Relationship Between Obesity and Periodontal Status in Vietnamese Patients

This study aims to investigate periodontal status, and the relationship between obesity and periodontal status in patients who first visited the Institute of Traditional Medicine, Ho Chi Minh City, Vietnam. 118 patients aged 18 or older, including 56 obese subjects (BMI≥27.5, mean age: 33.8, males: 11, females: 45) and 62 non-obese subjects (BMI<27.5, mean age: 34.3, males: 4, females: 58) were enrolled for a period of 5 months from February 2014 to June 2014. The information on socio-demographic characteristics and dental habits were collected by questionnaire. Periodontal status (PLI, GI, BOP, PD, CAL) was examined and the anthropometric index was measured. There was significantly higher prevalence of periodontitis (39.3%) in the obese group than the non-obese group (16.4%). Means of GI, BOP, PD, and CAL in obese subjects were significantly higher than those in non-obese subjects. Significantly higher percentages of subjects who had lower education, visited dental offices, scaled and polished their teeth regularly were in the non-obese group than in the obese group. Multiple logistic regression analysis revealed that age (OR=3.10), routine of dental visit (OR=3.34) and obesity (OR=2.79) were risk factors significantly related to periodontitis. Periodontal status in obese subjects was poorer than non-obese subjects. Obesity might be the risk factor for periodontitis in Vietnamese patients

High-rate groupwise STBC using low-complexity SIC based receiver

In this paper, using diagonal signal repetition with Alamouti code employed as building blocks, we propose a high- rate groupwise space-time block code (GSTBC) which can be effectively decoded by a low-complexity successive interference cancellation (SIC) based receiver. The proposed GSTBC and SIC based receiver are jointly designed such that the diversity repetition in a GSTBC can induce the dimension expansion to suppress interfering signals as well as to obtain diversity gain. Our proposed scheme can be easily applied to the case of large number of antennas while keeping a reasonably low complexity at the receiver. It is found that the required minimum number of receive antennas is only two for the SIC based receiver to avoid the error floor in performance. The simulation results show that the proposed GSTBC with SIC based receiver obtains a near maximum likelihood (ML) performance while having a significant performance gain over other codes equipped with linear decoders

A physical layer network coding based modify-and-forward with opportunistic secure cooperative transmission protocol

This paper investigates a new secure relaying scheme, namely physical layer network coding based modify-and-forward (PMF), in which a relay node linearly combines the decoded data sent by a source node with an encrypted key before conveying the mixed data to a destination node. We first derive the general expression for the generalized secrecy outage probability (GSOP) of the PMF scheme and then use it to analyse the GSOP performance of various relaying and direct transmission strategies. The GSOP performance comparison indicates that these transmission strategies offer different advantages depending on the channel conditions and target secrecy rates, and relaying is not always desirable in terms of secrecy. Subsequently, we develop an opportunistic secure transmission protocol for cooperative wireless relay networks and formulate an optimisation problem to determine secrecy rate thresholds (SRTs) to dynamically select the optimal transmission strategy for achieving the lowest GSOP. The conditions for the existence of the SRTs are derived for various channel scenarios

The effect of metal corrosion on the structural reliability of the Pre-Engineered steel frame

Nowadays, Pre-Engineered steel buildings are widely used in the field of the industrial construction. However, design standards often only care about the safety (or reliability) at the start time but not concerned about the deterioration of reliability during used under the metal corrosive of environment. Meanwhile, reliability and durability of steel structure depend heavily on metal corrosion of environmental, this is uncertainty parameters. In this research presents the effect of the safety of Pre-Engineered steel frames considering metal corrosion. The metal corrosion modeling used to propose by M.E. Komp. Reliability of the structure is evaluated using Monte Carlo simulation method and Finite Element Method (FEM). This computer program is written by using the MATLAB programming language. The results numbers are reliability and durability behaviors under corrosion are determined for exposure about from 10 - 50 years. Effects of input parameters are also investigated

Cardiac Arrest from Postpartum Spontaneous Coronary Artery Dissection

<p>We present the case of a 32-year-old woman who presented to the emergency department with a witnessed cardiac arrest. She was otherwise healthy with no cardiac risk factors and had undergone an uneventful repeated cesarean section 3 days priorly. The patient underwent defibrillation, out of ventricular fibrillation to a perfusing sinus rhythm, and was taken to the catheterization laboratory where coronary angiography findings showed spontaneous dissection of the left anterior descending artery. The patient received a total of 6 stents during her hospital stay and was eventually discharged in good condition. Spontaneous coronary artery dissection is a rare entity with a predilection for pregnant or postpartum women. Early diagnosis and treatment are key for survival, and when identified early, mortality is good. [West J Emerg Med. 2011;12(4):567–570.]</p

