Дисертації з теми "Defect prediction model"

Context. Quality assurance plays a vital role in the software engineering development process. It can be considered as one of the activities, to observe the execution of software project to validate if it behaves as expected or not. Quality assurance activities contribute to the success of software project by reducing the risks of software’s quality. Accurate planning, launching and controlling quality assurance activities on time can help to improve the performance of software projects. However, quality assurance activities also consume time and cost. One of the reasons is that they may not focus on the potential defect-prone area. In some of the latest and more accurate findings, researchers suggested that quality assurance activities should focus on the scope that may have the potential of defect; and defect predictors should be used to support them in order to save time and cost. Many available models recommend that the project’s history information be used as defect indicator to predict the number of defects in the software project. Objectives. In this thesis, new models are defined to predict the number of defects in the classes of single software systems. In addition, the new models are built based on the combination of product metrics as defect predictors. Methods. In the systematic review a number of article sources are used, including IEEE Xplore, ACM Digital Library, and Springer Link, in order to find the existing models related to the topic. In this context, open source projects are used as training sets to extract information about occurred defects and the system evolution. The training data is then used for the definition of the prediction models. Afterwards, the defined models are applied on other systems that provide test data, so information that was not used for the training of the models; to validate the accuracy and correctness of the models Results. Two models are built. One model is built to predict the number of defects of one class. One model is built to predict whether one class contains bug or no bug.. Conclusions. The proposed models are the combination of product metrics as defect predictors that can be used either to predict the number of defects of one class or to predict if one class contains bugs or no bugs. This combination of product metrics as defect predictors can improve the accuracy of defect prediction and quality assurance activities; by giving hints on potential defect prone classes before defect search activities will be performed. Therefore, it can improve the software development and quality assurance in terms of time and cost

Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put right. Defect Prediction is an important part of Software Engineering. It allows developers to prioritise the code that needs to be inspected when trying to reduce the number of defects in code. A small change in the number of defects found will have a significant impact on the cost of producing software. Aims. The aim of this dissertation is to investigate the factors which a ect the performance of defect prediction models. Identifying the causes of variation in the way that variables are computed should help to improve the precision of defect prediction models and hence improve the cost e ectiveness of defect prediction. Methods. This dissertation is by published work. The first three papers examine variation in the independent variables (code metrics) and the dependent variable (number/location of defects). The fourth and fifth papers investigate the e ect that di erent learners and datasets have on the predictive performance of defect prediction models. The final paper investigates the reported use of di erent machine learning approaches in studies published between 2000 and 2010. Results. The first and second papers show that independent variables are sensitive to the measurement protocol used, this suggests that the way data is collected a ects the performance of defect prediction. The third paper shows that dependent variable data may be untrustworthy as there is no reliable method for labelling a unit of code as defective or not. The fourth and fifth papers show that the dataset and learner used when producing defect prediction models have an e ect on the performance of the models. The final paper shows that the approaches used by researchers to build defect prediction models is variable, with good practices being ignored in many papers. Conclusions. The measurement protocols for independent and dependent variables used for defect prediction need to be clearly described so that results can be compared like with like. It is possible that the predictive results of one research group have a higher performance value than another research group because of the way that they calculated the metrics rather than the method of building the model used to predict the defect prone modules. The machine learning approaches used by researchers need to be clearly reported in order to be able to improve the quality of defect prediction studies and allow a larger corpus of reliable results to be gathered.

An analytical model based on linear superposition of momentum defects is developed for predicting depth-averaged velocity distributions in trapezoidal and rectangular channels with submerged and unsubmerged rigid cylinders (i.e., simulated vegetation). The model is founded on wake theory and there is an existing model that is similar except for using linear superposition of velocity defects. The momentum defects and velocity defects supposition models both require a criterion for deciding when wakes created by the rigid cylinders will be considered completely dissipated (cutoff criterion). Comparing the momentum defects and velocity defects models required developing a new cutoff criterion that would have an equivalent effect when applied to either model. The chosen cutoff criterion considers a wake to be completely dissipated when umax (the maximum defect caused by a cylinder) is less than or equal to 0.2 m/s. Predicted depth-averaged velocities from both models were compared to measured values. The predicted values differed from the measured values by less than 20% in general. It was concluded that the depth-averaged velocity predictions from the linear superposition of momentum defects model and the linear superposition of velocity defects model do not differ significantly and that the greatest advantage of the velocity defects model is that compared to the momentum defects model it is simpler to implement.

