Context: Games involve people to a large extent where they relate themselves with the game characters; this is commonly known as game immersion. Generally, some players play games for enjoyment, some for stress relaxation and so on.Game immersion is usually used to describe the degree of involvement with a game. When people play games, they don’t necessarily realize that they have been dissociated with the surrounding world. Real world dissociation (RWD) can be defined as the situation where a player is less aware of the surroundings outside the game than about what is happening in the game itself. The RWD factor has been expected to measure the losing track of time, lack of awareness of surroundings and mental transportation. Objectives: In this thesis, we measure and compare the difference in game immersion between experienced and inexperienced players using RWD factor. In addition, the study involves exploring the significance of game immersion and various approaches used to measure it. Methods: In this study literature review has been carried out to explore the meaning of game immersion and further user studies in the form of an experiment has been conducted to measure game immersion between experienced and inexperienced gamers. The game immersion has been measured using the real world dissociation (RWD) factor. After the experiment has been conducted, a statistical technique has been carried out to measure the difference in game immersion among the two groups. Results:The empirical investigation on the measurement of game immersion has been done using RWD factor. The results state that the significance value is less than 0.05 and hence null hypothesis is rejected for both the games. The measurable difference has been calculated by using Cohen’s d effect size between experienced and inexperienced players. The Cohen’s d value between experienced players and inexperienced players for Dota 2 is 0.7423 and CS:GO is 0.8383. Conclusions: After analyzing the data and calculating the effect size, the overall results state that inexperienced group of players are more immersed than the experienced group of players when measured by RWD factor. Hence it can be concluded that irrespective of the game played, inexperienced players are more dissociated from the real world than the experienced players.

The National Education Department of South Africa has mandated a policy of outcomes-based education for all learners and educators in this country. Two of the most important principles of outcomes-based education are collaborative work in groups and continuous assessment by the teacher and peers. In Computer Studies, taken on the higher grade, learners are expected to construct algorithms and programs by themselves. In the real world such algorithms and programs would be constructed by groups of people working together. The researcher’s purpose of conducting this study was to breach the gap that exists between what is done in accordance with the outcomes-based curriculum in schools - and what is expected in the real world where collaborative work is the norm. The researcher used Bloom’s high-order thinking skills as his point of departure for this study and examined the implications of how they contribute to real-world situations in the school environment. To evaluate the South African curriculum for Computer Studies on the higher grade, the researcher compared the South African curriculum was the curriculum used in Australia for learners of the same age group. The results led to an intervention in which South African learners were examined on high-order thinking skills and programming in the real world. / Dissertation (MEd (CIE))--University of Pretoria, 2005. / Curriculum Studies / unrestricted

Un grand nombre d'études ont été faites dans le domaine de l'Optimisation Stochastique en général et les Algorithmes Génétiques en particulier. L'essentiel des nouveaux développements ou des améliorations faites sont alors testés sur des jeux de tests très connus tels que BBOB, CEC, etc. conçus de telle manière que soient présents les principaux défis que les optimiseurs doivent relever : non séparabilité, multimodalité, des vallées où le gradient est quasi-nul, et ainsi de suite. La plupart des études ainsi faites se déroulent via une application directe sur le jeu de test, optimisant un nombre donné de variables pour atteindre un critère précis. La première contribution de ce travail consiste à étudier l'impact de la remise en cause de ce fonctionnement par deux moyens : le premier repose sur l'introduction d'un grand nombre de variables qui n'ont pas d'impact sur la valeur de la fonction optimisée ; le second quant à lui relève de l'étude des conséquences du mauvais conditionnement d'une fonction en grande dimension sur les performances des algorithmes d'optimisation stochastique. Une deuxième contribution se situe dans l'étude de l'impact de la modification des mutations de l'algorithme CMA-ES,où, au lieu d'utiliser des mutations purement aléatoires, nous allons utiliser des mutations quasi-aléatoires. Ce travail introduit également la ``Sieves Method'', bien connue des statisticiens. Avec cette méthode, nous commençons par optimiser un faible nombre de variables, nombre qui est ensuite graduellement incrémenté au fil de l'optimisation.Bien que les jeux de tests existants sont bien sûr très utiles, ils ne peuvent constituer que la première étape : dans la plupart des cas, les jeux de tests sont constitués d'un ensemble de fonctions purement mathématiques, des plus simples comme la sphère, aux plus complexes. Le but de la conception d'un nouvel optimiseur, ou l'amélioration d'un optimiseur existant, doit pourtant in fine être de répondre à des problèmes du monde réel. Ce peut-être par exemple la conception d'un moteur plus efficace, d'identifier les bons paramètres d'un modèle physique ou encore d'organiser des données en groupes.Les optimiseurs stochastiques sont bien évidemment utilisés sur de tels problèmes, mais dans la plupart des cas, un optimiseur est choisi arbitrairement puis appliqué au problème