Explore the vast range of titles available.

Small-scale transient jetlet activity and associated upflows from coronal hole plumes are potential sources of the solar wind. To elucidate the magnetic origins and driving mechanisms of such upflows, we perform three-dimensional (3D) radiative magnetohydrodynamic simulations using the MURaM code, spanning from the upper convection zone to the low corona. We synthesize Fe x 174 Å emission to capture the plume evolution comparable to observations, examining underlying plasma flows, thermal structures, and magnetic topologies. We identify a pronounced transition from cool downflows in the lower atmosphere to hot upflows in the corona at the interface between plume-rooted like-polarity flux concentrations. These upflows are threaded by a complex, filamentary network of quasi-separatrix layers (QSLs)—a topology distinct from standard interchange reconnection scenarios. The domain-averaged mass flux over a 38 minute interval ranges from 10−9 to 10−8 g cm−2 s−1, substantially exceeding observed solar-wind loss rates. Our results demonstrate that highly structured plasma outflows are channeled along strong QSLs at open–open field boundaries, providing a pathway to sustain the solar wind from coronal-hole plumes without requiring interchange reconnection triggered by opposite-polarity flux emergence.

Speech processing is very important research area where speaker recognition, speech synthesis, speech codec, speech noise reduction are some of the research areas. Many of the languages have different speaking styles called accents or dialects. Identification of the accent before the speech recognition can improve performance of the speech recognition systems. If the number of accents is more in a language, the accent recognition becomes crucial. Telugu is an Indian language which is widely spoken in Southern part of India. Telugu language has different accents. The main accents are coastal Andhra, Telangana, and Rayalaseema. In this present work the samples of speeches are collected from the native speakers of different accents of Telugu language for both training and testing. In this work, Mel frequency cepstral coefficients (MFCC) features are extracted for each speech of both training and test samples. In the next step Gaussian mixture model (GMM) is used for classification of the speech based on accent. The overall efficiency of the proposed system to recognize the speaker, about the region he belongs, based on accent is 91 %.

This study aims to assess the effectiveness of integrating Segment Anything Model (SAM) and its variant MedSAM into the automated mining, object detection, and segmentation (MODS) methodology for developing robust lung cancer detection and segmentation models without post hoc labeling of training images. In a retrospective analysis, 10,000 chest computed tomography scans from patients with lung cancer were mined. Line measurement annotations were converted to bounding boxes, excluding boxes < 1 cm or > 7 cm. The You Only Look Once object detection architecture was used for teacher-student learning to label unannotated lesions on the training images. Subsequently, a final tumor detection model was trained and employed with SAM and MedSAM for tumor segmentation. Model performance was assessed on a manually annotated test dataset, with additional evaluations conducted on an external lung cancer dataset before and after detection model fine-tuning. Bootstrap resampling was used to calculate 95% confidence intervals. Data mining yielded 10,789 line annotations, resulting in 5403 training boxes. The baseline detection model achieved an internal F1 score of 0.847, improving to 0.860 after self-labeling. Tumor segmentation using the final detection model attained internal Dice similarity coefficients (DSCs) of 0.842 (SAM) and 0.822 (MedSAM). After fine-tuning, external validation showed an F1 of 0.832 and DSCs of 0.802 (SAM) and 0.804 (MedSAM). Integrating foundational segmentation models into the MODS framework results in high-performing lung cancer detection and segmentation models using only mined clinical data. Both SAM and MedSAM hold promise as foundational segmentation models for radiology images.

A 51-kiloton magnetised Iron Calorimeter (ICAL) detector, using Resistive Plate Chambers (RPCs) as active detector elements, aims to study atmospheric neutrinos. A prototype - 1/600 of the weight of ICAL, called mini-ICAL was installed in the INO transit campus at Madurai. A modest proof-of-principle cosmic muon veto detector of about 1 m × 1 m × 0.3 m dimensions was setup a few years ago, using scintillator paddles. The measured cosmic muon veto efficiency of 99.98% and simulation studies of muon-induced background events in the ICAL detector surrounded by an efficient veto detector were promising. This led to the idea of constructing a bigger cosmic muon veto around the mini-ICAL detector. Details of the design and construction of the detector including the electronics, trigger and DAQ systems planned will be briefly presented.

CD133 expression enriches for tumor-initiating cells and is a negative prognostic factor in numerous cancers. We previously developed an immunotoxin against CD133 by fusing a gene fragment encoding the scFv portion of an anti-CD133 antibody to a gene fragment encoding deimmunized PE38KDEL. The resulting fusion protein, dCD133KDEL, demonstrated potent antitumor activity following intratumoral delivery into head neck cell carcinoma xenografts. However, the efficacy against other tumors and the tolerability of systemic administration remained unclear. The purpose of this study was to evaluate the tolerability and efficacy of dCD133KDEL in a systemic human breast carcinoma model. Time course viability studies showed that dCD133KDEL selectively inhibited MDA-MB-231 ductal breast carcinoma cells that contained a minority CD133+ subpopulation, implicating CD133+ cells as a source for self-renewal within this cell line. Furthermore, systemic administration of dCD133KDEL caused regression or inhibition of tumor growth in mice bearing an intrasplenic MDA-MB-231 tumor challenge as a model for metastatic disease. In the same model, combined therapy with dCD133KDEL and another immunotoxin designed to target the bulk tumor mass was the most effective therapy, supporting the idea that such combination therapies might better address tumor heterogeneity. dCD133KDEL shows promise as a therapeutic agent and as a biologic tool to study cancer stem cells.

CD133, also known as Prominin-1, is expressed on stem cells present in many tissues and tumors. In this work, we have identified and characterized a single-chain variable fragment (scFv) for the efficient and specific recognition of CD133. Phage display was used to develop the scFv from a previously reported anti-CD133 hybridoma clone 7, which was capable of recognizing both glycosylated and non-glycosylated forms of human CD133. The scFv immunostained CD133+ Caco-2 cells, but not CD133−/low U87 cells. Significantly, it immunostained CD133− cells transiently transfected with the mouse CD133 gene as well as CD133+ mouse cells. Co-immunostaining studies in mouse bone marrow cells, using anti-CD133 scFv-FITC and anti-mouse CD133-PE (clone 13A4) commercial antibody, indicated that the epitopes recognized by these reagents partially overlap. Taken together, these results suggest that the scFv can recognize mouse CD133 protein in addition to recognizing human CD133. This new scFv is expected to be valuable both as a molecular diagnostic reagent for identifying CD133+ cells and as a ligand for targeting therapeutics to CD133+ tumor-initiating cells.

With the continuous advancement of power elctronic components such as power MOSFETs and MLCCs, a primary trend is moving towards high operating frquencies. Also new design trends requires power supplies with low voltages and high currents. Forward converter is a promising topology for low and medium power applications where low voltages at high currents are required with higher efficiency. In this paper small signal model of an Active Clamp Forward Converter (ACFC) with high side clamp, small signal model of Peak Current Mode Control (PCMC) are derived, and finally the complete model of active clamp forward converter with peak current mode control is simulated in MATLAB/Simulink and the results are analysed.