Modeling of optical and electrical properties of In2O3:Sn coatings made by various techniques

The optical and electrical properties of two types of wet chemical processed tin doped indium oxide (ITO) films deposited by spin coating technique as well as of a commercial sputtered ITO film have been measured. The transmission and reflection spectra in the wavelength range 0.25 to 20 µm have been simulated using the Scout 2 software with different dielectric function models. The electrical parameters obtained from the modeling are compared with those obtained experimentally. The optical data of a low porosity (28%), low specific resistivity (? = 6.

Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients

Introduction. Patients with diabetes are exposed to various cardiovascular risk factors, which lead to an increased risk of cardiac complications. Therefore, the development of a diagnostic system for diabetes and cardiovascular disease (CVD) is a relevant research task. In addition, the identification of the most significant indicators of both diseases may help physicians improve treatment, speed the diagnosis, and decrease its computational costs.Aim. To classify subjects with different diabetes types, predict the risk of cardiovascular diseases in diabetic patients using machine learning methods by finding the correlational indicators.Materials and methods. The NHANES database was used following preprocessing and balancing its data. Machine learning methods were used to classify diabetes based on physical examination data and laboratory data. Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.Results. The developed model (Random Forest) achieved the accuracy of 93.1 % (based on laboratory data) and 88 % (based on pysicical examination plus laboratory data). The top five most common predictors in diabetes and prediabetes were found to be glycohemoglobin, basophil count, triglyceride level, waist size, and body mass index (BMI). These results seem logical, since glycohemoglobin is commonly used to check the amount of glucose (sugar) bound to the hemoglobin in the red blood cells. For CVD patients, the most common predictors inlcude eosinophil count (indicative of blood diseases), gamma-glutamyl transferase (GGT), glycohemoglobin, overall oral health, and hand stiffness.Conclusion. Balancing the dataset and deleting NaN values improved the performance of the developed models. The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. The result was an automated system for predicting CVD risk in diabetic patients.Introduction. Patients with diabetes are exposed to various cardiovascular risk factors, which lead to an increased risk of cardiac complications. Therefore, the development of a diagnostic system for diabetes and cardiovascular disease (CVD) is a relevant research task. In addition, the identification of the most significant indicators of both diseases may help physicians improve treatment, speed the diagnosis, and decrease its computational costs.Aim. To classify subjects with different diabetes types, predict the risk of cardiovascular diseases in diabetic patients using machine learning methods by finding the correlational indicators.Materials and methods. The NHANES database was used following preprocessing and balancing its data. Machine learning methods were used to classify diabetes based on physical examination data and laboratory data. Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.Results. The developed model (Random Forest) achieved the accuracy of 93.1 % (based on laboratory data) and 88 % (based on pysicical examination plus laboratory data). The top five most common predictors in diabetes and prediabetes were found to be glycohemoglobin, basophil count, triglyceride level, waist size, and body mass index (BMI). These results seem logical, since glycohemoglobin is commonly used to check the amount of glucose (sugar) bound to the hemoglobin in the red blood cells. For CVD patients, the most common predictors inlcude eosinophil count (indicative of blood diseases), gamma-glutamyl transferase (GGT), glycohemoglobin, overall oral health, and hand stiffness.Conclusion. Balancing the dataset and deleting NaN values improved the performance of the developed models. The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. The result was an automated system for predicting CVD risk in diabetic patients

Stimulated perturbation on the neutron flux distribution in the mutually-dependent source-to-absorber geometry

The complexity of the neutron transport phenomenon throws its shadows on every physical system wherever neutron is produced or absorbed. The Monte Carlo N-Particle Transport Code (MCNP) was used to investigate the flux perturbations in the neutron field caused by an absorber. The geometry of the present experiment was designed to reach a simulation of an isotopic neutron field. The neutron source was a ${}^{241}$AmBe with the production physics of neutrons is dependent only on alpha-beryllium interaction and is independent of what happened to the neutron after it was generated. The geometries have been designed to get a volume of uniform neutron densities within a spherical volume of radius 15 cm in every neutron energy group up to 10 MeV. Absorbers of different dimensions were placed within the volume to investigate the field perturbation. Different neutron absorbers were used to correlate the phenomenon to the integral cross-section of the absorber. Flux density inside and outside the absorber samples was determined, while the spatial neutron flux distribution produced by the AmBe source without an absorber was taken as a reference. This study displayed that absorbers of various dimensions perturb the neutron field in a way that is dependent on the absorption and scattering cross-sections, particularly in the neutron resonance region. Unlike the simple picture of reducing the number density of neutrons, the perturbation was found to influence the moderation of neutrons in the medium, significantly above 1 MeV.Comment: 10 pages, 13 figures, 26 reference

