10 research outputs found
Air Quality-Lung Cancer Data
No full textData comes from two different sources. Population-based lung cancer incidence rates for the period 2010-2014 (most updated data) were abstracted from National Cancer Institute state cancer profiles (Schwartz et al. 1996).This national county-level database of cancer data is collected by state public health surveillance systems. The domain specific county level environmental quality index (EQI) data for the period 2000-2005 were abstracted from United States Environmental Protection Agency (USEPA) profile. Complete descriptions of the datasets used in the EQI are provided in Lobdell’s paper (Lobdell 2011). Data were merged based on the Federal Information Processing Standards (FIPS) code. Out of 3144 counties in United States this study has available information for 2602 counties: Data was not available for four states namely Kansas, Michigan, Minnesota and Nevada due to state legislation and regulations which prohibit the release of county-level data to outside entities, county whose lung cancer mortality information is missing were omitted from the data set, the Union county, Florida is an outlier in terms of mortality information which was deleted from the data set, in the process of local control analysis this study experiences two (cluster 28 and 29) non-informative clusters (non-informative cluster is one for which either treatment or control group information is missing). For analysis, non-informative clusters information was deleted from the data set.
Three types of variables are used in this study: (i) lung cancer mortality as an outcome variable (ii) binary treatment indicator is the PM2.5 high (greater than 10.59 mg/m3) vs. low (less than 10.59 mg/m3) (iii) three potential X confounder for clustering namely land EQI, sociodemographic EQI and built EQI. For each index, higher values correspond to poorer environmental quality (Jagai et al. 2017). As PM2.5 is one of the indicators for measuring air EQI, that is why we do not consider the air EQI to avoid confounding effects
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
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
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
An efficient hybrid system for anomaly detection in social networks
Get PDFAnomaly 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)
Assessment of Research output on Bamboo in India: A Bibliometric Study
Get PDFThis 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
Acute Kidney Injury Diagnostic Accuracy and Implications of Different Baseline Creatinine Equations
No full textIntroduction: Acute kidney injury (AKI) definitions rely on known baseline serum creatinine (Crb), unavailable in up to 75% of hospitalized children. New equations (the AKI Baseline Creatinine [ABC] methods) for estimating Crb were derived from children without kidney disease. We aimed to externally validate ABC methods in an international cohort and assess how different Crb equations alter AKI epidemiology. Methods: AWARE was a prospective international study of critically ill children (aged 0–25 years) from 32 pediatric intensive care units (ICUs). A subset of AWARE (n = 2451) with known Crb (gold standard) was used to validate ABC methods using statistical measures of precision (R2) and accuracy (within 10% or 30% of gold standard). The entire cohort (N = 4984) was used to determine how different Crb estimating equations (3 ABC equations and 4 published estimated glomerular filtration rate [eGFR] equations imputing Crb) impact AKI incidence and its association with key clinical outcomes, including 28-day mortality, using univariate and multivariate analysis. Results: The ABC-Age equation (requiring only age) demonstrated similar accuracy and precision compared with existing Crb equations. The ABC-Creatinine equation (includes age and hospital creatinine value) outperformed existing Crb equations by up to 15% in precision (ABC-Creatinine, R2 = 0.51 vs. full-age spectrum [FAS], R2 = 0.36) and 32% in accuracy (ABC-Creatinine, 66% vs. original Schwartz, 34%). For the entire cohort, AKI incidence varied from 7% to 12%, depending on Crb definition. ABC equations were associated with clinical outcomes similarly to existing Crb equations. Conclusion: ABC-Creatinine equation (with minimal variables) outperformed existing Crb equations for accuracy and precision as the optimal method for Crb estimation. Crb definition variability alters AKI incidence and epidemiology, necessitating standardization
NAD depletion mediates cytotoxicity in human neurons with autophagy deficiency
Get PDF\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
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Advancements in Urban Environmental Studies : Application of Geospatial Technology and Artificial Intelligence in Urban Studies
No full textAdvancements in Urban Environmental Studie
