The Action of Introvit A + WS and Monocalcium Phosphate in the Prevention of Vitamin and Mineral Deficiency of Infertility in Cows

This article describes the clinical signs, blood tests, and diagnosis of nutritional infertility in dairy cows

Collaborative filtering approach in adaptive learning

© 2016,International Journal of Pharmacy and Technology. All rights reserved.Nowadays an adaptive approach in education is gaining in popularity. But what does this adaptive approach mean? Adaptive learning (also known as Adaptive education) means that the education system has a personal approach for each student or for groups of students that fits to the students’ abilities. Teacher must pick up the most relevant topic for explanation,exercises and tests for such an education process. Also the teacher should adapt the order of learning topics for the current student. It is a very big and hard job,but machine learning algorithms can solve some of these tasks instead of teacher. What do we have? We have a set of lessons and students. In each step of the education process we select one lesson that fits best for a current student. This problem can be solved by recommender systems of algorithms. Recommender systems predict rating or “preference’” that a user would give to the item,and by similar way an adaptive education system also predict lessons “ratings” for user. In the paper we will define “rating” of lessons and what does it mean “fits the best”. Also we give some explanations of a chosen machine learning algorithm

Modern tools and methods for ensuring the accuracy of measurement results

Production of high-quality products and provision of modern production hightech processes requires establishing the accuracy of measurements by introducing modern methods and tools.Производство качественной продукции и обеспечение современных производственных высокотехнологичных процессов требует установления точности производимых измерений путем внедрения современных методов и средств

Топография вновь созданных путей оттока при антиглаукомных операциях неоваскулярной глаукомы по данным ультразвуковой биомикроскопии

PURPOSE: The study of UBM capacity in drainage structures control after surgical treatment of neovascular glaucoma. METHODS: The study included 80 patients (80 eyes) with neovascular glaucoma. The mean age of the patients was 60.7±1.9. All patients were divided into two groups depending on the surgical technique: group I (main group) included 40 patients after a deep sclerectomy with xenocollagen drainage implantation (DSE with XDI), group II (control group) - 40 patients after standard deep sclerectomy (DSE). All study subjects underwent standard ophthalmological examination as well as ultrasound biomicroscopy (UBM) (“Paradigm Model P60™ UBM”, USA). The following parameters: filtration bleb - height, area, volume; scleral flap - thickness; intracleral cavity - height, area, volume and anterior chamber depth, anterior chamber angle were measured with the help of ultrasound biomicroscopy. RESULTS: Ultrasound biomicroscopic study helped identify the morphological components of each structure respon sible for postoperative drainage function of the eye after deep sclerectomy with implantation of xenocollagenic drainage in patients with neovascular glaucoma. UBM data research of the anterior segment of the eye in early and long-term follow-up revealed that the proposed modified method of deep sclerectomy with xenocollagen drainage implantation allows forming new constructed ways for intraocular fluid outflow from the anterior chamber in supraciliar, sub-Tenon’s space and subconjuctival spaces. This leads to unobstructed filtration of intraocular fluid and helps prevent scarring of the operation zone. CONCLUSION: Using the ultrasound biomicroscopy in patients with neovascular glaucoma allows monitoring the condition of the drainage system of the eye in the postoperative period in order to ensure the prevention of the postoperative hypertension.ЦЕЛЬ. Изучение возможностей ультразвуковой биомикроскопии (УБМ) для контроля дренажных структур глаза после хирургического лечения у больных с неоваскулярной (НВГ) глаукомой. МЕТОДЫ. Объектом исследования служили 80 больных (80 глаз) с неоваскулярной глаукомой. Средний возраст больных составил 60,7±1,9 года. Все больные разделены на две группы по 40 пациентов в зависимости от метода оперативного лечения: 1-я группа (основная) - 40 пациентов, которым была выполнена глубокая склерэктомия с имплантацией ксеноколлагенового дренажа (ГСЭ с ИКД); 2-я группа (контрольная) - 40 пациентов, которым была сделана глубокая склерэктомия (ГСЭ). Методы исследования: офтальмологические, инструментальные, общеклинические, статистические. Наряду с обычным офтальмологическим обследованием проводили УБМ переднего отрезка глазного яблока на аппарате «Paradigm Model P60™ UBM» (USA). При помощи метода ультразвуковой биомикроскопии измеряли следующие параметры: фильтрационную подушку (ФП) - высоту, площадь, объем; склеральный лоскут (СЛ) - толщину; интрасклеральную полость (ИСП) - высоту, площадь, объем, а также угол передней камеры и глубину передней камеры. РЕЗУЛЬТАТЫ. При УБМ исследовании определены морфологические составляющие каждой структуры, ответственной за дренажную функцию глаза после операции ГСЭ с ИКД при неоваскулярной глаукоме. Исследованием показателей УБМ переднего отдела глаз в ближайшие и отдаленные сроки наблюдения выявлено, что предлагаемый метод ГСЭ с ИКД позволяет формировать вновь созданные пути оттока внутриглазной жидкости из передней камеры в супрацилиарное, субтеноново и субконъюнктивальное пространства. Это приводит к свободной фильтрации внутриглазной жидкости и является профилактикой рубцевания в зоне операции. ЗАКЛЮЧЕНИЕ. Использование методики УБМ при неоваскулярной глаукоме позволяет проводить мониторинг состояния дренажной системы глаза в послеоперационном периоде для своевременного обеспечения мер профилактики послеоперационной гипертензии

