Focused Ion Beam Milling Strategies of Photonic Crystal Structures in Silicon

We report on optimisation of the side wall angle of focused ion beam (FIB) fabricated submicron diameter holes in silicon. Two optimisation steps were performed. First, we compare two different FIB scanning procedures and show the advantages of using a spiral scanning method for the definition of holes in photonic crystal slab structures. Secondly, we investigate the effect on the geometry, of parameters for reducing the tapering effect. Furthermore, we report on the initial results regarding effects of $Ga^{+}$ ion implantation during FIB milling on optical losses, both before and after an annealing step, showing over a decade reduction of optical loss

Bond-shear Behavior of FRP Rods as a Function of Attachment Configuration

The use of external reinforcement to improve or enhances the flexural capacity of a member depends on the transfer capacity, and the failure behavior of the composite between the reinforcement, the epoxy resin and the concrete. The most influencing factor is the bond-shear capacity between the rod and the epoxy, and the epoxy to the concrete. Fiber Reinforced Polymer (FRP) rods are the latest alternate for fulfilling the external reinforcement scheme. In the field, the mandated embedment depth as outlined by the ACI 440 code, could customary not be achieved since factors such as the depth of the concrete cover, and presence of stirrups limits the space. This study is aimed to evaluate the effect of FRP rod configurations with respect to the concrete surface, to the effectiveness of external reinforcement. The study looked into the bond-shear capacity as well as the mode of failure, influence by the rod attachment depth. It was shown that the embedment depth significantly influenced the failure mode, and therefore the strain transfer capacity from the concrete to the rods

Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordResource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning longterm memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using realworld workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads

In vivo diffusion tensor imaging in rat model of chronic spinal cord compression

Session 64: Advanced Spinal Cord Imaging - Oral presentationWe have employed DTI to investigate the pathophysiology of chronic spinal cord compression in this study. Average diffusion characteristic curves and fiber tracking have been done to evaluate the lesion and intact regions. DTI is sensitive to the damage and it is potential to monitor the progressive structural and functional changes in such chronic spinal cord diseases.published_or_final_versionThe 17th Scientific Meeting & Exhibition of the International Society of Magnetic Resonance in Medicine (ISMRM), Honolulu, HI., 18-24 April 2009. In Proceedings of ISMRM 17th Scientific Meeting & Exhibition, 2009, p. 63

In vivo diffusion tensor imaging of chronic spinal cord compression in rat model

Conference Theme: Engineering the Future of BiomedicineChronic spinal cord compression induced cervical myelopathy is a comon cause of spinal cord dysfunction. The exact mechanisms of underlying progressive cell death remain to be elucidated. In this study, in vivo diffusion tensor imaging (DTI) has been applied to investigate the microstructural changes of white matter (WM) in this neurodegenerative disease. Compared with conventional MRI techniques, DTI is believed to be more specific to pathological changes. Radial diffusivity (λ⊥) is higher in the ipilesional region, suggesting demyelination or axonal degradation may occur after prolonged compression. Near the epicenter of lesion, axial diffusivity (λ∥) is lower. Also, caudal-rostral asymmetry has been observed in λ∥. Feasibility of using DTI to detect microstructural changes in chronic disease has been demonstrated. ©2009 IEEE.published_or_final_versionThe 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009), Minneapolis, MN., 3-6 September 2009. In Proceedings of the 31st EMBC, 2009, p. 2715-271

On the Performance of a Multi Story Irregular Apartment Building Model Under Seismic Load in Indonesian Moderately High Seismicity Region

Purbalingga is regency with a potential moderately high seismicity requiring compliance of planning and implementation rules of the earthquake-resistant structural system. The purpose of this research is to evaluate the performance of a ten-story irregular apartment building model in Purbalingga due to the seismic load. The study is necessarily conducted to provide information on impacts and mitigation strategies that should be implemented. This research was conducted based on the seismic capacity of 2002 and 2012 Indonesian National Standard (SNI) including linear static analysis, dynamic response analysis, and pushover analysis. Based on the direct static review, it shows that the base shear is reduced and the drift ratio level decreases respectively for X and Y direction.Meanwhile, based on the dynamic response analysis, the drift ratio level also decreases respectively for X and Y direction. Also, the pushover analysis indicates that the performance of this apartment building model is still at Immediate Occupancy (IO) level as the post-earthquake damage state that remains safe to occupy, essentially retains the pre-earthquake design strength and stiffness of the structure. The risk of life-threatening injury as a result of structural damage is very low, and although some minor structural repairs may be appropriate, these would generally not be required before occupanc

