7 research outputs found
The Effectiveness of Chromotherapy on Youth
No full textThe majority of people view color therapy as an alternative therapy. It is a rather young field of study. There are a tonne of various elements in life that might affect your mood and mental health. It is proposed that colors and colored lighting can improve one’s physical or mental health. If you want to create a calm and clear workplace, it’s critical to understand how color influences your mood. The interaction between the human body and colors has been thoroughly explored in a variety of research. Despite the fact that color therapy has been used for thousands of years, people’s interest in it has grown more recently. Different body parts are related to different colors. These are the various energy centers’ inherent healing abilities. In today’s age of globalization, color therapy is one of the most well-liked complementary treatments used to affect people’s conduct and brains. In forensic psychology, color is a crucial element that helps to build our surroundings. Without color, our world would be lifeless and sad. It is essential to our built environment, especially for teenagers, people who are partially blind, adolescents, and those who, for one reason or another, feel confined and dissatisfied with their way of life. Our brains are programmed to focus on things that stand out from our surroundings in terms of color. Which qualities and traits we associate with a person are influenced by the color of her clothing. Depending on the context, it may be culturally prejudiced due to political movements or historical occurrences. This review clarified the significance of this therapy and its advancement in the field of psychology, raised awareness among today’s youth, and added a fresh perspective to this investigation
Simulation of Low Power Heater for Gas Sensing Application
No full textAbstractThis paper presents design, simulation and analysis of a platinum (Pt) based micro-heater for gas sensing applications. Finite element method (FEM) analysis is employed for the purpose of performing the tasks mentioned above thereby investigates the various properties of the high resistive material platinum (Pt) using COMSOL Multiphysics. The Micro-heater is principally designed to ensure minimum power consumption, low thermal mass and better temperature uniformity. Furthermore, the effect of variation of thickness of heating element with created temperature and power consumption of the MEMS micro-heater is observed and evaluated
Finite Difference Time Domain Simulation of Active Cancellation of Radar Echoes
No full textAbstractRadar evasion or Stealth is a technology most desirable among all the military research areas currently pursued. Research organisations have focused their attention on electronic stealth technology or cancellation of waves since it is feasible now due to the improvement of high end processing and fast electronic systems. In an attempt to increase our understanding of this field, we have analysed the phenomenon through computer aided simulation. In this paper, we have created an electromagnetic wave simulation platform and using finite difference time domain method, analysed a method of active cancellation. We have found results showing complete effectiveness of this method assured by the accuracy of FDTD method
A novel deep unsupervised learning method for sum-rate optimization in device-to-device networks with a quality-of-service constraint
No full textThis study introduces a new Deep Unsupervised Learning (DUL) approach based on an optimization problem with box constraints coupled with polytope constraints for maximizing the sum rate in Device-to-Device (D2D) networks, a key factor in enhancing network capacity and efficiency. Current deep learning methods struggle with managing resource-intensive projection steps and need multiple iterations to optimize the sum rate in varying D2D environments. The proposed approach overcomes these challenges by minimizing the loss function and satisfying constraints when dealing with a monotone matrix. The novel approach controls transmit power through a fully connected, multi-layer Deep Neural Network (DNN), solving the complex, non-convex optimization problem associated with optimizing the sum rate in a symmetric interference channel model. The result shows that this method outperforms other power control methods regarding average sum rate, hit rate, and complexity when applied to a standard symmetric K-user Gaussian interference channel.D2D communicationsum-rate optimizationdeep learning (DL)unsupervised learning (UL)box constraintsmonotone matri
Assessment of Research output on Bamboo in India: A Bibliometric Study
No full textThis study assessed the research output on Bamboo for a period of 29 years (1989-2018). The web of science database has been used to retrieve worldwide publication records on bamboo research. The records were analysed using the descriptive statistics. Based on the retrieved data, various aspect of literature on bamboo research analysed and interpreted. The performance of the most productivity countries, authors, journals, Institution wise, and Growth rate and doubling time have assessed. The articles were classified as Research, review and others and grouped under 22 subjects to identify the subject coverage of bamboo research. The study found a positive growth in research and review article while very sharp decrement was observed. The growth rate and doubling period were estimated 8.5 and 8.34 respectively. Most of the articles written on Agriculture, Material Science, building technology and chemistry. M. Das (Presidency University, Department Life Science, Kolkata) is the most prolific primary author while R. Kumar (National Institute of Technology, Department of Mechanical Engineer, Silchar, India) mostly occurred as secondary author. Local and National collaboration mostly observed in the paper. India is the most productive country of world followed by china and Tamilnadu is most productive state of India. Indian Institute of Technology, India is a premier institute in bamboo research activity
NAD depletion mediates cytotoxicity in human neurons with autophagy deficiency
No full text\ua9 2023 The Author(s)Autophagy is a homeostatic process critical for cellular survival, and its malfunction is implicated in human diseases including neurodegeneration. Loss of autophagy contributes to cytotoxicity and tissue degeneration, but the mechanistic understanding of this phenomenon remains elusive. Here, we generated autophagy-deficient (ATG5−/−) human embryonic stem cells (hESCs), from which we established a human neuronal platform to investigate how loss of autophagy affects neuronal survival. ATG5−/− neurons exhibit basal cytotoxicity accompanied by metabolic defects. Depletion of nicotinamide adenine dinucleotide (NAD) due to hyperactivation of NAD-consuming enzymes is found to trigger cell death via mitochondrial depolarization in ATG5−/− neurons. Boosting intracellular NAD levels improves cell viability by restoring mitochondrial bioenergetics and proteostasis in ATG5−/− neurons. Our findings elucidate a mechanistic link between autophagy deficiency and neuronal cell death that can be targeted for therapeutic interventions in neurodegenerative and lysosomal storage diseases associated with autophagic defect
An efficient hybrid system for anomaly detection in social networks
No full textAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. © 2021, The Author(s)
