Spin Relaxation Times of Single-Wall Carbon Nanotubes

We have measured temperature ($T$)- and power-dependent electron spin resonance in bulk single-wall carbon nanotubes to determine both the spin-lattice and spin-spin relaxation times, $T_1$ and $T_2$. We observe that $T_1^{-1}$ increases linearly with $T$ from 4 to 100 K, whereas $T_2^{-1}$ {\em decreases} by over a factor of two when $T$ is increased from 3 to 300 K. We interpret the $T_1^{-1} \propto T$ trend as spin-lattice relaxation via interaction with conduction electrons (Korringa law) and the decreasing $T$ dependence of $T_2^{-1}$ as motional narrowing. By analyzing the latter, we find the spin hopping frequency to be 285 GHz. Last, we show that the Dysonian lineshape asymmetry follows a three-dimensional variable-range hopping behavior from 3 to 20 K; from this scaling relation, we extract a localization length of the hopping spins to be $\sim$100 nm.Comment: 6 pages, 3 figure

Enhanced Raman Microprobe Imaging of Single-Wall Carbon Nanotubes

We explore Raman microprobe capabilities to visualize single wall carbon nanotubes (SWCNTs). Although this technique is limited to a micron scale, we demonstrate that images of individual SWCNTs, bundles or their agglomerates can be generated by mapping Raman active elementary excitations. We measured the Raman response from carbon vibrations in SWCNTs excited by confocal scanning of a focused laser beam. Carbon vibrations reveal key characteristics of SWCNTs as nanotube diameter distribution (radial breathing modes, RBM, 100-300 cm(exp -1)), presence of defects and functional groups (D-mode, 1300-1350 cm(exp -1)), strain and oxidation states of SWCNTs, as well as metallic or semiconducting character of the tubes encoded in the lineshape of the G-modes at 1520-1600 cm(exp - 1). In addition, SWCNTs are highly anisotropic scatterers. The Raman response from a SWCNT is maximal for incident light polarization parallel to the tube axis and vanishing for perpendicular directions. We show that the SWCNT bundle shape or direction can be determined, with some limitations, from a set of Raman images taken at two orthogonal directions of the incident light polarization

The Unique Origin of Colors of Armchair Carbon Nanotubes

The colors of suspended metallic colloidal particles are determined by their size-dependent plasma resonance, while those of semiconducting colloidal particles are determined by their size-dependent band gap. Here, we present a novel case for armchair carbon nanotubes, suspended in aqueous medium, for which the color depends on their size-dependent excitonic resonance, even though the individual particles are metallic. We observe distinct colors of a series of armchair-enriched nanotube suspensions, highlighting the unique coloration mechanism of these one-dimensional metals.Comment: 4 pages, 3 figure

Carbon Nanotube-Enhanced Carbon-Phenenolic Ablator Material

This viewgraph presentation reviews the use of PICA (phenolic impregnated carbon ablator) as the selected material for heat shielding for future earth return vehicles. It briefly reviews the manufacturing of PICA and the advantages for the use of heat shielding, and then explains the reason for using Carbon Nanotubes to improve strength of phenolic resin that binds carbon fibers together. It reviews the work being done to create a carbon nanotube enhanced PICA. Also shown are various micrographic images of the various PICA materials

Diabetic detection from images of the eye

This cross-sectional study aims to detect Diabetic Retinopathy (DR) in patients who have had retinal scans and ophthalmological exams. The research makes use of tailored retinal images together with the OPF (Optimum-Path Forest) and RBM (Restricted Boltzmann Machine) models to categorize images according to the presence or absence of DR. In this work, features were extracted from the retinal images using both the RBM and OPF models. In particular, after a thorough system training phase, RBM was able to extract between 500 and 1000 features from the images. The study included fifteen distinct trial series, each with thirty cycles of repetition. The research comprised 122 eyes, or 73 diabetic patients, with a gender distribution that was reasonably balanced and an average age of 59.7 years. Remarkably, the RBM-1000 model stood out as the top performer, with the highest overall accuracy of 89.47% in diagnosis. In terms of specificity, the RBM-1000 and OPF-1000 models surpassed the competition, correctly categorizing all images free of DR symptoms. These findings highlight the potential of machine learning, particularly the RBM model, for self-identifying illnesses. The potential of machine learning models—in particular, RBM and OPF—to automate the diagnosis of diabetic retinopathy is demonstrated by this work. The results show how well the RBM model diagnoses, how sensitive it is, and how well it can be applied for efficient DR screening and diagnosis. This information may be used to improve the effectiveness of systems that identify retinal illnesses

Novel KNN with Differentiable Augmentation for Feature-Based Detection of Cassava Leaf Disease and Mitigation of Overfitting: An Innovative Memetic Algorithm

