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Saccharum spontaneum is a grass-type plant abundantly found in the Indian subcontinent, known for its beautiful, lustrous white flowers. Fibres were extracted from the flower and analyzed for their physical, mechanical, and chemical properties. The chemical composition of the fibre is 90.9% holocellulose, with a moisture content of 10.97%, and an average fibre length of 25 mm. FTIR spectra confirmed the presence of functional groups similar to those found in other natural cellulosic fibres. Additionally, the fibre exhibits a tensile strength of approximately 63 cN/tex, which is significantly higher than that of cotton and jute fibres. However, its crystallinity is relatively high at about 75%, resulting in a low elongation at break of 1.9%. FESEM analysis revealed a hollow structure in the fibre, indicating its potential suitability for applications requiring high thermal insulation, excellent moisture management, vapor permeability, and microbial fuel cell development.

Rapid diagnostic tests are crucial for timely disease detection and management, yet accurate interpretation of test results remains challenging. In this study, we propose a novel approach to enhance the accuracy and reliability of rapid diagnostic test result interpretation by integrating artificial intelligence (AI) algorithms, including convolutional neural networks (CNN), within a smartphone-based application. The app enables users to take pictures of their test kits, which YOLOv8 then processes to precisely crop and extract the membrane region, even if the test kit is not centered in the frame or is positioned at the very edge of the image. This capability offers greater accessibility, allowing even visually impaired individuals to capture test images without needing perfect alignment, thus promoting user independence and inclusivity. The extracted image is analyzed by an additional CNN classifier that determines if the results are positive, negative, or invalid, providing users with the results and a confidence level. Through validation experiments with commonly used rapid test kits across various diagnostic applications, our results demonstrate that the synergistic integration of AI significantly improves sensitivity and specificity in test result interpretation. This improvement can be attributed to the extraction of the membrane zones from the test kit images using the state-of-the-art YOLO algorithm. Additionally, we performed SHapley Additive exPlanations (SHAP) analysis to investigate the factors influencing the model's decisions, identifying reasons behind both correct and incorrect classifications. By facilitating the differentiation of genuine test lines from background noise and providing valuable insights into test line intensity and uniformity, our approach offers a robust solution to challenges in rapid test interpretation.

This paper extends a previous model for p-i-n GaAs quantum dot solar cells (QDSC) by revising the equation of photocarrier generation rate in quantum dots (QDs) inside the intrinsic region. In our model, we address a notable discrepancy that arose from the previous model where they did not consider the volume of QDs within the intrinsic region, leading to an overestimation of the photocarrier generation rate. Our present model rectifies this by incorporating the volume of quantum dots, resulting in adjustments to the photocarrier generation rate. Additionally, we determine the absorption coefficient of the QDs based on Mie theory for different diameter sizes considering the constant volume fraction of the total number of QDs in the intrinsic region. We observe in our analysis that the absorption spectra of the QDs and host material may overlap in certain cases, although the previous model assumed no overlap. This finding suggests the need for caution when evaluating spectral overlap: if the spectra do not overlap, both the previous and current modified models can be reliably applied. However, in cases of overlap, careful consideration is required to ensure accurate predictions of photocarrier generation. Furthermore, investigating the effect of QD diameter size on the photocarrier generation rate in the intrinsic region, we find that smaller QD sizes result in a higher absorption coefficient as well as a higher generation rate for a constant volume of QDs in the region. Moreover, we establish the optimization of the QDs array size by varying the size and the total volume of QDs to improve the generation rate. Our analysis reveals that a higher volume of QDs and a smaller size of QDs result in the maximum generation rate. From an experimental perspective, we propose that the optimal arrangement of QDs in such solar cells is a 0.5 volume fraction with a QD diameter of 2 nm., Comment: 22 pages, 6 figures

In recent years, the 'kirigami' technique has gained significant attention for creating meta-structures and meta-materials with exceptional characteristics, such as unprecedented stretchability. These properties, not typically inherent in the original materials or structures, present new opportunities for applications in stretchable electronics and photovoltaics. However, despite its scientific and practical significance, the application of kirigami patterning on a monolayer of tungsten disulfide (WS2), a van der Waals material with exceptional mechanical, electronic, and optical properties, has remained unexplored. This study utilizes molecular dynamics (MD) simulations to investigate the mechanical properties of monolayer WS2 with rectangular kirigami cuts. We find that, under tensile loading, the WS2 based kirigami structure exhibits a notable increase in tensile strain and a decrease in strength, thus demonstrating the effectiveness of the kirigami cutting technique in enhancing the stretchability of monolayer WS2. Additionally, increasing the overlap ratio enhances the stretchability of the structure, allowing for tailored high strength or high strain requirements. Furthermore, our observations reveal that increasing the density of cuts and reducing the length-to-width ratio of the kirigami nanosheet further improve the fracture strain, thereby enhancing the overall stretchability of the proposed kirigami patterned structure of WS2., Comment: 19 pages, 5 figures

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge., Comment: 14 pages, 6 figures

Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to imbalanced insulin activity.The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with high accuracy,in order to identify and treat diabetes patients at an early age.Our training and test dataset is an accumulation of 9483 diabetes patients information.The training dataset is large enough to negate overfitting and provide for highly accurate test performance.We use performance measures such as accuracy and precision to find out the best algorithm deep ANN which outperforms with 95.14% accuracy among all other tested machine learning classifiers.We hope our high-performing model can be used by hospitals to predict diabetes and drive research into more accurate prediction models.

