Physicochemical and Biological Properties of Land and Water Bodies Surrounding Major Dumpsites in Kolkata

In this paper we have investigated the data acquired from the analysis of the soil and water samples from and around the dump sites in Kolkata and North 24 Paragana district of West Bengal where the population density is extremely high. The treatment of disposal of municipal solid wastes and waste water has been inadequate to negligible in these areas and as a result the quality of soil and water bodies is subject to deterioration. We have made use of GPS enabled Satellite acquired images and its associated softwares to identify and demarcate the areas that come under the direct impact of the dumping sites, and which ultimately are the areas prone to diseases and degradation in the coming years. The pH, salinity, Total Dissolved Solvents and oxidation reduction potential has been investigated for the basic characterization of the samples. An estimate of heavy metals has also been made. Estimation of salts and oxides from the various sediments and soil samples were acquired. Identification of bacteria, under the purview of biological studies and the dependence of their growth on physiochemical parameters of the surrounding has portrayed an alarming result.  Adjacent to these areas there are agricultural fields where leached water from the dumping site directly drain into and cause biomagnifications. Contamination of the ground water is also sizable. This research will help to control pollution and biological outbreaks as well as suggest areas where immediate care should be taken to set up environmental restoration. The findings of the paper will further enlighten the planning and designing of waste disposal in urban areas and assist in its policy making in urban areas and thereby improve the quality of life of the scavengers who are left to equate their survival with the garbage mounds

Image processing and Machine learning in Concrete Cube Crack detection

Concrete cube testing plays a crucial role in various aspects of modern construction. The structural performance of concrete cubes under direct compressive stress can result in failure through concrete cube breakout. Failure modes related to concrete can be classified into two types: acceptable and non-acceptable, with further classification into various modes. However, most of the time 80% to 90% of the cubes are inaccurately selected, leading to lower strength and sustainability of concrete. Moreover, the excessive usage of cement required due to these inaccuracies contributes to global warming and increases costs. To address these issues, this research aims to develop an industry 4.0 solution for the construction and civil engineering fields. The proposed solution will be reliable, efficient, and based on image processing techniques. Convolutional Neural Networks (CNN) is used to detect and analyze cracks in concrete cubes. By examining the crack patterns, the damage area can be determined. By leveraging industry 4.0 technologies and advanced analysis techniques, this research aims to revolutionize the way concrete cube testing is conducted. The proposed solution will provide a reliable and efficient method for evaluating concrete cube quality, mitigating the negative impacts associated with inaccurate cube selection, and improving the performance and environmental sustainability of concrete in construction applications

Skin Cancer Classification using Deep Learning

According to world health organization skin cancer is the one of the most common cancer types in the world. The abnormal growth of skin cells most often develops on the skin when exposed to the sun and occurs when there is a mutation in the DNA of skin cells, it begins at the top of the skin. More than five million people are affected by skin cancer each year. The proposed method aim at analyzing and detecting the significant class of skin cancer variant such as Melanoma, Basal cell Carcinoma, Nevus. Melanoma is the most dangerous form of skin cancer when compared to the other types. In this paper we have developed a webapp that could differentiate skin cancer. The data set has been taken from ISIC and the model is trained using Gcollab. The proposed work has used convolution neural network (CNN) as algorithm for deep learning as it has higher accuracy and flask is used to develop the web app and the class of cancer is classified based on historical data of dermoscopic images

Time Series Analysis for Tractor Sales using SARIMAX and Deep Learning Models

Time series forecasting is known for playing vital role in many industries to make important decisions and strategies. This study concentrates on providing accurate insights that can help manufactures and stakeholders of agriculture machinery industry on future sales of tractors by applying both traditional and deep learning models like SARIMAX which is extension of SARIMA and deep learning models. Research starts by observing history data which include years of tractor sales then preprocess the data to find its quality and stationarity further applying SARIMAX model to find trends and seasons and cycles in the data and this model is evaluated by famous metrics like Root Mean Squared Error (RMSE).Deep learning models like Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, CNN LSTM Encoder Decoder, Convolutional Neural networks (CNN). They can help in enhancing the forecasting accuracy by handling all the non-linear relationships and their dependencies in the timeseries and this study will provide comparative analysis of deep learning models and SARIMAX model. Where SARIMAX outperformed the deep learning models with RMSE score 0.01 and provide forecast of next year’s tractor sales using SARIMAX model from the study and use q-q plot, residual plots and ACF and PACF graphs to make sure forecast was done accurately

Utilizing Chlorophyll as a Natural Chelating Agent for the Remediation of Heavy Metal Pollution: A Density Functional Theory Study

Heavy metal pollution, driven by industrialization, urbanization, and inadequate waste management, poses significant environmental and health risks. Toxic elements such as lead (Pb), mercury (Hg), cadmium (Cd), and arsenic (As) persist in ecosystems and bioaccumulate within biological systems, leading to severe health effects. Major contamination sources include industrial processes, agricultural practices, and improper waste disposal. Unlike organic pollutants, heavy metals do not degrade over time, allowing long-distance transport and deposition in soils and sediments. Traditional remediation methods often generate secondary waste, while adsorption techniques face material regeneration challenges. Natural chelating agents like chlorophyll, integral to photosynthesis, offer a promising alternative due to their ability to form stable complexes with heavy metals, reducing their bioavailability and toxicity. This study explores chlorophyll's potential in sequestering heavy metals through Density Functional Theory (DFT) to analyze the electronic structure and bonding characteristics of metal-chlorophyll complexes, aiming to develop sustainable and eco-friendly remediation strategies

