94 research outputs found
IQ and socio-occupational functioning in relation to obsessive-compulsive symptoms severity in a clinical sample of adolescents
No full textAbstract Obsessive-compulsive disorder (OCD) is often comorbid with other psychiatric conditions, leading to severely impaired functioning and poor quality of life. Until now, the relationship between obsessive-compulsive symptoms (OCS) and functioning has been studied mainly through a categorical approach (i.e., presence/absence of the disorder), leaving more dimensional analyses almost unexplored. Therefore, the present study investigates the association between OCS and both intellectual functioning (IQ) and socio-occupational functioning across different levels of OCS severity. 341 help-seeking adolescents (65% female, mean age = 15.37 years, SD = 1.37) with different psychopathologies underwent an in-depth clinical examination using the Comprehensive Assessment of At Risk Mental States (CAARMS), through which OCS were also evaluated, and the Kiddie-Schedule for Affective Disorder and Schizophrenia - Present and Lifetime – DSM-5 (K-SADS-PL). Cognitive functioning was assessed using a full IQ test (WISC-IV or WAIS-IV), and socio-occupational functioning was assessed using the Social and Occupational Functioning Assessment Scale (SOFAS). A negative quadratic curvilinear (i.e. inverted U-shape) relationship was found between OCS severity and IQ (β=-1.11, p < .05), and a negative linear relationship was observed between OCS severity and socio-occupational functioning (β=-1.32, p < .01). While the association with IQ remained significant after controlling for sociodemographic variables and psychopathology symptoms (β=-0.471, p = .005), the association with socio-occupational functioning did not (β=-0.034, p = .487). These results indicate that OCS are differentially associated with IQ depending on their severity within the adolescent psychiatric population. In particular, mild OCS appear to be associated with a higher IQ relative to no OCS or severe OCS
Computermodel Golfdemping in rietkragen
No full textMet behulp van de kortegolf-theorie en de kennis uit de offshoretechniek is een model opgezet dat gebruikt is in het simulatieprogramma Reedsiml. Dit programma simuleert een golf die door een rietkraag loopt en daardoor energie verliest. Ten gevolge hiervan neemt de golfhoogte af. Dit programma neemt verschillende vormen van dissipatie mee, maar laat de dissipatie ten gevolge van turbulente stroming buiten beschouwing. Deze is met de huidige kennis niet te bepalen. Door nu de overige termen in beschouwing te nemen valt meer te zeggen over de grootte van de dissipatie ten gevolge van turbulentie en wervelingen in de rietkraag. Allereerst wordt de theoretisch achtergrond van het programma beschouwd. Dit is stap voor stap gedaan opdat precies bekend is waar het programma op gebaseerd is. Voor de parameters, die in het programma gebruikt worden, zijn redelijke waarden bepaald. Bij deze waarden blijkt dat de trends die bij variatie van waterdiepte, golflengte, bodemhelling en golfamplitude worden gevonden duidelijke overeenkomsten vertonen met het onderzoek uit Delft van Bouter [Bouter, 1989]. Het blijkt echter dat de met dit simulatieprogramma gevonden demping van de amplitude gemiddeld 10 procentpunten lager is dan de demping die gevonden is in de goot te Delft. Het programma is opgebouwd uit twee delen, een programma en een invoerbestand. Het invoerbestand, het deel waarmee de gebruiker te maken heeft, wordt uitvoerig besproken in dit verslag. De opbouw van het programma is modulair en gestructureerd opdat een vervolg onderzoeker het programma zonder problemen kan uitbreiden.Hydraulic EngineeringCivil Engineering and Geoscience
Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization: Including Real-World Medical Applications
No full textIn recent years, the use of Artificial Intelligence (AI) has become prevalent in a large number of societally relevant, real-world problems, e.g., in the domains of engineering and health care. The field of Evolutionary Computation (EC) can be considered to be a sub-field of AI, concerning optimization using Evolutionary Algorithms (EAs), which are population-based (meta-)heuristics that employ the Darwinian principles of evolution, i.e., variation and selection. Such EAs are historically mainly considered for the optimization of difficult, non-linear problems in a Black-Box Optimization (BBO) setting, because EAs can effectively optimize such problems even when very little is known about the optimization problem and its structure. This is in contrast to optimization methods that are specifically designed for certain problems of which the definition and structure are known, i.e., a White-Box Optimization (WBO) setting
Golfdemping door riet
