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Background: Groundnuts are immensely rich in potassium, calcium, phosphorus and B vitamins which offers you with a host of health benefits. Groundnuts are a great blend of healthy fats, protein and fibre that curbs your appetite, lowers the risk of heart disease and regulates blood glucose levels. Objectives: Effects of potassium levels and spacing on growth and yield of summer groundnut. Methods: With the goal of studying the effect of potassium and spacing on growth and yield of groundnut under a Randomized block design with 9 treatments (T1-T9) The experimental results revealed that 120 kg N/ha + 60 kg P/ha produced maximum plant height (49.7 cm) No. of Nodules/Plant (106) plant dry weight (22.9) No of pods (19.3) no of kernals (2.7) pod yield (3.62 ta/ha) Halum yield (4.65 ta/ha). Conclusion: The combination of 40 kg potassium and 30x10 proved to be the most advantageous to farmers, resulting in, plant height (49.7 cm) No. of Nodules/Plant(106) plant dry weight (22.9) No of pods (19.3) no of kernals (2.7) pod yield (3.62 ta/ha) Halum yield (4.65 ta/ha) respectively.

The condition known as diabetes is brought on when the immune system, which protects us from infection, targets and kills the pancreatic cells that produce insulin. This is the pathogenesis of type 1 diabetes. When cells in the muscles and liver grow resistant to insulin, type 2 diabetes develops. Undiagnosed diabetes can cause dangerous illnesses including renal damage and heart attacks. In order to discover and analyse gestational diabetes, there is a need for sound research and the improvement of learning models. This significant flaw is been resolved by advances in machine learning techniques. The goal of this study is to create a system that, using machine learning algorithms, can more accurately forecast a patient's risk of developing diabetes at an early stage. The suggested model yields the most accurate diabetic prediction results, and the findings shown that the prediction system is capable of accurately, quickly, and most significantly, instantaneously predicting the diabetes illness. Sklearn has been utilized by us to train, test, and determine the model's correctness, IJSRED - International Journal of Scientific Research and Engineering Development

The main objective of this research is to study the impact of supplementary cementitious materials (SCMs) on mitigating Alkali-Silica Reactivity (ASR) of hardened concrete. ASR is a deleterious reaction between alkali content within the cement and highly reactive silica content available in some aggregate sources across the United States. ASR results in the formation of an expansive white-like gel that adds internal tensile stresses within hardened concrete. Increased tensile stress results in the formation of cracks, dissipation of additional moisture, and reinforcement corrosion. In this research project, large scale concrete blocks, with aggregates from different sources, were poured using different percentages of SCMs. An exposure site was developed for the poured blocks to investigate ASR possible impact on concrete. ASR impact was measured by calculating concrete blocks expansion over a time span of two years. The outcomes of the research showed that different aggregates result in different rates of expansion. The use of SCMs resulted in mitigating ASR and limit concrete expansion. The replacement of 25% to 30% of cement weight with SCMs (fly ash) is sufficient to halt ASR, and improve the long- time performance of concrete structures.

ObjectivesTo integrate morphological, haemodynamic and mechanical analysis of carotid atheroma driving plaque disruption.Materials and methodsFirst, we analysed the phenotypes of carotid endarterectomy specimens in a photographic dataset A, and matched them with the likelihood of preoperative stroke. Second, laser angioscopy was used to further define the phenotypes in intact specimens (dataset B) and benchmark with histology. Third, representative vascular geometries for each structural phenotype were analysed with Computational Fluid Dynamics (CFD), and the mechanical strength of the complicated atheroma to resist penetrating forces was quantified (n=14).ResultsIn dataset A (n=345), ulceration (fibrous cap disruption) was observed in 82% of all plaques, intraplaque haemorrhage in 68% (93% subjacent to an ulcer) and false luminal formation in 48%. At least one of these ‘rupture’ phenotypes was found in 97% of symptomatic patients (n=69) compared with 61% in asymptomatic patients. In dataset B (n=30), laser angioscopy redemonstrated the structural phenotypes with near-perfect agreement with histology. In CFD, haemodynamic stress showed a large pulse magnitude, highest upstream to the point of maximal stenosis and on ulceration the inflow stream excavates the necrotic core cranially and then recirculates into the true lumen. Based on mechanical testing (n=14), the necrotic core is mechanically weak and penetrated by the blood on fibrous cap disruption.ConclusionsFibrous cap ulceration, plaque haemorrhage and excavation are sequential phenotypes of plaque disruption resulting from the chiselling effect of haemodynamic forces over unmatched mechanical tissue strength. This chain of events may result in thromboembolic events independently of the degree of stenosis.

In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per-pixel reconstruction loss. To achieve good reconstruction results using color videos, the CNN needs to have a certain level of understanding of the scene dynamics and predict plausible, temporally coherent contents. We further explore to jointly reconstruct both color frames and flow fields. By exploiting the statistical temporal structure of images, we show that the learned representations capture meaningful spatiotemporal structures from raw videos. We validate the effectiveness of our approach for CNN pre-training on action recognition and action similarity labeling problems. Our quantitative results demonstrate that our method compares favorably against learning without external data and existing unsupervised learning approaches. Master of Science

Laufman and Saphir first described primary signet ring cell carcinoma of the colon and rectum in 1951 [1]. Primary signet ring cell carcinoma of colon and rectum is a distinctive malignant disease and rarer than conventional adenocarcinoma, with a reported incidence ranging from 0.01 to 2.6 % [2]. Signet cell carcinoma is a poorly differentiated type of adenocarcinoma which behaves more aggressively and is more common in younger patients (especially under 40) compared to other types of colorectal cancers. Herein, we use a typical patient along with relevant literature to illustrate the multimodality approach to signet ring cell cancer rectum (SRCC) and discuss important factors associated with the treatment and prognosis.