the speed, gear is very much needed. Such machineries are now days are compact design with less weight. Gears must occupy minimum spaces and materials ...reduction is required. For that, one of the best options is rimmed gears. The aim of the paper is to design and analysis of different thin-rimmed gear for high strength with high speed of rotations under harsh condition. The composite materials are in trend in recent years. High heat resistance and low wear characterized composite materials such as Nylon66 with TiO2, with and without glass; fibres are implemented in thinned rimmed gears. With TiO2, volume of percentage varies from 0% to 6%, which are used in analysing the individual gear models. The effect of web thickness, web positions, rim thickness, module, hub thickness and face width parameters are analysed to find out the strength that depends on the type of materials and gears, which are analysed in this paper.
The advent of globalization in the market has led to huge competition among the companies in various fields to achieve best supply chain practices. Increasing focus on environmental concerns has ...driven critical changes in industries’ strategy by incorporating sustainability in their supply chain. A supply chain which does not threaten the opportunities for future generations by considering environmental and social impact in addition to the economic impact leads to the concept of Sustainable Supply Chain Management (SSCM). Firms adopt sustainability by implementing specific practices - named as SSCM practices, in supply chain. However they struggle to identify the influential practices.This Paper intends to analyze the SSCM practices in plywood Industries in Visakhapatnam using Interpretive Structural Modeling (ISM). ISM method is used to develop a structural model to identify the influential practices. The SSCM practices are identified through literature review and from domain experts and managers of industries. Then practices are grouped under the dimensions of sustainability namely economic, environmental and social. And ISM model is built through which the most dominant practice among them in each dimension is identified.
In Medical Imaging, deep learning techniques are gaining the attention and trust of experts for predicting diseases at early stages. The mixture of artificial intelligence and medical knowledge ...facilities improves the accuracy of detecting diseases. Motivated by these advances, this paper performs a literature review focused on screening eye diseases, classification, and segmentation based on eye images. Artificial Intelligence has shown high accuracy with high specificity and sensitivity in screening and excavation images. This paper presents a systematic survey on the two major types of eye diseases, detection of glaucoma and diabetic retinopathy. Accordingly, we confirmed whether Artificial Intelligence strategies might be useful in distinguishing eye illnesses with high precision and minimal expense, which might advance patient strengthening and help clinical specialists.
Suicide is increased in recent years compared with the past and social media has a great impact on these suicides among youngsters. The internet and social media have a great influence on ...suicide-related behavior. Between 2007 and 2019, technology emerged into human life as the most important aspect of society. Twitter has Increased in investigating as a means of detecting mental health status, including suicidality and depression in the population. In twitter, knowledge will mechanically analyze the feelings of those tweets. To investigate a tool data mining to extract more helpful information for the classification of tweets. Suicidal ideation is detected by using several informative sets of features, including syntactic, statistical, linguistic and topic features. To examine whether suicide-related posts on tweeter could be determined by human coders and then simulate by machine learning, which verifies the effectiveness of performance in terms of precision, recall, and accuracy on sentient analysis.
This study examined the potential of two cutting-edge computer programmes, ResNet50 and Inception V3, to assist in the diagnosis of various eye disorders. To create a wider variety of ocular images ...for training, they employed a unique technique. Four different eye conditions were depicted in these images: normal, diabetic, cataract- and glaucoma-afflicted. To ensure that there were adequate images of each kind, applied data preprocessing with GAN model and also using a technique known as transfer learning, these programmes were trained to identify eye disorders after initially being trained on a large collection of photographs (ImageNet). This study used two models on the dataset where it has divided into 80% train and 20% test, and then modifying the learning process as necessary. This model reached the accuracy of 89%, which held true for many output parameters such as recall and precision. Additionally, the deep learning techniques performed well on testing using different new data, proving their possibility for dependability in practical settings and then 90% accuracy rate gives positive impact that they can be helpful resources for ophthalmologists.
Background: The spread of novel corona virus disease (COVID-19) contributed to a global crisis in the world. The virus was identified a pandemic by the World Health Organization on 11 March 20. And ...also more than 200 countries have been affected by the outbreak, with more than 37 million cases reported and over 1 million fatalities as of 10 October 2020. The SARS-CoV-2 virus triggered the COVID-19 disease. The human respiratory system deteriorates from this severe disease. In the first four to ten days after infection, patients with COVID-19 may develop symptoms similar to pneumonia as well as other respiratory diseases. As a reason, there could be a misdiagnosis between COVID-19 and common pneumonia patients. So for that, Computer-Aided Diagnosis (CAD) system can be considered an effective technique for physicians to use medical imaging methods to help the detection of pneumonia and COVID-19. Method: Some methods of deep learning can assist doctors in achieving an accurate pre-diagnosis. This paper proposed a specific and precise method for classification of pneumonia, COVID- 19 and healthy patients using x-ray images, even in the case of a small number of labeled samples being available. EfficientNet-B4 is used to train & predict precise result it is a transfer learning approach. To bring the proposed model to reality, additional layers are added to the EfficientNet-B4 model. Results: The proposed work has procured a high training accuracy of 99% by 3886 (1200:COVID-19, 1345: pneumonia and 1341: healthy) X-ray images. And it accurately classifies the classes of Covid 19, Pneumonia and Healthy images with testing accuracy 99%, 93% and 100% respectively. Conclusion: Due to a high spreading of corona- virus, the identification of COVID-19 in early age plays an important role in implementing preventive steps. The developed model will accurately identify The diseases called COVID-19 and Pneumonia in X- Ray images at early stages. The obtained results specify that the proposed work attained best results compared with the previous approaches.
The cultivated Gossypium spp. (cotton) represents the single most important, natural fiber crop in the world. In addition to its fiber, the oil and protein portion of the cottonseed also represents ...significant economic value. To protect the worldwide economic value of cotton fiber and cotton byproducts, coordinated efforts to collect and maintain cotton genetic resources have increased over the last 100 yr. The classified genetic resources of cotton are extensive and include five tetraploid species in the primary gene pool, 20 diploid species in the secondary gene pool, and 25 diploid species in the tertiary gene pool. This report provides information on the status and contents of eight major cotton germplasm collections present across the world. Based on the findings of this report, a number of classified Gossypium species are not maintained in these collections, and several are underrepresented and vulnerable to extinction. This report presents several critical challenges and opportunities facing international efforts to enhance and preserve the world's Gossypium genetic resources. Multinational communication and collaboration are essential to protect, secure, and evaluate the global cotton germplasm resources. Without global, collaborative efforts, the rarest and most unique cotton germplasm resources are vulnerable to extinction.
SOC1
, a MADS-box type II transcription factor, integrates environmental and endogenous cues to promote flowering in angiosperms. Recent reports implicating
SOC1
in roles beyond floral transition ...prompted functional characterization of
SOC1
in polyploid rapeseed mustard genomes. Gene characterization in Brassicas necessitates analysis of composite homeolog function. While insertional mutagenesis is untenable in Brassicas owing to gene redundancy, gain-of-function approach entails serial characterization of individual homeologs. Herein, we demonstrate modulated floral promotive effects in natural variants of
Brassica SOC1
and provide lateral branching as a probable outcome of polyploidy-induced gene diversification. Ectopic expression of two B genome specific
SOC1
variants in
Arabidopsis thaliana
resulted in differential floral acceleration and manifestation of multiple vegetative rosettes. Characterization of composite homeolog function in
B. juncea
via introgression of
Brassica SOC1
specific artificial miRNA, designed to target homeologs, also exhibited modifications in floral transition and lateral branching. Comprehensive analysis of field performance of
B. juncea
transgenics displayed altered fitness across 11 agronomic traits. Crucially, reduced
SOC1
levels directly impacted two developmental traits, namely, flowering time and number of lateral branches which in turn influenced several dependent agronomic traits. While delayed flowering and crop maturity resulted in altered fatty acid composition with higher SFA and lower PUFA in transgenics relative to controls, reduction in overall count of lateral branches caused a concomitant decrease in silique count which ultimately impacted total seed yield in transgenics. Statistical analysis revealed number of secondary branches as the most critical trait influencing seed yield. Based on our findings, we propose enhancing levels
Brassica SOC1
, a key target, for achieving earliness in flowering, improved seed yield and oil quality, and studying trait trade-offs.
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