Tuning Co valence state in cobalt oxyhydrate superconductor by post reduction

We report a successful tuning of Co valence state in cobalt oxyhydrate superconductor via a facile post reduction using NaOH as reducing agent. The change in Co valence was precisely determined by measuring the volume of the released oxygen. The possible hydronium-incorporation was greatly suppressed in concentrated NaOH solution, making the absolute Co valence determinable. As a result, an updated superconducting phase diagram was obtained, which shows that the superconducting transition temperature increases monotonically with increasing Co valence in a narrow range from +3.58 to +3.65.Comment: 17 pages, 5 figures and 1 table. Chem. Mat. in pres

Numerical investigation of airborne contaminant transport under different vortex structures in the aircraft cabin.

Airborne contaminants such as pathogens, odors and CO2 released from an individual passenger could spread via air flow in an aircraft cabin and make other passengers unhealthy and uncomfortable. In this study, we introduced the airflow vortex structure to analyze how airflow patterns affected contaminant transport in an aircraft cabin. Experimental data regarding airflow patterns were used to validate a computational fluid dynamics (CFD) model. Using the validated CFD model, we investigated the effects of the airflow vortex structure on contaminant transmission based on quantitative analysis. It was found that the contaminant source located in a vorticity-dominated region was more likely to be "locked" in the vortex, resulting in higher 62% higher average concentration and 14% longer residual time than that when the source was on a deformation dominated location. The contaminant concentrations also differed between the front and rear parts of the cabin because of different airflow structures. Contaminant released close to the heated manikin face was likely to be transported backward according to its distribution mean position. Based on these results, the air flow patterns inside aircraft cabins can potentially be improved to better control the spread of airborne contaminant

Designing and Evaluating the MULTICOM Protein Local and Global Model Quality Prediction Methods in the CASP10 Experiment

Background: Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results: MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions: Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy

SMOQ: A Tool for Predicting the Absolute Residue-Specific Quality of a Single Protein Model with Support Vector Machine

Background: It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results: We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusions: SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/

Solar radiation pressure enabled femtosatellite based Earth remote sensing

Recent developments in electronics have pushed miniaturised satellites to the femto-scale, with masses between 10 and 100 g. Although femtosatellites have been proven as a feasible concept, most designs are limited in mission capacity and lifetime due to the lack of environmental protection and onboard propellant. In this paper, a novel concept for femtosatellites for Earth remote sensing is proposed. In particular, a swarm of femtosatellites are used as elements of a sparse array in orbit to receive radar echoes. They also feature active orbit control enabled by solar radiation pressure to extend their lifetime. A simple active orbit control algorithm has been demonstrated. A mission concept based on a Sun-synchronous circular orbit is proposed to maximise the benefit for both Earth remote sensing and active orbit control. A synthetic aperture radar mission has been used to characterise their performance

Micro-Doppler based recognition of ballistic targets using 2D gabor filters

The capability to recognize ballistic threats, is a critical topic due to the increasing effectiveness of resultant objects and to economical constraints. In particular the ability to distinguish between warheads and decoys is crucial in order to mitigate the number of shots per hit and to maximize the ammunition capabilities. For this reason a reliable technique to classify warheads and decoys is required. In this paper the use of the micro-Doppler signatures in conjunction with the 2-Dimensional Gabor filter is presented for this problem. The effectiveness of the proposed approach is demonstrated through the use of real data