Behavior of CAD/CAM ceramic veneers under stress: A 3D holographic study

International audienceObjectivesCeramic veneers restorations may undergo damages, such as cracks, fractures, or debonding. Full-field measurements must be carried out in order to visualize and analyze the strain fields. This paper demonstrates that digital holography permits to investigate the mechanical behavior under stress of a natural incisor and a natural incisor reconstructed with CAD/CAM ceramic veneer.MethodsThe facial surface of a maxillary central incisor is prepared to receive a monolithic ceramic reconstruction manufactured using a chairside computer-aided design and computer aided manufacturing (CAD/CAM) system (Cerec AC® system, Sirona Dental System®, Bensheim, Germany). One incisor is kept intact for comparison. The samples are sectioned longitudinally to obtain a planar observation of the region of interest. A mechanical indentation head and digital holographic set-ups permit a full-field, contact-less and single-shot measurement of the three-dimensional displacement fields at the surface of the tooth sample when subjected to load. Stain fields are then estimated and comparison of the results between two samples can be carried out.Results3D displacement, fields and strain fields are measured and highlight the behavior of the region of interest in three directions of space for the ceramic veneer and the natural incisor. The strain maps reveal the local behavior, especially the concentration or the sudden change in strain. The transition zones are clearly observed, particularly for the veneered sample.ConclusionDigital holography highlights the localization of stress concentration zones in regions of interest and yields comparative analysis between samples with different tooth preparations.Significanceholography permits to visualize and compare the mechanical response of the ceramic veneer and natural tooth. This helps choosing the mechanical properties of the bonding interface

Causes and clinical characteristics of drug abuse

Background: Drug abuse is considered one of the most serious public health problems, especially among young people at working ages. In this study, we assessed clinical characteristics of patients presenting with drug abuse who were referred to the addiction treatment clinics of Al-Mamoura mental hospital (Alexandria, Egypt), including sociodemographic variables, clinical symptoms, physical complications and psychiatric co-morbidities in comparison to other substance abuse. Methods: A descriptive cross sectional and comparative study was conducted on 516 patients attending outpatient addiction treatment clinics in 2013–14 on the Structured Clinical Interview for DSM-IV (SCID-I and SCID-II) as well as social and addiction scales. Results: in our sample, drug addiction was more common in males than females. Importantly, among many potential factors, it was found that peer pressure (friends) was the most common cause for drug abuse. Second, it was also found that psychiatric symptoms were more common among patients with substance abuse than legal or financial problems. Conclusions: Future behavioral treatments should take into account the role played by friends that lead to drug abuse and the maintenance of such habits. Further, pharmacological and behavioral therapies should consider psychiatric aspects of drug abuse as these are very common and may impact effective recovery

Interaction Effects of Nitrogen Source and Irrigation Regime on Tuber Quality, Yield, and Water Use Efficiency of Solanum tuberosum L.

Two field experiments were conducted to investigate the effects of three drip irrigation regimes (G1: 120% crop evapotranspiration (ETc), G2: 100% ETc, and G3: 80% ETc) and four nitrogen (N) source treatments (S0: non-fertilized; S1: urea, S2: ammonium nitrate, and S3: ammonium sulfate on water consumption use, water utilization efficiency, chlorophyll, yield and tubers quality of potato (Solanum tuberosum L.; cv Diamond) under a drip irrigation system during two successive winter seasons (2015/16 and 2016/17)). Nitrogen fertilization was applied at 380 kg ha−1 as standard application for potato in the investigated area. The highest tubers yield was obtained from potato grown with G1 S2 (65.8 Mg ha−1), G1 S3 (63.6 Mg ha−1), G2 S2 (64.1 Mg ha−1), and G2 S3 (62.4 Mg ha−1), while the lowest tubers yield was obtained from potato grown with G3 S0 (10.1 Mg ha−1) and G2S0 (17.4 Mg ha−1). Different treatments of N source resulted in a significant increase for water use efficiency (WUtE) compared with unfertilized treatment. For the interaction effect, the highest WUtE was obtained from potato grown with G3 S2 (18.1 kg m−3), followed by G3 S3 (17.6 kg m−3), while the lowest WUtE was obtained from plants grown with G3S0 (3.0 kg m−3). However, the highest chlorophyll content was obtained from plants grown with G1 and any N source, followed by G2S1-3, while the lowest chlorophyll content was obtained from those grown with G3S0. The highest N, S, protein, and P contents in tubers were obtained from plants grown with G3S3, G3S2, and G2S2, while the highest K content in tubers was obtained from plants grown with G1S1 and G1S2. In concussion, the integrative effects of G1 or G2 with S2 or S3 is recommended for high productivity, while the integrative effects of G3S3 and G3S2 are recommended for high quality tubers

