A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Drug-Related Problems in Coronary Artery Diseases

Coronary artery disease (CAD) remains the leading cause of mortality among cardiovascular diseases, responsible for 16% of the world’s total deaths. According to a statistical report published in 2020, the global prevalence of CAD was estimated at 1655 per 100,000 people and is predicted to exceed 1845 by 2030. Annually, in the United States, CAD accounts for approximately 610,000 deaths and costs more than 200 billion dollars for healthcare services. Most patients with CAD need to be treated over long periods with a combination of drugs. Therefore, the inappropriate use of drugs, or drug-related problems (DRPs), can lead to many consequences that affect these patients’ health, including decreased quality of life, increased hospitalization rates, prolonged hospital stays, increased overall health care costs, and even increased risk of morbidity and mortality. DRPs are common in CAD patients, with a prevalence of over 60%. DRPs must therefore be noticed and recognized by healthcare professionals. This chapter describes common types and determinants of DRPs in CAD patients and recommends interventions to limit their prevalence

Лексические средства связности в речи телеведущего в жанре интерактивной белорусскоязычной телебеседы

Материалы XII Междунар. науч. конф. студентов, магистрантов, аспирантов и молодых ученых, Гомель, 16–17 мая 2019 г

Practice regarding tuberculosis care among physicians at private facilities: A cross-sectional study from Vietnam.

ObjectivesTo evaluate the practice of TB care among physicians at private facilities.MethodsA cross-sectional study was conducted using questionnaires on knowledge, attitude, and practice related to TB care. The responses to these scales were used to explore latent constructs and calculate the standardized continuous scores for these domains. We described the percentages of participant's responses and explored their associated factors using multiple linear regression.ResultsA total of 232 physicians were recruited. The most important gaps in practice included requesting chest imaging to confirm TB diagnosis (~80%), not testing HIV for confirmed active TB cases (~50%), only requesting sputum testing for MDR-TB cases (65%), only requesting follow-up examination at the end of the treatment course (64%), and not requesting sputum testing at follow-up (54%). Surgical mask was preferred to N95 respirator when examining TB patients. Prior TB training was associated with better knowledge and less stigmatizing attitude, which were associated with better practice in both TB management and precautions.ConclusionThere were important gaps in knowledge, attitude, and practice of TB care among private providers. Better knowledge was associated with positive attitude towards TB and better practice. Tailored training may help to address these gaps and improve the quality of TB care in the private sector

Knowledge and Systems EngineeringProceedings of the Fifth International Conference KSE 2013, Volume 2 /

XII, 448 p. 132 illus.online resource

The Effect of Fermented Kefir as Functional Feed Additive in Post-Weaned Pigs

The control of the immune system of pigs after weaning is important in pig farming because productivity depends on the survival of the post-weaned pigs. Previously, antibiotics would have been administered in the case of infectious diseases to increase the survival rate of post-weaned pigs, but now, the use of antibiotics is strictly restricted in order to prevent other problems such as the occurrence of antibiotic-resistant pathogens. In this study, the effect of fermented kefir as a functional feed additive as a replacement to antibiotics was evaluated in terms of the microbial profile in fecal samples, immunological factors in the blood of pigs, growth performance measured as average daily gain (ADG) and the feed conversion rate (FCR) of post-weaned pigs. In the kefir-treated group, the number of lactic acid bacteria and Bacillus spp. in the fecal samples of the pigs increased with the kefir treatments. Interestingly, the number of coliform groups as opportunistic pathogens was reduced in the fecal samples of pigs treated with kefir. We found out that treatment with kefir enhanced the innate immunity of post-weaned pigs though the reduction of IL-6 as a proinflammatory cytokine and an increase in IgG as an immunoglobulin, enhancing immunological defense against pathogens. Finally, after treatment with kefir, we observed that the ADG of post-weaned pigs increased to 135.6% but FCR decreased to 92.2%. Therefore, this study shows that fermented kefir can be used as a functional feed additive and an antibiotic alternative in order to improve both the innate immune system and growth performance of post-weaned pigs

Application of machine learning-based surrogate models for urban flood depth modeling in Ho Chi Minh City, Vietnam [Formula presented]

Rapid flood prediction in coastal urban areas is an important but challenging task. However, multi-driver floods in coastal areas and their non-linearity in physical processes are hard to represent in physics-based numerical models (PBNMs). In this study, we investigated the performance of surrogate machine learning (ML) models and their flood prediction capability. Initially, we utilize the MIKE+ coupled 1D–2D model to simulate coastal urban flooding in one of the severely flood-affected areas of Ho Chi Minh City (HCMC), Vietnam. Then, nine ML models, including AdaBoost (AB), Decision Tree (DT), Gaussian Process (GP), k-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM) are employed to surrogate the PBNM flood prediction performance and engaged to predict flood depths of the study area domain. 806 simulation scenarios of MIKE+ modeling having a spatial grid of 1107 ×1513, grid size = 2 m, extracting 270,000 inundation points to generate input data for nine ML models are used to simulate surface flood depths for the study area. Results show three ML models, GP, RF, and NN, outperform the remaining models, with R2 value of 0.997, 0.996, and 0.995, respectively. Thus, applying ML models can significantly reduce the simulation time by a PBNM, improve accuracy, and potentially be adopted for real-time forecasting and emergency management.</p