considéré. Nous savons comment les optimiseurs se comparent les uns par rapport aux autres sur des fonctions artificielles, mais peu de travaux portent sur leur efficacité sur des problèmes réels. L'un des principaux aspects de des travaux présentés ici consiste à étudier le comportement des optimiseurs les plus utilisés dans la littérature sur des problèmes inspirés du monde réel, voire des problèmes qui en viennent directement. Sur ces problèmes, les effets des mutations quasi-aléatoires de CMA-ES et de la``Sieves Method'' sont en outre étudiés. / A lot of research is being done on Stochastic Optimisation in general and Genetic Algorithms in particular. Most of the new developments are then tested on well know testbeds like BBOB, CEC, etc. conceived to exhibit as many pitfalls as possible such as non-separability, multi-modality, valleys with an almost null gradient and so on. Most studies done on such testbeds are pretty straightforward, optimising a given number of variables for there cognized criterion on the testbed. The first contribution made here is to study the impact of some changes in those assumptions, namely the effect of supernumerary variables that don't change anything to a function evaluation on the one hand, and the effect of a change of the studied criterion on the other hand. A second contribution is in the modification of the mutation design for the algorithm CMA-ES, where we will use Quasi-Random mutations instead of purely random ones. This will almost always result in a very clear improvement ofthe observed results. This research also introduces the Sieves Method well known in statistics, to stochastic optimisers: by first optimising a small subset of the variables and gradually increasing the number of variables during the optimization process, we observe on some problems a very clear improvement. While artificial testbeds are of course really useful, they can only be the first step: in almost every case, the testbeds are a collection of purely mathematical functions, from the simplest one like the sphere, to some really complex functions. The goal of the design of new optimisers or the improvement of an existing one is however, in fine, to answer some real world question. It can be the design of a more efficient engine, finding the correct parameters of a physical model or even to organize data in clusters. Stochastic optimisers are used on those problems, in research or industry, but in most instances, an optimiser ischosen almost arbitrarily. We know how optimisers compare on artificial functions, but almost nothing is known abouttheir performances on real world problems. One of the main aspect of the research exposed here will be to compare someof the most used optimisers in the literature on problems inspired or directly coming from the real-world. On those problems, we will additionally test the efficiency of quasi-random mutations in CMA-ES and the Sieves-Method.

<p>Socially engaged design programs, community development coalitions, and intentional and unintentional design spaces are rich with expertise and thinkers who are developing solutions to very pressing, yet complicated problems. Little research has been conducted on the expertise and sense-making of the community partners who participate in these situations. The goal of this research endeavor is to unpack the ways various community partners make meaning of their design experiences by answering the question: What evidence of system’s thinking can be seen in the way community partners describe their work or context? A qualitative research study was conducted in which three community partners were interviewed at various points during their engagement with socially engaged design programs. They demonstrated their systems thinking ability most strongly across the following domains: differentiate and qualify elements, explore multiple perspectives, consider issues appropriately, recognize systems, identify and characterize relationships. These findings imply that the community partners are not only capable of systems thinking but have the potential to be more deeply involved in <a>developing solutions</a> within these settings. Future studies should investigate systems thinking beyond socially engaged design in formal settings and should consider investigation protocols that more directly surface systems thinking domains. Overall, this study contributes to existing work in systems thinking by calling for a more expansive and inclusive engagement of community partners in socially engaged work.</p>

<p>This qualitative study was conducted to understand the instructional strategies used by high school integrated STEM (iSTEM) teachers to apply real-world problem solving in their classrooms in the state of Indiana. The problem addressed by this study was the need to understand the instructional strategies employed by iSTEM teachers in their classrooms. Using a basic qualitative approach, data was collected through teacher interviews, classroom observations, and documents. The thematic analysis revealed several themes: (a) there is no single instructional strategy, but teachers adapt their strategies to the context, (b) the importance of preparation using various sources and building on student’s prior knowledge, (c) a focus on asking "why" questions as a priority, (d) the necessity of making group work tangible, (e) the use of modeling as a common strategy, including data collection and analysis, sketching and documentation, (f) the promotion of student independence by being aware and performing tasks independently, (g) the integration of real-world issues to relate learning to student lives, and (h) the challenges posed by time and diversity of student abilities. These findings suggest that iSTEM teachers should be flexible in their approach and emphasize preparation, questioning, modeling, group work, and real-world connections to improve student learning in an integrated STEM approach. The findings contribute to the existing literature on iSTEM teaching and have implications for iSTEM teachers, school administrators, and policymakers. The findings of this study can inform professional development programs for iSTEM teachers and can help school administrators design collaborative and problem-solving learning environments. Lastly, policymakers can use the findings to develop policies that promote the integration of real-world problem-solving into STEM education, thereby contributing to the development of a workforce that is prepared to meet the challenges of the 21st century.</p>

