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While many educators suggest active, rather than passive learning, this is not always the best solution, especially when learners are novices. Sweller and Cooper found learners who passively studied worked examples were significantly more efficient than those who actively solved problems (Cooper & Sweller, 1987; Sweller & Cooper, 1985) later described as the "worked-example effect" (Sweller & Chandler, 1991). The current study tested the claims of Lewis (2005) who suggested animated demonstrations act as worked examples. It compared the performance of groups of preservice teachers who: studied animated demonstrations (demo); studied animated demonstrations and practiced procedures (demo+practice & demo2+practice), or practiced procedures (practice). Two MANOVAs were used to compare group performance. During week one, it was hypothesized that the demonstration learners would out-perform those in the practice condition given performance time and accuracy. It was found that there was a significant difference between groups, Wilks’ Λ=0.68, F (2, 68) = 6.83, p <0.0001, η 2 =0.32. Post hoc comparisons with Scheffé’s test (p<0.025) revealed that the demonstration groups (demo+practice and demo2+practice groups) assembled the problem, in significantly less time than the practice group, which is positive evidence for the worked-example effect (Sweller & Chandler, 1991) given animated demonstrations. During week two, a similar MANOVA revealed no differences between groups. While this study considered learner performance from a human computer interaction (HCI) perspective, it also considered learners from a cognitive load perspective, by measuring relative condition efficiency (Paas & van Merriënboer, 1993). In addition, it developed a new measure called performance efficiency. During week one, the demonstration conditions were found to be significantly different F (2, 68) = 3.69, p =0.03, given relative condition efficiency. This is positive evidence of the variability effect. However in post hoc comparisons these instructional conditions were not found to differ. Performance efficiency was found to be significantly different, during week one, F (2, 68) = 12.95, p<0.0001, and post hoc comparisons with Scheffé’s test (p<0.05) revealed the demonstration learners were significantly more efficient, than the practice learners. During week two, groups were not significantly different, so once learners had practiced procedures, they performed equally well.

This paper deals with theory of measuring enterprise performance in relation to its market value. Main objective is to explain the principles of value-based management and answer the question why managers prefer this concept of financial management. It is extended about less used financial performance indicators which respects principles of value-based management and implements to this traditional instrument new dynamic components. This theory is supported by case study.

Today, power consumption costs supercomputer centers millions of dollars annually and the heat produced can reduce system reliability and availability. Achieving high performance while reducing power consumption is challenging since power and performance are inextricably interwoven; reducing power often results in degradation in performance. This thesis aims to address these challenges by providing theories, techniques, and tools to 1) accurately predict performance and improve it in systems with advanced hierarchical memories, 2) understand and evaluate power and its impacts on performance, 3) control power and performance for maximum efficiency. Our theories, techniques, and tools have been applied to high-end computing systems. Our theroetical models can improve algorithm performance by up to 59% and accurately predict the impacts of power on performance. Our techniques can evaluate power consumption of high-end computing systems and their applications with fine granularity and save up to 36% energy with little performance degradation. / Ph. D.

This diploma thesis describes a simulator of home elevator driven by an EC engine (external combustion engine). The introductory chapter of the thesis deals with the history of elevators. It also contains the description of EC engines, the description of the construction of the simulator and of the mechanical, electrical, and software adjustments carried out as based on the thesis. The main part of the work concentrates on the description of the changes of the drive algorithms, the production of the 3D simulator and the animation in the background of 3D Solid Works. Moreover, it also deals with the experimental performance measurement based on the loading of the stimulator drive.

<p class="MsoNormal" style="">The study shows the financial impacts on costs per student at academic module level, at departmental level, at faculty level, and at institutional level, thereby showing the effects of cross-subsidisation at all levels of management. The reports were developed in termsof the guidelines compiled in terms of Llewellyn's five levels of theorisation.</p> <p class="MsoNormal" style=""><span style="font-family: Garamond / "><o:p></o:p></span></p>

Este trabajo analiza el rendimiento académico de los estudiantes mediante la estimación de fronteras de eficiencia, método que permite distinguir el efecto de características del alumno y su entorno (edad, sexo, condición laboral, entre otros) del impacto de factores exógenos (características de la universidad, etc.). Entre los principales resultados, se encuentra que los alumnos que trabajan en tareas vinculadas a la carrera se ubican más cerca de la frontera de rendimientos, mientras que quienes trabajan en tareas no relacionadas se alejan de ella por factores ajenos a su comportamiento (por ejemplo, institucionales). Para los estudiantes que no trabajan, los resultados varían. / This paper analyses students' academic performance by estimating efficiency frontiers, a method that allows to distinguish the effect of characteristics of the student and his background (age, gender, employment status, among others) from the impact of exogenous factors (university characteristics, etc.). Among the main results, it is found that students who work in jobs related to their career lie closer to the performance frontier, whereas for those employed in other kinds of jobs, departures from the frontier are explained by factors beyond their behavior (e.g. institutional). For students who don't work, results are variable.

