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Using service learning to fuel multi-disciplinary research in Indian HEIs: A novel approach
The current work proposes a novel approach that can allow Indian HEIs to offer service-learning-based curricula while enhancing the institutional research output. The proposed model suggests a unique linkage between existing volunteer and academic departments at institutions of higher education such that data already being generated through existing outreach programs can be utilised for meaningful social science research. The proposed model thus utilizes existing resources already available with an institution to bolster research output, enhances the institutional capacity to include a pedagogical approach with proven benefits, and facilitates institutional compliance with regulatory directives mandating the inclusion of Service-learning-based courses at UG and PG levels. 2024, IGI Global. All rights reserved. -
Importance of Genetic Model in Huntingtons Disease
Huntingtons disease (HD) is the first defect-mapped autosomal-dominant, progressive neurodegenerative disorder with a distinct phenotype found in 1983, which contributed to the concept of human genome project. Thus, the search for genetic defects pioneered various mapping and gene study technological prototypes that culminated in identification of distinct disorders. Huntingtons disease has many symptoms, including chorea and dystonia, incoordination, cognitive decline and dementia, and behavioral difficulties. This disease can manifest any time over the age of 30 years, with the first sign and symptom being behavioral changes, which might include lack of emotions, periods of aggression, excitement, and anger. HD duration varies, ranging from 10 to 25 years or more depending on the individual. It is caused by a single gene defect on chromosome number 4, wherein a person requires only a single copy of the defected gene to show the symptoms; that is, a person with parent with the HD gene has a 50% chance of suffering. The disease becomes prominent in a human being as a result of mutations in a gene called Huntington that is located on the p arm (short arm) of chromosome 4 (4p 16.3). This chapter discusses the genetic model of Huntingtons disease and its importance. An increase in the normal number of repeat CAG (cytosine, adenine, and guanine) segments, (i.e. > 35 CAG) is seen in the Huntington (HTT) mutation that causes the disease. The severity of the disease depends on the sized expansion (i.e. increasing CAG repeats will accelerate the age of onset of the disease). Continuing studies of genetic modifiers-genes whose natural polymorphic variation contribute to the alteration and development of the D gene-offers to open new gateways for early diagnosis by unlocking the biochemical changes that occur years before diagnosis, thereby providing validated target protein and pathways for rational therapeutic interventions. This is also added as a section in this chapter. 2025 selection and editorial matter, Sachchida Nand Rai, Sandeep Singh, Santosh Kumar Singh. -
Use of popular paintings in advertising /
Western paintings are constantly been in multiple mediums used because of its high recall value; these paintings have had a huge impact on global advertisement by multiple companies to promote their products and services. These popular western paintings have been the source of inspiration for various reason, the age old classic paintings have been deconstructed analysed and meanings have derived. -
Vicarious Trauma in Law Students: Role of Gender, Personality, and Social Support
Law student trainees are exposed to trauma-related work which puts them at higher risk of being adversely affected by it. Since they are not directly related to the event, their distress goes unnoticed. The repetitive account of traumatic instances leads to traumatization of their own which is referred to as vicarious traumatization. The purpose of this paper was to delve into the degree to which the role of gender, personality, and social support impact law students vulnerability to vicarious trauma. For the current research, exploratory design was utilized. All one hundred and twenty participants were selected using purposive sampling. Self-report measures were employed to investigate social support, personality traits, and vicarious trauma in sixty male and sixty female law students. The results revealed that female law students and those law students who are high on Neuroticism and low on Extraversion are more vulnerable to experiencing vicarious trauma. Implications for trainees and educators are discussed and suggestions are provided for future research. 2021 International Journal of Criminal Justice Sciences. All Rights Reserved. -
Optimal Disassembly Sequence Generation Using Tool Information Matrix
Just as the assembly sequence plays an important role in the early part of the product, the disassembly sequence plays an important part in the final stage of the product. The disassembly sequence determines how efficiently the product can be recycled or it can be disassembled for maintenance purposes. In this study, the disassembly sequence is generated using the Tool Information Matrix (TIM) and the contact relations. In this study the feasible sequences are generated using the TIM and contact relations, afterward, the time required is considered as a fitness equation for generating the optimal disassembly sequence. The proposed methodology is applied to 10-part crankshaft assembly to test the performance in generating the optimal disassembly sequences. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hike in student suicides Consequence of online classes?