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Novel forms of brain-inspired programming models related to novel computer architecture are required to both understand the mysteries of intelligence as well as break barriers in computational complexity, and computer parallelism. Artificial Intelligence is focused on developing complex programs based on abstract, statistical prediction engines that require large datasets, vast amounts of computational power, and unbounded computation time. By contrast, the brain utilizes relatively few experiences to make decisions in unpredictable, time-constrained situations while utilizing relatively small amounts of physical computational space and power with high degrees of complexity and parallelism. We observe that intelligence requires the accommodation of ambiguity, conflict, and paradox. From a structural perspective, this means the same set of inputs leads to conflicting results that are likely produced in isolated regions of the brain that function independently until an answer must be chosen. We further observe that, unlike computer programs, brains constantly interact with the physical world where external constraints force the selection of the best available response in time-quality trade-offs ranging from fight-or-flight to deep thinking. For example, when intelligent beings are presented with a set of inputs, those inputs can be processed with different levels of thinking, utilizing heterogeneous algorithms to produce answers dependent upon the time available to process them. We introduce the Troop meta-approach, which is a novel meta computer architecture and programming. Experiments demonstrate our approach in modeling conflict when the same set of inputs are heterogeneously processed independently using maze solving and ordered search in real-world environments with unpredictable, random time constraints. Across one hundred trials, on average, the Troop solution solves mazes almost six times faster than the only other solution, which does not accommodate conflict but can always produce a result when required. Two other experiments based on ordered search show that, on average, the Troop solution returns a position that is over twice as accurate as the other solutions which do not accommodate conflict but always produce a result when required. This work lays the foundation for more research in algorithms that utilize time-accuracy trade-offs consistent with our approach. / This material is based upon work supported by the National Science Foundation under Grant No. 2204780. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. / Master of Science / New types of brain-inspired computer architectures and programming models are needed to break barriers that hinder traditional methods in computer parallelism as well as to understand better the phenomenon by which intelligence emerges from the structure of the human brain. Traditional research in the field of Artificial Intelligence is focused on developing complex programs based on simulating low-level models of the brain such as artificial neural networks. The most advanced of these methods are processed on large supercomputers that use vast amounts of power and have unlimited amounts of time to process a task producing a single result. By contrast, the human brain is relatively small and uses very little power. Furthermore, it can use relatively few experiences to make very quick and inaccurate but necessary decisions to survive in unpredictable environments. But the brain can produce many different and conflicting decisions to the same problem. Given more time, the human brain can use higher levels of thinking located in different parts of the brain to produce better decisions. Thus, we observe that intelligence requires the ability to handle conflicting answers to the same problem. From a highlevel perspective, this means different and independent structures of the brain can simultaneously produce conflicting answers that solve the same problem. We further observe that, unlike traditional computer programs, the brain constantly interacts with the physical world, where different circumstances within the environment force the best available decision to be carried out. Based on these observations, this research introduces novel approaches that we collectively reference as the Troop meta-approach to develop computer architectures that solve real-world problems, such as maze solving. This research demonstrates the approaches by first introducing scenarios inspired by humans solving problems in environments where unforeseeable events occur that force decisions to be made that are not the most accurate but necessary not to fail the overall objective. For example, military and law enforcement trainees use square mazes to prepare for unpredictable environments. When a threat presents itself, if a soldier or officer does not react to a circumstance in time, their failure may be fatal. To demonstrate that our approaches are feasible, this research then presents three experiments based on the problems of the scenarios and uses the Troop meta-approach to solve each one. Across three experiments, on average, the computer architectures and related algorithms developed using the Troop meta-approach solve mazes or search databases while responding to unpredictable real-world events faster or more accurately than traditional architectures and algorithm pairs that do not handle simultaneous decisions that conflict. This work lays the foundation for more research in methods and computer architectures that utilize multiple conflicting decisions.

