Classification of Metals used in the Sand Casting Process

The Sand Casting Process And The Different Methods Used To Cast The Metals Are The Subjects Of This Paper. Casting Is A Manufacturing Process For Creating Complex Material Shapes. A Large Classification Is Needed To Understand The Metals Used By Researches, Why They Were Used, And What The Most Commonly Used Metal For Their Research Is. We Studied And Understanded The Various Types Of Sand Casting Processes Used By The Researchers In This Review Paper. And We Categorized And Graded All The Papers We Reviewed And Classified Based On Their Respective Study Areas And Material Use

The Nlrp3 inflammasome regulates acute graft-versus-host disease

The success of allogeneic hematopoietic cell transplantation is limited by acute graft-versus-host disease (GvHD), a severe complication accompanied by high mortality rates. Yet, the molecular mechanisms initiating this disease remain poorly defined. In this study, we show that, after conditioning therapy, intestinal commensal bacteria and the damage-associated molecular pattern uric acid contribute to Nlrp3 inflammasome-mediated IL-1β production and that gastrointestinal decontamination and uric acid depletion reduced GvHD severity. Early blockade of IL-1β or genetic deficiency of the IL-1 receptor in dendritic cells (DCs) and T cells improved survival. The Nlrp3 inflammasome components Nlrp3 and Asc, which are required for pro-IL-1β cleavage, were critical for the full manifestation of GvHD. In transplanted mice, IL-1β originated from multiple intestinal cell compartments and exerted its effects on DCs and T cells, the latter being preferentially skewed toward Th17. Compatible with these mouse data, increased levels of active caspase-1 and IL-1β were found in circulating leukocytes and intestinal GvHD lesions of patients. Thus, the identification of a crucial role for the Nlrp3 inflammasome sheds new light on the pathogenesis of GvHD and opens a potential new avenue for the targeted therapy of this severe complication

Lack of the purinergic receptor P2X7 results in resistance to contact hypersensitivity

Engagement of P2X7 on mouse dendritic cells, presumably by ATP released in response to contact allergen, is needed for IL-1β production and the sensitization phase of contact hypersensitivity

Probing high temperature ferromagnetism and its paramagnetic phase change due to Eu3+ incorporation in ZnO nanophosphors

Ferromagnetic oxide semiconductors exhibiting efficient luminescent properties together with robust ferromagnetism above room temperature form an exclusive class of spintronic materials endowed with both charge and spin degrees of freedom. Herein, we report on the occurrence of high temperature ferromagnetism (>600 K) in zinc oxide nanophosphors attributed to the presence of defects in the host lattice and wherein incorporation of rare earth ions contributed to a gradual reduction in the ferromagnetic character and steady transformation to paramagnetic behavior. Although undoped ZnO nanophosphors exhibit a high coercive field and saturation magnetization along with a prominent green emission (536 nm) attributed to the presence of oxygen vacancies V-o, Eu3+ doping results in a decrease in green emission along with coercivity as well as magnetization efficient line emission in the orange red region (618-622 nm) pointing to a definite correlation between the V-o and ferromagnetism. The temperature dependence of the magnetization shows stable ferromagnetism with Curie temperature above 600 K for undoped ZnO and a ferromagnetic to paramagnetic transition with an increase in Eu3+ concentration that has been explained through an F+ center exchange mechanism

PREVALENCE OF BEHAVIORAL PROBLEMS AMONG SCHOOL GOING CHILDREN IN SOUTH INDIA

<p><strong>Background: </strong>Children can have mental, emotional and behavioural problems that are real, painful and costly. These problems often can lead to development of disorders if neglected which are the sources of stress for children and their families, schools and communities.</p><p><strong>Aims & Objectives: </strong>To assess the prevalence of behavioral problems among school going children.</p><p><strong>Methodology: </strong>Researcher adopted quantitative approach and cross-sectional descriptive study. 1600 school going children screened by child behavior checklist and who fulfilled inclusion and exclusion criteria, were selected as samples by using a non-probability purposive sampling technique. Written informed consent was obtained from the parent and assent obtained from school going children.</p><p><strong>Results:</strong>210(13.13%) school going children had behavioral problems.In experimental group the mean behavioral problem score was 51.83 with SD of 9.75 and in control group the mean behavioral problem score was 53.26 with SD of 11.32. There is a significant difference between experimental and control group behaviour score.</p><p><strong>Conclusion:</strong>The study concluded that majority of the school going children had moderate level of behavioral problems. Regular screening of behavioral problems and early intervention help the school going children to cope and grow in a better way. </p><p> </p&gt

A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes

The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards to human life and habitats and also reflects a prodigious amount of economic losses. Therefore, it is necessary to build models to improve the prediction accuracy and to avoid such significant losses in all aspects. However, it is impractical to predict or monitor every storm formation in real time. Though various techniques exist for diagnosing the tropical cyclone intensity such as convolutional neural networks (CNN), convolutional auto-encoders, recurrent neural network (RNN), etc., there are some challenges involved in estimating the tropical cyclone intensity. This study emphasizes estimating the tropical cyclone intensity to identify the different categories of hurricanes and to perform post-disaster management. An improved deep convolutional neural network (CNN) model is used for predicting the weakest to strongest hurricanes with the intensity values using infrared satellite imagery data and wind speed data from HURDAT2 database. The model achieves a lower Root mean squared error (RMSE) value of 7.6 knots and a Mean squared error (MSE) value of 6.68 knots by adding the batch normalization and dropout layers in the CNN model. Further, it is crucial to predict and evaluate the post-disaster damage for implementing advance measures and planning for the resources. The fine-tuning of the pre-trained visual geometry group (VGG 19) model is accomplished to predict the extent of damage and to perform automatic annotation for the image using the satellite imagery data of Greater Houston. VGG 19 is also trained using video datasets for classifying various types of severe weather events and to annotate the weather event automatically. An accuracy of 98% is achieved for hurricane damage prediction and 97% accuracy for classifying severe weather events. The results proved that the proposed models for hurricane intensity estimation and its damage prediction enhances the learning ability, which can ultimately help scientists and meteorologists to comprehend the formation of storm events. Finally, the mitigation steps in reducing the hurricane risks are addressed

A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes

The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards to human life and habitats and also reflects a prodigious amount of economic losses. Therefore, it is necessary to build models to improve the prediction accuracy and to avoid such significant losses in all aspects. However, it is impractical to predict or monitor every storm formation in real time. Though various techniques exist for diagnosing the tropical cyclone intensity such as convolutional neural networks (CNN), convolutional auto-encoders, recurrent neural network (RNN), etc., there are some challenges involved in estimating the tropical cyclone intensity. This study emphasizes estimating the tropical cyclone intensity to identify the different categories of hurricanes and to perform post-disaster management. An improved deep convolutional neural network (CNN) model is used for predicting the weakest to strongest hurricanes with the intensity values using infrared satellite imagery data and wind speed data from HURDAT2 database. The model achieves a lower Root mean squared error (RMSE) value of 7.6 knots and a Mean squared error (MSE) value of 6.68 knots by adding the batch normalization and dropout layers in the CNN model. Further, it is crucial to predict and evaluate the post-disaster damage for implementing advance measures and planning for the resources. The fine-tuning of the pre-trained visual geometry group (VGG 19) model is accomplished to predict the extent of damage and to perform automatic annotation for the image using the satellite imagery data of Greater Houston. VGG 19 is also trained using video datasets for classifying various types of severe weather events and to annotate the weather event automatically. An accuracy of 98% is achieved for hurricane damage prediction and 97% accuracy for classifying severe weather events. The results proved that the proposed models for hurricane intensity estimation and its damage prediction enhances the learning ability, which can ultimately help scientists and meteorologists to comprehend the formation of storm events. Finally, the mitigation steps in reducing the hurricane risks are addressed.</jats:p