Study of Hydraulic Properties and Design Criteria for a River Subsurface Intake without Cut Off

Experimental Studie

Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models

© 2018 Esmaili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries can provide an insight into the health of the affected parties, allow improved health system resource planning, and provide a baseline against which any future system-level interventions can be evaluated. Therefore, this research aims to use time series of service utilization provided by a compensation agency to identify groups of claimants with similar utilization patterns, describe such patterns, and characterize the groups in terms of demographic, accident type and injury type. Methods To achieve this aim, we have proposed an analytical framework that utilizes latent variables to describe the utilization patterns over time and group the claimants into clusters based on their service utilization time series. To perform the clustering without dismissing the temporal dimension of the time series, we have used a well-established statistical approach known as the mixture of hidden Markov models (MHMM). Ensuing the clustering, we have applied multinomial logistic regression to provide a description of the clusters against demographic, injury and accident covariates. Results We have tested our model with data on psychology service utilization from one of the main compensation agencies for transport accidents in Australia, and found that three clear clusters of service utilization can be evinced from the data. These three clusters correspond to claimants who have tended to use the services 1) only briefly after the accident; 2) for an intermediate period of time and in moderate amounts; and 3) for a sustained period of time, and intensely. The size of these clusters is approximately 67%, 27% and 6% of the number of claimants, respectively. The multinomial logistic regression analysis has showed that claimants who were 30 to 60-year-old at the time of accident, were witnesses, and who suffered a soft tissue injury were more likely to be part of the intermediate cluster than the majority cluster. Conversely, claimants who suffered more severe injuries such as a brain head injury or anon-limb fracture injury and who started their service utilization later were more likely to be part of the sustained cluster

Bathymetric modelin from satellite imagery via Single Band Algorithm (SBA) and Principal Components Analysis (PCA) in southern Caspian Sea

Remotely sensed imagery is proving to be a useful tool to estimate water depths in coastal zones. Bathymetric algorithms attempt to isolate water attenuation and hence depth from other factors by using different combinations of spectral bands. In this research, images of absolute bathymetry using two different but related methods in a region in the southern Caspian Sea coasts has been produced. The first method used a Single Band Algorithm (SBA) and assumed a constant water attenuation coefficient throughout the blue band. The second method used Principal Components Analysis (PCA) to adjust for varying water attenuation coefficients without additional ground truth data. PCA method (r=-0.672394) appears to match our control points slightly better than single band algorithm (r=-0.645404). It is clear that both methods can be used as rough estimates of bathymetry for many coastal zone studies in the southern Caspian Sea such as near shore fisheries, coastal erosion, water quality, recreation siting and so forth. The presented methodology can be considered as the first step toward mapping bathymetry in the southern Caspian Sea. Further research must investigate the determination of the nonlinear optimization techniques as well as the assessment of these models’ performance in the study area

ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked improvement over a state-of-the-art system.Comment: Accepted at NAACL-HLT 201

Gene Diversity of Trichomonas vaginalis Isolates

Background: Trichomonas vaginalis is protozoan parasite responsible for trichomoniasis and is more common in high-risk behavior group such as prostitute individuals. Interest in trichomoni­asis is due to increase one's susceptibility to viruses such as herpes, human papillomavirus and HIV. The aim of this study was to find genotypic differences between the isolates.Methods: Forty isolates from prisoners' women in Tehran province were used in this study. The random amplified polymorphic DNA (RAPD) technique was used to determine genetic differ­ences among isolates and was correlated with patient's records. By each primer the banding pat­tern size of each isolates was scored (bp), genetic differences were studied, and the genealogical tree was constructed by using NTSYS software program and UPGMA method.Results: The least number of bands were seen by using primer OPD8 and the most by using OPD3. Results showed no significant difference in isolates from different geographical areas in Iran. By using primer OPD1 specific amplified fragment with length 1300 base pair were found in only 8 isolates. All these isolates were belonged to addicted women; however, six belonged to asymptomatic patients and two to symptomatic ones.Conclusion: There was not much genetic diversity in T vaginalis isolates from three different geo­graphical areas

The relationship between body image and marital adjustment in infertile women

Introduction: Body image is one of the most important issues in women's mental health and due to its relationship with physical, cognitive and emotional aspects of women’s life; it can predict their healthy or unhealthy behaviors. According to some research reports, women’s negative or positive body image can affect their marital relationships. Considering the impact of infertility on both body image and marital adjustment and the lack of evidence regarding the relationship of these two variables in infertility, this study aimed to investigate the relationship of body image with marital adjustment in infertile women in 2010 in Mashhad. Methods: This correlational study was carried out on 130 infertile women referred to Montaserie Infertility Research Center in Mashhad who were selected through convenient sampling. Research tools were consisted of valid and reliable demographic questionnaire including personal and infertility-related information, modified Younesi Body Image Questionnaire and Spanier marital adjustment scale (DAS) which were completed by subjects. Data analysis was carried out by SPSS software (version 15/5) using t-tests, one way ANOVA, and Spearman and Pearson correlation test. Results: 93/1% of women reported positive body image and 76/9% had high marital adjustment. There was a direct correlation between the overall score of body image with marital adjustment (P<0/001). There was also a direct correlation between the scores of body image subscales including body in loneliness (P= 0/001), real body (P=0/014), public image of body (P=0/016), spouse image of body (P<0/001) and spouse family image of body (P<0/001) with marital adjustment. However, this relationship was not observed between the subscale of ideal body and marital adjustment. Conclusion: The presence of a direct correlation between body image and marital adjustment could guide developing educational or counseling programs particularly for infertile women who suffer from marital disputes. Keywords: Body image, Marital adjustment, Infertilit

Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed

ReWE: Regressing word embeddings for regularization of neural machine translation systems

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked improvement over a state-of-the-art system

Leveraging Discourse Rewards for Document-Level Neural Machine Translation

Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspects of the discourse at document level. However, document-level translation models are usually not trained to explicitly ensure discourse quality. Therefore, in this paper we propose a training approach that explicitly optimizes two established discourse metrics, lexical cohesion (LC) and coherence (COH), by using a reinforcement learning objective. Experiments over four different language pairs and three translation domains have shown that our training approach has been able to achieve more cohesive and coherent document translations than other competitive approaches, yet without compromising the faithfulness to the reference translation. In the case of the Zh-En language pair, our method has achieved an improvement of 2.46 percentage points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time improving 0.63 pp in BLEU score and 0.47 pp in F_BERT.Comment: Accepted at COLING 202

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists. DESIGN: This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting. SETTING: The study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020. PARTICIPANTS: Eleven consultant diagnostic radiologists of varying levels of experience participated in this study. PRIMARY AND SECONDARY OUTCOME MEASURES: Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed. RESULTS: Of 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy. CONCLUSIONS: Use of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice