Study and interpretation of the millimeter-wave spectrum of Venus

The effects of the Venus atmospheric constituents on its millimeter wavelength emission are investigated. Specifically, this research describes the methodology and the results of laboratory measurements which are used to calculate the opacity of some of the major absorbers in the Venus atmosphere. The pressure broadened absorption of gaseous SO2/CO2 and gaseous H2SO4/CO2 has been measured at millimeter wavelengths. We have also developed new formalisms for computing the absorptivities of these gases based on our laboratory work. The complex dielectric constant of liquid sulfuric acid has been measured and the expected opacity from the liquid sulfuric acid cloud layer found in the atmosphere of Venus has been evaluated. The partial pressure of gaseous H2SO4 has been measured which results in a more accurate estimate of the dissociation factor of H2SO4. A radiative transfer model has been developed in order to understand how each atmospheric constituent affects the millimeter wave emissions from Venus. Our results from the radiative transfer model are compared with recent observations of the micro-wave and millimeter wave emissions from Venus. Our main conclusion from this work is that gaseous H2SO4 is the most likely cause of the variation in the observed emission from Venus at 112 GHz

Understanding the variation in the millimeter-wave emission of Venus

Recent observations of the millimeter-wave emission from Venus at 112 GHz (2.6 mm) have shown significant variations in the continuum flux emission that may be attributed to the variability in the abundances of absorbing constituents in the Venus atmosphere. Such constituents include gaseous H2SO4, SO2, and liquid sulfuric acid (cloud condensates). Recently, Fahd and Steffes have shown that the effects of liquid H, SO4, and gaseous SO2 cannot completely account for this measured variability in the millimeter-wave emission of Venus. Thus, it is necessary to study the effect of gaseous H2SO4 on the millimeter-wave emission of Venus. This requires knowledge of the millimeter-wavelength (MMW) opacity of gaseous H2SO4, which unfortunately has never been determined for Venus-like conditions. We have measured the opacity of gaseous H2SO4 in a CO2 atmosphere at 550, 570, and 590 K, at 1 and 2 atm total pressure, and at a frequency of 94.1 GHz. Our results, in addition to previous centimeter-wavelength results are used to verify a modeling formalism for calculating the expected opacity of this gaseous mixture at other frequencies. This formalism is incorporated into a radiative transfer model to study the effect of gaseous H2SO4 on the MMW emission of Venus

Predicting student performance in a blended learning environment using learning management system interaction data

Purpose: Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale. Design/methodology/approach: This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment. Findings: Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods. Originality/value: The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning

Long-term culture of human pancreatic slices as a model to study real-time islet regeneration.

The culture of live pancreatic tissue slices is a powerful tool for the interrogation of physiology and pathology in an in vitro setting that retains near-intact cytoarchitecture. However, current culture conditions for human pancreatic slices (HPSs) have only been tested for short-term applications, which are not permissive for the long-term, longitudinal study of pancreatic endocrine regeneration. Using a culture system designed to mimic the physiological oxygenation of the pancreas, we demonstrate high viability and preserved endocrine and exocrine function in HPS for at least 10 days after sectioning. This extended lifespan allowed us to dynamically lineage trace and quantify the formation of insulin-producing cells in HPS from both non-diabetic and type 2 diabetic donors. This technology is expected to be of great impact for the conduct of real-time regeneration/developmental studies in the human pancreas.post-print3.907 K

A Comparison of Continuous-Wave and Pulsed Free Space 2-Port Photonic Vector Network Analyzers for Terahertz Characterization

Photonic Vector Network Analyzers (PVNA) provide a viable solution to the rising demand for efficient, accurate, and affordable characterization instruments for benchmarking terahertz devices and components. This paper compares continuous-wave and pulsed versions of the free space 2-port PVNA and their aptitudes for THz characterization using a distributed Bragg reflector as an application example

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Recent studies report doubling numbers of deaths due to dementia. With such an escalating mortality rate related to cognitive decline diseases, like dementia, timely information on contributing factors and knowledge discovery from evidence-based repositories is warranted. A large amount of scholarly knowledge extracted from research findings on dementia can be understood only using human intelligence for arriving at quality inferences. Due to the unstructured data presented in such a massive dataset of scientific articles available online, gaining insights from the knowledge hidden in the literature is complex and time-consuming. Hence, there is a need for developing a knowledge management model to create, query and maintain a knowledge repository of key elements and their relationships extracted from scholarly articles in a structured manner. In this paper, an innovative knowledge discovery computing model to process key findings from unstructured data from scholarly articles by using the design science research (DSR) methodology is proposed. The solution caters to a novel composition of the cognitive script of crucial knowledge related to dementia and its subsequent transformation from unstructured into a structured format using graph-based next-generation infrastructures. The computing model contains three phases to assist the research community to have a better understanding of the related knowledge in the existing unstructured research articles: (i) article collection and construction of cognitive script, (ii) generation of Cypher statements (a knowledge graph query language) and (iii) creation of graph-based repository and visualization. The performance of the computing model is demonstrated by visualizing the outcome of various search criteria in the form of nodes and their relationships. Our results also demonstrate the effectiveness of visual query and navigation highlighting its usability

Oral Candidiasis Susceptibility of Mice Lacking Interferon Regulatory Factor 3, A Preliminary Report

Candidiasis is prevalent when the host defense is compromised. For example, individuals with diabetes mellitus, HIV infection, chronic systemic immune-suppressor drugs usage, or patients in intensive care units or having cancer are at high risk to develop persistent candida infection or recurrent episodes [1,2]

SARS-CoV-2 infection of the pancreas promotes thrombofibrosis and is associated with new-onset diabetes

Evidence suggests an association between severe acute respiratory syndrome-cornavirus-2 (SARS-CoV-2) infection and the occurrence of new-onset diabetes. We examined pancreatic expression of angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2), the cell entry factors for SARS-CoV-2, using publicly available single-cell RNA sequencing data sets, and pancreatic tissue from control male and female nonhuman primates (NHPs) and humans. We also examined SARS-CoV-2 immunolocalization in pancreatic cells of SARS-CoV-2-infected NHPs and patients who had died from coronavirus disease 2019 (COVID-19). We report expression of ACE2 in pancreatic islet, ductal, and endothelial cells in NHPs and humans. In pancreata from SARS-CoV-2-infected NHPs and COVID-19 patients, SARS-CoV-2 infected ductal, endothelial, and islet cells. These pancreata also exhibited generalized fibrosis associated with multiple vascular thrombi. Two out of 8 NHPs developed new-onset diabetes following SARS-CoV-2 infection. Two out of 5 COVID-19 patients exhibited new-onset diabetes at admission. These results suggest that SARS-CoV-2 infection of the pancreas may promote acute and especially chronic pancreatic dysfunction that could potentially lead to new-onset diabetes

Exploiting an Elitist Barnacles Mating Optimizer implementation for substitution box optimization

Barnacles Mating Optimizer (BMO) is a new metaheuristic algorithm that suffers from slow convergence and poor efficiency due to its limited capability in exploiting the search space and exploring new promising regions. Addressing these shortcomings, this paper introduces Elitist Barnacles Mating Optimizer (eBMO). Unlike BMO, eBMO exploits the elite exponential probability (Pelite) to decide whether to intensify search process via swap operator or to diversify search by randomly exploring new regions. Furthermore, eBMO uses Chebyshev map instead of random numbers to generate quality S-boxes. Experimental results of eBMO on the generation of 8 × 8 substitution-box are competitive against other existing works

Intelligent ultra-light deep learning model for multi-class brain tumor detection

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2−3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery