NON-DIMENSIONAL COMBINED TREATMENT IN HETEROGENEOUS MEDIA OF SAMPLES OBTAINED BY SELECTIVE LASER MELTING

The paper presents the results of a study of chemical polishing in heterogeneous media of samples prepared by selective laser alloying from a titanium alloy VT6. The results of changes in the roughness of the sample surface in the longitudinal and transverse directions after polishing with solutions containing various concentrations are given. The polishing effect is analyzed for different contents of the solid phase in abrasive electrolyte paste with su¬perposition of ultrasonic vibrations. A recommendation is given on the optimal composition and concentration in the polishing of articles made of titanium alloys produced by selective laser fusion

Технологии комплексного интеллектуального анализа клинических данных

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient’s features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented.Background: Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality.Aims: the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center.Materials and methods: Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as «negation» (indicates that the disease is absent), «no patient» (indicates that the disease refers to the patient’s family member, but not to the patient), «severity of illness», «disease course», «body region to which the disease refers». Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the method for determining the most informative patients’ features are also proposed.Results: Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records of patients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases.Conclusions: The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare. Обоснование. Медицинские учреждения генерируют большой поток как структурированных, так и неструктурированных данных, содержащих важную информацию о пациентах. В структурированном виде, как правило, хранятся результаты анализов, однако подавляющее количество данных хранится в неструктурированной форме в виде текстов на естественном языке (анамнезы, результаты осмотров, описания результатов обследований, таких как УЗИ, ЭКГ, рентгеновских исследований и др.). Используя методы интеллектуальной обработки накопленных массивов структурированных и неструктурированных данных, можно автоматизировать решение многих задач, возникающих в клинической практике и повысить качество медицинской помощи.Цель исследования: создание комплексной системы интеллектуальной обработки данных в многопрофильном педиатрическом центре.Методы. Извлечение информации из клинических текстов на русском языке осуществляется на основе полного лингвистического анализа. Извлекаются упоминания заболеваний, симптомов, областей тела, лекарственных препаратов. В тексте также распознаются атрибуты заболеваний: «отрицание» (указывает на то, что заболевание отсутствует), «не пациент» (указывает на то, что заболевание относится не к пациенту, а к его родственнику), «тяжесть заболевания», «течение заболевания», «область тела, к которой относится заболевание». Для извлечения информации используются медицинские тезаурусы, набор вручную составленных шаблонов, а также различные методы на основе машинного обучения. Полученные из текстов данные используются для решения задачи автоматической диагностики хронических заболеваний. Предложен метод на основе машинного обучения для классификации пациентов со схожими нозологиями, а также метод для определения наиболее информативных признаков.Результаты. Экспериментальное исследование разработанных методов проводилось на обезличенных историях болезни пациентов педиатрического центра. Проведена оценка качества разработанных методов извлечения информации из клинических текстов на русском языке. Проведена экспериментальная оценка метода автоматической диагностики на данных пациентов с аллергическими заболеваниями и болезными органов дыхания, нефрологическими и ревматическими заболеваниями. Определены наиболее подходящие методы машинного обучения для классификации пациентов для каждой группы заболеваний, а также наиболее информативные признаки. Использование данных, извлеченных из клинических текстов совместно со структурированными данными, позволило повысить качество диагностики хронических заболеваний по сравнению с использованием лишь доступных структурированных данных. Получены также шаблонные комбинации признаков заболеваний.Заключение. Разработанные методы были реализованы в системе интеллектуальной обработки данных в многопрофильном педиатрическом центре. Проведенные исследования свидетельствуют о перспективности использования системы для повышения качества медицинской помощи пациентам детской возрастной категории

The Crossing Statistic: Dealing with Unknown Errors in the Dispersion of Type Ia Supernovae

We propose a new statistic that has been designed to be used in situations where the intrinsic dispersion of a data set is not well known: The Crossing Statistic. This statistic is in general less sensitive than `chi^2' to the intrinsic dispersion of the data, and hence allows us to make progress in distinguishing between different models using goodness of fit to the data even when the errors involved are poorly understood. The proposed statistic makes use of the shape and trends of a model's predictions in a quantifiable manner. It is applicable to a variety of circumstances, although we consider it to be especially well suited to the task of distinguishing between different cosmological models using type Ia supernovae. We show that this statistic can easily distinguish between different models in cases where the `chi^2' statistic fails. We also show that the last mode of the Crossing Statistic is identical to `chi^2', so that it can be considered as a generalization of `chi^2'.Comment: 14 pages, 5 figures. Paper restructured and extended and new interpretation of the method presented. New results concerning model selection. Treatment and error-analysis made fully model independent. References added. Accepted for publication in JCA

Acute complicated anaerobic paraproctitis: a case from practice

Anaerobic paraproctitis is a life-threatening disease, accompanied by a high mortality rate, requiring absolutely innovative diagnostic and therapeutic approaches. The development of acute paraproctitis is brought about by a number of predisposing factors: weakening of the immune system due to concurrent acute or chronical infection, microcirculation disorders, gastrointestinal disorders, complications of hemorrhoids, fissures, cryptitis. Not infrequently acute paraproctitis is complicated by Fournier’s gangrene. Objectives. To describe the clinical case of acute anaerobic non-clostridial paraproctitis. Material and methods. The paper presents a patient with suspected anaerobic non-clostridial paraproctitis. Results. We managed to control the process. The patient was discharged in a satisfactory condition for the outpatient treatment. Conclusions. As a result of complex treatment of the patient with symptoms of acute anaerobic non-clostridial paraproctitis, including a wide dissection of purulent streaks and antibiotic therapy, it was possible to stop the given disease

Peculiarities of electronic structure and composition in ultrasound milled silicon nanowires

The combined X-ray absorption and emission spectroscopy approach was applied for the detailed electronic structure and composition studies of silicon nanoparticles produced by the ultrasound milling of heavily and lowly doped Si nanowires formed by metal-assisted wet chemical etching. The ultrasoft X-ray emission spectroscopy and synchrotron based X-ray absorption near edges structure spectroscopy techniques were utilize to study the valence and conduction bands electronic structure together with developed surface phase composition qualitative analysis. Our achieved results based on the implemented surface sensitive techniques strongly suggest that nanoparticles under studies show a significant presence of the silicon suboxides depending on the pre-nature of initial Si wafers. The controlled variation of the Si nanoparticles surface composition and electronic structure, including band gap engineering, can open a new prospective for a wide range Si-based nanostructures application including the integration of such structures with organic or biological systems. © 202

Spin asymmetry A_1^d and the spin-dependent structure function g_1^d of the deuteron at low values of x and Q^2

We present a precise measurement of the deuteron longitudinal spin asymmetry A_1^d and of the deuteron spin-dependent structure function g_1^d at Q^2 < 1 GeV^2 and 4*10^-5 < x < 2.5*10^-2 based on the data collected by the COMPASS experiment at CERN during the years 2002 and 2003. The statistical precision is tenfold better than that of the previous measurement in this region. The measured A_1^d and g_1^d are found to be consistent with zero in the whole range of x.Comment: 17 pages, 10 figure

Gluon polarization in the nucleon from quasi-real photoproduction of high-pT hadron pairs

We present a determination of the gluon polarization Delta G/G in the nucleon, based on the helicity asymmetry of quasi-real photoproduction events, Q^2<1(GeV/c)^2, with a pair of large transverse-momentum hadrons in the final state. The data were obtained by the COMPASS experiment at CERN using a 160 GeV polarized muon beam scattered on a polarized 6-LiD target. The helicity asymmetry for the selected events is = 0.002 +- 0.019(stat.) +- 0.003(syst.). From this value, we obtain in a leading-order QCD analysis Delta G/G=0.024 +- 0.089(stat.) +- 0.057(syst.) at x_g = 0.095 and mu^2 =~ 3 (GeV}/c)^2.Comment: 10 pages, 3 figure

The Deuteron Spin-dependent Structure Function g1d and its First Moment

We present a measurement of the deuteron spin-dependent structure function g1d based on the data collected by the COMPASS experiment at CERN during the years 2002-2004. The data provide an accurate evaluation for Gamma_1^d, the first moment of g1d(x), and for the matrix element of the singlet axial current, a0. The results of QCD fits in the next to leading order (NLO) on all g1 deep inelastic scattering data are also presented. They provide two solutions with the gluon spin distribution function Delta G positive or negative, which describe the data equally well. In both cases, at Q^2 = 3 (GeV/c)^2 the first moment of Delta G is found to be of the order of 0.2 - 0.3 in absolute value.Comment: fits redone using MRST2004 instead of MRSV1998 for G(x), correlation matrix adde