Search

This paper describes an idea of a simple analytical microfluidics device that is used to infer levels of potassium concentration in a sample. The project is based on a paper-based device with capable barriers and a 50 mm microchannel which is subjected to wick the solution containing potassium salts. The wicking rate and the wicking time is calculated. The barrier is made out of cheap processes by using paraffin wax which is heated to 70 oC for about 10 minutes and then carefully applied and infused into the filter paper for required length. Various simulations are carried out and studies were analysed by using COMSOL Multiphysics to understand the wicking phenomenon and the impact of filter paper parameters such as porosity, pore size and pore distribution index on wicking rate. The dimensions of the microstrip is inferred from the simulation results and the designed strip is tested with KOH solution of closely varying concentrations. A common time is set and the wicking rate and fluid uptake is observed. It is realised that the output shows that wicking rate is inversely proportional to the concentration of solution. [ABSTRACT FROM AUTHOR]

Paper is constantly in demand worldwide for use in printing, packaging, and other applications. This is because additional contemporary uses of paper have been discovered. Alternatives to wood and less costly raw materials are currently being sourced in order to meet the rising demand brought on by a beneficial technological disruption. The majority of papers are crafted from virgin wood pulp from hardwood sources, but recently, softwood has earned enormous potential as an alternative to hard wood since the hardwood takes a substantial number of years to grow sufficiently to be used in the paper and pulp industry in contrast to softwood, which are primarily annual plants. It has been suggested that a number of agricultural food crop leftovers, such as rice husks, maize straw, okra stalks, corn stalks, banana bunches, pineapple leaves, and corn husks, which in many places do not have immediate practical applications, could be sources of pulp. This investigation examines the strength characteristics of paper samples made from a pulp mix of corn husks. [ABSTRACT FROM AUTHOR]

The study in this paper presents factors related to knowledge-sharing activity as the basis of cooperative-decision making. Using the Decision Support System (DSS) as the research object, the paper aims to understand better how managers foster knowledge sharing in the DSS implementation. The proposition departs from most managers not fully supported by the knowledge or alternatives given by the DSS to make decisions. We proposed that there is a 'loose' connection between DSS and users, and knowledge sharing bridges them. To provide empirical evidence, we approached a university as the research context in which they implement a so-called DSS. Our finding suggests that university managers share knowledge before making decisions with variations on how they interpret the information on DSS. We underlined that this activity has been institutionalized as their habit and norm. Furthermore, the significant factors influenci n g cooperative decision-making include personal and social values and technology literacy. [ABSTRACT FROM AUTHOR]

This paper presents a computational procedure, based on the theory of B-spline and NURBS curves and surfaces, for the reconstruction of 3-dimensional ship hull surfaces from 2-dimensional drawings. The paper focuses on the numerical procedure for defining and manipulating curves and surfaces. A software platform was also developed to support the complete computational procedure in a semi-automated interactive manner, through an advanced Graphical User Interface (GUI). [ABSTRACT FROM AUTHOR]

This paper presents a new and easy-to-use analytical model for the debonding of steel reinforce grout (SRG)-reinforced concrete specimens subjected to single lap shear tests. The non-linearity is concentrated at the interface between mortar and textile supposed linear elastic, assuming a perfect bond between the support – infinitely rigid and resistant – and mortar. The behavior at the interface, assumed discontinuous and multilinear, is characterized by a tangential stress-slip relationship consisting of four phases: three elastic perfectly brittle and one elastic perfectly ductile. A pseudo-linear hardening phase is observed experimentally in global force-displacement curves when detachment triggers, and consequently, it is essential to assume a non-zero residual tangential resistance at the interface. Admitting such assumptions, the slip at the interface between reinforcement and substrate can be described through a second order differential field equation which states closed form solutions, properly derived. This paper also proposes an implementation in a commercial finite element (FE) software, which discretizes the interface between mortar and steel cords by means of three in parallel cutoff bars, two of which are elastic perfectly brittle and one elastic perfectly ductile, using rigid beams for the connections and modelling SRG with elastic quadrilateral elements. Finally, the reliability of the proposed procedure and model is validated against experimental tests carried out by the authors using five SRG-strengthened specimens tested with direct shear test. [ABSTRACT FROM AUTHOR]

This paper describes the initial design of the experimental apparatus placed in the wind tunnel to obtain experimental data for the analysis of convective heat transfer coefficients on the surface of porous building materials. The experiment is based on the utilization of a unique closed-circuit wind tunnel solution allowing to measure large-scale samples in the aerodynamic section. The main objective of this paper is to design an experimental apparatus and verify its capability to detect responses (heat flux changes) to varying experimental factors, such as wind speed and temperature gradient. The pilot experiment described in this paper provides an initial set of data for further assessment, as well as practical information for particularization and detailed specification of the subsequent stages of the experimental research. [ABSTRACT FROM AUTHOR]

As the healthcare industry increasingly combines data-driven approaches to improve patient care. Data sharing across different systems becomes difficult for effective decision-making. This paper offers a comprehensive empirical review of existing models that facilitate healthcare data sharing for efficient query processing operations. The study aims to investigate the four-core metrics of precision, accuracy, complexity, and scalability systematically comparing these models to understand their capabilities and limitations. comparative analysis reveals salient features of each model and offers insights into their relative merits and drawbacks. This paper not only serves as a guide to navigate the landscape of existing solutions but also provides customized recommendations on selecting the most suitable model(s) based on individual and combined metrics. findings are expected to cover the way for future research in this field and contribute to creating more robust, efficient, and interoperable healthcare data sharing systems. [ABSTRACT FROM AUTHOR]

In early 2020s, Large Language Models (LLMs) like GPT-four, LaMDA and Llama 2 have emerged as modern equipment within the subject of synthetic intelligence. This overview paper delves into their profound impact on AI, industry, and society. The evolution of LLMs, driven with the aid of deep mastering techniques and large datasets, is explored, highlighting their pivotal function in shaping natural language information, technology, and translation, and their packages in unlikely locations like outside sports, architecture, mechanical and electrical engineering, presenting both possibilities and demanding situations for industries. The paper addresses a few societal implications of LLMs, examining their effects at the personnel, training, and conversation, even as scrutinizing moral concerns, privacy problems, and regulatory responses. [ABSTRACT FROM AUTHOR]

The advancement of diagnostic technologies in the healthcare sector has raised the bar for healthcare professionals who must effectively manage and integrate diverse yet complementary data generated during routine medical practice. Breast cancer is the main reason of women death in recent years. Breast cancer detection methods include self-exams, clinical exams, mammography, ultrasound, MRI, biopsies, ductal lavage, and thermography. These screenings help find abnormalities like lumps or changes in breast tissue. Early detection is crucial for successful treatment. To detect the breast cancer in its early stage is a challenging task. For that many computer-aided applications have been developed. Many new Machine learning and deep learning models are introduced to diagnose the breast cancer in early stages. In machine learning, the model teaches a computer to learn from examples and experiences and it learns to make decisions or predictions without being explicitly programmed. In deep learning, these artificial neural networks learn to solve more complicated problems by breaking them down into smaller, manageable parts, much like how our brain processes information in layers to understand things. The primary goal of this paper is to review the unimodal and multi-modal methods used for detecting breast cancer. In unimodal, a single diagnostic test, tool, or technique is employed to make a determination. This approach combines several diagnostic tools or sources of information to arrive at a more comprehensive and accurate diagnosis. The personalized diagnosis and treatment planning for an individual breast cancer patient relies on a multitude of data types, including various image data such as Mammography, Magnetic Resonance Imaging (MRI), X-ray, radiology, pathology, and camera images, as well as non-image data like clinical and genomic information. However, these decision-making processes can often be subjective, qualitative, and depend on significant variations between individuals. In this paper, literature survey is done on different unimodal and multimodal techniques to predict the detection of breast cancer in early stages. The study includes comparison of various factors such as dataset used, features extracted, evaluation matrix, machine learning and deep learning techniques. This paper offers a comprehensive review of recent research endeavors aimed at addressing this question. [ABSTRACT FROM AUTHOR]

Home Automation Changed the way people live and it is one of the major growing Industries. There are different targeted consumers for home automation some seek luxurious and some seek help from the developing technology by keeping second set of consumers in mind, we proposed Bluetooth Based Home Automation This paper gives Design and Framework of wireless based Bluetooth Home Automation. Our method is to provide the safety, security and comfort of elderly and disabled people during ON and OFF of electrical appliances. So, this paper gives the model for Bluetooth Based Home Automation using Arduino UNO Hardware and Arduino IDE software. [ABSTRACT FROM AUTHOR]

