The term "serious games" became popularized in 2002 as a result of an initiative to promote the use of games for education, training, and other purposes. Today, many companies are using games for training and development, often with hefty price tags. For example, the development budget for the U.S. Army recruiting game, "America's Army" was estimated at $7 million. Given their increasing use and high costs, it is important to understand whether game-based learning systems perform as billed. Research suggests that games do not always increase learning outcomes over conventional instruction. However, certain game features (e.g., rules/goals, fantasy, challenge) might be more beneficial for increasing learner motivation and learning outcomes than other game features. This study manipulated two specific game features: multimedia-based fantasy (vs. text-based fantasy) and reward (vs. no reward) in a computer-based training program on employment law. Participants (N=169) were randomly assigned to one of the four experimental conditions or to a traditional computer-based training condition. Contrary to hypotheses, the traditional PowerPoint-like version was found to lead to better declarative knowledge outcomes on the learning test than the most game-like version, although no differences were found between conditions on any of the other dependent variables. Participants in all conditions were equally motivated to learn, were equally satisfied with the learning experience, completed an equal number of practice exercises, performed equally well on the declarative knowledge and skill-based practice, and performed equally well on the skill-based learning test. This suggests that adding the "bells and whistles" of game features to a training program won't necessarily improve learner motivation and training outcomes.

The main challenge in studying genetics has evolved from identifying variations and their impact on traits to comprehending the molecular mechanisms through which genetic variations affect human biology, including disease susceptibility. Despite having identified a vast number of variants associated with human traits through large scale genome wide association studies (GWAS) a significant portion of them still lack detailed insights into their underlying mechanisms [1]. Addressing this uncertainty requires the development of precise and scalable approaches to discover how genetic variation precisely influences phenotypes at a molecular level. In this study, we developed a pipeline to automate the annotation of structural variant feature effects. We applied this pipeline to a dataset of 33,942 variants from the ClinVar and GnomAD databases, which included both pathogenic and benign associations. To bridge the gap between genetic variation data and molecular phenotypes, I implemented Non-negative Matrix Factorization (NMF) on this large-scale dataset. This algorithm revealed 6 distinct clusters of variants with similar feature profiles. Among these groups, two exhibited a predominant presence of benign variants (accounting for 70% and 85% of the clusters), while one showed an almost equal distribution of pathogenic and benign variants. The remaining three groups were predominantly composed of pathogenic variants, comprising 68%, 83%, and 77% of the respective clusters. These findings revealed valuable insights into the underlying mechanisms contributing to pathogenicity. Further analysis of this dataset and the exploration of disease-related genes can enhance the accuracy of genetic diagnosis and therapeutic development through the direct inference of variants that are likely to affect the functioning of essential genes.

Yes / In recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side-view of face images. Here we exploit this by using a pre-trained deep residual neural network (ResNet) to extract features. We then utilize a sparse partial least squares regression approach to estimate ages. Despite having less information as compared to frontal images, our results show that the extracted deep features achieve a promising performance.

<p>Synthesis of terrains with complex features has been a challenging problem in computer graphics since most of the existing methods are based on the height field representation. Complex features in terrains adds to the overall realism of the terrain. Hence, there is a need to synthesize terrains in real-time with complex features that adhere to user input. The methodology described in this thesis describes a novel way to synthesize terrains with complex features based on user drawn sketches. Layered stack data structure is used to ensure that the resulting terrain has complex features. Since, Neural Networks are used to generate the terrains, the process is real-time.</p>

Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods.

Although digital media has been exploited to improve digital libraries, social networking sites, and book promotion for adult and child stakeholders, but encouraging children who have the choice to either read from a book or on a screen remains limited worldwide, including Jordan. This interest has meant that data about children¿s reading habits were needed, and the present study was intended as a contribution towards this aim. Interviews were conducted with Jordanian writers, publishers, child specialists, and various children¿s cultural centres. The managers and personnel unanimously showed that Jordanian children are not good readers and that a limited number of books are published for children as there are actual boundaries preventing Jordanian writers from publishing books. In particular, subjecting the typical sorts of children¿s websites ¿ 'Club Penguin', 'PBS Kids', 'A Story before Bed', 'Baraem', 'Storyline Online', and 'Raneen' ¿ to evaluation showed that 'Club Penguin' got the highest rank among the other websites in terms of multimodal features, usability, and language, while 'PBS Kids' got the highest rank regarding interactivity, and 'A Story before Bed' got the highest rank in reading activities. Although it was realised that most children were satisfied with the aspects of usability and ease of use rather than the structure or the aesthetic of the website, and were more attracted to the websites that provide multimodal features such as special characters, narration, gesture, and interactivity. The targeted websites¿ parameters obtained from the survey were used as guidance in the design structure of the KITABAK website, as a virtual reading environment for children¿s reading practices. The evaluation results that were obtained showed that there is a significant correlation towards encouraging children¿s reading habits and reading from printed books accompanying the website; girls showed more interest in reading iv than boys; and there is an obvious willingness for the adaptation of the website as a part of the Jordanian school curriculum. In addition, the KITABAK website was accepted significantly more than 'Club Penguin', mainly because the KITABAK website has facilities, games and reading activities. Also, results showed that children who were subjected to testing the KITABAK website for a one-week period proved to accept the website significantly more than those who were subjected to testing it once. / Applied Science University

The dissertation is devoted to comparisons in the texts of modern printing. In the work given full structural analysis comparisons, identify the meanings of words in the object position, comparisons are set fixed and variable signs comparative constructions. Identified their stylistic features. / Диссертация посвящена исследованию сравнений в текстах современной печати. В работе дан полный структурный анализ сравнений, определены значения слов в позиции объекта сравнения, установлены постоянные и переменные признаки сравнительной конструкции. Выявлены их стилистические особенности.

The availability of industrial machinery is crucial to any business operating in the manufacturingsector. Mechanical failures halt production and unplanned downtime canbe disruptive and costly. Small failures can compound to serious failures which exponentiallyincreases downtime and repair costs. Therefore Identifying a degradationcondition before reaching failure is key to maintaining machine availability. On theother hand, it’s undesirable to spend resources performing maintenance that is notrequired. For these reasons a large field of academic work is dedicated to analyzingthe health of a machine, it’s remaining life and in turn preventing failures. This thesis analyses data from a tube straightening machine used in the steel industrywith the goal of implementing a condition monitoring strategy. The data comes froma real world application provided by a multinational manufacturer of steel products.It was obtained using the existing sensors and data acquisition system. The projectserves as a study of the existing infrastructure (available sensors) and it’s suitability forimplementing a condition monitoring strategy. The work is the first step in a largerstudy and does not attempt to perform any implementation or fault identification. Ina broad sense the aim of the project is to identify relationships and patterns in the datathat could be varying with time as the machine degrades. The data consists of twelve channels taken over a two week duration. It is prepossessedto isolate periods where the machine is operating and separated into cycles. Each ofthese is then further processed to extract time and frequency domain features. Thefeatures within each channel are compared with each other using the R2 coefficientof determination to find combinations that are correlated. A semi automated processis used to select the feature combinations. The same process is performed betweensignals for each feature. A number of linear regression models are created based on the results from the correlatedfeatures as well as some multivariate models. These are then compared usinga goodness of fit metric, Normalized Root Mean Square Error (NRMSE). Potentialclustering of machine states are highlighted based on observations in the feature combinations.The conclusions drawn from this study include identification of correlationsbetween signals, potential non-linear relationships and suggestions for future data collectionand analysis going forward. No one feature was identified as correlated betweenall signals.

