Adsorption of Ammonium (NH4+) Ions onto various Vietnamese biomass residue-derived biochars (wood, rice husk and bamboo)

This study evaluates adsorption of ammonium nitrogen (NH4+-N) ions onto various biochars produced from biomass residues in Vietnam as a function of their physicochemical characteristics. Three biochars, including wood biochar (WBC), rice husk biochar (RBC), and bamboo biochar (BBC), were produced under limited oxygen conditions using Top-Lid Updraft Drum technology at temperatures of 450-550oC. Physicochemical characterization (BET surface area, Cation exchange capacity (CEC), Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy) of the biochars was performed in order to link their porosity and surface functional groups with their NH4+-N capture capacities. Please click on the file below for full content of the abstract

Cluster ionization via two-plasmon excitation

We calculate the two-photon ionization of clusters for photon energies near the surface plasmon resonance. The results are expressed in terms of the ionization rate of a double plasmon excitation, which is calculated perturbatively. For the conditions of the experiment by Schlipper et al., we find an ionization rate of the order of 0.05-0.10 fs^(-1). This rate is used to determine the ionization probability in an external field in terms of the number of photons absorbed and the duration of the field. The probability also depends on the damping rate of the surface plasmon. Agreement with experiment can only be achieved if the plasmon damping is considerably smaller than its observed width in the room-temperature single-photon absorption spectrum.Comment: 17 pages and 6 PostScript figure

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

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

NGHIÊN CỨU NHIỄU LOẠN TẦNG ĐIỆN LY TỨC THỜI BẰNG MÁY THU TÍN HIỆU TẦN SỐ RẤT THẤP TẠI NHA TRANG

Kỹ thuật sử dụng tín hiệu tần số rất thấp (VLF- Very Low Frequency) là một công cụ rất hữu hiệu để nghiên cứu nhiễu loạn tầng điện ly tức thời do tín hiệu này hầu như bị phản xạ tại lớp D tầng điện ly (ở độ cao 60-90 km) khi xãy ra nhiễu loạn. Với yêu câu nhanh chóng nắm bắt các kỹ thuật và công nghệ tiên tiến để làm chủ các thiết bị nghiên cứu, chúng tôi mạnh dạn đề ra mục tiêu tự chế tạo thiết bị phục vụ cho nghiên cứu và ứng dụng. Trong bài báo này, nhóm nghiên cứu xin trình bày kết quả ứng dụng máy thu tín hiệu VLF được chế tạo tại Nha Trang để nghiên cứu nhiễu loạn tầng điện ly tức thời cho mục đích giáo dục thời tiết không gian

Adaptive network alignment with unsupervised and multi-order convolutional networks

Network alignment is the problem of pairing nodes between two graphs such that the paired nodes are structurally and semantically similar. A well-known application of network alignment is to identify which accounts in different social networks belong to the same person. Existing alignment techniques, however, lack scalability, cannot incorporate multi-dimensional information without training data, and are limited in the consistency constraints enforced by an alignment. In this paper, we propose a fully unsupervised network alignment framework based on a multi-order embedding model. The model learns the embeddings of each node using a graph convolutional neural representation, which we prove to satisfy consistency constraints. We further design a data augmentation method and a refinement mechanism to make the model adaptive to consistency violations and noise. Extensive experiments on real and synthetic datasets show that our model outperforms state-of-the-art alignment techniques. We also demonstrate the robustness of our model against adversarial conditions, such as structural noises, attribute noises, graph size imbalance, and hyper-parameter sensitivity

Network Alignment by Representation Learning on Structure and Attribute

Network alignment is the task of recognizing similar network nodes across different networks, which has many applications in various domains. As traditional network alignment methods based on matrix factorization do not scale to large graphs, a variety of representation learning based approaches has been proposed recently. However, these techniques tend to focus on topology consistency between two networks while ignoring other valuable information (e.g. network nodes attribute), which makes them susceptible to structural changes. To alleviate this problem, we propose RAN, a representation-based network alignment model that couples both structure and node attribute information. Our framework first constructs multi-layer networks to represent topology and node attribute information, then computes the alignment result by learning the node embeddings for source and target network. The experimental results show that our method is able to outperform other techniques significantly even on large datasets

Network Alignment with Holistic Embeddings (Extended Abstract)

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different types of network information or modalities. In this paper, we propose a novel end-to-end alignment framework that can leverage different modalities to compare and align network nodes in an efficient way. A comprehensive evaluation on various datasets shows that our technique outperforms state-of-the-art approaches. Our source code is available at https://github.com/ thanhtrunghuynh93/holisticEmbeddingsNA.LSI

A comparative study on network alignment techniques

Network alignment is a method to align nodes that belong to the same entity from different networks. A well-known application of network alignment is to map user accounts from different social networks that belong to the same person. As network alignment has a wide range of applications from recommendation to link prediction, there are several proposed approaches to aligning nodes from different networks. These techniques, however, have been rarely compared and analyzed under the same setting, rendering a right choice for a particular set of networks very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of network alignment methods. Specifically, we integrate several state-of-the-art network alignment techniques in a comparable manner, and measure distinct characteristics of these techniques with various settings. We then provide in-depth analysis of the benchmark results, obtained by using both real data and synthetic data. We believe that the findings from the benchmark will serve as a practical guideline for potential applications. (C) 2019 Elsevier Ltd. All rights reserved