Матричная модель для создания логических фильтров электронного каталога протезных модулей при персонифицированном синтезе протеза

Introduction. When synthesizing a prosthesis from ready-made prosthesis units, the prosthetist is faced with the problem of selecting from a large range of components that differ in properties and characteristics. This challenge can be overcome by the creation of a system for processing the patient's biomedical information and its further use as criteria for selecting prosthetic nodes from a global database. For this purpose, an appropriate knowledge base must be incorporated into the system software.Aim. Substantiation of the expediency of presenting the knowledge base about the requirements for the lower limb prosthesis nodes in the form of a matrix model for creating a system of logical filters in the process of selecting nodes from an electronic catalog.Materials and methods. Theoretical research methods were used, including analysis, synthesis and analogy. An expert survey among leading specialists was carried out. To unify the description of the structural and functional state of a disabled person, the terms of the International Classification of Functioning (ICF), Disability and Health were used.Results. At the main stage of filtering, prosthetic modules optimally meeting the patient’s needs are selected using a specialized software application, depending on the patient’s health status and various healthrelated factors. A model of the knowledge base is presented, which describes the logic of selecting prosthetic nodes and their filtering in an electronic catalog.Conclusion. The matrix representation of the knowledge base that contains rules for selecting components of lower limb prostheses, taking into account the patient's condition, is a basis for creating a system of logical filters when searching for prosthetic modules in an electronic catalog for creating customized prostheses. The use of the ICF conceptual language for describing the factors influencing the choice of prosthetic modules is a step towards the formation of a patient’s digital profile, which corresponds to the strategy of transition to digital medicine technologies.Введение. При синтезе протеза из промышленно выпускаемых узлов возникает проблема выбора из большой номенклатуры комплектующих, различающихся по своим свойствам и характеристикам. Решить проблему может создание информационно-измерительной системы для измерения и анализа биомедицинской информации о состоянии пациента и использование полученных результатов как критериев выбора моделей узлов протеза из глобальной базы данных. С этой целью в программное обеспечение системы должна быть заложена соответствующая база знаний.Цель работы. Обоснование целесообразности представления базы знаний о требованиях к характеристикам узлов протеза нижней конечности в виде матричной модели для создания системы логических фильтров выбора моделей узлов из электронного каталога.Материалы и методы. В качестве методов исследования применены: теоретический метод, включающий анализ, синтез и аналогию; экспертный опрос ведущих специалистов. Для унификации описания структурно-функционального состояния инвалида используется понятийный язык Международной классификации функционирования (МКФ), ограничения жизнедеятельности и здоровья.Результаты. На основном этапе фильтрации протезных модулей требуется посредством специализированного программного обеспечения под запросы пользователя сформировать выборку моделей модулей, наиболее релевантных потребностям протезируемого пациента, которые определяются показателями состояния его здоровья, а также связанными со здоровьем факторами. Представлена форма модели базы знаний для отражения логики процедуры выбора протезных узлов и решения основной проблемы организации фильтрации этих объектов в электронном каталоге.Заключение. Матричное представление базы знаний, отражающее правила выбора комплектующих протеза нижней конечности с учетом показателей состояния пациента, является базой для создания системы логических фильтров в электронном каталоге протезных модулей при персонифицированном синтезе протеза. Использование понятийного языка МКФ при описании факторов, влияющих на выбор протезных модулей, является шагом по пути формирования цифрового профиля протезируемого, что соответствует стратегии перехода на технологии цифровой медицины