<p class="MsoNormal" style="">The study shows the financial impacts on costs per student at academic module level, at departmental level, at faculty level, and at institutional level, thereby showing the effects of cross-subsidisation at all levels of management. The reports were developed in termsof the guidelines compiled in terms of Llewellyn's five levels of theorisation.</p> <p class="MsoNormal" style=""><span style="font-family: Garamond / "><o:p></o:p></span></p>

Over the last two centuries, there has been a continual evolution of how occupied rooms are heated, with inventors competing to design new heating devices. In particular, there is a wide range of convector types, which vary in shape, size, design, material, operating medium and application. With approximately 190 million convectors installed in the UK alone, the question arises regarding the dependencies on the efficiency of heat distribution through convector-induced filling flows. A standard approach to evaluate convector performance is based on the convector strength only, the implication being the stronger the convector the better the performance. This work has gone beyond the limits of a stereotypical assessment in pursuit of answers regarding the physics of convector-induced filling and a new objective method to evaluate the efficiency of this transient process. The ultimate goal has been to provide a deep understanding of filling and stratification induced by a convector, in order to heat rooms rapidly and effectively. An experimental facility has been designed that approximates dynamic similarity between the experimental set-up and a real-life room with a convector. In the experiments, a rectangular sectioned water tank represents a room and a saline source rectangular sectioned panel with sintered side walls provides a convector representation. Experiments have been performed in water with a saline solution to ensure high Rayleigh numbers. Diagnostic techniques involve a combination of a shadowgraph method, a dye-attenuation method, direct salinity measurements and a new application of Particle Image Velocimetry (PIV). Interesting insight into convector-induced buoyancy-driven flows has been gained. As a result, new guidelines aimed at heating rooms more rapidly and effectively have been proposed. The key outcome that can be immediately applied is that, for a given convector strength, heat distribution with height can be improved by adjusting the convector position. For instance, faster filling leading to more uniform heat distribution occurs in rooms with convectors detached from side walls, due to large-scale mixing flows in the early period of filling. Also shorter convectors relative to the room height, positioned close to the floor level, promote faster and more uniform filling. An attempt to describe the transient filling has been made and to do so statistical methods, application specific, have been developed. As a result, the empirical equations describing both the filling rates in different stages of filling and the development of stratification have been derived, which rank the governing parameters, based on their importance, as either dominant or subordinate. Two dominant parameters governing filling flows are the non-dimensional accumulation parameter B and the Rayleigh number ΔRa, which are related to the convector strength. The impact of these two parameters is constant throughout the process. The parameters accounting for the system geometry and filling time (T) are subordinate parameters. Their impact, visible in the early period, decreases as filling continues.

Eftersom samhället blir allt mer datadrivet revolutionerar användningen av AI och maskininlärning sättet företag fungerar och utvecklas på. Denna studie utforskar användningen av AI, Big Data och Natural Language Processing (NLP) för att förbättra affärsverksamhet och intelligens i företag. Huvudsyftet med denna avhandling är att undersöka om den nuvarande klassificeringsprocessen hos värdorganisationen kan upprätthållas med minskade driftskostnader, särskilt lägre moln-GPU-kostnader. Detta har potential att förbättra klassificeringsmetoden, förbättra produkten som företaget erbjuder sina kunder på grund av ökad klassificeringsnoggrannhet och stärka deras värdeerbjudande. Vidare utvärderas tre tillvägagångssätt mot varandra och implementationerna visar utvecklingen inom området. Modellerna som jämförs i denna studie inkluderar traditionella maskininlärningsmetoder som Support Vector Machine (SVM) och Logistisk Regression, tillsammans med state-of-the-art transformermodeller som BERT, både Pre-Trained och Fine-Tuned. Artikeln visar att det finns en avvägning mellan prestanda och kostnad vilket illustrerar problemet som många företag, som Valu8, står inför när de utvärderar vilket tillvägagångssätt de ska implementera. Denna avvägning diskuteras och analyseras sedan mer detaljerat för att utforska möjliga kompromisser från varje perspektiv i ett försök att hitta en balanserad lösning som kombinerar prestandaeffektivitet och kostnadseffektivitet. / As society is becoming more data-driven, Artificial Intelligence (AI) and Machine Learning are revolutionizing how companies operate and evolve. This study explores the use of AI, Big Data, and Natural Language Processing (NLP) in improving business operations and intelligence in enterprises. The primary objective of this thesis is to examine if the current classification process at the host company can be maintained with reduced operating costs, specifically lower cloud GPU costs. This can improve the classification method, enhance the product the company offers its customers due to increased classification accuracy, and strengthen its value proposition. Furthermore, three approaches are evaluated against each other, and the implementations showcase the evolution within the field. The models compared in this study include traditional machine learning methods such as Support Vector Machine (SVM) and Logistic Regression, alongside state-of-the-art transformer models like BERT, both Pre-Trained and Fine-Tuned. The paper shows a trade-off between performance and cost, showcasing the problem many companies like Valu8 stand before when evaluating which approach to implement. This trade-off is discussed and analyzed in further detail to explore possible compromises from each perspective to strike a balanced solution that combines performance efficiency and cost-effectiveness.