[No abstract available] -
Suicide among children during Covid-19 pandemic: An alarming social issue
[No abstract available] -
An efficient optimization based lung cancer pre-diagnosis system with aid of feed forward back propagation neural network ( FFBNN)
Vol. 56. No.2, October. ISSN: 1817-3195 -
Feature selection based on the classifier models: Performance issues in the pre-diagnosis of lung cancer /
Journal of Theoretical and Applied Information Technology, Vol-59(3), pp.549-555. ISSN-1992-8645. -
Feature selection based on the classifier models: Performance issues in the prediagnosis of lung cancer
Dimensionality reduction is generally carried out to reduce the complexity of the computations in the large data set environment by removing redundant or de-pendent attributes. For the Lung cancer disease prediction, in the pre-diagnosis stage, symptoms and risk factors are the main information carriers. Large number of symptoms and risk attributes poses major challenge in the computation. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are preferred based on relevancy of the models with respect to the data types chosen, which are based on statistical, rule based, logic based and artificial neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Based on the confusion matrix parameters the models prediction outcomes are found out in the supervisory training mode. The Confusion matrix of the models before and after dimensionality reduction is computed. Models are compared based on weighted Reader Operator Characteristics. Normalized weights are assigned based for the result of individual models and predictive model is developed. Predictive models performance is studied with target under supervised classifier model and it is observed that it is tallying with the expected outcome. 2005 - 2014 JATIT & LLS. All rights reserved. -
An efficient optimization based lung cancer pre-diagnosis system with aid of feed forward back propagation neural network (FFBNN)
World Health Organization (WHO) reports that worldwide 7.6 million deaths are caused by cancer each year. Uncontrollable cell development in the tissues of the lung is called as lung cancer. These uncontrollable cells restrict the growth of healthy lung tissues. If not treated, this growth can spread beyond the lung in the nearby tissue called metastasis and, form tumors. In order to preserve the life of the people who are suffered by the lung cancer disease, it should be pre-diagonized. So there is a need of pre diagnosis system for lung cancer disease which should provide better results. The proposed lung cancer prediagnosis technique is the combination of FFBNN and ABC. By using the Artificial Bee Colony (ABC) algorithm, the dimensionality of the dataset is reduced in order to reduce the computation complexity. Then the risk factors and the symptoms from the dimensional reduced dataset are given to the FFBNN to accomplish the training process. In order to get higher accuracy in the prediagnosis process, the FFBNN parameters are optimized using ABC algorithm. In the testing process, more data are given to well trained FFBNN-ABC to validate whether the given testing data predict the lung disease perfectly or not. 2005-2013 JATIT & LLS.All rights reserved. -
Dimensionality reduction based on the classifier models: Performance Issues in the prediction of Lung cancer
Dimensionality reduction is an essential feature to reduce the complexity of the computations in the large data set environment. When handling large quantum of medical data set, as in the case like, Lung cancer prediction, based on symptoms and Risk factors, number of attributes/ dimensions pose a major challenge. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are chosen, which are based on statistical, rule based, logic based and artificial Neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Confusion matrix of the models before and after dimensionality reduction is computed. Based on the confusion matrix result the models are compared and based on the performance optimal model is chosen. It is found that Multi-layer perceptron based artificial neural network model gives better performance compared to other approaches. 2012 IEEE. -
Message from IEEE InC4 2023 General Chair
[No abstract available] -
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Extraction of preformed mixed phase graphene sheets from graphitized coal by fungal leaching
The potential use of coal as source of carbon nano structure is seldom investigated. Herein we report a facile fungal solubilization method to extract mixed phase carbon structure from low grade coal. Coal had been used as a primary source for the production of carbon nanostructure with novel property, in addition to its main utility as a fuel. The major hurdle in its application is the inherent mineral embedded in it. An environmentally benign demineralization procedure make coal as a widely accepted precursor for the novel carbon materials. With Aspergiilus niger leaching, the randomly oriented preformed crystalline mixed phase nanocarbon in coal can be extracted. Raman studies revealed the presence of E2g scattering mode of graphite. The sp3 domains at ~1355 cm-1 (D band) is an indication of diamond like structure with disorder or defect. In the 2D region, multilayer stacking of graphene layers is noticed. The ratio of the defect to graphitic bands was found to be decreasing with increasing rank of coal. Bio leaching of coal enhances the carbon content in coal while eliminating the associated minerals in it. These defected carbon is an ideal material for graphene quantum dots and carbon dots, which are useful in drug delivery and bio imaging applications. 2017, IGI Global. All rights reserved. -
A multi-layer memory enriched staticmemory system /
Patent Number: 202141039100, Applicant: Debarka Mukhopadhyay.The present invention is configured with the generation of multiprocessor core of each 15 bit CAM cell and a section works at a lower supply area which will drive a part to work at a more prominent induced voltage for the CAM core processor. The present device is configured in such a way so that no level converters are required. The system operates avoiding the power overhead and low supply area as illustrated in Fig1. This process is used assorting voltage with less area interface inside the CAM cells, related by the applying product on D-FF. The match line indicates the search word and stored word are indistinguishable (the match case) or are extraordinary (a confounding case, or miss) of the CAM cell. -