No full textReeds eerder zijn onderzoeken naar de golfdempende werking van rietkragen uitgevoerd. In 1981 is een aantal metingen verricht in het Waardkanaal door een samenwerkingsverband van de Landbouwhogeschool en de Rijksdienst voor de IJsselmeerpolders. Bonham publiceerde in 1983 de resultaten van een onderzoek naar golfdemping in een oeverzone van de Thames. Vervolgens is in 1984 opnieuw een onderzoek in het Waardkanaal uitgevoerd, ditmaal door de Rijkswaterstaat (dienst Verkeerskunde) en de TU-Delft. Vooral na deze laatste metingen bleek de noodzaak van een meer 'klinisch' onderzoek van rietkragen. Proeven die in-situ worden uitgevoerd geven meetresultaten waarin een aantal neveneffecten zijn verborgen (zie ook rig.3): de oever is meestal niet vlak; hierdoor ontstaat een vertekend beeld van de demping (shoaling-effect) de gemeten golftreinen zijn vrij kort, daardoor ook de meetduur windgolven geven verstoringen op de gemeten scheepsgolven niet alle parameters die van belang zijn voor de demping kunnen gevarieerd worden (bijv. waterdiepte) Bovendien is het onder natuurlijke omstandigheden slechts mogelijk om een beperkt aantal type golven (wat betreft lengte en hoogte) te onderzoeken. Dit alles pleitte voor een onderzoek onder laboratoriumomstandigheden. Het doel van het onderhavige onderzoek is om te bepalen welke parameters een rol spelen in de interactie tussen waterbeweging en rietstengel. Getracht is om door middel van een systematisch onderzoek naar de invloed van die parameters tot een aantal conclusies omtrent de golfdemping door rietkragen te komen. Met behulp van de verzamelde gegevens en de conclusies wordt een aanzet gegeven voor de modellering van de onderzochte interactie.Hydraulic EngineeringCivil Engineering and Geoscience
Designing the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm and Applying it to Substantially Improve the Efficiency of Multi-Objective Deformable Image Registration
No full textThe recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) for discrete variables has been shown to be able to efficiently and effectively exploit the decomposability of optimization problems, especially in a grey-box setting, in which a solution can be efficiently updated after a modification of a subset of its variables. GOMEA is considered to be state of the art, but currently no version of GOMEA for real-valued variables exists. In this thesis, we design a real-valued version of GOMEA, for both single-objective and multi-objective optimization. Our novel GOMEA variant is then applied to the Deformable Image Registration (DIR) problem, which was adapted to allow for efficient partial evaluations. DIR concerns the calculation of a deformation that transforms one image to another, and is of great importance for many medical applications. Experiments are performed to assess GOMEA’s performance in black-box and grey-box settings on a range of single-objective and multi-objective benchmark problems, including comparisons with it to the state-of-the-art real-valued optimization algorithm AMaLGaM. From the results of these experiments, we find that GOMEA performs substantially better on all considered single-objective and multi-objective benchmark problems in a grey-box setting, in terms of required time and number of evaluations. Moreover, the improvement becomes larger as problem dimensionality increases. In a black-box setting, GOMEA still performed better than AMaLGaM in terms of time, and comparable in terms of the number of evaluations. On DIR problems, GOMEA achieved solutions of similar quality while achieving a speed-up of up to a factor of 1600.Electrical Engineering, Mathematics and Computer ScienceSoftware TechnologyAlgorithmic
CG Variants for General-Form Regularization with an Application to Low-Field MRI
No full textIn an earlier paper, we generalized the CGME (Conjugate Gradient Minimal Error) algorithm to the ℓ2-regularized weighted least-squares problem. Here, we use this Generalized CGME method to reconstruct images from actual signals measured using a low-field MRI scanner. We analyze the convergence of both GCGME and the classical Generalized Conjugate Gradient Least Squares (GCGLS) method for the simple case when a Laplace operator is used as a regularizer and indicate when GCGME is to be preferred in terms of convergence speed. We also consider a more complicated ℓ1-penalty in a compressed sensing framework.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Numerical AnalysisSignal Processing System
GPU-Accelerated GOMEA: Solving the max-cut problem by large-scale parallelisation of GOMEA using GPGPU