Many tropical countries depend on cassava, which is susceptible to deadly illnesses. These abnormalities can be diagnosed accurately and quickly to ensure food security. This study compares healthy and sick cassava leaves for four diseases: bacterial blight, brown streak, green mottle, and mosaic. Leaf images were systematically feature extracted to reveal color patterns, morphology, and textural qualities. Model learning methods use this extracted feature dataset. A new KNN+DA method may improve disease identification. Differentiable Augmentation uses data unpredictability to create alternative training samples to increase KNN performance. KNN+DA was compared to SVM, KNN, LR, and a memetic-tuned KNN to comprehend it better. We reached calculation speed, accuracy, recall, precision, and F1-score. KNN+DA outperformed older approaches in accuracy and resilience. KNN with differentiable augmentation improved classification accuracy and reduced overfitting, improving model generalizability for real-world use. Memetic algorithm-tuned KNN is another potential hybrid technique for disease diagnosis. Integrating current machine learning algorithms with cassava leaf photos can provide reliable early disease detection. More environmentally friendly agriculture would resul

Enhancing the Optimization of BI-LSTM Classifier with Ensemble Methods, Regularization, and Cross-Validation Techniques for Email Spam Detection

Email spam, a persistent and escalating issue, continues to disrupt the digital communication landscape, causing inconvenience and time loss for users worldwide. With technological advancements, spammers continually adapt and refine their tactics to infiltrate email inboxes. Staying current with state-of-the-art anti-spam techniques is imperative to secure emails and eliminate unwanted messages. Our research work embarks on an exploration of supercharging email spam detection through the augmentation of a Bidirectional Long Short-Term Memory (BI-LSTM) classifier. Our approach integrates ensemble methods, regularization techniques, and cross-validation into the fabric of the BI-LSTM model, creating a formidable spam detection system. Our paper delves into the intricate technical aspects of these methodologies, elucidating their synergy in fortifying the classifier\u27s performanc

Resnet for blood sample detection: a study on improving diagnostic accuracy

Automated blood cell analysis plays a crucial role in medical diagnostics, enabling rapid and accurate assessment of a patient\u27s health status. In this paper, we provide a unique technique for detecting and classifying WBCs,RBCs, and platelets inside blood smear pictures using ResNet (Residual Neural Network), a deep learning architecture. Because of its capacity to efficiently train very deep neural networks while minimizing the vanishing gradient problem, the ResNet architecture has exhibited excellent performance in a variety of image recognition applications. Leveraging the power of ResNet, we developed a multi-class classification model capable of distinguishing between WBCs, RBCs, and platelets within microscopic images of blood smears. Our methodology involved preprocessing the blood smear images to enhance contrast and remove noise, followed by image segmentation to isolate individual blood cells and platelets. The segmented images were then used to train and fine-tune a ResNet model, utilizing a large annotated dataset of labeled blood cell images. The trained model exhibited remarkable accuracy in identifying and classifying different blood cell types, even in the presence of overlapping cells or artifacts. We extensively tested our suggested technique, on a range of blood smear images to evaluate its performance. The findings demonstrated that ResNet effectively identifies and categorizes WBCs, (RBCs) and platelets. When compared to methods our approach showcased superior accuracy, robustness and generalization capabilities. After training the model with the Resnet algorithm we got 92% of Accuracy

A study on the prevalence of obesity and metabolic syndrome among students of a medical college

Background: Obesity is emerging as a serious problem throughout the world. The overall life expectancy is significantly shortened and the quality of life decreased in those who are excessively overweight. Metabolic syndrome (MetS) is characterized by a constellation of individual risk factors of cardiovascular disease. Central obesity is a key feature of this syndrome, reflecting the fact that the syndrome’s prevalence is driven by strong relationship between waist circumference and increasing obesity. Awareness about MetS in medical students is the need of the hour.Methods: This cross-sectional study was conducted at Dr. PSIMS and RF, Chinnoutpalli, Andhra Pradesh, India involving 400 medical students. A pre-tested questionnaire, measurement of blood pressure, fasting glucose level, fasting lipid profile, anthropometric variables such as height, weight, waist circumference and hip circumference were taken. Metabolic syndrome was defined based on the International Diabetes Federation criteria. Data was processed using SPSS version 16. T-test, chi-square test, fisher’s exact test, anova and odd’s ratio were used for statistical analysis.Results: 59% of the study population was female. The prevalence of obesity was 4%, with majority being males (81.25%) The MetS prevalence as per the International diabetes federation (IDF) criteria was 6% (n=24). The prevalence of MetS in males was 12.19% (n=20) and in females 1.69%. (n=4). The risk of developing metabolic syndrome is high among those who smoke, consume alcohol, consume junk food and sleep for longer durations.Conclusions: The prevalence of metabolic syndrome is 6%. A significant association is established between life style habits like smoking, alcohol consumption, junk food consumption, sleep duration and MetS