Journal of Computer Systems, Virtualization and Languages

Computer Simulation of a Capillary “Jump” of a Mercury Drop or an Air Bubble Under Conditions of Short-Term Weightlessness

Michael Shoikhedbrod — Wed, 08 May 2024 00:00:00 +0000

The capillary “jump” of drop of mercury or an air bubble that occurs during the transition of gravity acceleration from earth to microgravity is called a drop of mercury sharp “jump” starting from the bottom of a container with water, where it is located, to its top or a sharp “jump” of the surface of the air shell, which has turned into a spherical air bubble, starting from the top of a container with water down to the bottom of the container. Article presents both a computer model of a capillary “jump” of a mercury drop in a container with water and computer model of a capillary “jump” of an air bubble in a container with water, previously developed by author during the transition of gravitational acceleration from earthly to microgravity. The developed computer models, implemented in the Turbo C and Turbo Basic computer languages, permitted to simulate on the computer display a capillary “jump” of mercury drop and an air bubble in container with water; quantitatively to calculate the energy of motion of a capillary “jump” of a mercury drop and an air bubble in container with water during the transition of the acceleration of gravity from earth to microgravity. The results of calculations of the energy of motion of a capillary “jump” of a mercury drop showed that the surface tension of a mercury drop plays a significant role in the driving of a mercury drop compared to its density. Flight tests have shown that the developed computer models can be successfully used in main systems of spacecraft under conditions of microgravity to predict the occurrence of a capillary “jump” of wide range real mercury drops and air bubbles in these systems.

Jarvis-Virtual Voice Assistant

Vaibhav saxena, Aditya Kumar, Gagan Arya, Mukesh Kumar Bhardwaj — Fri, 10 May 2024 00:00:00 +0000

Although voice recognition technology has advanced significantly, there are still obstacles in the way of obtaining high accuracy, especially in a variety of environmental settings. This research uses deep learning models to increase the robustness and accuracy of voice recognition systems. Implementing different deep learning architectures, such as recurrent and convolutional neural networks (RNNs), and training them on a wide range of datasets with different noise levels and accents was part of the technique. Important results show a 15% improvement in recognition accuracy over current systems for the suggested models, which perform especially well in noisy contexts and with accented speech. These outcomes demonstrate the effectiveness of deep learning techniques in resolving issues that traditional voice recognition systems encounter. The results of this study have important significance for real-world applications, since they may enable smooth communication in a variety of linguistic and environmental contexts. Context: Since voice recognition technology has come a long way, it still has problems with accuracy, particularly in noisy situations and different locations with different accents. Even with advancements, traditional systems find it difficult to function consistently and dependably under these circumstances. This lays the groundwork for the need to investigate more durable ways to raise voice recognition's accuracy and dependability

BridgeConnect: A Comprehensive Study of an Interactive Q&A Platform for Enhanced Citizen-Organization Communication

Manas, Raghav Verma, Jayshree Upadhyay, Prince Raj, Mahesh Kumar Singh — Fri, 14 Jun 2024 00:00:00 +0000

This paper presents BridgeConnect, an innovative Question-Answering (Q&A) application aimed at bridging the communication gap between large organizations and the common populace in India. The portal allows users to post reviews and problems about government services and organizations, contributing to public discourse and accountability. The paper covers planning, database design, backend and frontend development, integration and testing, deployment, maintenance, and ongoing enhancements. Through comprehensive user feedback and analytical evaluation, BridgeConnect demonstrates significant potential in enhancing transparency and interaction between citizens and organizations.