No full textWith the advances in General-Purpose computing on Graphics Processing Units (GPGPU), it is worthwhile to explore whether other areas in the field of Artificial Intelligence (AI) can reap the benefits. One such area is Evolutionary Algorithms (EAs), which—among other processes—involves the repetitive exchange of genes among individuals. This repetitive nature aligns with our intuition for parallel optimisation, precisely what GPGPU is designed for. Currently, the state-of-the-art approach in EA is known as Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), which capitalises on the information embedded within the population by computing the linkage between genes across the entire population. However, when it comes to parallelising the exchange of complete linkage sets, particularly in the context of our specific problem of interest, the challenge becomes more intricate.In the case of our problem, known as Max-cut, there are dependencies between genes that must be considered when constructing parallel sets of linkage sets, referred to as packages. We propose three solutions: contamination, revision, and association. Contamination fully utilises parallel capabilities but deviates from the concept of linkage sets. Revision constructs the linkage sets as described by GOMEA, but keeps the dependencies between linkage sets within a package untouched. Association on the other hand attempts to resolve the dependencies by generating a dependency graph to create the set of packages.From our experiments, we can conclude that parallel acceleration using GPGPU is roughly on par with—and sometimes even outperforms—its non-parallelised counterpart. Out of the three solutions, it is evident that association demonstrates the most promising performance profile in terms of approaching the optimal solution. However, the performance falls significantly short of matching the capabilities exhibited by GOMEA. Furthermore, all of the solutions face a significant burden when evaluating the fitness for each exchanged linkage set. An option to consider as an extension to the current setup is known as partial evaluation, although the performance exhibited by contamination implies that simplicity could be the key to success. Further exploration of the acceleration process using widely employed parallel operators—such as those found in linear algebra—has the potential to yield valuable insights for enhancing performance.Applied Mathematic
Penerapan total task presentation untuk meningkatkan kemampuan menyikat gigi pada anak dengan disabilitas intelektual berat
No full textThe aim of this research is to figure out whether the implementation of total task presentation technique can possibly be the tool to enhance toothbrushing skill in middle childhood child with severe intellectual disability. In this research the author used the pretest-posttest design with N=1; in which the treatment applied to one participant. This research was conducted in one of SLB-C, Mampang Prapatan, South Jakarta and it was done when the participant had finished eating his breakfast and lunch. The participant is one of the student in SLB-C with severe intellectual disability who has not yet been able of doing toothbrushing activity independently and he has the oral health problem. In addition, in this research, the author has used the toothbrushing task analysis with some adjustment. The task analysis has referred to two previous research that had been done by Horner and Keilitz (1975) and Smeets, Bouter and Bouter (1976) and with some adjustment from from the DepKes RI. The intervention conducted within 10 days or equal to 20 training sessions. The result shows that the total task presentation technique can be a possible tool to enhance the toothbrushing skill for middle childhood child with severe intellectual disability. Moreover, the improvement can be seen from the comparison between the pretest and posttest results; in which the final result shows that E's toothbrushing skill has increased for approximately 75%
No association between methylphenidate use and psychotic experiences in a population-based sample of adolescents at risk of emotional and behavioral problems
No full textConstraint Handling in RV-GOMEA
No full textThe Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) is a state-of-the-art algorithm for single-objective, real-valued optimization. As many practical applications are inherently constrained, evolutionary algorithms are equipped with constraint handling techniques to allow optimizing constrained problems. The approach currently in use with RV-GOMEA prioritizes solution feasibility over the objective value in all cases, pressuring the algorithm to find feasible solutions. However, this can be inefficient if the constrained optimum is located at the constraint boundary, as search is discouraged from exploring the search space close to infeasible solutions.In this thesis, several well-known constraint handling techniques from literature are adapted for use with RV-GOMEA and evaluated on different benchmark problems, identifying the strengths and limitations of the various techniques. Furthermore, the inefficiency of the current technique is investigated in detail. Based on the insights gained, modifications to the existing techniques are proposed, leading to promising preliminary results.Computer Science | Artificial Intelligenc
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