24 research outputs found
Captain Botsis’ Odyssey: On the Biography of the Russian Naval Commander
No full textThe article was submitted on 18.02.2022.В статье на основании изученных архивных данных РГАДА (фонда «Сношения с Венецией») приводятся новые сведения о малоизвестном периоде жизни Ивана Боциса (1700–1703), связанные с его переходом на русскую службу и непосредственно ему предшествовавшие. До сих пор нет полноценного жизнеописания выдающегося сподвижника Петра I, и настоящая публикация дополняет сведения о его полной опасностей жизни. Боцис сыграл важную роль в успехах России на Балтийском море в период Северной войны, поэтому заполнение лакуны в его биографии представляет особый интерес. Из вводимых в научный оборот сведений автор статьи черпает информацию о его деятельности в период, непосредственно следующий за Морейской войной (1684–1699). Хотя Боцис известен в первую очередь как российский флотоводец, на основании изученных данных становится очевидно, что у себя на родине он ярко проявил себя в военном деле как верный подданный своей новой родины – Венецианской республики, а по окончании конфликта и после завоевания Пелопоннеса Венецией, осыпанный почестями, пытался преуспеть и в мирной профессии – торговом деле, причем не столько как купец, сколько как координатор коммерческой активности своих соотечественников для частной и государственной пользы. Предприимчивый и даже авантюрный характер греческого капитана привел его, находившегося на пике своей карьеры, к конфликту с венецианскими и османскими властями, а надежда на русского царя как на избавителя греков от турецкого владычества и сочувствие его военным предприятиям в ходе Северной войны – к решению отказаться от привилегированного положения в своем отечестве и отправиться в полное опасностей путешествие в Россию, чтобы служить Петру I. Подробности его рискованного побега, описанные в челобитной на имя царя, напоминают отрывок из приключенческого романа. В приложениях к статье публику ются неизданные прежде документы – челобитная капитана Боциса и отпуск с указа царя Петра о выделении ему жалованья.Referring to archival data of the RGADA (Relations with Venice Fund), this article provides new information about the little-known period of life (1700–1703) of Ivan Botsis related to his transition to the Russian service and directly preceding it. Until now, there is no full-fledged biography of the outstanding associate of Peter the Great, and this study supplements the information about his life full of dangers. Botsis played a significant role in Russia’s success in the Baltic Sea during the Northern War, so filling the gap in his biography is of particular interest. Based on the information introduced into scholarly circulation, the author draws information about his activities immediately after the Morean War (1684–1699). Although he is primarily known as a Russian naval commander, based on the data studied, one may conclude that in his homeland, he proved himself to be a loyal subject of his new homeland, the Venetian Republic, in military affairs. And after the end of the conflict and the conquest of the Peloponnese by Venice, showered with honors, he tried to succeed in a peaceful profession, trading business, and not so much as a merchant but as a coordinator of the commercial activity of his compatriots for private and public benefit. At the peak of his career, the enterprising and even adventurous nature of the Greek captain led him to a conflict with the Venetian and Ottoman authorities, and the hope for the Russian tsar as the deliverer of the Greeks from Turkish rule and sympathy for his military enterprises during the Northern War made him abandon a privileged position in his homeland and embark on a perilous journey to Russia to serve Peter I. The details of his risky escape, described in a petition addressed to the tsar, resemble an excerpt from an adventure novel. The appendix of the article contains previously unpublished documents, i. e. a petition of Captain Botsis and an excerpt of a decree of Tsar Peter on the allocation of his salary
Oncology: breakthroughs in research and practice/ Information Resources Management Association, editor.
