Nanoscale Prediction of Graphite Surface Erosion by Highly Energetic Gas - Molecular Dynamics Simulation -

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.In order to understand the fundamental essence in the erosion of graphite by hot gas molecules, in this study we investigate the mechanical properties of a single layer of graphite (e.g. graphene) and the bombardment of CO2 and H2O on graphene at high temperature by using extensive molecular dynamics (MD) simulations. The Reactive Empirical Bond Order (REBO) potential is employed to model the C-C bonds. The stress-strain curve shows that the stiffness of graphene decreases with increase in temperature. The strength of graphene at 2400 K is 60% less than the strength of graphene at 300 K. Also, we observe that the collision with CO2 and H2O provokes the bond breaking of C-C bonds in graphene at high temperature. The bombardment of gas molecules is carried out for different temperatures ranging between 300 K and 3000 K. Until 2400 K, both H2O and CO2 molecules are reflected back from the surface. However, at a critical temperature i.e., 2700 K and beyond, the bombardment of gas molecules breaks the C-C bond in the graphene. As the temperature increases, the graphene is destroyed quickly. This study shows that even the real gas molecules can induce the fracture of graphene at high temperature

Similarities in Dielectrophoretic and Electrophoretic Trap

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.In this study we present a universal theoretical formulation of the particle motions in electrophoretic and dielectrophoretic traps. It is extended from the well-known Mathieu equation based theories for Paul trap. The white noise random force model is utilized to form the Brownian motion of particle in the traps and the instantaneous dielectrophoretic force is employed rather than the time-averaged ponderomotive expression. The new approach enables many interesting properties of dielectrophoretic traps about stability and random motion. This study will be expected to provide a concrete protocol for the design of nanoscale traps which is essential in single molecule analysis

Trust Management In Ad-hoc Networks: A Social Network Based Approach

A social network is a social structure made up of individuals called œnodes, which are connected by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, or relationships of beliefs, knowledge or prestige.Social network analysis views social relationships in terms of network theory consisting of nodes and ties (also called edges, links, or connections). Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.We propose a social network based approach for trust management in ad-hoc networks, where nodes trust, help and interact with each other to create a complex trust-worthy network. Keywords: Trust management, ad-hoc network, social network, attack

Optimizing nitrogen and potassium for aerobic rice (Oryza sativa L.) in elevated -temperature environments

Increasing atmospheric temperature is the consequence of global warming, which is anticipated to impact crop growth and development and decrease the productivity of crops in tropical regions. The field experiments were conducted during the summer season of 2020 and 2021 at College farm, Kerala Agricultural University, Thrissur, to study the response of different rice varieties to elevated temperatures (2-3°C above ambient condition) during the flowering stage with different N and K levels under aerobic conditions. The experiments were laid out in Randomized Block Design consisting of eight treatments viz. V1F0 – Vaishak + 60 kg N and 30 kg K2O (Control – ambient temperature); V1F1- Vaishak+60 kg N and 30 kg K2O; V1F2- Vaishak+90 kg N and 45 kg K2O; V1F3- Vaishak+120 kg N and 60 kg K2O (under stress); V2F0- Aiswarya+60 kg N and 30 kg K2O (Control – No stress);V2F1- Aiswarya+60 kg N and 30 kg K2O; V2F2- Aiswarya+90 kg N and 45 kg K2O; V2F3- Aiswarya+120 kg N and 60 kg K2O (under stress). The tallest plant, higher number of tillers per hill, leaf area index, grain protein, grain and straw yield were observed with higher N and K levels (120: 60 kg/ha). The study revealed that the application of 90 kg N and 45 kg K2O produced comparable grain yields of Vaishak (2365 kg/ha in 2020 and 2186 kg/ha in 2021) and Aiswarya (2395 kg/ha in 2020 and 2104 kg/ha in 2021) to that of 120 kg N and 60 kg K2O/ha in both Vaishak and Aiswarya. Under elevated temperatures, the variety Aiswarya and Vaishak gave better yields to the farmers when supplied with 90 kg N and 45 kg K2O.  

Pretreatment quality assurance of volumetric modulated arc therapy on patient CT scan using indirect 3D dosimetry system

Purpose: Aim of this study is to clinically implement the COMPASS 3D dosimetry system for pretreatment quality assurance of volumetric modulated arc therapy (VMAT-RapidArc) treatment plans. Methods: For this study, 10 head and neck (H&N) and 10 pelvis VMAT plans dose response from Linac was measured using COMPASS system along with MatriXXEvolution and 3D dose was reconstructed in the patient computed tomography (CT) scan. Dose volume histograms and 3D gamma were used to evaluate the difference between the measured and calculated values. In order to validate the COMPASS system, dose response for open fields were acquired for both homogeneous and inhomogeneous phantoms. Results: The average dose difference between Eclipse treatment planning system (TPS) calculated and COMPASS measured (homogenous medium) in normalization region, inner region, penumbra region and buildup region was less than ±2%. In inhomogeneous phantom, there was a maximum difference of -3.17% in lung, whereas the difference other densities was within ±2%. The systematic increase in the average 3D gamma between the TPS calculated and COMPASS measured for VMAT plans with known dose errors and multi-leaf collimator (MLC) offset errors shows that COMPASS system was sensitive enough to find clinical significant errors. The 3D dose parameters (D95, D1, and average dose) of all H&N and pelvis patients were well within the clinically acceptable tolerance level of ±5%. The average 3D gammas for planning target volumes (PTV) and organ at risks (OAR) of the patients were less than 0.6. Conclusion: The results from this study show that COMPASS along with MatriXXEvolution can be effectively used for pretreatment verification of VMAT plans in the patient anatomy

