Inward vapor diffusion due to high temperature gradients in experimentally tested large-scale wall assemblies

Inward vapor flow due to high temperature gradients as a moisture source in building envelope has been documented, specifically when the exterior cladding is wetted by rain and then exposed to solar radiation. This phenomenon can bring large amount of undue moisture across the envelope assembly with a risk of damage of the wall elements. In this paper, inward vapor flow is applied to five large-scale monitored wall assemblies using a large-scale experimental facility consisting of a spraying array, a radiation array, and a test hut to provide controlled interior conditions. The variables studied include type of cladding (brick and cement stucco), presence and ventilation of air space, type of exterior sheathing (oriented strand board and extruded polystyrene) and type of interior finish (vinyl wall covering and paint). The results show that the presence of vapor tight interior finishes leads to the accumulation of moisture in the interior gypsum board, even in the presence of a vapor tight exterior sheathing. The presence of an air space reduces, but does not prevent moisture accumulation, while connecting the air space to the outdoor seems sufficient to short-circuit the inward vapor flow and prevent moisture accumulation.     

Features extraction for soccer video semantic analysis: current achievements and remaining issues

This paper presents a state of the art review of features extraction for soccer video summarization research. The all existing approaches with regard to event detection, video summarization based on video stream and application of text sources in event detection have been surveyed. As regard the current challenges for automatic and real time provision of summary videos, different computer vision approaches are discussed and compared. Audio, video feature extraction methods and their combination with textual methods have been investigated. Available commercial products are presented to better clarify the boundaries in this domain and future directions for improvement of existing systems have been suggested.     

Circulating Non-Coding RNAs as a Signature of Autism Spectrum Disorder Symptomatology

Autism spectrum disorder (ASD) is a multifaced neurodevelopmental disorder that becomes apparent during early childhood development. The complexity of ASD makes clinically diagnosing the condition difficult. Consequently, by identifying the biomarkers associated with ASD severity and combining them with clinical diagnosis, one may better factionalize within the spectrum and devise more targeted therapeutic strategies. Currently, there are no reliable biomarkers that can be used for precise ASD diagnosis. Consequently, our pilot experimental cohort was subdivided into three groups: healthy controls, individuals those that express severe symptoms of ASD, and individuals that exhibit mild symptoms of ASD. Using next-generation sequencing, we were able to identify several circulating non-coding RNAs (cir-ncRNAs) in plasma. To the best of our knowledge, this study is the first to show that miRNAs, piRNAs, snoRNAs, Y-RNAs, tRNAs, and lncRNAs are stably expressed in plasma. Our data identify cir-ncRNAs that are specific to ASD. Furthermore, several of the identified cir-ncRNAs were explicitly associated with either the severe or mild groups. Hence, our findings suggest that cir-ncRNAs have the potential to be utilized as objective diagnostic biomarkers and clinical targets.     

The effect of polystyrene as a porogen on the fluoride ion adsorption of Zr(IV) surface-immobilized resin

A novel adsorbent, Zr(IV)-immobilized resin, was prepared by surface template polymerization. This adsorbent, prepared by adding polystyrene (PS) to resin, has a high adsorption capacity for the removal of fluoride ion from aqueous solutions. The adsorption capacity increased with rising PS concentration, since the specific surface area can be effectively increased by adding polystyrene as a porogen. The adsorption isotherm has been modeled by the Langmuir and Freundlich equations. The adsorption isotherms were well described by the Langmuir equation. The removal of fluoride was also carried out using a column method. The presence of PS in the resin exerted a remarkable influence on the uptake of fluoride ion. The fluoride adsorbed on the resin was quantitatively eluted with 0.1 M NaOH.     

The effects of high school career education on social–cognitive variables.

The authors investigated the influence of a 9-week career education class on career decision-making self-efficacy, vocational skills self-efficacy, perceived educational barriers, outcome expectations, educational plans, and career expectations among a sample of 166 high school sophomores. Using a nonrandomized, within-subjects crossover design, the authors collected pretest, posttest, and follow-up data with a health education class as the control condition. Post- and follow-up testing suggest that the class resulted in increased career decision-making self-efficacy, vocational skills self-efficacy, and short-term gains in outcome expectations but did not influence perceived educational barriers. Participants enrolled in the career education class in the first quarter were more likely to change career plans than were those in the control condition. Implications for practice and future research are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation‐based selection method and as the output, the best features are selected. These selected features are validated through feed‐forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.     

Study of electrical conductivity changes and phase transitions in TiO2 doped ZrO2

The electrical conductivity in ZrO₂ doped with various molar ratios of TiO₂ has been measured at different temperatures. Phase transitions depending on the temperature for different molar compositions were investigated by doping of the samples. The conductivity is also found to increase with rise in temperature till 200 °C and thereafter decreases due to collapse of the fluorite framework. A second rise in conductivity in the doped samples around 462 °C is observed due to phase transition of ZrO₂. X-ray powder diffraction recorded at high temperature show the effect of doping and the phase relationships of doped materials. DTA was also carried out for confirming doping effect and phase transition in samples.