No full textChapter 8 is reprint of chapter originally published in Handbook of research on advanced techniques in diagnostic imaging and biomedical applications / [edited by] Themis P. Exarchos, Athanasios Papadopoulos, Dimitrios I. Fotiadis. 2009.Includes bibliographical references and index."This book features international perspectives on cancer identification, treatment, and management methodologies in addition to patient considerations and outlooks for the future"--Provided by publisherThe role of big data in radiation oncology : challenges and potentials / Issam El Naqa -- The evolving role of pharmacoinformatics in targeting drug-related problems in clinical oncology practice / Kevin Yi-Lwern Yap -- Computational grids : an introduction to potential biomedical uses and future prospects in oncology : neuro-oncology applications as a model for cancer sub-specialties / Ribhi Hazin, Ibrahim Qaddoumi, Francisco Pedrosa -- Improving access to oncology care for individuals and families through telehealth / Johanna den Duyf, Lars Apland, Valerie Ashworth, Arminee Kazanjian, Margarita Loyola, Sarah Robertson -- Image registration algorithms for applications in oncology / Katia Marina Passera, Luca Tommaso Mainardi -- Implementation of a computerized system in an oncology unit / Taxiarchis Botsis, Konstantinos Syrigos -- Participatory online platforms and the construction of citizen autonomy in health issues / Nuno Duarte Martins, Heitor Alvelos, Rita Espanha -- Image registration algorithms for applications in oncology / Katia Marina Passera, Luca Tommaso Mainardi -- Image registration for biomedical information integration / Xiu Ying Wang, Dagan Feng.1 online resourc
The fetus that is small for gestational age
No full textThe symmetric small for gestational age (SGA) fetus presents a complex management problem for the obstetrician, but the growth restriction affects morbidity and mortality at all stages of life. The differential diagnosis in symmetric growth aberration includes the constitutionally small fetus, the fetus with pathology, and the cases with incorrect dating of pregnancy. The ultrasonographic examination focuses in the detection of anomalies, signs of intrauterine infection, and serial assessment of fetal growth. Accuracy of fetal biometry may be improved by using individualized fetal growth curves. From the available surveillance tools, the uterine artery Doppler has a value in predicting poor perinatal outcome. Magnetic resonance imaging is also useful in the evaluation of anomalies. Cesarean section is not justified for all symmetric SGA fetuses that may carry a guarded prognosis
Response to hormonal treatment of young females with primary or very premature ovarian failure
No full textSonohysterography is superior to transvaginal sonography for the diagnostic approach of irregular uterine bleeding in women of reproductive age
No full textPurpose. To evaluate and compare the accuracy of transvaginal sonography (TVS) and sonohysterography (SHG) in the investigation of women of reproductive age presenting with irregular uterine bleeding (IUB). Methods. This prospective study included 104 women presenting with IUB. All patients underwent TVS, SHG, and hysteroscopy, during which endometrial biopsies were obtained and any endometrial mass was treated with hysteroscopic surgery. Statistical analysis was performed by calculating the sensitivity, specificity, and positive and negative predictive values of TVS and SHG in diagnosing endometrial polyp, submucous myoma and all endometrial pathologies (polyp, submucous myoma, endometrial hyperplasia, and endometrial carcinoma) with the histopathological report of the tissues obtained by hysteroscopy serving as the end point for the analysis. Results. The sensitivity, specificity, and positive and negative predictive values, respectively of TVS were 61.2%, 90.9%, 85.7%, and 72.5% for diagnosing endometrial polyps; 75.0%, 92.0%, 63.1%, and 95.3% for diagnosing submucous myomas; and 75.0%, 80.6%, 87.9%, and 63.0% for diagnosing any kind of pathology. The corresponding diagnostic values of SHG were 83.7%, 96.4%, 95.3%, and 86.9% for polyps; 87.5%, 98.9%, 93.3%, and 97.8% for submucous myomas; and 88.2%, 91.7%, 95.2%, and 80.5% for any kind of pathology. Conclusions. SHG showed superior sensitivity, specificity, and positive and negative predictive values compared with TVS in diagnosing intrauterine lesions in women of reproductive age with IUB. (c) 2006 Wiley Periodicals, Inc
Increased impedance in the uterine arteries of women with unexplained recurrent pregnancy loss
No full textRainfal - runoff simulation with machine learning methods