Development of a Knowledge-Based Decision Support System to predict ancestry of the remains of missing World War Two soldiers in South-East Asia. By utilizing biological and historical information provided by the user, ancestry is assigned based on complex statistical analyses searching for distinctive patterns in the DNA that distinguish between the Australian and Japanese populations. Important features taken into consideration are the detection of a rare event, the effect of sample size and the impact of natural variation.

Impulsivity is a hallmark of two of the three DSM-IV ADHD subtypes and is associated with myriad adverse outcomes. Limited research, however, is available concerning the mechanisms and processes that contribute to impulsive responding by children with ADHD. The current study tested predictions from two competing models of ADHD--working memory (WM) and behavioral inhibition (BI)--to examine the extent to which ADHD-related impulsive responding was attributable to model-specific mechanisms and processes. Children with ADHD (n = 21) and typically developing children (n = 20) completed laboratory tasks that provided WM (domain-general central executive (CE), phonological/visuospatial storage/rehearsal) and BI indices (stop-signal reaction time (SSRT), stop-signal delay, mean reaction time). These indices were examined as potential mediators of ADHD-related impulsive responding on two diverse laboratory tasks used commonly to assess impulsive responding (CPT: continuous performance test; VMTS: visual match-to-sample). Bias-corrected, bootstrapped mediation analyses revealed that CE processes significantly attenuated between-group impulsivity differences, such that the initial large-magnitude impulsivity differences were no longer significant on either task after accounting for ADHD-related CE deficits. In contrast, SSRT partially mediated ADHD-related impulsive responding on the CPT but not VMTS. This partial attenuation was no longer significant after accounting for shared variance between CE and SSRT; CE continued to attenuate the ADHD-impulsivity relationship after accounting for SSRT. These findings add to the growing literature implicating CE deficits in core ADHD behavioral and functional impairments, and suggest that cognitive interventions targeting CE rather than storage/rehearsal or BI processes may hold greater promise for alleviating ADHD-related impairments.
ID: 030646239; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 41-55).
M.S.
Masters
Psychology
Sciences
Psychology Clinical

Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica, com antecedência de até três dias para o período seco. Para o úmido, este fato foi observado apenas para o primeiro dia de antecedência. A concordância entre os valores estimados pelo modelo e a partir da estação para o método de Penman-Monteith foi muito baixa, em conseqüência do desempenho do modelo de previsão do tempo em prognosticar o saldo de radiação e a velocidade do vento.
The performance of a numeric weather forecast model (GFS- Forecast System, former AVN - AvatioN model, National Center for Environmental Prediction-NCEP) was evaluated for predicting weather variables, like air temperature and vapor pressure deficit, net radiation and wind speed, as well as reference evapotranspiration calculated by Thornthwaite (1948) and Penman-Monteith (Allen et al., 1948) methods, by the comparison with data obtained by an automatic weather station, in Piracicaba, State of São Paulo, Brazil. Temperature and vapor pressure deficit were the variables predicted with the best accuracy, with a "very good" and "good" performance, according to the index of confidence proposed by Camargo and Sentelhas (1997), for the maximum of four and three days in advance, respectively, during the dry season. For the wet season, only vapor pressure deficit was predicted with a "good" performance of the model. The predictions of net radiation and wind speed were very poor for both seasons. As the weather forecast model predicted temperature well, ETo estimated by Thornthwaite method showed a good agreement with ETo values estimated by observed data from the weather station, with till three days in advance for the dry season. For the wet season, such agreement was observed just for one day in advance. When ETo estimated by Penman-Monteith method with data from the weather forecast model and from weather station were compared any agreement was observed, which was caused by the poor performance of the numeric weather forecast model to predict net radiation and wind speed.

The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In the next part of the thesis there are results of testing of selected methods presented. For logopaedia speech defects recognition algorithms are used in order to extract the features MFCC and PLP. The segmentation of words to speech sounds is performed on the base of Differential Function method. The extracted features of a sound are classified into either a correct or an incorrect pronunciation class with one of tested methods of pattern recognition. To classify the features, the k-NN, SVN, ANN, and GMM methods are tested.

