The Genetic Landscape and Epidemiology of Phenylketonuria

Phenylketonuria (PKU), caused by variants in the phenylalanine hydroxylase (PAH) gene, is the most common autosomal-recessive Mendelian phenotype of amino acid metabolism. We estimated that globally 0.45 million individuals have PKU, with global prevalence 1:23,930 live births (range 1:4,500 [Italy]-1:125,000 [Japan]). Comparing genotypes and metabolic phenotypes from 16,092 affected subjects revealed differences in disease severity in 51 countries from 17 world regions, with the global phenotype distribution of 62% classic PKU, 22% mild PKU, and 16% mild hyperphenylalaninemia. A gradient in genotype and phenotype distribution exists across Europe, from classic PKU in the east to mild PKU in the southwest and mild hyperphenylalaninemia in the south. The c.1241A gt G (p.Tyr414Cys)-associated genotype can be traced from Northern to Western Europe, from Sweden via Norway, to Denmark, to the Netherlands. The frequency of classic PKU increases from Europe (56%) via Middle East (71%) to Australia (80%). Of 758 PAH variants, c.1222C gt T (p.Arg408Trp) (22.2%), c.1066-11G gt A (IVS10-11G gt A) (6.4%), and c.782G gt A (p.Arg261Gln) (5.5%) were most common and responsible for two prevalent genotypes: p.[Arg408Trp];[Arg408Trp] (11.4%) and c.[1066-11G gt A];[1066-11G gt A] (2.6%). Most genotypes (73%) were compound heterozygous, 27% were homozygous, and 55% of 3,659 different genotypes occurred in only a single individual. PAH variants were scored using an allelic phenotype value and correlated with pre-treatment blood phenylalanine concentrations (n = 6,115) and tetrahydrobiopterin loading test results (n = 4,381), enabling prediction of both a genotype-based phenotype (88%) and tetrahydrobiopterin responsiveness (83%). This study shows that large genotype databases enable accurate phenotype prediction, allowing appropriate targeting of therapies to optimize clinical outcome

Error detection and analysis in implementation of dynamic discrete inventory control models

Social sciences / Operations researchPredmet istraţivanja ove doktorske disertacije je pristup za detekciju i analizu grešaka u dinamiĉkim diskretnim spredšit modelima upravljanja zalihama, zasnovan na karakteristikama problema i naĉinu modeliranja. Postojeći pristupi za obezbeĊenje kvaliteta spredšit modela pokazali su se kao perspektivni, ali su nedovoljno ispitani i prilagoĊeni konkretnim problemima. Spredšit modeli korišćeni za evaluaciju ovih pristupa su najĉešće namenski kreirani, nisu realni primeri, relativno su mali i nisu prilagoĊeni kompleksnim problemima sa velikim brojem zavisnosti, tako da njihova primena u realnim okolnostima, kao i skalabilnost nisu potvrĊeni. Ovi pristupi, u najvećem broju sluĉajeva, zasnovani su na osobinama spredšit aplikacija, ali i idejama iz razliĉitih oblasti kao što su: softversko inţenjerstvo, operaciona istraţivanja i druge. Postojeći pristupi ne uzimaju u obzir karakteristike problema i naĉin modeliranja, koji znaĉajno utiĉu na nastanak, ali i mogućnost otkrivanja grešaka. Oni podrazumevaju da su izlazne vrednosti modela unapred poznate ili da korisnik moţe da obezbedi sve informacije o strukturi i ograniĉenima modela, što veoma ĉesto nije moguće. U skladu sa navedenim, opravdan je i neophodan razvoj novog pristupa, koji bi omogućio unapreĊenje kvaliteta spredšit modela za upravljanja zalihama, odnosno detekciju i analizu grešaka u njima. Osnovni cilj ove disertacije je kreiranje novog pristupa za obezbeĊenje višeg kvaliteta dinamiĉkih diskretnih modela upravljaĉkih problema operacionog menadţmenta, konkretno upravljanja zalihama, razvijenih u spredšitovima, razvojem algoritma za detekciju i analizu grešaka u navedenim modelima i utvrĊivanje njihovih uzroka na brz i efikasan naĉin.The subject of this doctoral thesis refers to error detection and debugging approach for dynamic discrete spreadsheet models of inventory control problems, based on problem characteristics and modelling method. Existing quality assurance approaches are very perspective, but insufficiently tested and adapted to actual problems. Spreadsheet models used for evaluation of those approaches are usually created just in that purpose and aren’t real practical examples. Experimental spreadsheet models are generally small and aren’t adjusted to complex problems with many dependencies. Applicability and scalability of existing quality assurance approaches has not been proven in wider context. In most cases, approaches are based on spreadsheet application characteristics and ideas from different scientific areas, such as: software engineering, operations reaserch and others. Existing approaches do not consider problem characteristics and modelling method, which significantly influence error occurrence and error detection. Those approaches consider that model output values are known in advance or that all informations about model structure and constraints are provided by user. Very often, both presumptions are unacceptable for models used in practice. In accordance with aforementioned, development of new improved quality assurance approach for inventory control spreadsheet models is justified and necessary. Main goal of this dissertation is to create new spreadsheet quality assurance approach for dynamic discrete models of operations management problems, specifically inventory control problems, developed in spreadsheets, by developing algorithm for error detection and quick and efficient debugging for mentioned models. Newly developed error detection and debugging approach for dynamic discrete spreadsheet models of inventory control problems, presented in this dissertation, is adapted to users and allows quality improvement of inventory control spreadsheet models