No full textThe conversion of precipitation to runoff is considered one of the most important research areas in the science of hydrology. The rainfall-runoff modelling provides considerable information about the hydrologic behavior of a watershed and given the probability of appearance of flood events which usually caused from extreme weather phenomena. The watershed management and the planning of hydraulic technical infrastructure depends on water influx, losses and final discharges. The main water influx in the watershed comes from precipitation (rainfall and snowfall) and the losses include infiltration, evapotranspiration and interception. Finally the remaining water from the difference between influx and losses is the water discharge. The complexity of the rainfall-runoff relationship and the numerous parameters it involves make it difficult to determine a mathematical function that will describe precisely the final runoff. Artificial intelligence and machine learning methods have the important advantage that they can approach the hydrological phenomena without requiring the exact knowledge of the physical parameters that affect the rainfall-runoff relationship. The fact that these methods have the ability to determine a relationship between inputs and target data makes them powerful computational tools for the simulation of complex water processes such as the transformation of precipitation to runoff. In the present dissertation four models were developed for the rainfall-runoff simulation, each one using a different machine learning approach. More specifically the author has developed models of artificial neural networks, Bayesian neural networks, support vector regression models and ensemble methods (Boosting and Bagging). We make a special mention to the Boosting learning algorithm, which was developed in order to be adaptable to the special characteristics of the meteorological and hydrological data, providing accurate predictions both of normal and extreme runoff values. This particular model named HydroBAL which is the initialism for “Hydrologic Boosting ALgorithm”. The models were applied to three watersheds for which data of precipitation and runoff were available. The application of the models showed that all of them simulate efficiently the rainfall-runoff relationship and their differences are minor. An important aspect of the models is their considerable accuracy in forecasting the extreme values. These values rarely appear in the time series data (inputs-targets) and diverge from the normal data range, so their prediction is very difficult. However these values are important because they are directly connected to flood events. Finally the effectiveness of these models at all levels of runoff simulation and forecasting create positive expectations for their generalization applicability.Το υδρολογικό φαινόμενο του μετασχηματισμού των κατακρημνίσεων σε απορροή αποτελεί ένα από τα σημαντικότερα πεδία έρευνας στην επιστήμη της υδρολογίας. Η μοντελοποίηση της σχέσης βροχόπτωσης-απορροής παρέχει αξιόλογες πληροφορίες για την υδρολογική συμπεριφορά μίας λεκάνης απορροής και την πιθανότητα εμφάνισης πλημμυρικών γεγονότων που προκαλούνται συνήθως από ακραία καιρικά φαινόμενα. Η διαχείριση των νερών σε επίπεδο λεκάνης απορροής και ο σχεδιασμός υδραυλικών τεχνικών έργων στηρίζονται στις εισροές, απώλειες και τελικές εκροές νερού, δηλαδή στις κατακρημνίσεις, στις απώλειες που δημιουργούνται από τη διήθηση, εξατμισοδιαπνοή και υδατοσυγκράτηση και τελικά στο νερό που απομένει και απορρέει. Η πολυπλοκότητα του φαινομένου και ο μεγάλος αριθμός παραμέτρων από τους οποίους επηρεάζεται καθιστούν δύσκολη την εύρεση μίας μαθηματικής συνάρτησης που να προσδιορίζει με ακρίβεια την τελική απορροή. Οι μέθοδοι τεχνητής νοημοσύνης και μηχανικής μάθησης έχουν το τεράστιο πλεονέκτημα της προσέγγισης υδρολογικών φαινομένων, όπως η σχέση βροχόπτωσης-απορροής, χωρίς να είναι απαραίτητη η γνώση των φυσικών παραμέτρων που την επηρεάζουν. Η ικανότητά τους να δημιουργούν μία σχέση μεταξύ δεδομένων εισόδων και στόχων τα καθιστά πανίσχυρα υπολογιστικά εργαλεία για την προσομοίωση των πολύπλοκων διεργασιών του νερού, όπως ο μετασχηματισμός των κατακρημνίσεων σε απορροή. Στην παρούσα ερευνητική εργασία αναπτύχθηκαν μοντέλα για την προσομοίωση της σχέσης βροχόπτωσης-απορροής που εντάσσονται σε τέσσερις διαφορετικές προσεγγίσεις μηχανικής μάθησης. Ειδικότερα δημιουργήθηκαν μοντέλα τεχνητών νευρωνικών δικτύων, Bayesian νευρωνικών δικτύων, παλινδρόμησης των μηχανών διανυσμάτων υποστήριξης και συνδυαστικών αλγορίθμων μάθησης (Boosting και Bagging). Ειδική αναφορά πρέπει να γίνει σε ένα μοντέλο συνδυαστικού αλγόριθμου μάθησης Boosting το οποίο κατασκευάστηκε με σκοπό να προσαρμόζεται στις ιδιαιτερότητες των μετεωρολογικών και υδρολογικών δεδομένων παρέχοντας τη μέγιστη δυνατή ακρίβεια πρόβλεψης κανονικών και ακραίων τιμών απορροής ταυτόχρονα. Για το συγκεκριμένο μοντέλο προτάθηκε η ονομασία HydroBAL που αποτελεί το ακρωνύμιο της έκφρασης «Hydrologic Boosting ALgorithm». Ως πεδίο εφαρμογής των μοντέλων επιλέχθηκαν τρεις λεκάνες απορροής, για τις οποίες υπήρχαν διαθέσιμα στοιχεία κατακρημνίσεων και απορροών. Η εφαρμογή των μοντέλων κατέδειξε τη μεγάλη αποτελεσματικότητα τους στην προσομοίωση του υδρολογικού φαινομένου και οι μεταξύ τους διαφορές διαπιστώθηκε ότι είναι πολύ μικρές. Αξιοσημείωτη είναι η καλή συμπεριφορά των μοντέλων στις πολύ ακραίες τιμές, οι οποίες παρατηρούνται σποραδικά στις χρονοσειρές των προτύπων εισόδων-στόχων και αποκλίνουν από το σύνηθες εύρος τιμών των δεδομένων με αποτέλεσμα η πρόβλεψη τους να είναι δύσκολη. Η σημαντικότητα του γεγονότος έγκειται στο ότι οι ακραίες τιμές είναι αυτές που δίνουν τα πλημμυρικά γεγονότα. Τελικά η αποτελεσματικότητα των μοντέλων σε όλα τα επίπεδα προσομοίωσης και πρόβλεψης της απορροής δημιουργεί θετικές προοπτικές για τη γενικευμένη εφαρμογή τους
Concentrations of Soluble Fas in Maternal Serum and Amniotic Fluid During Uncomplicated Pregnancies
No full textPredicting bulk density using pedotransfer functions for soils in the Upper Anthemountas basin, Greece
No full text