Performance-based contracting (PBC) is an outcome-based product support strategy that provides efficient performance solutions for buyers. Suppliers under performance-based contracting are rewarded after achieving desired performance objectives. While current scholarship has deepened our knowledge of the benefits of PBC, the particular factors behind effective and efficient performance-based contracts (PBCs) are still vague. Thus, this dissertation will focus on essential dimensions for the successful PBC. There remains a great deal that is not understood about the success factors for effective PBCs. When looking at the critical criteria for the selection of suppliers in the context of PBC, even less is known. This dissertation contains three essays with the purpose of: (1) investigating the effect of supply chain collaboration and upfront investments on the benefits of the PBC; (2) exploring supplier selection criteria for successful PBC; and (3) examining the effect of contract length and fleet size on upfront investments for effective and efficient PBC. These three essays offer a solid foundation for theoretical and practitioner understanding for effective PBCs.

Finding if color has an effect on depth perception in virtual reality (VR) is important, as it could be important for e.g. surgeons to perceive the depth correctly if they were to be trained in VR environments as a preparation for surgeries on real patients. If color has an effect on perceived depth in VR then producers of these simulations have to take their color choices into account when creating simulations. Previous research has shown that luminosity and hue can have effects on depth perception. It is also perceived that depth underestimation is prevalent in VR. Discerning if either the color of the focal object or the background is affecting the depth perception is important. Therefore finding what effect different color attributes of a focal object and background has on the depth perception in a VR environment is important. This experimental study examined this through a case study performed in a VR environment built in Unity. The tests were set up to emulate the piercing of a catheter into a plane, where the user pressed a button the moment the plane was pierced. To test different colors of the focal object, in this case a plane, the background was assigned neutral colors (white or black) and while testing the background the plane had a neutral color (white). Results from the study show that colors have a small effect, namely up to 13.2 mm error (for the yellow hue with high luminosity and high saturation), on users’ depth perception in VR. No single attribute was better than another but on the object, blue hue gave the largest error while red hue gave the smallest error. For the background, there was more variation on the data but green and blue hue gave the smallest errors and red and yellow the largest. In sum, color has differing effects on depth perception in VR depending on if the color is applied to a background or an object. Red color gave the most accurate depth perception when applied to the object. For color applied to the background, green hue with high luminosity and blue hue with low luminosity resulted in the most accurate depth perception. / Att ta reda på om färg har en påverkan på djupseende i virtuell verklighet (VR) är viktigt, eftersom det skulle vara viktigt för t.ex. kirurger att uppfatta djupet korrekt om de skulle bli tränade i VR miljöer som en förberedelse inför operationer på riktiga patienter. Om färg har en effekt på upplevd djup i VR, då måste tillverkarna av dessa simulationer ha deras färgval i åtanke när de skapar simulatorerna. Tidigare forskning har visat att ljusintensitet och kulörton kan ha en effekt på djupseende. Det har också upptäckts att djupunderskattning är allmänt förekommande i VR. Att urskilja om antingen färgen på fokusobjektet eller på bakgrunden påverkar djupseendet är viktigt. Således att hitta vilken effekt olika färg attribut av ett fokusobjekt och bakgrund har på djupseendet i en VR miljö. Studien undersökte detta genom en fallstudie i en VR miljö byggd i Unity. Testen var uppbyggda för att efterlikna en kateter som genomtränger ett plan där användaren trycker på en knapp då den trängde igenom planet. För att testa olika färger på fokusobjektet, i detta fall ett plan, blev bakgrunden tilldelad neutrala färger (vit och svart) och när bakgrunden testades var planet tilldelad en neutral färg (vit). Resultaten från studien visar att färg har en liten effekt, upp till 13.2 mm i fel (för den gula kulörtonen med hög ljusintensitet och hög mättnad), på djupseende i VR. Inget enskilt attribut var bättre än ett annat, men på objektet gav blå kulörton det största felet medan röd kulörton gav det minsta felet. För bakgrunden var det mer variation på data men grön och blå kulörton gav de minsta felen och röd och gul gav de största felen. Färgen har olika påverkan på djupseende i VR beroende på om färgen är applicerad på en bakgrund eller ett objekt. Röd färg gav det mest korrekta djupseendet när den var applicerad på objektet. För färg applicerad på bakgrund, resulterade grön kulörton med hög ljusintensitet och blå kulörton med låg ljusintensitet i det mest korrekta djupseendet.