This paper explores the development of a firefighting robot designed to minimize firefighter risk in hazardous situations. The robot is remotely controlled, eliminating the need for firefighters to enter burning buildings. It can move forward, backward, and turn left or right, allowing for precise maneuvering. A sensitive flame sensor detects fires, even small ones, while a wireless sensor network facilitates fire extinguishing measures. This robot represents a potential future for firefighting technology, aiming to replace traditional methods with automation. The paper delves deeper into the robot's functionalities and the role of automation in firefighting. Firefighting robots can proactively find and locate fires before they become large-scale emergencies. They can also work alongside firefighters to minimize human injury risks and detect hazardous gas leaks. The technical aspects of the robot are also explained here. The design incorporates three main components: hardware, electronics, and programming. The robot uses two DC motors for movement and a steering wheel. A water pump and servo motor enable water spraying with directional control. An Arduino Uno board serves as the central processing unit, interfacing with various sensors and interpreting sensor data to control the robot's movements. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of cancer in women all over the world. In 2023, there will be approximately 2.2 million new cases of breast cancer, with 662000 deaths worldwide. As of the end of 2021, 7.9 million women had been diagnosed with breast cancer in the previous five years, making it the world's second most common cancer. Primary breast cancer detection is critical for increasing survival rates. After 5 years, breast cancer survival rates range from more than 93% in developed countries to 63% in India and 42% in South Africa. Early detection and treatment have been shown in developed countries to be effective and should be expanded to other countries [1]. Mammography is the utmost operative method for detecting early-stage breast cancer that is presently available. Naturally, a radiologist will look for signs of cancer in a mammogram. Radiologists are directed by the computer-aided diagnosis system to re-examine the mammogram for any suspicious areas. The computer programme can analyse the mammogram and detect abnormalities. The goal of this paper is to look into the value of textural properties, specifically fractal textures, in describing benign and malignant microcalcifications, as well as their role in improving classification accuracy. To investigate classification and labelling, textural measures were used. For fractal analysis, the differential box counting method, Fractal dimensions, and the Gray level Difference Method were used (GLDM). Fractal measures were used to classify and label benign and malignant microcalcifications. The importance of fractal texture measures in achieving automated Breast cancer detection and classification accuracy is emphasised in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we show a straightforward and cost-effective approach for constructing a silicon cantilever-based sensor platform with piezoresistive transduction. The suggested platform may be utilised to identify the presence of nitrogen in soil, according to 18crown6 ether. The design and FEM analysis of a PZT sensor for the detection of soil macronutrients using COMSOL multiphysics software are presented in this paper. The sensor component is made of p-type silicon, whereas the device component is made of n-type silicon. [ABSTRACT FROM AUTHOR]

Automated detection of breast boundary is an important and fundamental step involved in the identification and classification of benign and malignant tissues of the breast from analysis of Digital Mammograms. In this paper, Preprocessing of the mammogram dataset is presented in detail to obtain a processed mammogram image with clear demarcation between breast and background. Most of the breast cancer detection and location identification algorithms developed for diagnosis from digital mammograms start with an input image, properly enclosing only the breast region, not covering the pectoral muscle, and a clear background image with no artifacts. In practice, digital mammograms, used by radiologists have a background marked by varying intensity levels and have labels to indicate details of the patient's id and breast. Removal of the scanning artifacts, labels, and varying background intensity presents a complex problem in presenting a clear demarcation between breast and background image, drawing a clear boundary on the Skin air interface. This paper approaches the problem of extracting skin-air interface employing size and rotation-invariant template matching to remove labels, and an advanced lee filter for suppressing speckle noise, while preserving texture information to obtain mammogram image, cleared of scanning artifacts. Then, the Local Binary Pattern method is applied to compute cross-correlation analysis. Then, the resultant image is given to the Support Vector Machine (SVM) classifier. SVM is employed to classify breast and background. From the classified image, the breast boundary is extracted and overlaid on the mammogram image. Results were compared with actual breast boundary using the AMinDist procedure and are found to be 96% accurate with an error distance of fewer than 2 pixels. [ABSTRACT FROM AUTHOR]

Pedestrian detection is the significant tasks in recent computer vision applications for providing smart applications like intelligence surveillance, autonomous driving, and much more. Many applications were introduced in recent years regarding pedestrian detection using various methodologies. In this paper, we aim to analyze various existing system related to pedestrian detection and related object detection systems for the research purpose and to conclude which is the better and efficient technique. In this, various methods for building pedestrian detection system has been discussed that were used by various authors in recent years, that is about the existing environments for implementing pedestrian detection, and their drawbacks and the methods to overcome that. This paper will be useful for future references and research purpose. [ABSTRACT FROM AUTHOR]

Biomaterials are needed for human survival. Hydroxyapatite (HA) is a biomaterial that has biocompatibility properties suitable for use in the human body. HA can be found in natural materials such as fish bones, cow bones, pig bones, clam shells, eggshells and others. There are various methods to synthesize HA. The calcination method is used because it is simpler and low-cost. Calcination temperature can affect the Ca/P ratio of the natural sources used. This paper reviews several journals related to HA sources using different fish bones. Differences in calcination temperature and trace elements can affect the Ca and P ratio in fish bone powder. Choosing the right temperature and fish source will produce Ca and P that are in compliance with the HA standard of 1.67. [ABSTRACT FROM AUTHOR]

This paper evaluates the shock vibration response of a dynamics system equipped with nonlinear spring. The nonlinear spring has a quasi-zero-stiffness (QZS) characteristic with low dynamics and large static stiffness. The QZS stiffness behavior is designed numerically using in-house FEM software. The experimental setup of QZS spring is realized using a simple supported beam system with a preload condition. The shock load is applied to the vibration system using free-fall conditions. The experimental data shows that the maximum acceleration under shock load is small for the vibration system with QZS spring compared to those using linear spring. [ABSTRACT FROM AUTHOR]

This paper studies the shock response of a vibration system with a nonlinear spring. The analysis is performed for one-degree and two degrees of freedom vibration models under free fall conditions. The acceleration and rebound displacement, which indicate the system's comfortability and stability, are evaluated in the time domain. The simulation results show that a vibration system with a nonlinear spring performs better than that with a linear spring in reducing the acceleration and rebound displacement of the main mass under shock load excitation. [ABSTRACT FROM AUTHOR]