In this study, a series of nanocrystalline ferrites of Ni0.7Cu0.30AlxFe2-xO4 (x=0.00 to 0.10 with a step of 0.02) has been synthesized through the sol-gel auto combustion technique. The structural, morphological, dielectric, and electrical properties of the Ni-Cu spinel ferrite nanoparticles are analyzed due to the substitution of Al3+ content. The crystalline and structural characteristics of the prepared nanoparticles (NPs) have been studied employing the x-ray diffraction (XRD) spectra and FTIR analysis. The extracted XRD patterns assure the single-phase cubic spinel structure of all samples with homogeneity and no impurity, which indicates the yielding of high crystalline NPs. A slight decrease of average grain size with increment of Al3+ content is observed in the surface morphological study carried out by the field emission scanning electron microscopy (FESEM). The studied materials are found in semi-spherical shapes, showing the multi-domain grains separated by grain boundaries with some agglomerations. The chemical composition study for the synthesized NI-Cu spinel ferrites using energy dispersive x-ray (EDX) ensures the presence of each component in appropriate proportions in each sample.

RF sensors have been recently proposed as a new modality for sign language processing technology. They are non-contact, effective in the dark, and acquire a direct measurement of signing kinematic via exploitation of the micro-Doppler effect. First, this work provides an in depth, comparative examination of the kinematic properties of signing as measured by RF sensors for both fluent ASL users and hearing imitation signers. Second, as ASL recognition techniques utilizing deep learning requires a large amount of training data, this work examines the effect of signing kinematics and subject fluency on adversarial learning techniques for data synthesis. Two different approaches for the synthetic training data generation are proposed: 1) adversarial domain adaptation to minimize the differences between imitation signing and fluent signing data, and 2) kinematically-constrained generative adversarial networks for accurate synthesis of RF signing signatures. The results show that the kinematic discrepancies between imitation signing and fluent signing are so significant that training on data directly synthesized from fluent RF signers offers greater performance (93% top-5 accuracy) than that produced by adaptation of imitation signing (88% top-5 accuracy) when classifying 100 ASL signs.

COVID-19 is currently one the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection is essential to identify, take better decisions and ensure treatment for the patients which will help save their lives. In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. Both X-ray and CT scan images are considered to evaluate the proposed technique. We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network model for segmenting the regions infected by COVID-19. The detection model shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in CT-images. A novel quantitative analysis strategy is also proposed in this paper to determine the percentage of infected regions in X-ray and CT images. The qualitative and quantitative results demonstrate promising results for COVID-19 detection and infected region localization., Comment: 11 pages, 15 figures

Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional approaches to this problem either proceed from handcrafted features or require large datasets to combat the m >> n problem. In this paper, we show that the source of the problem---signal dynamics---can be used to our advantage and noticeably improve classification performance on a range of discrimination tasks when training data is scarce. We demonstrate that self-supervised pre-training guided by signal dynamics produces embedding that generalizes across tasks, datasets, data collection sites, and data distributions. We perform an extensive evaluation of this approach on a range of tasks including simulated data, keyword detection problem, and a range of functional neuroimaging data, where we show that a single embedding learnt on healthy subjects generalizes across a number of disorders, age groups, and datasets., Comment: 11 pages, 12 figures. arXiv admin note: text overlap with arXiv:1911.06813

In mobile ad-hoc networks, nodes have to transmit HELLO or Route Maintenance messages at regular intervals, and all nodes residing within its radio range, reply with an acknowledgment message informing their node identifier, current location, and radio-range. Regular transmitting these messages consume a significant amount of battery power in nodes, especially when the set of down-link neighbors does not change over time and the radio-range of the sender node is large. The present article focuses on this aspect and tries to eliminate the number of HELLO messages in existing state-of-art protocols. Also, it shortens radio-ranges of nodes whenever possible. Simulation results show that the average lifetime of nodes greatly increases in proposed Minus HELLO devoid routing protocols along with a great increase in network throughput. Also, the required number of route re-discovery reduces., Comment: 33 pages, 20 figures, pre-print of under review manuscript

Images of handwritten digits are different from natural images as the orientation of a digit, as well as similarity of features of different digits, makes confusion. On the other hand, deep convolutional neural networks are achieving huge success in computer vision problems, especially in image classification. BDNet is a densely connected deep convolutional neural network model used to classify (recognize) Bengali handwritten numeral digits. It is end-to-end trained using ISI Bengali handwritten numeral dataset. During training, untraditional data preprocessing and augmentation techniques are used so that the trained model works on a different dataset. The model has achieved the test accuracy of 99.775%(baseline was 99.40%) on the test dataset of ISI Bengali handwritten numerals. So, the BDNet model gives 62.5% error reduction compared to previous state-of-the-art models. Here we have also created a dataset of 1000 images of Bengali handwritten numerals to test the trained model, and it giving promising results. Codes, trained model and our own dataset are available at: {https://github.com/Sufianlab/BDNet}., Comment: 23 pages, 11 figures, 7 tables, Accepted Manuscript. Journal of King Saud University - Computer and Information Sciences (2019)