Radial basis functions versus geostatistics in spatial interpolations

A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method. Actually there is no “best” method, universally valid. Choosing a particular method implies to make assumptions. The understanding of initial assumption, of the methods used, and the correct interpretation of the interpolation results are key elements of the spatial interpolation process. A powerful alternative to geostatistics in spatial interpolation is the use of the soft computing methods. They offer the potential for a more flexible, less assumption dependent approach. Artificial Neural Networks are well suited for this kind of problems, due to their ability to handle non-linear, noisy, and inconsistent data. The present paper intends to prove the advantage of using Radial Basis Functions (RBF) instead of geostatistics in spatial interpolations, based on a detailed analyze and modeling of the SIC2004 (Spatial Interpolation Comparison) dataset.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Radial basis functions versus geostatistics in spatial interpolations

A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method. Actually there is no “best” method, universally valid. Choosing a particular method implies to make assumptions. The understanding of initial assumption, of the methods used, and the correct interpretation of the interpolation results are key elements of the spatial interpolation process. A powerful alternative to geostatistics in spatial interpolation is the use of the soft computing methods. They offer the potential for a more flexible, less assumption dependent approach. Artificial Neural Networks are well suited for this kind of problems, due to their ability to handle non-linear, noisy, and inconsistent data. The present paper intends to prove the advantage of using Radial Basis Functions (RBF) instead of geostatistics in spatial interpolations, based on a detailed analyze and modeling of the SIC2004 (Spatial Interpolation Comparison) dataset.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Radial basis functions versus geostatistics in spatial interpolations

A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method. Actually there is no “best” method, universally valid. Choosing a particular method implies to make assumptions. The understanding of initial assumption, of the methods used, and the correct interpretation of the interpolation results are key elements of the spatial interpolation process. A powerful alternative to geostatistics in spatial interpolation is the use of the soft computing methods. They offer the potential for a more flexible, less assumption dependent approach. Artificial Neural Networks are well suited for this kind of problems, due to their ability to handle non-linear, noisy, and inconsistent data. The present paper intends to prove the advantage of using Radial Basis Functions (RBF) instead of geostatistics in spatial interpolations, based on a detailed analyze and modeling of the SIC2004 (Spatial Interpolation Comparison) dataset.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Infecția cu coronavirus de tip nou (COVID-19): protocol clinic național (ediția VIII) PCN-371

Acest protocol este elaborat și revizuit sistematic de grupul de lucru al Ministerului Sănătății al Republicii Moldova (MS), constituit din reprezentanți ai Comisiilor de specialitate ale MS, angajați ai Universității de Stat de Medicină și Farmacie „Nicolae Testemițanu” și Agenției Naționale pentru Sănătate Publică. Documentul nu este unul exhaustiv și se bazează pe recomandările actuale ale Organizației Mondiale a Sănătății privind infecția cu coronavirus de tip nou (COVID-19) și alte date disponibile la acest moment. Protocolul clinic național servește drept referință pentru elaborarea protocoalelor clinice instituționale, reieșind din posibilitățile reale ale fiecărei instituții medico-sanitare

Infecția cu coronavirus de tip nou (COVID-19): protocol clinic naţional (ediția VII) PCN-371

Acest protocol este elaborat și revizuit sistematic de grupul de lucru al Ministerului Sănătății al Republicii Moldova (MS), constituit din reprezentanți ai Comisiilor de specialitate ale MS, angajați ai Universității de Stat de Medicină și Farmacie „Nicolae Testemițanu” și Agenției Naționale pentru Sănătate Publică. Documentul nu este unul exhaustiv și se bazează pe recomandările actuale ale Organizației Mondiale a Sănătății privind infecția cu coronavirus de tip nou (COVID-19) și alte date disponibile la acest moment. Protocolul clinic național servește drept referință pentru elaborarea protocoalelor clinice instituționale, reieșind din posibilitățile reale ale fiecărei instituții medicosanitare

