Choice procedures and quantitative models of choice behavior have been used to assess the reinforcing efficacy of drugs. Few studies, however, have used quantitative models of choice for the study of behavior maintained by alcohol. In addition, no studies have assessed the usefulness of quantitative models of concurrent-chains performance for the study of drug-associated cues. The purpose of the present series of experiments was to test the generality of the matching law with alcohol as a reinforcer and extend the use of quantitative models of concurrent-chains performance to behavior maintained by alcohol and alcohol-associated cues. In the first experiment (Chapter 2), rats responded for an alcohol solution on concurrent variable-interval schedules of reinforcement. Across conditions, relative rates of alcohol reinforcement were varied, which allowed for estimates of the parameters of the generalized matching law. Overall, the matching law accounted for changes in rats’ relative allocation of behavior with changes in the relative rate of alcohol delivery. The second and third experiments (Chapter 3) extended the use of the concurrent-chains procedure to rats responding to gain access to stimulus contexts associated with different rates of alcohol delivery. These experiments examined whether initial-link preference would change as a result of changes in the relative rate of alcohol deliveries in the terminal links and whether increases in the initial-link schedules would result in a decrease in preference (i.e., initial-link effect), as predicted by models of concurrent-chains performance. Results showed that choice between two terminal links depended on the different rates of alcohol delivered in each terminal-link stimulus context. When the initial-link schedules were increased, preference for the preferred context decreased. Future studies can benefit from the use of quantitative models of behavior on concurrent and concurrent-chains schedules as a framework for the assessment of potential behavioral and pharmacological treatments of drug abuse and dependence.

Econometric studies of firm-level behavior are gaining acceptance among some industrial organization economists. This is a potentially useful tool for detecting noncompetitive behavior. Policy makers and antitrust enforcement officials are interested in the results of these studies as they are applied to specific industries to help enforce current antitrust regulations and develop new policies. These New Empirical Industrial Organization (NEIO) econometric behavioral studies typically require detailed price, quantity, and cost data regarding the industry being studied. The models used are derived from the profit maximization problem of individual firms. In spite of this fact, many previous studies have relied on publicly available industry aggregate data, often also aggregated over time to the quarterly or yearly-level. This study investigates the sensitivity of empirical estimates of market power obtained from econometric conjectural variations studies to the level data aggregation used for the analysis. In addition, the sensitivity of the results to model specification is also explored. The focus of this study is on measurement of oligopsony power in the U. S. beef packing/processing industry. Using Monte Carlo techniques, weekly plant or firm-level data are simulated to be representative of the U. S. beef packing industry in two broadly defined geographical procurement regions. To broaden the scope of the experiment, the assumed underlying technology of the beef packing industry is varied across a broad range of possibilities. In addition, alternative assumptions regarding the conduct of industry participants in the live cattle procurement market are imposed on the data generation process. The disaggregate data sets are aggregated over plants and firms to weekly industry aggregates, and over time to quarterly industry aggregates. At each level of aggregation, the data are tested using 3 alternative specifications of an NEIO econometric market power testing model, that differ by functional form. Results of the tests are compared across aggregation levels, and across model specifications. The results reveal that in general the actual size of the test of the null hypothesis of no market power is much higher than the chosen nominal size of the test. The power of the test for market power is quite high. Data aggregation tends to bias the results of tests for market power. In addition, an adequately flexible functional form must be specified to capture the underlying technology of the industry when using econometric methods to test for market power. Therefore, in order to be useful for antitrust policy enforcement, econometric behavioral studies must make use of detailed firm (or plant )-level disaggregate data, and must use carefully specified models. / Ph. D.

