Use of CR-39 films for nuclear radiation shielding efficacy evaluation of lining materials for combat vehicles

All materials provide, to a lesser or greater extent, shielding against nuclear radiations. Armoured fighting vehicles (AFVs) have steel as the structural material, which appears to be a reasonably good gamma and neutron shield material but a shield of pure iron would not be equally effective against whole range of neutron energies as it has a few resonances in electron volt range, and it reduces energy of fast neutrons to lower energy neutrons. These neutrons will be absorbed through radiative capture and emit gamma radiations. Thus it is essential that an effective shield should contain a large amount of moderating material, hydrogen being preferred with low atomic number materials (B, C, Li) and lead (Pb) to ensure that the neutrons do not diffuse at intermediate energies in the shield as well as gamma attenuation will also take place. In order to have a suitable shield material for armoured vehicles which serves as neutron and gamma radiation attenuator, polyethylene polymer with fillers lining materials are preferred. These materials were evaluated against gamma and fast neutrons using radioactive sources for suitability to fitment into combat vehicle as per the requirement of protection factor values. The detector for gamma radiation was used as Nal(Tl) while for neutron, CR-39 film was used.Use of CR-39 films for nuclear radiation shielding efficacy evaluation of lining materials for combat vehicles Deepak Gopalani1*, A S Jodha1, M K Das1, R K Singh2 and G L Baheti1 1Defence Laboratory, Jodhpur-342 011, Rajasthan, India 2Defence Material & Store Research & Developement Establishment, Kanpur-208 013, Uttar Pradesh, India E-mail : [email protected] Laboratory, Jodhpur-342 011, Rajasthan, India 2Defence Material & Store Research & Developement Establishment, Kanpur-208 013, Uttar Pradesh, Indi

Effect of Thermoelectric Cooling in Nanoscale Junctions

We propose a thermoelectric cooling device based on an atomic-sized junction. Using first-principles approaches, we investigate the working conditions and the coefficient of performance (COP) of an atomic-scale electronic refrigerator where the effects of phonon's thermal current and local heating are included. It is observed that the functioning of the thermoelectric nano-refrigerator is restricted to a narrow range of driving voltages. Compared with the bulk thermoelectric system with the overwhelmingly irreversible Joule heating, the 4-Al atomic refrigerator has a higher efficiency than a bulk thermoelectric refrigerator with the same $ZT$ due to suppressed local heating via the quasi-ballistic electron transport and small driving voltages. Quantum nature due to the size minimization offered by atomic-level control of properties facilitates electron cooling beyond the expectation of the conventional thermoelectric device theory.Comment: 8 figure

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

Supplemental Data Supplemental Data include one figure and five tables and can be found with this article online at http://dx.doi.org/10.1016/j.ajhg.2016.08.016. Supplemental Data Document S1. Figure S1 and Tables S1–S5 Download Document S2. Article plus Supplemental Data Download Web Resources ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/ dbNSFP, https://sites.google.com/site/jpopgen/dbNSFP Human Gene Mutation Database, http://www.hgmd.cf.ac.uk/ REVEL, https://sites.google.com/site/revelgenomics/ SwissVar, http://swissvar.expasy.org/ The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10−12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046–0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027–0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale

The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights

Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights. Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking

Interpolation Technique in Computed Tomography Image Visualisation(Short Communication)

An interpolation technique has been developed for generation of enlarged dataset from a limited one-dimesional acquired dataset for improving the image quality in quick-scan tomography. The effectiveness of the technique has been tested using data acquired from the first-generation. The CT images generated using this technique have been compared with the CT images generated from the acquired dataset for the same number of projections. The image quality has been improved on account of (i) enhancement of features, (ii) reduction in reconstruction artifacts, and (iii) magnification of the image without pixelisation