Sliding stability analysis of a retaining wall constructed by soilbags

Model tests were conducted to analyse the sliding stability of a retaining wall constructed by soilbags. The aim was to obtain an equation that calculates the active resultant earth pressure of sand acting on the wall in the ultimate state. Additionally, shear tests on multi-layers of vertically stacked soilbags were designed to investigate how the interlayer friction resistance varied with the height of the wall. The results show that the active earth pressure acting on the soilbag-constructed retaining wall in the ultimate state is non-linear, but it can be calculated from the force equilibrium of a differential element. The interlayer friction resistance of soilbags is found to be related to the shape of the sliding surface. Based on the obtained equation and the unique shear test results, the sliding stability of the retaining wall constructed by soilbags could be appropriately analyse

Boundary recovery for 3D Delaunay triangulation

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Moxifloxacin Replacement in Contemporary Tuberculosis Drug Regimens Is Ineffective against Persistent Mycobacterium tuberculosis in the Cornell Mouse Model

Tuberculosis (TB) caused by Mycobacterium tuberculosis remains a leading killer worldwide, and disease control is hampered by ineffective control of persistent infections. Substitution of moxifloxacin for isoniazid or ethambutol in standard TB regimens reduces treatment duration and relapse rates in animal studies and four-month regimens were not non-inferior in clinical trials. Resuscitation promoting factor (RPF) dependent bacilli have recently been implicated in M. tuberculosis persistence. We aimed to investigate the therapeutic effects of moxifloxacin substitution in the standard drug regimen for eradicating colony forming count (CFU) positive and RPF-dependent persistent M. tuberculosis using the Cornell murine model. M. tuberculosis infected mice were treated with regimens in which either isoniazid or ethambutol were replaced by moxifloxacin to the standard regimen. The efficacy of the regimens was compared to the standard regimen for bacterial CFU count elimination and removal of persistent tubercle bacilli evaluated using culture filtrate (CF) derived from M. tuberculosis strain H37Rv. We also measured disease relapse rates. Moxifloxacin-isoniazid substituted regimen achieved total organ CFU count clearance at 11 weeks post-treatment, faster than standard regimen (14 weeks), and with a 34% lower relapse rate. Moxifloxacin-ethambutol substituted regimen was similar to standard regimens in these regards. Importantly, neither moxifloxacin-substituted regimens nor the standard regimen could remove CF-dependent persistent bacilli. Evaluation of CF-dependent persistent M. tuberculosis requires confirmation in human studies, and has implications in future drug design, testing and clinical applications

Design of hybrid continuous-time discrete-time delta-sigma modulators

Recent attention has been drawn to the hybrid Delta-Sigma (ΔΣ) structure featuring the integration of continuous-time (CT) and discrete-time (DT) structures in the loop filter. It combines the accurate loop filter characteristic of a DT ΔΣ modulator and the inherent anti-aliasing of a CT ΔΣ modulator. We present a design methodology for building a CT-DT ΔΣ modulator via the transformation from a DT ΔΣ modulator prototype. We also demonstrate the tradeoff of applying this structure to cascaded Delta-Sigma modulators compared to pure CT or DT implementations. ©2008 IEEE.published_or_final_versio

Air quality monitoring for vulnerable groups in residential environments using a multiple hazard gas detector

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents a smart “e-nose” device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multiple hazard gas detector (MHGD) system that is mounted on a motor vehicle-based robot which can be remotely controlled. First, we optimized the sensor array for the classification of three hazardous gases, including cigarette smoke, inflammable ethanol, and off-flavor from spoiled food, using an e-nose with a mixing chamber. The mixing chamber can prevent the impact of environmental changes. We compared the classification results of all combinations of sensors, and selected the one with the highest accuracy (98.88%) as the optimal sensor array for the MHGD. The optimal sensor array was then mounted on the MHGD to detect and classify the target gases without a mixing chamber but in a controlled environment. Finally, we tested the MHGD under these conditions, and achieved an acceptable accuracy (70.00%)

Scaling of three-dimensional InN islands grown on GaN(0001) by molecular-beam epitaxy

The scaling property of three-dimensional InN islands nucleated on GaN(0001) surface during molecular-beam epitaxy (MBE) is investigated. Due to the large lattice mismatch between InN and GaN (∼10%), the islands formed from the Stranski-Krastanow growth mode are dislocated. Despite the variations in (residual) strain and the shape, both the island size and pair separation distributions show the scaling behavior. Further, the size distribution resembles that for submonolayer homoepitaxy with the critical island size i = 1, suggesting that detachment of atoms is not significant. The above results also indicate strain is insignificant in determining the nucleation and growth of dislocated islands during heteroepitaxy by MBE.published_or_final_versio

Primary gastric chorioadenocarcinoma: a needle in a haystack

Primary gastric chorioadenocarcinoma (PGC) is an exceedingly rare neoplasm which is often misdiagnosed as gastric adenocarcinoma at presentation. A markedly elevated serum beta human chorionic gonadotrophin (Beta HCG) level is a characteristic feature of this tumor. A 44 year old white male presented with generalized abdominal pain and fullness, tarry black stools and weight loss of 3 months duration. Medical work-up including imaging with CT scans revealed the presence of a gastric mass and multiple liver metastases. Tumor markers were significant for a Betahuman chorionic gonadotrophin (Beta HCG) of 23717.5 MIU/ML. Scrotal ultrasound did not show the presence of a testicular mass. Upper GI endoscopy with biopsy was positive for a poorly differentiated adenocarcinoma with Beta HCG staining on immunohistochemistry. The patient was diagnosed with metastatic PGC. He received four cycles of chemotherapy with Bleomycin, Etoposide and Cisplatinum. At the end of the fourth cycle, Beta HCG was 23 MIU/ML. CT scan for restaging, however showed an increase in the size of the metastatic lesions. The patient subsequently became profoundly pancytopenic, developed disseminated intravascular coagulation (DIC) and expired 12 months after initial presentation. PGC genetically and morphologically represents an adenocarcinoma and a choriocarcinoma. The significance of an elevated serum Beta HCG is controversial and it may have a role in evaluating response to treatment and tumor recurrence. Curative resection, appropriate chemotherapy and the absence of metastatic lesions is associated with improved survival. Hence, a high index of suspicion must be maintained to diagnose this tumor correctly at presentation and tailor therapy accordingly

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefit-ting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and chebyshev fitting, the proposed shape sig-nature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature can act as a plug-and-play component and ablation study shows its effectiveness and good scalabilityComment: Code is available at https://github.com/xinge008/SS