Le développement du séquençage de l’ADN à haut débit a grandement facilité le criblage de variations génétiques dans le génome des patients. Désormais, l’un des principaux défis de la génétique médicale n’est donc plus la détection des variations, mais leur interprétation fonctionnelle et clinique. Récemment, nous avons montré, à l’aide de tests fonctionnels basés sur l’utilisation de minigènes, que bien que le nombre de mutations d’épissage, et en particulier celles qui affectent sa régulation, est actuellement sous-estimé, l’effet de ces variations pourrait être dorénavant prédit à l’aide d’outils bioinformatiques spécifiques. Nous avons ainsi étendu l’évaluation du caractère prédictif de ces quatre nouvelles approches bioinformatiques par une étude comparative des scores générés par ces approches avec des données expérimentales obtenues pour un total d’environ 1200 variations exoniques. Nos travaux ont ainsi démontré la fiabilité de ces approches, utilisées seules ou en combinaison, et ont permis de proposer des recommandations quant à leur utilisation en tant qu’outils de filtration pour prioriser les variations à analyser dans des tests fonctionnels axés sur l’épissage. Néanmoins, une analyse mutationnelle exhaustive ciblée sur l’exon 7 de MLH1, a mis en évidence l’échec apparent de ces approches, pourtant validées par des études menées sur l’exon 7 de BRCA2, l’exon 10 de MAPT et l’exon 5 de MSH2, laissant suggérer que ces méthodes pourraient ne pas s’appliquer de manière équivalente à tous les exons et/ou à tous les gènes. En effet, nous avons montré que cet exon était doté de caractéristiques particulières, i.e. de sites d’épissage remarquablement forts, lui conférant une résistance totale aux mutations de régulation d’épissage et mettant en échec les outils de prédictions. Ces données contribuent à mieux déterminer les limitations de ces outils bioinformatiques tout en contribuant à leur amélioration. En dépit de ces avancées, l'évaluation de la pathogénicité des mutations d'épissage reste complexe, en particulier celles conduisant à des anomalies d'épissage en phase et/ou partielles. En utilisant, comme modèle d’étude, des variations à l’origine du saut partiel de l’exon 3 de BRCA2, nos résultats ont révélé que l’activité tumeur-suppressive de BRCA2 tolère une réduction substantielle du niveau d’expression, étant donné qu’un allèle produisant jusqu’à 70% de transcrit codant une protéine déficiente n’est pas nécessairement associé à un risque élevé de développer un cancer. L’ensemble de ces données a d’importantes implications dans le diagnostic moléculaire et la prise en charge des patients et de leurs apparentés, avec un bénéfice direct pour les familles évocatrices d’une prédisposition héréditaire et devrait contribuer à l’interprétation de VSI identifiées par séquençage à haut débit dans toute autre pathologie d’origine génétique
The development of high-throughput DNA sequencing has greatly facilitated the screening of genetic variations within patient genome. Henceforth, one of the main challenges in medical genetics is no longer the detection of variations, but their functional and clinical interpretation. Recently, we showed by using splicing reporter minigene assays, that although splicing mutations, and in particular those affecting its regulation, are more prevalent than initially estimated, they could now be predicted by using dedicated bioinformatics tools. We thus extended the evaluation of the predictive power of these four newly developed computational approaches by a comparative study of the scores obtained by these approaches with experimental data for a total of about 1200 exonic variations. Our findings have demonstrated the reliability of these approaches, used alone or in combination, and allow to offer recommendations for their use as a filtration tool to prioritize the variations to be analysed as a priority in splicing-dedicated functional assays. Nevertheless, an exhaustive mutational analysis targeting MLH1 exon 7, has highlighted the apparent failure of these approaches, yet validated by studies focused on BRCA2 exon 7, MAPT exon 10 and MSH2 exon 5, suggesting that these methods might not be equivalently applicable to all exons and/or genes. Indeed, we have shown that this exon has particular characteristics, i.e. remarkably strong splice sites, conferring it a total resistance to splicing regulation mutations and defeating prediction tools. These findings help to better determine the limitations of these bioinformatics tools while contributing to their improvement. In spite of these advances, the pathogenicity assessment of splicing mutations remains complicated, especially of those leading to in-frame and/or partial splicing anomalies. By using variant-induced partial BRCA2 exon 3 skipping as a model system, we showed that BRCA2 tumor suppressor function tolerates a substantial reduction in expression level, as BRCA2 allele producing as much as 70% of transcript encoding deficient protein may not necessarily confer high-risk of developing cancer. Altogether, these data have important implications in the molecular diagnosis and clinical management of patients and their relatives, with a direct benefit for hereditary cancer-suspected families and should contribute to the interpretation of VSI identified by high throughput sequencing in any other genetic disease

