Abstract Medical image segmentation plays a crucial role in various clinical applications, including disease diagnosis and treatment planning. In the context of spine imaging, accurate segmentation is essential for precise analysis and intervention. This study presents a comparative analysis of two prominent segmentation algorithms: fuzzy c-means (FCM) and region growing, applied to spine image segmentation. The dataset consists of spine images obtained from medical imaging modalities, preprocessed to enhance clarity and remove noise. Both FCM and region-growing algorithms are implemented with appropriate parameter settings and evaluated using quantitative metrics such as the Dice similarity coefficient, sensitivity, and specificity. Additionally, qualitative assessments are conducted through visual inspection of segmented images. The results reveal distinct performance characteristics of each algorithm, highlighting their respective strengths and weaknesses in spine image segmentation tasks. Through comprehensive analysis and discussion, this study provides valuable insights into the effectiveness of FCM and region-growing algorithms, aiding clinicians and researchers in selecting suitable segmentation approaches for spine imaging applications.

Abstract The authors have requested that this preprint be removed from Research Square.

AbstractAn ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract

Abstract Related to various fields like Academia, Government Organizations, Hospitals, and Mythology, where a lot of information is available in hard copies. These documents need to get digitized to text, to make the hard copies data more accessible and maintainable. To create a paperless environment Automatic handwritten text recognition system plays a major role which is possible through digitizing and processing handwritten copies. The proposed Automatic text recognition system works on image input to generate digital output. To develop Automatic handwritten text recognition system various domains like machine learning with computer vision and deep learning techniques are required to create abstract models for recognizing letters and words initially. The proposed model uses sequence to sequence architecture to generate the sequential output of digitized lines of text. The proposed model uses Conventional Neural Network model to perform feature extraction from the handwritten image. The extracted features are modeled with a sequence-to-sequence methodology and submit to ResNet-Transformer for encoding and decoding from the Input Image of visual features and the sequence of letters. The proposed model uses synthetic data augmentation for image preprocessing. The IAM dataset which contains a large amount of data will be used for the implementation of proposed model.

The effective management of heat transfer processes is critical in various engineering applications, including thermal management systems and advanced cooling technology. Nanofluids, with their distinct thermal properties, present significant opportunities for improving heat transmission. The Arrhenius activation model is used to investigate the thermal behaviour of nanofluids. This study investigates the joint effect of Arrhenius activation on the non-darcy hydromagnetic convective heat and mass transfer of a nanofluid in a vertical channel using both SWCNT and MWCNT. The Runge-Kutta fourth-order shooting technique was used to analyze the coupled nonlinear equations governing flow heat transfer. Graphical research has been conducted to investigate the impact of different activation energies, viscosity levels, heat sources, and chemical reactions on flow characteristics. A numerical analysis is performed to determine the skin friction, the rate of heat transfer, and the mass transfer for various parametric variables.

Abstract This study looks into the mix design process for Stone Matrix Asphalt (SMA) and Bituminous Concrete (BC) in compliance with IRC: SP: 79-2008 and MoRT&H-2013 requirements. In comparison to standard dense and open-graded asphalt mixes, SMA has a better structure characterized by gap-graded aggregate, mastic, and fiber. The study uses coconut shell biochar as a filler in both SMA and BC, acting as a carbon-neutral and sequestration material. Using gap-graded aggregates, the study applies STAB (Simple Tool for Aggregate Blending) software for aggregate blending to establish ideal amounts. Following confirmation of the blends using Bailey's gradation technique, the Optimum Bitumen Content (OBC) is calculated using the Marshall method. The initial bitumen concentration is 4% for BC and 6% for SMA, with 0.5% increases up to 7%. Theoretical specific gravity is determined at 6% for the loose mix using ASTM D 2041. Gse is then computed, with OBC set at 6% for SMA and 4.2% for BC, meeting a 4% average air voids criteria among other volumetric parameters. At the appropriate bitumen percentage, all blends are subjected to a variety of tests, including indirect tensile strength, rutting, and resilient modulus. The study replicates moisture resistance deterioration by freezing, thawing, and humidifying materials. The number of blows used to compute refusal density ranges from 25 to 150. ITS determined retained tensile strength to be 93.88% and 98.8% for freezing and thawing BC samples and 93.88% and 98.8% for humidity-conditioned BC samples, respectively. The equivalent figures for SMA are 84% and 89%. Proportional rut depth and wheel tracking speed measurements are given for freezing, humidity, and unconditional samples, and robust modulus values are also supplied. Refusal density air voids that do not decrease below 4% up to 100 blows for BC are within the specified range of 3-5%. This extensive laboratory investigation demonstrates the feasibility of using biochar as a filler in bituminous concrete and stone matrix asphalt. This sustainable method helps eco-friendly and long-lasting road construction practices in addition to improving the durability and performance of highways.

AbstractThis paper presents the design and prototype development of a compact frequency‐selective surface (FSS) for shielding WLAN signals. The FSS unit‐cell geometry is evolved by modifying the arms of the conventional crossed dipole element. The footprint of the unit cell is only 6 mm × 6 mm × 1.6 mm. The proposed FSS offers stop band characteristics at 5 GHz for both transverse electric (TE) and transverse magnetic (TM) modes. In addition, the FSS offers angular stability up to 60°. A prototype of the proposed FSS is fabricated and measured to validate its simulations. Measured bandwidth of the stop band is 410 MHz, referenced at −20 dB for both TE and TM modes. Equivalent circuit model of the FSS is derived and the results are compared with the simulations.

Abstract Citations are increasingly being used to evaluate institutional and individual performance, suggesting a need for rigorous research to understand what behaviors citations are reflecting and what these behaviors mean for the institution of science. To overcome challenges in accurately representing the citation generation process, we use postretraction citations to test competing theories under two different citation search processes, empirically testing predictions on the spread of retracted references. We find that retracted papers are continually cited after the retraction, and that these citations are more likely to come from audiences likely to be unfamiliar with the field of the retracted paper. In addition, we find this association to be much stronger among those citing high-status journals, consistent with the behavior of scientists relying on heuristic search instead of engaged search process. While the current policy debate on misinformation in science emphasizes increasing the visibility of retraction labels to discourage the use of such publications, we argue that institutional-level interventions may be more effective, as such interventions are more consistent with the heuristic citation process. As such citation behavior may not be limited to the case of postretraction citations, we discuss the implications for current science studies as well as science policy.