Oxidation of copper electrodes on flexible polyimide substrates for non-enzymatic glucose sensing

The integration of non-enzymatic glucose sensing entities into device designs compatible with industrial production is crucial for the broad take-up of non-invasive glucose sensors. Copper and its oxides have proven to be promising candidates for electrochemical glucose sensing. They can be fabricated in situ enabling integration with standard copper metallisation schemes for example in printed circuit boards (PCBs). Here, copper oxide electrodes are prepared on flexible polyimide substrates through direct annealing of patterned electrode structures. Both annealing temperature and duration are tuned to optimise the sensor surface for optimum glucose detection. A combination of microscopy and spectroscopy techniques is used to follow changes to the surface morphology and chemistry under the varying annealing conditions. The observed physico-chemical electrode characteristics are directly compared with electrochemical testing of the sensing performance, including chronoamperommetry and interference experiments. A clear influence of both aspects on the sensing behaviour is observed and an anneal at 250 °C for 8 h is identified as the best compromise between sensor performance and low interference from competing analytes

Nonlinear 3D Model of Double Shear Lap Tests for the Bond of Near-surface Mounted FRP Rods in Concrete Considering Different Embedment Depth

The utilization of near-surface mounted Fiber Reinforced Polymer (FRP) reinforcement as a method of strengthening in reinforced concrete structures has increased considerably in recent years. Moreover, the application of double-shear lap tests for this rein-forcement method leads to the achievement of a local bond-slip behavior in a bonded joint. This research, therefore, focused on 3-D modeling of this type of test to suitably characterize the bond mechanics between FRP rods and concrete at various embedment depth. The use of different alternatives to represent the interface between the FRP rod and concrete were analyzed after which a comparison was drawn between the numerical finite element (FE) simulations and experimental measurements. The results showed the prediction of the load–slip corresponded with the data obtained from the experiment. Finally, the proposed model has the ability to express the relationship between the penalty stiffness parameters in shear direction Kss = (Ktt) and the embedment depth of FRP rods

Extracellular vesicles from amniotic fluid, milk, saliva, and urine expose complexes of tissue factor and activated factor VII

Background Tissue factor (TF) is expressed in the adventitia of the vessel wall and on extracellular vesicles (EVs) in body fluids. TF and activated coagulation factor (F) VII(a) together form the so-called extrinsic tenase complex, which initiates coagulation. Aim We investigated whether EVs in amniotic fluid, milk, saliva, and urine expose functional extrinsic tenase complexes that can trigger coagulation. Methods Milk, saliva, and urine were collected from healthy breastfeeding women (n = 6), and amniotic fluid was collected from healthy women undergoing routine amniocentesis (n = 7). EVs were isolated from body fluids by size exclusion chromatography (SEC) and clotting experiments were performed in the presence and absence of antibodies against TF and FVIIa in normal plasma and in FVII-deficient plasma. The ability of body fluids to generate FXa also was determined. Results Amniotic fluid, milk, saliva, and urine triggered clotting of normal plasma and of FVII-deficient plasma, which was almost completely inhibited by an anti-FVII antibody and to a lesser extent by an anti-TF antibody. Fractionation of body fluids by SEC showed that only the fractions containing EVs triggered clotting in normal plasma and FVII-deficient plasma and generated FXa, which again was almost completely inhibited by an anti-FVII antibody and partially by an anti-TF antibody. Conclusion Here we show that EVs from amniotic fluid, milk, saliva, and urine expose complexes of TF and FVIIa (i.e., extrinsic tenase complexes) that directly activate FX. Based on our present findings we propose that these EVs from normal body fluids provide hemostatic protection

Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordMulti-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts. Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts. However, these methods have weak adaptability to new environments because they have low sample efficiency and need full retraining to learn updated policies for new environments. To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To efficiently train the seq2seq network, we propose a method that synergizes the first order approximation and clipped surrogate objective. The experimental results demonstrate that this new offloading method can reduce the latency by up to 25% compared to three baselines while being able to adapt fast to new environments