Introduction: Autologous stem cell transplantation (ASCT) is the standard treatment for younger patients with newly-diagnosed multiple myeloma (MM). However, due to restrictive exclusion criteria, more than half of eligible patients are usually excluded from transplant-studies. Methods: This retrospective monocentric analysis included 540 patients with MM who received an ASCT between 1996 and 2019. Results: Up to 2005, induction therapy consisted mainly of conventional chemotherapies, e.g. vincristine/doxorubicin/dexamethasone (VAD). In the following years, the triple-combinations based on bortezomib coupled with doxorubicin/dexamethasone (PAD), melphalan/prednisolone (VMP), cyclophposphamide/dexamethasone (VCD) or bendamustine/prednisolone (BPV) became the most popular treatment options. A progressive improvement in PFS was observed in patients treated with the two current induction therapies BPV (47 months) or VCD (54 months) compared to VAD (35 months, p<0.03), PAD (39 months, p<0.01 and VMP (36 months, p<0.01). However, there was no significant difference in median OS (VAD 78, PAD 74, VMP 72, BPV 80 months and VCD not reached). In our analysis, we also included 139 patients who do fulfill at least one of the exclusion criteria for most phase 3 transplant-studies (POEMS/amyloidosis/plasma cell leukemia, eGFR<40 mL/min, severe cardiac dysfunction or poor general condition). Outcome for these patients was not significantly inferior compared to patients who met the inclusion criteria for most of the transplant studies with PFS of 36 vs 41 months (p=0.78) and OS of 78 vs 79 months (p=0.34). Conclusions: Our real world data in unselected pts also stress the substantial value of ASCT during the first line treatment of younger MM pts.:Inhaltsverzeichnis Bibliographische Beschreibung …………………………………………………...………………..……….……………………......2 Inhaltsverzeichnis …………………………………………………………………………………………………………………...…........3 Abkürzungsverzeichnis ………………………………………………………………………………………………………………..…….4 1. Einführung …………………………………………………………………..…………………..…...................................7 1.1. Das multiple Myelom ………………………………………………….……………………………..………………………..7 1.2. Klinik und Diagnostik ……………………………………………………………………………..…………………………….7 1.3. Stadieneinteilung ……………………………………………………………….…………..…………………………………...8 1.4. Remissionsbeurteilung ……………………………………………………………………………………………………….10 1.5. Therapie …………………………………………………………………………………………….……………………………...12 1.5.1. Konventionelle Chemotherapie ……………………………………………………………………………………….…12 1.5.2. Neue Substanzen in der Myelomtherapie …………………………………………………………………………..13 1.5.2.1. Immunmodulatorische Substanzen ……………………………………………………………………………….……13 1.5.2.2. Proteasomen-Hemmer ……………………………………………………………………………………….………………13 1.5.2.3. HDAC Hemmer ……………………………………………………………………………………………………………………13 1.5.2.4. Monoklonale Antikörper …………………………………………………………………………………………………….14 1.5.2.5. BCMA basierte Therapien …………………………………………………………………………………………………..14 1.5.3. Autologe Stammzelltransplantation in der Ära der konventionellen Chemotherapie ………...15 1.5.4. Allogene Stammzelltransplantation …………………………………………………………………………………….15 1.5.5. Autologe Stammzelltransplantation in der Ära der neuen Substanzen ………………………………..16 1.6. Aufgabenstellung der Arbeit ……………………………………………………………………………………………….17 2. Publikation …………………………………………………………………………………………………………………….……18 3. Zusammenfassung der Arbeit ……………………………………………………………………………………………..33 Literatur …………………………………………………………………………………………………………………………………..…..…37 Tabellenverzeichnis …………………………………………………………………………………………………………………………49 Darstellung des eigenen Beitrags …………………………………………………………………………………………………….50 Selbständigkeitserrklärung ………………………………………………………………………………………………………………52 Lebenslauf ……………………………………………………………………………………………………………………………………….53 Verzeichnis der wissenschaftlichen Veröffentlichungen……………………………………………………………………54 Danksagung ……………………………………………………………………………………………………………………………………..56