Error detection and analysis in implementation of dynamic discrete inventory control models

Social sciences / Operations researchPredmet istraţivanja ove doktorske disertacije je pristup za detekciju i analizu grešaka u dinamiĉkim diskretnim spredšit modelima upravljanja zalihama, zasnovan na karakteristikama problema i naĉinu modeliranja. Postojeći pristupi za obezbeĊenje kvaliteta spredšit modela pokazali su se kao perspektivni, ali su nedovoljno ispitani i prilagoĊeni konkretnim problemima. Spredšit modeli korišćeni za evaluaciju ovih pristupa su najĉešće namenski kreirani, nisu realni primeri, relativno su mali i nisu prilagoĊeni kompleksnim problemima sa velikim brojem zavisnosti, tako da njihova primena u realnim okolnostima, kao i skalabilnost nisu potvrĊeni. Ovi pristupi, u najvećem broju sluĉajeva, zasnovani su na osobinama spredšit aplikacija, ali i idejama iz razliĉitih oblasti kao što su: softversko inţenjerstvo, operaciona istraţivanja i druge. Postojeći pristupi ne uzimaju u obzir karakteristike problema i naĉin modeliranja, koji znaĉajno utiĉu na nastanak, ali i mogućnost otkrivanja grešaka. Oni podrazumevaju da su izlazne vrednosti modela unapred poznate ili da korisnik moţe da obezbedi sve informacije o strukturi i ograniĉenima modela, što veoma ĉesto nije moguće. U skladu sa navedenim, opravdan je i neophodan razvoj novog pristupa, koji bi omogućio unapreĊenje kvaliteta spredšit modela za upravljanja zalihama, odnosno detekciju i analizu grešaka u njima. Osnovni cilj ove disertacije je kreiranje novog pristupa za obezbeĊenje višeg kvaliteta dinamiĉkih diskretnih modela upravljaĉkih problema operacionog menadţmenta, konkretno upravljanja zalihama, razvijenih u spredšitovima, razvojem algoritma za detekciju i analizu grešaka u navedenim modelima i utvrĊivanje njihovih uzroka na brz i efikasan naĉin.The subject of this doctoral thesis refers to error detection and debugging approach for dynamic discrete spreadsheet models of inventory control problems, based on problem characteristics and modelling method. Existing quality assurance approaches are very perspective, but insufficiently tested and adapted to actual problems. Spreadsheet models used for evaluation of those approaches are usually created just in that purpose and aren’t real practical examples. Experimental spreadsheet models are generally small and aren’t adjusted to complex problems with many dependencies. Applicability and scalability of existing quality assurance approaches has not been proven in wider context. In most cases, approaches are based on spreadsheet application characteristics and ideas from different scientific areas, such as: software engineering, operations reaserch and others. Existing approaches do not consider problem characteristics and modelling method, which significantly influence error occurrence and error detection. Those approaches consider that model output values are known in advance or that all informations about model structure and constraints are provided by user. Very often, both presumptions are unacceptable for models used in practice. In accordance with aforementioned, development of new improved quality assurance approach for inventory control spreadsheet models is justified and necessary. Main goal of this dissertation is to create new spreadsheet quality assurance approach for dynamic discrete models of operations management problems, specifically inventory control problems, developed in spreadsheets, by developing algorithm for error detection and quick and efficient debugging for mentioned models. Newly developed error detection and debugging approach for dynamic discrete spreadsheet models of inventory control problems, presented in this dissertation, is adapted to users and allows quality improvement of inventory control spreadsheet models

What about the Chief Digital Officer? A Literature Review

Digital transformation is becoming more ingrained into everyday life and is a talking point among researchers today. It has been evident from the growing number of publications that have focused on various aspects of digital transformation in the last few years. Based on the number of papers published, one of them is of much less interest to researchers: the managerial role of Chief Digital Officer (CDO) inherent in digital transformation. Having this in mind, a systematic literature review was conducted to elucidate the CDO’s role, duties, and required competencies. In this paper, the results and conclusions of 22 papers are presented based on the search criteria outlined by the Web of Science and Scopus index databases. It is obvious that managing digital transformation is becoming more important as digital transformation affects almost every sector of business. The purpose of this study is to examine the position of Chief Digital Officer in terms of its position, role, and responsibilities as well as its necessary competencies. The results of this study could serve as the basis for future research in this area, which judging by the relevance of the topic, will certainly be more intensive

Control model for ground crew scheduling problem at small airports: case of Serbia

Present-day airline industry is quite a competitive field and crew scheduling represents one of the crucial problems due to significant impact on the airline’s cost. The crew scheduling problem is based on the assignment of crew members to operate different tasks of route. The main goal of this paper is to provide an analysis and a solution to one of the biggest problems detected on a small airport in the Serbia - the problem of ground crew scheduling. The paper presents the main characteristics, goals and limitations of a real-life problem identified at this small airport. In order to solve the problem, we developed a dynamic discrete simulation model. The model is developed in a spreadsheet environment of Microsoft Excel. Some of the main limitations found in the development of the model are strong constraints and multiple goals. The model presented in the paper is designed as a useful management tool for smaller airports and is aimed at the improvement of operative processes