Клинико-функциональные результаты глубокой склерэктомии с имплантацией ксеноколлагенового дренажа у больных с неоваскулярной глаукомой

PURPOSE: The analysis of the results of deep sclerectomy with xenocollagen drainage implantation in patients with neovascular glaucoma during dynamic observation process. METHODS: The study included 80 patients (80 eyes) with neovascular glaucoma. All patients were divided into two groups depending on the surgical technique: group I (main group) included 40 patients after a deep sclerectomy with xenocollagen drainage implantation (DSE with XDI), group II (control group) - 40 patients after standard deep sclerectomy. All patients underwent a standard eye examination including visometry, perimetry, tonometry, tonography, biomicroscopy, gonioscopy, ophthalmoscopy, A-, B- scan («А/В 3D-OTI Scan 2000», Canada), ultrasound biomicroscopy («Paradigm Model P60™ UBM», USA). RESULTS: There was a significant difference in the persistence of post-operative hypotensive effect in the two studied groups. IOP decompensation rate in the control group exceed that in the study group (15 vs 3%, respectively) in long-term follow-up. Statistically significant positive changes in the visual functions were noted in the main group within 1 month after surgery, which remained stable during the 12 months of the observation period. The scarring of the filtration area was the main cause of glaucoma relapse in patients of the control group. CONCLUSION: According to the observation data, there was a statistically significant improvement in clinical and functional indicators in patients of the main group, which remained stable during late postoperative period, as compared to the control group. Using the modified operation technique with xenocollagen drainage implantation in the treatment of neovascular glaucoma promotes the stabilization of the clinical and functional results.ЦЕЛЬ. Анализ результатов глубокой склерэктомии с имплантацией ксеноколлагенового дренажа у пациентов с неоваскулярной глаукомой в процессе динамического наблюдения. МЕТОДЫ. Объектом исследования служили 80 больных (80 глаз) с неоваскулярной глаукомой. Средний возраст больных составил 60,7+1,9 года. Все больные были разделены на две группы по 40 пациентов в зависимости от метода оперативного лечения: 1-я группа (основная) - 40 пациентов, которым проведена глубокая склер-эктомия с имплантацией ксеноколлагенового дренажа, 2-я группа (контрольная) - 40 пациентов, которым была сделана глубокая склерэктомия. Всем пациентам проводили стандартное комплексное офтальмологическое обследование, включавшее проведение визометрии, периметрии, тонометрии, тонографии, биомикроскопии, гониоскопии, офтальмоскопии, А-, В-сканирования («А/В 3D-OTI Scan 2000», Канада), ультразвуковой биомикроскопии на аппарате «Paradigm Model P60™ UBM» (USA). РЕЗУЛЬТАТЫ. По результатам проведенных исследований была отмечена значительная разница в двух сравниваемых группах в сохранности гипотензивного эффекта, получаемого после антиглаукоматозной операции. В основной группе отмечалось преобладание случаев с компенсированным внутриглазным давлением (ВГД) по сравнению с контрольной. В отдаленные сроки наблюдения количество случаев декомпенсации ВГД в контрольной группе превосходили аналогичные в основной группе (15 против 3% соответственно). В основной группе была отмечена значительная статистически достоверная положительная динамика в отношении зрительных функций в течение 1-го мес. после операции с последующей стабилизацией до 12 мес. наблюдения. Основной причиной развития рецидивов глаукомы в контрольной группе явилось рубцевание зоны фильтрации. ЗАКЛЮЧЕНИЕ. У больных основной группы в отличие от контрольной, по данным динамического наблюдения, отмечается статистически значимое улучшение клинико-функциональных показателей и сохранение их в отдаленные сроки после операции. Использование модифицированного способа c имплантацией ксеноколлагенового дренажа в хирургическом лечении пациентов с неоваскулярной глаукомой дает стабильный гипотензивный эффект, что приводит к положительной динамике в отношении зрительных функций