Recent developments in melamine detection: Applications of gold and silver nanostructures in colorimetric and fluorometric assays
The purity of milk, traditionally regarded as a symbol of health and nourishment, has been undermined by the alarming issue of melamine (MLM) adulteration. This nitrogen-rich compound is illicitly introduced to falsely enhance protein content, posing significant health risks. Traditional detection methods are often labor-intensive, time-consuming, or require expensive equipment. In response, researchers have developed colorimetric detection techniques to efficiently screen milk for MLM contamination. These methods are particularly promising due to their ease of preparation, rapid detection, high sensitivity, and capability for naked-eye detection. Furthermore, the unique optical properties of advanced nanomaterials have facilitated fluorometric detection, wherein the presence of contaminants induces detectable changes in fluorescence intensity or wavelength. This study offers an in-depth review of recent advancements in colorimetric and fluorometric probes based on silver (Ag) and gold (Au) nanostructures, exploring their application in food analysis. It delves into the underlying sensing mechanisms of these probes, showcasing their efficacy in detecting food contaminants. Despite the numerous advantages of Ag and Au nanostructure-based probes, challenges remain, particularly in addressing the complexity of food matrices, achieving simultaneous detection of multiple analytes, and mitigating interference from testing conditions. Additionally, this review highlights the emergence of immunoassay-based sensors, noting that many commercially available MLM testing kits utilize ELISA and LFIA platforms. For the first time, a comprehensive list of MLM testing devices and assay kits is presented, accompanied by key findings from recent studies and recommendations for future research directions. 2024 The Author(s) -
Rubitics: The Smarter GCMS for Mars
A GCMS stands for a Gas Chromatograph and Mass Spectrometer. These two instruments are used to identify compounds from both soil and atmospheric samples. The GCMS usually has a mass of around 40 kilograms and is the size of a microwave oven, but what if we could downsize it? Downsizing the GCMS means that the number of equipment and instruments that can be used and carried by a rover can drastically increase. Rubitics is essentially a GCMS, only smaller and more efficient. This paper discusses the way Rubitics functions and how a GCMS can be remodeled and used to its fullest potential. The column of the Gas Chromatograph is replaced with composite materials to increase the flexibility of the tube, thereby increasing the number of columns along with finger-like projections on the interiors, which will aid in a much more precise separation of compounds. The inert carrier gas container is changed with a more durable, strong composite that will be instrumental in reducing the mass of the cylinder, and a safer chemically unreactive material will ensure complete pure storage. Rubitics will also contain a cooling system so as to be more power-efficient and aid in obtaining precise results. The material of the oven used in the gas chromatograph will be of much more insulating capacity (thermal resistance), lighter in mass, and smaller in size. Rubitics maintains the optimum shape to provide the most temperature and energy-efficient GCMS ever. Rubitics houses a compact electronic bay with sensors and a microprocessor for analysing the different components. The detectors' values are processed in the onboard microprocessor with the help of TinyML. This light algorithm can help in reducing the bandwidth consumed in transmitting unnecessary data to the ground station through providing in-situ data filtration. The paper also contemplates using such an algorithm to improve the efficiency of GCMS. In conclusion, Rubitics will be the future of GCMS technologies and sample analysis on different planetary terrains. Due to its re-engineered structure, it occupies lesser weight, size, and space. Rubitics thereby changes the number and quality of experiments that can be performed on Mars, leading to better insights for successful future habitation. Copyright 2022 by Ms. Harshini K Balaji. Published by the IAF, with permission and released to the IAF to publish in all forms. -
Artificial Intelligence for Enhanced Anti-Money Laundering and Asset Recovery: A New Frontier in Financial Crime Prevention
The incorporation of artificial intelligence (AI) into asset recovery and anti-money laundering (AML) procedures signifies a revolutionary change in the handling of financial crime. This article investigates the use of AI techniques to improve AML compliance, detect suspicious activity, and improve transaction monitoring. Financial institutions can now analyze massive volumes of transaction data in real-time, find anomalies, and lower false positives thanks to AI-based solutions, which include machine learning algorithms and predictive modeling. The research also outlines the difficulties and advantages of implementing AI, such as enhancing the effectiveness and caliber of suspicious activity reports (SARs) while resolving security and privacy issues with data. The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention. 2024 IEEE. -
The Evolving Prospects of Bharatanatyam: An Enquiry on Changing Religious Landscape
As cultural boundaries expand, symbols of cultural identity, like dance forms, evolve in terms of content and practice. Bharatanatyam, originally a temple dance, originated in the Hindu culture and had long been considered a religious art. However, the art form has gradually expanded its scope beyond its religious context. Contemporary evidence suggests that artists increasingly engage in performances addressing themes that are secular and even compositions based on other religious beliefs, but not without challenges. This article brings to light the evolving religious aspects of Bharatanatyam and investigates novel elements being introduced by cross-religious practices, such as thematic innovations, choreographic patterns and symbolic representations. By analysing data from in-depth interviews with twenty artists from diverse religious backgrounds, the authors argue that religious conservatism in society hinders the evolution of art forms such as Bharatanatyam that have the potential to adapt across and beyond religions. Edinburgh University Press.