Radial Basis Functions Versus Geostatistics in Spatial Interpolations

Abstract. A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method. Actually there is no "best" method, universally valid. Choosing a particular method implies to make assumptions. The understanding of initial assumption, of the methods used, and the correct interpretation of the interpolation results are key elements of the spatial interpolation process. A powerful alternative to geostatistics in spatial interpolation is the use of the soft computing methods. They offer the potential for a more flexible, less assumption dependent approach. Artificial Neural Networks are well suited for this kind of problems, due to their ability to handle non-linear, noisy, and inconsistent data. The present paper intends to prove the advantage of using Radial Basis Functions (RBF) instead of geostatistics in spatial interpolations, based on a detailed analyze and modeling of the SIC2004 (Spatial Interpolation Comparison) dataset

STUDY OF THE BODY VALUES IN THE CONTEXT OF THE NEW MASTER PROGRAMS. A COMPARATIVE ANALYSIS OF THE STUDENTS FROM THREE MASTER PROGRAMS OF PHYSICAL EDUCATION AND SPORT FACULTY CLUJ-NAPOCA

With the establishment of new Master programs, the option of students includes a wide range which reflects, in most cases, some tangible values. When choose follow the Masters program, not all students are aware, not all of them take it into account. The Magister students came from different social, cultural and geographical backgrounds. Their basic body values have different aspects like a social, cultural and geographical reflection. Those reflections differ from one society to another from one individual to another. The type of body values dominant in Romanian society are mostly those which are connected to the hedonistic, esthetic and emotional dimension of the body itself. Therefore, our study aims to prioritize and emphasize those values that Master students of the Faculty of Physical Education and Sport of the Babes-Bolyai University in Cluj-Napoca are endowed as part of their studies in order to facilitate their integration into social life, highlighted by the changes of modern society post EU accession. Studiu privind valorile corporale în contextul noilor programe Masterale. Analiza comparativă a studenților din cadrul a trei programe Master a Facultății de Educație Fizică și Sport din Cluj-Napoca. Odată cu înființarea noilor programe Masterale, opțiunea studenților cuprinde o paletă largă ce reflectă, în majoritatea cazurilor, anumite valori corporale de care, în momentul alegerii programului masteral de urmat, nu toți studenții sunt conștienți și de care nu toți țin cont. Studenții masteranzi provin din medii sociale și geografice diferite în care valorile corporale capătă aspecte diferite, de la o societate la alta de la un individ la altul. În general, în România, tipologia valorilor umane este asociată cu dimensiunile hedonistice, estetice și emoționale ale corpului uman. Tocmai de aceea, studiul nostru are ca obiectiv să ierarhizeze și să scoată în evidență acele valori cu care studenții masteranzi ai Facultății de Educație Fizică și Sport ai Universității Babeș-Bolyai din Cluj-Napoca sunt înzestrați ca parte integrantă a formării caracterului lor ce le va facilita integrarea în structurile vieții sociale puse sub influența transformărilor societății moderne post aderare la Uniunea Europeană.  Cuvinte Cheie: studenți, master, programe, educați

ORPHAcodes use for the coding of rare diseases: comparison of the accuracy and cross country comparability

Abstract Background Estimates of rare disease (RD) population impact in terms of number of affected patients and accurate disease definition is hampered by their under-representation in current coding systems. This study tested the use of a specific RD codification system (ORPHAcodes) in five European countries/regions (Czech Republic, Malta, Romania, Spain, Veneto region-Italy) across different data sources over the period January 2019-September 2021. Results Overall, 3133 ORPHAcodes were used to describe RD diagnoses, mainly corresponding to the disease/subtype of disease aggregation level of the Orphanet classification (82.2%). More than half of the ORPHAcodes (53.6%) described diseases having a very low prevalence (< 1 case per million), and most commonly captured rare developmental defects during embryogenesis (31.3%) and rare neurological diseases (17.6%). ORPHAcodes described disease entities more precisely than corresponding ICD-10 codes in 83.4% of cases. Conclusions ORPHAcodes were found to be a versatile resource for the coding of RD, able to assure easiness of use and inter-country comparability across population and hospital databases. Future research on the impact of ORPHAcoding as to the impact of numbers of RD patients with improved coding in health information systems is needed to inform on the real magnitude of this public health issue