Investigation of the energy performance of a novel modular solar building envelope

The major challenges for the integration of solar collecting devices into a building envelope are related to the poor aesthetic view of the appearance of buildings in addition to the low efficiency in collection, transportation, and utilization of the solar thermal and electrical energy. To tackle these challenges, a novel design for the integration of solar collecting elements into the building envelope was proposed and discussed. This involves the dedicated modular and multiple-layer combination of the building shielding, insulation, and solar collecting elements. On the basis of the proposed modular structure, the energy performance of the solar envelope was investigated by using the Energy-Plus software. It was found that the solar thermal efficiency of the modular envelope is in the range of 41.78–59.47%, while its electrical efficiency is around 3.51% higher than the envelopes having photovoltaic (PV) alone. The modular solar envelope can increase thermal efficiency by around 8.49% and the electrical efficiency by around 0.31%, compared to the traditional solar photovoltaic/thermal (PV/T) envelopes. Thus, we have created a new envelope solution with enhanced solar efficiency and an improved aesthetic view of the entire building

The Vasculome of the Mouse Brain

The blood vessel is no longer viewed as passive plumbing for the brain. Increasingly, experimental and clinical findings suggest that cerebral endothelium may possess endocrine and paracrine properties – actively releasing signals into and receiving signals from the neuronal parenchyma. Hence, metabolically perturbed microvessels may contribute to central nervous system (CNS) injury and disease. Furthermore, cerebral endothelium can serve as sensors and integrators of CNS dysfunction, releasing measurable biomarkers into the circulating bloodstream. Here, we define and analyze the concept of a brain vasculome, i.e. a database of gene expression patterns in cerebral endothelium that can be linked to other databases and systems of CNS mediators and markers. Endothelial cells were purified from mouse brain, heart and kidney glomeruli. Total RNA were extracted and profiled on Affymetrix mouse 430 2.0 micro-arrays. Gene expression analysis confirmed that these brain, heart and glomerular preparations were not contaminated by brain cells (astrocytes, oligodendrocytes, or neurons), cardiomyocytes or kidney tubular cells respectively. Comparison of the vasculome between brain, heart and kidney glomeruli showed that endothelial gene expression patterns were highly organ-dependent. Analysis of the brain vasculome demonstrated that many functionally active networks were present, including cell adhesion, transporter activity, plasma membrane, leukocyte transmigration, Wnt signaling pathways and angiogenesis. Analysis of representative genome-wide-association-studies showed that genes linked with Alzheimer’s disease, Parkinson’s disease and stroke were detected in the brain vasculome. Finally, comparison of our mouse brain vasculome with representative plasma protein databases demonstrated significant overlap, suggesting that the vasculome may be an important source of circulating signals in blood. Perturbations in cerebral endothelial function may profoundly affect CNS homeostasis. Mapping and dissecting the vasculome of the brain in health and disease may provide a novel database for investigating disease mechanisms, assessing therapeutic targets and exploring new biomarkers for the CNS

Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification

In hyperspectral image classification, both spectral and spatial data distributions are important in describing and identifying different materials and objects in the image. Furthermore, consistent spatial structures across bands can be useful in capturing inherent structural information of objects. These imply that three properties should be considered when reconstructing an image using sparse coding methods. First, the distribution of different ground objects leads to different coding coefficients across the spatial locations. Second, local spatial structures change slightly across bands due to different reflectance properties of various object materials. Finally and more importantly, some sort of structural consistency shall be enforced across bands to reflect the fact that the same object appears at the same spatial location in all bands of an image. Based on these considerations, we propose a novel joint spectral-spatial sparse coding model that explores structural consistency for hyperspectral image classification. For each band image, we adopt a sparse coding step to reconstruct the structures in the band image. This allows different dictionaries be generated to characterize the band-wise image variation. At the same time, we enforce the same coding coefficients at the same spatial location in different bands so as to maintain consistent structures across bands. To further promote the discriminating power of the model, we incorporate a graph Laplacian sparsity constraint into the model to ensure spectral consistency in the dictionary generation step. Experimental results show that the proposed method outperforms some state-of-the-art spectral-spatial sparse coding methods