碩士
元智大學
工業工程與管理學系
96
It has highly correlation between TEG(Test Element Group) electric measure value on Array’s process and Array NG Rank what is abnormal defect on Cell’s process. The panel be judged as Array NG Rank 4 will be scrapped.It can reduce the cost waste resulted from unnecessary material be input in scrapped panel if we can predict what’s Rank in Array NG precisely.Therefore,the article’ topical subject research in construct prediction model of the abnormal defect by analyzing electric character on Array’s process.It will be divide into two parts to proceed. In first phase,by different method:Multinomial Logit model and a decision tree algorithm C5.0 to find out the better predictive rate in predicting Array NG’s Rank and induce the key factor from the inside of TEG(Test Element Group) electric measure value: Ion,Ioff,Vth, Ufe,Idl,Gm,Rgl,Rs1,Rc1,Rge1,Rge2, Rsd1 and Rsd2. In second phase,i will construct prediction model of the abnormal defect by analyzing electric character on Array’s process to understand that TEG electric measure value influence the degree of Array NG Rank’s judgement. According to analysis result in article,in first phase, the models were evaluated based on the predictive accuracy rate for test sets.The Multinomial Logit model had better predictive rate(96.17%) than the predictive rate(96.10%) of C5.0 model and the key factors included Vth,Rs1,Rge2,Rsd1 and Rsd2.In second phase,the prediction model be constructed by analyzed the correlation between TEG electric measure value and Array NG Rank can have higher ability to predict Array NG Rank judged as 4.The ability can reach to 96.15% and it can save the cost includes NT 840,600 dollars and 45 hours in work per month.

Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiros
Software defects are the critical threat to increase life-cycle costs, delay project schedule, reduce the reliability of software systems, and even cause catastrophic disasters. Since the concept of software engineering has been proposed, people have developed many technologies to prevent the introduction of software defects. However, the effects are not optimistic. So far although tremendous resources have been devoted to software testing, defects are still the major threat to the reliability of software systems. The proactive defense against software defects can be a promising philosophy to reduce costs and improve reliability. However, conventional relevant technologies such as defect prediction and defect prevention can hardly prevent the introduction of software defects. It is the time to prompt a thorough reflection on the conventional ways: the conventional technologies trend to focus on the improvement of software process, but ignore the underlying mechanisms that cause software defects. Essentially, programs are the “expression” of human thoughts, while software defects are mainly caused by human cognitive failures. Conventional software engineering technologies are designed to control and improve the process of software production, rather than directly impact on the key factor---programmer’s cognition, thus, they can only influence software quality indirectly. Once we have failed to capture the mechanisms of software defects, we can neither predict them precisely, nor prevent them fundamentally. To address these gaps, this thesis proposes the concept of defect defense based on human error mechanisms. Logically, prediction and prevention should be interconnected, since only when an event can be predicted, it can be prevented. That is to say, prediction normally provides implications for prevention. However, due to the omission of mechanisms, the conventional defect prediction is unable to achieve sufficient accuracy at early stages of software development. Thus, conventional predictions can provide little information for defect prevention. That’s why the conventional defect prediction and prevention are completely irrelevant. In this thesis, bonded by the human error mechanisms, prediction and prevention are integrated, to defend against the introduction of software defects together. The research is first carried out by summarizing the relevant research about program design cognition, with an integrated cognition model of program design constructed. Then integrate the classical theories of human errors with the domain characteristics of programming, a base of human error modes for software defects is developed. Based on the integrated cognition model and human error modes, three approaches are proposed, designed and validated. “Conventional defect prevention (DP) based on the structural taxonomy of root causes” is an improved defect prevention approach in the framework of conventional DP. Conventional DP framework is effective in preventing defects due to process problems. However, it is strongly depended on experts’ experiences and brain storms, which have limit its applications in small companies. Even for companies at high process maturity levels, it is hard to replicate the benefits of conventional DP. A structural taxonomy of root causes is proposed and validated, and the core knowledge required for root cause analysis is solidified in the knowledge base. An application case has been carried out, results show that with the assistance of the taxonomy and knowledge base, the small company at the CMM initial level can implement conventional DP effectively. “Defect Prevention by Improving Software Developers’ Meta-cognitive Ability to Prevent Human Errors” (HEDP) is an approach in the framework that is completely different from conventional DP. This approach is proposed for the reason that, individual cognitive failures are the main cause of software defects, but conventional DP has little power in affecting individual’s cognitive performances. HEDP aims to prevent defects by improving programmers’ awareness and regulation abilities under error-prone situations. HEDP is designed in the framework of meta-cognition, including two stages. The first stage concerns meta-cognitive training on human error knowledge and the second stage aims to build programmers’ experience in meta-cognitive regulation. The knowledge training consists of knowledge about program designing cognition, human error mechanisms, and error prevention strategies. The meta-cognitive regulation experience is built by the reflection in the course of problem solving and self-reviews after the defects are detected. Two application cases are studied, with the self-assessment and defect data collected. Both kinds of results show that, HEDP is effective in improving programmers’ meta-cognitive ability to prevent software defects. Furthermore, HEDP is independent of process maturity, that is to say, all organizations can implement HEDP, no matter at CMM level 5 or level 1. Most important of all, HEDP can be used to guide any programmer pursuing self-improvement in human error prevention, no matter experts or novices. “Software defect prediction based on human error mechanisms”(HEFP) is an new approach to predict the location and format of defects at the early phases of software development, i.e. phases of requirement analysis and design. Such prediction is implemented by human error scenario analysis. A controlled experiment has been designed to validate HEFP and provides empirical evidences for relevant concepts. The results show that, HEFP has predominant advantages in predicting coincident defects. HEFP has precisely predicted the location and format of 88.9% coincident defects, which are committed by 96.5% of the subjects who has committed coincident defects. Meanwhile, what the HEFP predicts are the defects at high risk. Though the number of defects predicted by HEFP only constitutes 30.8% of the total defects, but they are committed by 78.6% subjects who commit any error. In comparison, conventional predictors based on program metrics can only account for 26.8% variance of the total defects, and they can not output the accurate locations and formats of the defects. Results show that HEFP performs much better than prediction models based on program metrics, both in the accuracy and efficiency. Most important of all, HEFP can perform at the early phases of the software development, thus it can provide implications for defect prevention. In summary, the two sets of basic theories and three approaches works together, constituting the comprehensive system to defend against software defects.

Software defects entail a highly-significant cost penalty in lost productivity and post-release maintenance. Early defect prevention and removal techniques can substantially enhance the profit realized on software products. The motivation for software quality improvement is most often expressed in terms of increased customer satisfaction with higher product quality, or more generally, as a need to position SAP Inc as a leader in quality software development. Thus, knowledge about how many defects to expect in a software product at any given stage during its development process is a very valuable asset. The great challenge, however, is to devise efficient and reliable prediction models for software defects. The first problem addressed in this thesis is software reliability growth modeling. We introduce an anisotropic Laplace test statistic that not only takes into account the activity in the system but also the proportion of reliability growth within the model. The major part of this thesis is devoted to statistical models that we have developed to predict software defects. We present a software defect prediction model using operating characteristic curves. The main idea behind our proposed technique is to use geometric insight in helping construct an efficient prediction method to reliably predict the number of failures at any given stage during the software development process. Our predictive approach uses the number of detected faults in the testing phase. Data from actual SAP projects is used to illustrate the much improved performance of the proposed method in comparison with existing prediction approaches

碩士
國立高雄第一科技大學
運籌管理所
95
In the TFT-LCD (Thin-film transistor liquid-crystal display) industry prediction of production cycle time is critical for customer orders. To promptly respond customer request about delivery date and time, manufacturers have to approximate production cycle time as precisely as possible. However, the prediction of production cycle time is especially difficult on the defect inspection station because this process is purely manual operation. Besides, the defect inspection station is also the major window of TFT-LCD manufacturing department to reply the stocking information to the production control department and sales department. Hence, it’s important for the TFT-LCD module manufacturers to have good prediction tools for defect inspection time because it will affect the delivery reply time of customer order and the percentage rate of on time delivery. This research applies classical time series approach and multiple regression analysis to establish the prediction models for defect inspection cycle time in TFT-LCD module process. Then an accuracy comparison between the above two prediction methods is discussed through case study. We found out that the model of multiple regression analysis has better prediction power than the model of classical time series. Our analysis also showed the major factors effecting the defect inspection. Manufacturers in TFT-LCD industry could refer our finding to build their own prediction module of defect inspection cycle time for better customer delivery response.

碩士
國立高雄應用科技大學
土木工程與防災科技研究所
91
Abstract The geomembrane is partially damaged in the landfill construction and operation due to its thin thickness (60 mils). This damage was called the defect. The leachate in landfill may leakage through the defect, then polluting the groundwater. At present, the up-gradient and down-gradient wells are installed to monitor whether the quality of background water does change at regular frequencies. However, the leak detection of the monitoring well could not provide an early warning. The purpose of the research is to set up a model for simulating and predicting the leakage through the defects in the composite liner. The model not only can predict the time of breakthrough for the leakage in a mudstone liner, but also can provide an early warning for landfill operation and management. The scope of work is to monitor the in-situ leakage in a composite liner and to predict the leakage due to geomembrane defects. In this study, the test pad was used to simulate the leakage through the composite liner. The electrical sensors were installed at the same depths and positions in three different simulated defects. Then, the mathematical model (FEMWATER) was used to analyze the monitoring data for adjusting the parameters of the early warning model. The result shows that an early warning model has well established and has well accepted on the leakage simulation. As the leakage passed through 3-cm defect in diameter, the time of breakthrough from 30-cm and 60-cm thick liner is approximately 11.84 years and 21.45 years, respectively. For 6-cm defect in diameter, the time of breakthrough from 30-cm and 60-cm thick liner is 5.75 years and 11.01 years, respectively. It can be concluded that the diameter of defect has a decisive influence on the time that the leakage passed through the mudstone liner under constant-head condition.

Caregivers of children with autism spectrum disorder (ASD) or attention-deficit/hyperactivity disorder (ADHD) experience heightened levels of distress. Further, research on Model A has demonstrated that caregiver-centric variables are significant predictors of distress in caregivers of children with ASD (Bones et al., 2019; Falk et al., 2014), ADHD, and typically developing children (Scott, 2018). The current study aimed to replicate this research, and further, to validate Model A in caregivers of children with comorbid ASD and ADHD. Participants were 205 caregivers of 5- to 17-year-old children with ASD (n = 41), ADHD (n = 44), comorbid ASD and ADHD (n = 53), or who were typically developing (n = 67). Participants completed an online questionnaire with measures of caregiver distress, socio-economic support, maladaptive caregiver cognitions, child externalised behaviour, and child social and interpersonal deficits. Using hierarchical regression analyses, Model A was validated by significantly predicting distress in caregivers of children with ADHD, and comorbid ASD and ADHD. Results indicated that child social and interpersonal deficits, social support, and parental locus of control predicted caregiver distress to the greatest extent in each group. It is argued that caregiver distress be considered a key target for improving outcomes for families impacted by ASD and/or ADHD

Attention-Deficit/Hyperactive Disorder is a well studied but poorly understood disorder. Given that the underlying neurological mechanisms involved in the disorder have yet to be established, diagnosis is dependent upon behavioural markers. However, recent research has begun to associate a dopamine system dysfunction with ADHD; though, consensus on the nature of dopamine’s role in ADHD has yet to be established. Here, I use a computational modelling approach to investigate two opposing theories of the dopaminergic dysfunction in ADHD. The hyper-active dopamine theory posits that ADHD is associated with a midbrain dopamine system that produces abnormally large prediction errors signals; whereas the dynamic developmental theory argues that abnormally small prediction errors give rise to ADHD. Given that these two theories center on the size of prediction errors encoded by the midbrain dopamine system, I have formally investigated the implications of each theory within the framework of temporal-difference learning, a reinforcement learning algorithm demonstrated to model midbrain dopamine activity. The results presented in this thesis suggest that neither theory provides a good account for the behaviour of children and animal models of ADHD. Instead, my results suggest ADHD is the result of asymmetrically effective reinforcement learning signals encoded by the midbrain dopamine system. More specifically, the model presented here reproduced behaviours associated with ADHD when positive prediction errors were more effective than negative prediction errors. The biological sources of this asymmetry are considered, as are other computational models of ADHD.

Genetic and Hormonal Regulation of Children's Growth MUDr. Jan Vosáhlo Abstract Growth in childhood is a complex process of changing the body, which can be disrupted by various illnesses including endocrine disorders, particularly growth hormone deficiency. Tumors or other processes affecting hypothalamic-pituitary area can be a postnatal cause of GHD; prenatal causes include 1) developmental disorders of the pituitary as part of complex syndromes, 2) developmental disorders of the pituitary due to defects in regulatory genes and 3) defects in genes involved in the synthesis and secretion of GH. The first topic of the thesis was septo-optic dysplasia - a complex syndrome involving optic nerve hypoplasia, structural brain abnormalities and pituitary dysfunctions. We extensively described phenotype in 11 Czech patients; we observed both complete SOD and incomplete forms variously combining two of the three main components of the syndrome. The cohort then became a part of an international study of 68 patients, in which we studied the phenotype in dependence on the brain morphology. We found correlation between the severity of clinical symptoms and the degree of septum pellucidum abnormities and also a correlation between hippocampus and falx abnormities and neurological symptoms. As the second topic we studied...