Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups

This study reports the impact of high sensitivity to early exchange in 11th-grade, CSCL triads solving well- and ill-structured problems in Newtonian Kinematics. A mixed-method analysis of the evolution of participation inequity (PI) in group discussions suggested that participation levels tended to get locked-in relatively early on in the discussion. Similarly, high (low) quality member contributions made earlier in a discussion did more good (harm) than those made later on. Both PI and differential impact of member contributions suggest a high sensitivity to early exchange; both significantly predicting the eventual group performance, as measured by solution quality. Consequently, eventual group performance could be predicted based on what happened in the first 30–40% of a discussion. In addition to drawing theoretical and methodological implications, implications for scaffolding CSCL groups are also discussed.     

Dynamics of growth and weight transitions in a pediatric cohort from India

Background  

There is paucity of information regarding time trends of weight status in children from rapidly developing economies like India. The aim of the study was to analyse the dynamics of growth and weight transitions in a cohort of school children from India.     

Computer-aided materials processing for silicon diffused impurity bipolar-circuit technology

A computer study has been made to infer materials processes for bipolar integrated circuits. The scheme is based on relatively simple device terminal measurements and microphotography. These yield final process parameters such as diffusion temperatures and times, oxide thicknesses and the other processing conditions. Thus, several intermediate process steps, required to obtain desirable device properties, can be eliminated once a prototype has been obtained and batch runs are planned.     

Cytokine Secretion Dynamics of Isolated PBMC after Cladribine Exposure in RRMS Patients

Cladribine (CLD) treats multiple sclerosis (MS) by selectively and transiently depleting B and T cells with a secondary long-term reconstruction of the immune system. This study provides evidence of CLD’s immunomodulatory role in peripheral blood mononuclear cells (PBMCs) harvested from 40 patients with untreated relapsing-remitting MS (RRMS) exposed to CLD. We quantified cytokine secretion from PBMCs isolated by density gradient centrifugation with Ficoll–Paque using xMAP technology on a FlexMap 3D analyzer with a highly sensitive multiplex immunoassay kit. The PBMC secretory profile was evaluated with and without CLD exposure. PBMCs isolated from patients with RRMS for ≤12 months had significantly higher IL-4 but significantly lower IFN-γ and TNF-α secretion after CLD exposure. PBMCs isolated from patients with RRMS for >12 months had altered inflammatory ratios toward an anti-inflammatory profile and increased IL-4 but decreased TNF-α secretion after CLD exposure. CLD induced nonsignificant changes in IL-17 secretion in both RRMS groups. Our findings reaffirm CLD’s immunomodulatory effect that induces an anti-inflammatory phenotype.     

Quarter-point elements for curved crack fronts

Ingraffea et al.[1] have presented a consistent method to compute stress-intensity factors from wedge quarter-point element nodal displacements for the straight crack front. The method was generalized to permit functional evaluation of stress-intensity factors along the crack front. Here the method is generalized for curved crack fronts.     

Size‐Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature‐Based Differential Targeting in Triple Negative Breast Cancer

Rapid sequestration and prolonged retention of intravenously injected nanoparticles by the liver and spleen (reticuloendothelial system (RES)) presents a major barrier to effective delivery to the target site and hampers clinical translation of nanomedicine. Inspired by biological macromolecular drugs, synthesis of ultrasmall (diameter ≈12–15 nm) porous silica nanoparticles (UPSNs), capable of prolonged plasma half‐life, attenuated RES sequestration, and accelerated hepatobiliary clearance, is reported. The study further investigates the effect of tumor vascularization on uptake and retention of UPSNs in two mouse models of triple negative breast cancer with distinctly different microenvironments. A semimechanistic mathematical model is developed to gain mechanistic insights into the interactions between the UPSNs and the biological entities of interest, specifically the RES. Despite similar systemic pharmacokinetic profiles, UPSNs demonstrate strikingly different tumor responses in the two models. Histopathology confirms the differences in vasculature and stromal status of the two models, and corresponding differences in the microscopic distribution of UPSNs within the tumors. The studies demonstrate the successful application of multidisciplinary and complementary approaches, based on laboratory experimentation and mathematical modeling, to concurrently design optimized nanomaterials, and investigate their complex biological interactions, in order to drive innovation and translation.     

A position and rotation invariant framework for sign language recognition (SLR) using Kinect

Multimedia Tools and Applications - Sign language is the only means of communication for speech and hearing impaired people. Using machine translation, Sign Language Recognition (SLR) systems...     

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.