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

Pharmacist-Led Intervention to Enhance Medication Adherence in Patients With Acute Coronary Syndrome in Vietnam:A Randomized Controlled Trial

Background: Patient adherence to cardioprotective medications improves outcomes of acute coronary syndrome (ACS), but few adherence-enhancing interventions have been tested in low-income and middle-income countries. Objectives: We aimed to assess whether a pharmacist-led intervention enhances medication adherence in patients with ACS and reduces mortality and hospital readmission. Methods: We conducted a randomized controlled trial in Vietnam. Patients with ACS were recruited, randomized to the intervention or usual care prior to discharge, and followed 3 months after discharge. Intervention patients received educational and behavioral interventions by a pharmacist. Primary outcome was the proportion of adherent patients 1 month after discharge. Adherence was a combined measure of self-reported adherence (the 8-item Morisky Medication Adherence Scale) and obtaining repeat prescriptions on time. Secondary outcomes were (1) the proportion of patients adherent to medication; (2) rates of mortality and hospital readmission; and (3) change in quality of life from baseline assessed with the European Quality of Life Questionnaire - 5 Dimensions - 3 Levels at 3 months after discharge. Logistic regression was used to analyze data. Registration: ClinicalTrials.gov (NCT02787941). Results: Overall, 166 patients (87 control, 79 intervention) were included (mean age 61.2 years, 73% male). In the analysis excluding patients from the intervention group who did not receive the intervention and excluding all patients who withdrew, were lost to follow-up, died or were readmitted to hospital, a greater proportion of patients were adherent in the intervention compared with the control at 1 month (90.0% vs. 76.5%; adjusted OR = 2.77; 95% CI, 1.01-7.62) and at 3 months after discharge (90.2% vs. 77.0%; adjusted OR = 3.68; 95% CI, 1.14-11.88). There was no significant difference in median change of EQ-5D-3L index values between intervention and control [0.000 (0.000; 0.275) vs. 0.234 (0.000; 0.379); p = 0.081]. Rates of mortality, readmission, or both were 0.8, 10.3, or 11.1%, respectively; with no significant differences between the 2 groups. Conclusion: Pharmacist-led interventions increased patient adherence to medication regimens by over 13% in the first 3 months after ACS hospital discharge, but not quality of life, mortality and readmission. These results are promising but should be tested in other settings prior to broader dissemination

Petrographic Characteristics and Depositional Environment Evolution of Middle Miocene Sediments in the Thien Ung - Mang Cau Structure of Nam Con Son Basin

This paper introduces the petrographic characteristics and depositional environment of Middle Miocene rocks of the Thien Ung - Mang Cau structure in the central area of Nam Con Son Basin based on the results of analyzing thin sections and structural characteristics of core samples. Middle Miocene sedimentary rocks in the studied area can be divided into three groups: (1) Group of terrigenous rocks comprising greywacke sandstone, arkosic sandstone, lithic-quartz sandstone, greywacke-lithic sandstone, oligomictic siltstone, and bitumenous claystone; (2) Group of carbonate rocks comprising dolomitic limestone and bituminous limestone; (3) Mixed group comprising calcareous sandstone, calcarinate sandstone, arenaceous limestone, calcareous claystone, calcareous silty claystone, dolomitic limestone containing silt, and bitumen. The depositional environment is expressed through petrographic characteristics and structure of the sedimentary rocks in core samples. The greywacke and arkosic sandstones are of medium grain size, poor sorting and roundness, and siliceous cement characterizing the alluvial and estuarine fan environment expressed by massive structure of core samples. The mixed calcareous limestone, arenaceous dolomitic limestone, and calcareous and bituminous clayey siltstone in the core samples are of turbulent flow structure characterizing shallow bay environment with the action of bottom currents. The dolomitic limestones are of relatively homogeneous, of microgranular and fine-granular texture, precipitated in a weakly reducing, semi-closed, and relatively calm bay environment

Identifying Computer-Translated Paragraphs using Coherence Features

We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.Comment: 9 pages, PACLIC 201

A metric learning-based method for biomedical entity linking

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here