Enhancing the Usability, Visibility, and Responsiveness of an Airline Reservation System: A User-Centered Design Approach

This paper presents the idea, design, and prototype of a flight search and airline booking system based on the perspective of user-centered design. The system is first sketched roughly on paper in the form of a sketched plan and implemented through the proper system by connecting with the rapid API to develop a responsive web application. Booking travel tickets is a hassle and quite stressful because there is a chance that the webpages take time, and several decisions to make, hard to choose a discounted or less expensive flight, and the user will have to put in a lot of effort with many browser tabs may leave open. If a user is looking for the lowest travel options within a range of dates, they need to search a lot of websites looking for better options. As UX designers, it is our responsibility to do some user research and identify the problem areas, then we will recommend some design options based on the research findings. After that, we will create a wireframe and prototype before jumping into web design by collecting all the requirements and analyzing the problems. We will be focusing on UI controls such as location picker, date picker, color contrast, accessibility, and so on. In this paper, we present the design and development of a user-centered flight search and booking system for the airline industry. Our goal is to create a system that would meet the needs and preferences of a diverse set of users. This paper will summarize the design, development, and implementation of an airline reservation system. We have used bubble.io to design the overall system and MYSQL as the database management system for this webpage. Our objective is to upgrade the current website by improving the usability, visibility, and responsiveness of the functions that the user will experience while buying a flight ticket. We have generated and managed the design documentation and a perfect user-based online flight booking system

Possibility of formation of stationary structures in relativistically degenerate magnetized quantum plasma with exchange-correlation energy

In the present paper we have studied the possibility of stationary structure formation in ion acoustic wave in a relaivistically degenerate quantum plasma in presence of magnetic field quantum diffraction parameter and localized exchange correlation energy. Recent authors include exchange correlation term in many plasma configurations including quantum and relativistic regime. We have analyzed the applicability of certain mathematical tools like the Sagdeev pseudo-potential method in dealing with the analysis of the formation and properties of large amplitude solitary structures, double layers, shocks etc. The findings of this paper will help future researchers to select analytical methods while studying wave phenomena in plasma

Design and Optimization of Piezoelectric Pressure Sensor with AlN as piezo electric material for High-Temperature Application using COMSOL 5.3

Dynamic pressure sensors in contrast to static pressure sensors measure pressure changes in liquids or gases generated due to a blast, a propulsion or an explosion, where the temperature is normally high which is above 700°C[1]. Piezoelectric pressure sensors with their inherent advantage of direct transduction capability are drawing attention for high-temperature applications. Lead Zirconate Titanate (PZT) and Zinc Oxide (ZnO) are popular ferroelectric materials for Piezoelectric sensor applications.  Aluminum Nitride (AlN) is a suitable candidate for high-temperature applications with its high melting point of 2673 K, the piezoelectric property remaining stable even up to 1423K, the energy band gap of 6.2eV, piezoelectric coefficient d33 of 7pC/N and pressure handling capacity up to 10 MPa. In this study, COMSOL Multiphysics 5.3 was used to analyse the pressure sensing capability of AlN film by optimizing the crystal orientation and the dimension of AlN in addition to studying suitability of using at high temperature. Also a comparison is done on the high temperature performance of pressure sensor using Silicon and Silicon Carbide as diaphragm

A Thermal Study on Foam-Based Eco-friendly Cinder Tiles

This study focusses on usage of fly ash and metakaolin as industrial waste to aerate lightweight foam concrete (LFGC) using hydrogen peroxide. By designing and optimising the components of metakaolin, hydrogen peroxide, and fly ash, physical properties such as mechanical strength, thermal characteristics, and heat resistance are assessed. Lightweight foam concrete has a dry density between 1400 and 1800 kg/m3, a bending strength between 0.7 and 1 MPa, and thermal conductivity between 0.1 and 0.7 W/mK, all of which indicate that it is more lightweight than normal concrete. By dumping solid waste on land, the environment suffers, and it leads to the release of toxic gases into the atmosphere and get polluted. As a byproduct, swapping out the cement with concrete would be a practical and cost-effective way to use the refuse. Alkaline solutions such as NaOH and Na2Sio3 are used to prepare geopolymer concrete. Samples of geopolymer concrete made with fly ash and metakaolin are cured in the oven for 24 hours. Geopolymer concrete is a form of material-based construction in which industrial raw materials supplied by businesses are incorporated into it. This lowers carbon emissions and makes the concrete more environmentally friendly. Using MATLAB, we predict which is the best value of bending strength, thermal conductivity, heat resistance, and dry density of various ash concrete. Effective input variables for geopolymer concrete include the amounts of fly ash, metakaolin, NaOH, and Na2SiO3, Fine aggregate, and the ratio of NaOH and Na2SiO3 and the output variables are bending strength, dry density, thermal conductivity, heat resistance

Digital Prescription for Hospital Database Management using ASR

According to American Medical Association (AMA), handwritten prescriptions are associated with larger risk of pharmaceutical errors when compared to electronic prescriptions. The solution to this problem is to create a digital prescription. This application leverages the usage of automated speech recognition (ASR) technology with digital prescription to make flawless and legible prescriptions. Automatic speech recognition reduces transcribing errors and speeds up prescription processing as well as ensures smooth interface with hospital database management by translating spoken instructions into text in real-time. This innovation not only simplifies clinical workflows but also improves patient safety and database management by providing a reliable and automated method for prescription documentation. This paper presents a digital prescription system for hospital database management using automatic speech recognition (ASR) technology, integrated with MySQL for database management and Java Script for application development. This approach aims to streamline the prescription process, minimize pharmaceutical errors and improve the overall patient care