Observation of a nuclear recoil peak at the 100 eV scale induced by neutron capture

Coherent elastic neutrino-nucleus scattering and low-mass Dark Matter detectors rely crucially on the understanding of their response to nuclear recoils. We report the first observation of a nuclear recoil peak at around 112 eV induced by neutron capture. The measurement was performed with a CaWO$_4$ cryogenic detector from the NUCLEUS experiment exposed to a $^{252}$Cf source placed in a compact moderator. The measured spectrum is found in agreement with simulations and the expected peak structure from the single-$\gamma$ de-excitation of $^{183}$W is identified with 3 $\sigma$ significance. This result demonstrates a new method for precise, in-situ, and non-intrusive calibration of low-threshold experiments

Collaborative filtering approach in adaptive learning

© 2016,International Journal of Pharmacy and Technology. All rights reserved.Nowadays an adaptive approach in education is gaining in popularity. But what does this adaptive approach mean? Adaptive learning (also known as Adaptive education) means that the education system has a personal approach for each student or for groups of students that fits to the students’ abilities. Teacher must pick up the most relevant topic for explanation,exercises and tests for such an education process. Also the teacher should adapt the order of learning topics for the current student. It is a very big and hard job,but machine learning algorithms can solve some of these tasks instead of teacher. What do we have? We have a set of lessons and students. In each step of the education process we select one lesson that fits best for a current student. This problem can be solved by recommender systems of algorithms. Recommender systems predict rating or “preference’” that a user would give to the item,and by similar way an adaptive education system also predict lessons “ratings” for user. In the paper we will define “rating” of lessons and what does it mean “fits the best”. Also we give some explanations of a chosen machine learning algorithm

MODERN METHODS FOR DETERMINATION OF COWS 'DISORDERS AND INFERTILITY

The article presents the results of ultrasound detection of cow calving and infertility. Annotation: In this article, the results of the use of ultrasound equipment in the diagnosis of pregnancy and infertility in cows

Collaborative filtering approach in adaptive learning

© 2016,International Journal of Pharmacy and Technology. All rights reserved.Nowadays an adaptive approach in education is gaining in popularity. But what does this adaptive approach mean? Adaptive learning (also known as Adaptive education) means that the education system has a personal approach for each student or for groups of students that fits to the students’ abilities. Teacher must pick up the most relevant topic for explanation,exercises and tests for such an education process. Also the teacher should adapt the order of learning topics for the current student. It is a very big and hard job,but machine learning algorithms can solve some of these tasks instead of teacher. What do we have? We have a set of lessons and students. In each step of the education process we select one lesson that fits best for a current student. This problem can be solved by recommender systems of algorithms. Recommender systems predict rating or “preference’” that a user would give to the item,and by similar way an adaptive education system also predict lessons “ratings” for user. In the paper we will define “rating” of lessons and what does it mean “fits the best”. Also we give some explanations of a chosen machine learning algorithm

Collaborative filtering approach in adaptive learning

© 2016,International Journal of Pharmacy and Technology. All rights reserved.Nowadays an adaptive approach in education is gaining in popularity. But what does this adaptive approach mean? Adaptive learning (also known as Adaptive education) means that the education system has a personal approach for each student or for groups of students that fits to the students’ abilities. Teacher must pick up the most relevant topic for explanation,exercises and tests for such an education process. Also the teacher should adapt the order of learning topics for the current student. It is a very big and hard job,but machine learning algorithms can solve some of these tasks instead of teacher. What do we have? We have a set of lessons and students. In each step of the education process we select one lesson that fits best for a current student. This problem can be solved by recommender systems of algorithms. Recommender systems predict rating or “preference’” that a user would give to the item,and by similar way an adaptive education system also predict lessons “ratings” for user. In the paper we will define “rating” of lessons and what does it mean “fits the best”. Also we give some explanations of a chosen machine learning algorithm