Pretreatment quality assurance of volumetric modulated arc therapy on patient CT scan using indirect 3D dosimetry system

Purpose: Aim of this study is to clinically implement the COMPASS 3D dosimetry system for pretreatment quality assurance of volumetric modulated arc therapy (VMAT-RapidArc) treatment plans. Methods: For this study, 10 head and neck (H&amp;N) and 10 pelvis VMAT plans dose response from Linac was measured using COMPASS system along with MatriXXEvolution and 3D dose was reconstructed in the patient computed tomography (CT) scan. Dose volume histograms and 3D gamma were used to evaluate the difference between the measured and calculated values. In order to validate the COMPASS system, dose response for open fields were acquired for both homogeneous and inhomogeneous phantoms. Results: The average dose difference between Eclipse treatment planning system (TPS) calculated and COMPASS measured (homogenous medium) in normalization region, inner region, penumbra region and buildup region was less than ±2%. In inhomogeneous phantom, there was a maximum difference of -3.17% in lung, whereas the difference other densities was within ±2%. The systematic increase in the average 3D gamma between the TPS calculated and COMPASS measured for VMAT plans with known dose errors and multi-leaf collimator (MLC) offset errors shows that COMPASS system was sensitive enough to find clinical significant errors. The 3D dose parameters (D95, D1, and average dose) of all H&amp;N and pelvis patients were well within the clinically acceptable tolerance level of ±5%. The average 3D gammas for planning target volumes (PTV) and organ at risks (OAR) of the patients were less than 0.6. Conclusion: The results from this study show that COMPASS along with MatriXXEvolution can be effectively used for pretreatment verification of VMAT plans in the patient anatomy.</p

Impact of Arbuscular Mycorrhizal Fungi on Photosynthesis, Water Status, and Gas Exchange of Plants Under Salt Stress–A Meta-Analysis

Soil salinization is one of the most serious abiotic stress factors affecting plant productivity through reduction of soil water potential, decreasing the absorptive capacity of the roots for water and nutrients. A weighted meta-analysis was conducted to study the effects of arbuscular mycorrhizal fungi (AMF) inoculation in alleviating salt stress in C3 and C4 plants. We analyzed the salt stress influence on seven independent variables such as chlorophyll, leaf area, photosynthetic rate (Amax), stomatal conductance (Gs), transpiration rate (E), relative water content (RWC), and water use efficiency (WUE) on AMF inoculated plants. Responses were compared between C3 and C4 plants, AMF species, plant functional groups, level of salinity, and environmental conditions. Our results showed that AMF inoculated plants had a positive impact on gas exchange and water status under salt stress. The total chlorophyll contents of C3 plants were higher than C4 plants. However, C3 plants responses regarding Gs, Amax, and E were more positive compared to C4 plants. The increase in Gs mainly maintained E and it explains the increase in Amax and increase in E. When the two major AMF species (Rhizophagus intraradices and Funnelliformis mosseae) were considered, the effect sizes of RWC and WUE in R. intraradices were lower than those in F. mosseae indicating that F. mosseae inoculated plants performed better under salt stress. In terms of C3 and C4 plant photosynthetic pathways, the effect size of C4 was lower than C3 plants indicating that AMF inoculation more effectively alleviated salt stress in C3 compared to C4 plants

Association between internalized stigma and depression among HIV-positive persons entering into care in Southern India

Background: In India, which has the third largest HIV epidemic in the world, depression and HIV–related stigma may contribute to high rates of poor HIV–related outcomes such as loss to care and lack of virologic suppression. Methods: We analyzed data from a large HIV treatment center in southern India to estimate the burden of depressive symptoms and internalized stigma among Indian people living with HIV (PLHIV) entering into HIV care and to test the hypothesis that probable depression was associated with internalized stigma. We fitted modified Poisson regression models, adjusted for sociodemographic variables, with probable depression (PHQ–9 score ≥10 or recent suicidal thoughts) as the outcome variable and the Internalized AIDS–Related Stigma Scale (IARSS) score as the explanatory variable. Findings: 521 persons (304 men and 217 women) entering into HIV care between January 2015 and May 2016 were included in the analyses. The prevalence of probable depression was 10% and the mean IARSS score was 2.4 (out of 6), with 82% of participants endorsing at least one item on the IARSS. There was a nearly two times higher risk of probable depression for every additional point on the IARSS score (Adjusted Risk Ratio: 1.83; 95% confidence interval, 1.56–2.14). Conclusions: Depression and internalized stigma are highly correlated among PLHIV entering into HIV care in southern India and may provide targets for policymakers seeking to improve HIV–related outcomes in India

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies