Expanding an Education-based Collision Detection System Created on Virtual Reality and Augmented Reality

Virtually enhancing the real environment with augmented reality (AR) has lots of potential but is still in the early stages of research. The definition of appropriate user interfaces (UIs) is complicated by the absence of standards and the growing complexity of interaction opportunities. Several educators have discussed the advantages of XR for students as well as the use of AR and VR in the laboratory. Utilizing AR and VR to create immersive learning experiences is challenging since it takes time and effort to construct instructional AR and VR tools, apps, or educational settings. Because of this, even though these new technologies are said to help today’s students, their implementation in education may be postponed or stopped. In this research, the usage of XR technologies in education has been investigated through the examination of websites, technical papers, reports, and mobile app stores. This research study proposes a collision detection algorithm (CDA) utilizing machine learning. In order to aid in the identification of the meeting of two objects in the virtual environment, the collision detection method is employed in applications that support augmented reality and simulated reality technologies. In this study, mean, standard deviation and error parameters were utilized to analyze competitions that were related to augmented reality and virtual reality

An Automated System to Detect Plant Disease using Deep Learning

Crop diseases, particularly in places with weak infrastructure, represent a severe danger to the security of the global food supply. To address this challenge, a platform for accurate identification of plant diseases is needed. The paper proposes deep learning techniques to identify plant diseases. The platform uses the “CNN” algorithm, widely known for its high accuracy in image classification, enabling it to accurately identify plant diseases from images. Furthermore, the platform offers automated suggestions for preventing and provide supplements for that disease and managing the spread of crop diseases using its user-friendly web interface

Tensile And Formability Studies on AISI310 Austenitic Stainless Steel

Stainless steel is an alloy of iron, chromium, and, occasionally, nickel and other metals that resists corrosion. Metal is made into thin, flat pieces through an industrial process called sheet metal. One of the fundamental shapes used in metalworking, it can be cut and bent into many other shapes. Metal sheet is used to create a vast array of common items. The aim of the current work is to examine the 310 austenitic stainless steel’s formability at room temperature with different strain rates (i.e 0.1&0.01mm/s). The study’s outcomes were achieved through the utilization of the Nakazima test during stretch forming. Before performing formability test, The mechanical properties of a high-strength stainless steel AISI 310 were examined by conducting tensile tests at room temperature with 0.1,0.01mm/s strain rates. The failure modes, stress-strain curves of all the test specimens were obtained and analyzed. In the current experiment, the stretch forming of different shaped metal was tested in servo electric hot forming machine with different strain rates i.e, (0.1,0.01) at room temperature and plotted forming limit diagrams based on the results. Then simulations of the experiments were performed in LS-dyna software and compared with the practical experiment results

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

International audienceA seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in)

Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort

Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure.People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m2 or more to first eGFR of less than 30 mL/min per 1·73 m2 (the therapeutic trial window).Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p Background Methods Findings Interpretation Funding</p