Combining subspace approach and short time Fourier analysis for locating structural damage storeys

This study proposes a novel and efficient approach for locating the floors of a building whose stiffnesses change after being subject to a strong earthquake. The floors that may be damaged are determined by comparing the unitary stiffness matrix in different stages in the life cycle of a building. To evaluate the coefficient matrices of a state-space model, the proposed procedure applies a subspace approach in conjunction with the short time Fourier analysis. The dynamic characteristics of a structure are determined from the coefficient matrices. Next, the unitary stiffness matrix is constructed by identifying natural frequencies and mode shapes. The effectiveness of the proposed procedure is verified using the measured earthquake acceleration responses of a three-storey structure that sustains damage on one or two floors and an eight-storey steel frame under a 200 Gal and a 1200 Gal earthquake, such as the Chi-chi earthquake that shook Taiwan on September 1999. The proposed scheme is compared to mode shape based approaches in identifying damaged floors, and is demonstrated to be superior to both MAC (Modal Assurance Criterion) and COMAC (Coordinate Modal Assurance Criterion)

SCintillation and IONosphere eXtended (SCION-X): A 12U CubeSat for Ionospheric and Atmospheric Science

SCION-X (SCintillation and IONosphere eXtended) is a 12U CubeSat that is being designed and developed by Upper Air Dynamics Laboratory, National Central University (NCU). SCION-X is the second funded CubeSat project being developed by NCU and is the largest self-developed spacecraft to date. This mission will help to further understand the variation of ionospheric irregularities, remote sensing methods for PM2.5 pollution distribution, and thermospheric photochemistry while serving as a relay station for amateur radio. Furthermore, it will help increase the communication and cooperation between universities in developing spaceflight capacity

Loss of Cofilin 1 Disturbs Actin Dynamics, Adhesion between Enveloping and Deep Cell Layers and Cell Movements during Gastrulation in Zebrafish

During gastrulation, cohesive migration drives associated cell layers to the completion of epiboly in zebrafish. The association of different layers relies on E-cadherin based cellular junctions, whose stability can be affected by actin turnover. Here, we examined the effect of malfunctioning actin turnover on the epibolic movement by knocking down an actin depolymerizing factor, cofilin 1, using antisense morpholino oligos (MO). Knockdown of cfl1 interfered with epibolic movement of deep cell layer (DEL) but not in the enveloping layer (EVL) and the defect could be specifically rescued by overexpression of cfl1. It appeared that the uncoordinated movements of DEL and EVL were regulated by the differential expression of cfl1 in the DEL, but not EVL as shown by in situ hybridization. The dissociation of DEL and EVL was further evident by the loss of adhesion between layers by using transmission electronic and confocal microscopy analyses. cfl1 morphants also exhibited abnormal convergent extension, cellular migration and actin filaments, but not involution of hypoblast. The cfl1 MO-induced cell migration defect was found to be cell-autonomous in cell transplantation assays. These results suggest that proper actin turnover mediated by Cfl1 is essential for adhesion between DEL and EVL and cell movements during gastrulation in zebrafish

The regulated secretory pathway and human disease: insights from gene variants and single nucleotide polymorphisms

The regulated secretory pathway provides critical control of peptide, growth factor, and hormone release from neuroendocrine and endocrine cells, and neurons, maintaining physiological homeostasis. Propeptides and prohormones are packaged into dense core granules (DCGs), where they frequently undergo tissue-specific processing as the DCG matures. Proteins of the granin family are DCG components, and although their function is not fully understood, data suggest they are involved in DCG formation and regulated protein/peptide secretion, in addition to their role as precursors of bioactive peptides. Association of gene variation, including single nucleotide polymorphisms (SNPs), with neuropsychiatric, endocrine and metabolic diseases, has implicated specific secreted proteins and peptides in disease pathogenesis. For example, a SNP at position 196 (G/A) of the human brain-derived neurotrophic factor (BDNF) gene dysregulates protein processing and secretion and leads to cognitive impairment. This suggests more generally that variants identified in genes encoding secreted growth factors, peptides, hormones, and proteins involved in DCG biogenesis, protein processing, and the secretory apparatus, could provide insight into the process of regulated secretion as well as disorders that result when it is impaired

Complete Blood Cell Detection and Counting Based on Deep Neural Networks

Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping

A Computer Mouse Using Blowing Sensors Intended for People with Disabilities

The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer mouse, but they all have some operational inconveniences. In this paper, we propose an innovative blowing-controlled mouse assistive tool to replace the conventional hand-controlled mouse. Its main contribution is that it uses microphones to induce small signals through the principle of airflow vibration, and it then converts the received signal into the corresponding pulse width. The co-design of software programming enables various mouse functions to be implemented by the identification of the blowing pulse width of multiple microphones. The proposed tool is evaluated experimentally, and the experimental results show that the average identification rate of the proposed mouse is over 85%. Additionally, compared with the other mouse assistive tools, the proposed mouse has the benefits of low cost and humanized operation. Therefore, the proposed blowing control method can not only improve the life quality of people with disabilities but also overcome the disadvantages of existing assistive tools