44 research outputs found

    In and Ga Codoped ZnO Film as a Front Electrode for Thin Film Silicon Solar Cells

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    Doped ZnO thin films have attracted much attention in the research community as front-contact transparent conducting electrodes in thin film silicon solar cells. The prerequisite in both low resistivity and high transmittance in visible and near-infrared region for hydrogenated microcrystalline or amorphous/microcrystalline tandem thin film silicon solar cells has promoted further improvements of this material. In this work, we propose the combination of major Ga and minor In impurities codoped in ZnO film (IGZO) to improve the film optoelectronic properties. A wide range of Ga and In contents in sputtering targets was explored to find optimum optical and electrical properties of deposited films. The results show that an appropriate combination of In and Ga atoms in ZnO material, followed by in-air thermal annealing process, can enhance the crystallization, conductivity, and transmittance of IGZO thin films, which can be well used as front-contact electrodes in thin film silicon solar cells

    Risk Factors of Streptococcus suis Infection in Vietnam. A Case-Control Study

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    Background: Streptococcus suis infection, an emerging zoonosis, is an increasing public health problem across South East Asia and the most common cause of acute bacterial meningitis in adults in Vietnam. Little is known of the risk factors underlying the disease. Methods and Findings: A case-control study with appropriate hospital and matched community controls for each patient was conducted between May 2006 and June 2009. Potential risk factors were assessed using a standardized questionnaire and investigation of throat and rectal S. suis carriage in cases, controls and their pigs, using real-time PCR and culture of swab samples. We recruited 101 cases of S. suis meningitis, 303 hospital controls and 300 community controls. By multivariate analysis, risk factors identified for S. suis infection as compared to either control group included eating "high risk" dishes, including such dishes as undercooked pig blood and pig intestine (OR1 = 2.22; 95% CI = [1.15-4.28] and OR2 = 4.44; 95% CI = [2.15-9.15]), occupations related to pigs (OR1 = 3.84; 95% CI = [1.32-11.11] and OR2 = 5.52; 95% CI = [1.49-20.39]), and exposures to pigs or pork in the presence of skin injuries (OR1 = 7.48; 95% CI = [1.97-28.44] and OR2 = 15.96; 95% CI = [2.97-85.72]). S. suis specific DNA was detected in rectal and throat swabs of 6 patients and was cultured from 2 rectal samples, but was not detected in such samples of 1522 healthy individuals or patients without S. suis infection. Conclusions: This case control study, the largest prospective epidemiological assessment of this disease, has identified the most important risk factors associated with S. suis bacterial meningitis to be eating 'high risk' dishes popular in parts of Asia, occupational exposure to pigs and pig products, and preparation of pork in the presence of skin lesions. These risk factors can be addressed in public health campaigns aimed at preventing S. suis infectio

    Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

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    Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

    Making distributed edge machine learning for resource-constrained communities and environments smarter: contexts and challenges

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    Publisher Copyright: © 2022, The Author(s).The maturity of machine learning (ML) development and the decreasing deployment cost of capable edge devices have proliferated the development and deployment of edge ML solutions for critical IoT-based business applications. The combination of edge computing and ML not only addresses the development cost barrier, but also solves the obstacles due to the lack of powerful cloud data centers. However, not only the edge ML research and development is still at an early stage and requires substantial skills normally missed in resource-constrained communities, but also various infrastructure constraints w.r.t. network reliability and computing power, and business contexts from the resource-constrained environments require different considerations to make edge ML applications context aware through smart and intelligent runtime strategies. In this paper, we analyze representative real-world business scenarios for edge ML solutions and their contexts in resource-constrained communities and environments. We identify andmap the key distinguished contexts of distributed edge ML and discuss the impacts of these contexts on data and software components and deployment models. Finally, we present key research areas, how we should approach them, and possible tooling for making edge machine learning solutions smarter in resource-constrained communities and environments.Peer reviewe

    Parallel G-quadruplex-mediated protein dimerization and activation

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    We studied parallel G4-mediated protein dimerization and activation by incorporating a RHAU peptide with a fluorescent protein FRET pair CFP/YFP and an apoptotic casp9. Occurrence of energy tranfer (from donor CFP to acceptor YFP) and enhancement of 60-fold cleavage efficiency of casp9 were observed in the presence of parallel G4, which indicated that parallel G4 can induce dimerization and activation of proteins. This novel approach holds a great promise for studying G4-targeting functional dimeric proteins in celllular biology

    MAppGraph: Mobile-App Classification on Encrypted Network Traffic using Deep Graph Convolution Neural Networks

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    Identifying mobile apps based on network traffic has multiple benefits for security and network management. However, it is a challenging task due to multiple reasons. First, network traffic is encrypted using an end-to-end encryption mechanism to protect data privacy. Second, user behavior changes dynamically when using different functionalities of mobile apps. Third, it is hard to differentiate traffic behavior due to common shared libraries and content delivery within modern mobile apps. Existing techniques managed to address the encryption issue but not the others, thus achieving low detection/classification accuracy. In this paper, we present MAppGraph, a novel technique to classify mobile apps, addressing all the above issues. Given a chunk of traffic generated by an app, MAppGraph constructs a communication graph whose nodes are defined by tuples of IP address and port of the services connected by the app, edges are established by the weighted communication correlation among the nodes. We extract information from packet headers without analyzing encrypted payload to form feature vectors of the nodes. We leverage deep graph convolution neural networks to learn the diverse communication behavior of mobile apps from a large number of graphs and achieve a fast classification. To validate our technique, we collect traffic of a hundred mobile apps on the Android platform and run extensive experiments with various experimental scenarios. The results show that MAppGraph significantly improves classification accuracy by up to 20% compared to recently developed techniques and demonstrates its practicality for security and network management of mobile services.Peer reviewe

    On-chip Silicon Photonic Controllable 2 × 2 Four-mode Waveguide Switch

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    Multimode optical switch is a key component of mode division multiplexing in modern high-speed optical signal processing. In this paper, we introduce for the first time a novel 2 × 2 multimode switch design and demonstrate in the proof-of-concept. The device composes of four Y-multijunctions and 2 × 2 multimode interference coupler using silicon-on-insulator material with four controllable phase shifters. The shifters operate using thermo-optic effects utilizing Ti heaters enabling simultaneous switching of the optical signal between the output ports on four quasi-transverse electric modes with the electric power consumption is in order of 22.5 mW and the switching time is 5.4 µs. The multimode switch exhibits a low insertion loss and a low crosstalk below − 3 dB and − 19 dB, respectively, in 50 nm bandwidth in the third telecom window from 1525 to 1575 nm. With a compact footprint of 10 µm × 960 µm, this device exhibits a relatively large width tolerance of ± 20 nm and a height tolerance of ± 10 nm. Furthermore, the conceptual principle of the proposed multimode switch can be reconfigurable and scalable in multifunctional on-chip mode-division multiplexing optical interconnects
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