Background and Motivation. Requirements engineering is decision intensiveand involves many roles and stakeholders. As humans are often subjective in theirdecision-making and biased by subjective criteria, we are interested in exploring howthis impacts requirements prioritization. Each requirements prioritization techniquehas its advantages and limitations to use on software products for single/multiplepurposes in the software field. Understanding how human aspects affect requirementsprioritization remains greatly unexplored. Objectives. This thesis aims to understand how human factors impact requirementsprioritization. The primary goal is to address and understand the various human as-pects that affect people when they make decisions. The secondary goal is to identifyvarious human aspects that receive more attention while prioritizing requirements. Methods. Systematic Literature Review (SLR) and survey were chosen as the re-search methods for this thesis. A snowballing method was used to extract empiricalresearch papers that were used for implementing the survey questionnaire. Each em-pirical paper from snowballing method has identified some human aspects throughone or more prioritization techniques and prioritization criteria. Using these humanaspects as input a survey questionnaire is designed for gaining insights on occur-rences/experiences of these human aspects in a large organization of Agile practi-tioners. Results. From the literature review, we identified 21 papers through the snow-balling method. And we identified more than two human aspects from each SLRpaper that impact requirements prioritization that were grouped into 11 categories.We also discovered many requirements prioritization techniques and their criteriawhere we included the top 15 RP techniques, 11 human aspects, and 17 RP cri-teria in the web-based survey questionnaire that were extracted through the SLRapproach. Our survey respondents considered the human aspects as very importantare Domain Knowledge of Individuals/ Stakeholders/ Analysts; Ability to consid-er/understand multiple perspectives; Ability to build/reach Consensus; Cognitiveskills and Limitations; Group Cohesion/ Team Maturity; and Accept Diversity as-pects as having the largest impact when prioritizing requirements. We have alsodiscovered that Emotions/ Emotional Cohesion which is also rated by the surveyrespondents as very important and is having the least impact as a human aspectwhen prioritizing requirements. Conclusions. Our study focus on the human aspects in requirements prioritizationmethod, the actual human aspects are least graded and human behavior that is con-sidered as an human aspect is highly graded by the practitioners in the survey. So aclear map is needed to identify the human aspect bias for requirements prioritizationand the results of this study can be helpful to all the researchers who want to carryour research on requirements prioritization in relation with human aspects.

Huntington's disease (HD) belongs to neurodegenerative disorders. It is a monogenic disease caused by trinucleotic CAG expansion in exon 1 of gene coding protein huntingtin. Even though the cause of HD is known since 1993, the pathophysiology and cure for HD reminds to be found. The animal models are being used for better understanding of HD. The most common animal models for HD are rodents, especially mice but it was also important to create large animal models, which will be more like human. Therefore, TgHD minipig was created in Academic of Science in Liběchov in 2009. This model was created by microinjection of lentiviral vector carrying N-terminal part of human HTT with 124 repetitive CAG in exon 1. This model is viable and in every generation, is part of the offspring transgenic. In this thesis, I specialized to biochemical and behavioral changes of this model. I compared transgenic and wild type siblings. I found that biochemical changes are manifested mostly by increased level of mtHtt fragments in testes and brain. In behavioral part of this thesis I established new methods for testing behavioral changes in this model. The introduced methods showed some changes between wild type and transgenic animals at the tested ages but these changes were not significant due to the low number of...

Au cours des 20 dernières années, de nombreux modèles animaux ont émergé, permettant le développement de nouvelles approches pour l'étude préclinique du cerveau sain et pathologique. Les rongeurs sont ainsi devenus des acteurs incontournables des avancées thérapeutiques. Dans ce contexte, l'imagerie radioisotopique, qui permet de quantifier des traceurs radioactifs avec une sensibilité excellente, constitue un outil de choix l'étude des processus cérébraux in vivo. Mais, jusqu'à présent, les techniques de radioimagerie les plus courantes imposent l'anesthésie ou l'immobilisation de l'animal. Or, les anesthésiants affectent les processus biologiques étudiés. De plus, il existe un vif intérêt pour l'étude simultanée du comportement de l'animal. L'acquisition d'une image dynamique des processus cérébraux concomitante à la mesure du comportement de l'animal éveillé et libre de ses mouvements est une information précieuse pour l'étude de l'addiction, de la mémoire, etc.À IMNC, nous avons abordé la neuroimagerie comportementale par une approche originale basée sur des sondes intracérébrales qui mesurent la concentration du traceur radioactif par détection directe des positons in situ. La sonde PIXSIC, basée sur un capteur pixelisé à diodes de silicium, a démontré leur pertinence dans le cadre d'études pharmacologiques chez l'animal totalement libre de ses mouvements. Toutefois, PIXSIC a montré quelques limitations pour son utilisation longitudinale : un niveau de bruit élevé dû aux perturbations électromagnétiques, une forte sensibilité au rayonnement gamma d'annihilation et une grande fragilité mécanique de l'implant aminci à 200 micromètres. En nous appuyant sur l'avènement des technologies CMOS pour la détection des particules chargées en physique des hautes énergies, nous avons pour ambition de concevoir MAPSSIC, une sonde qui réponde aux difficultés mises en avant par PIXSIC. Les capteurs CMOS permettent d'inclure l'amplification au niveau des pixels, limitant ainsi le bruit d'origine électromagnétique. Le volume sensible peut être réduit à une épaisseur de quelques dizaines de micromètres, réduisant ainsi fortement la sensibilité aux gammas et autorisant l'augmentation de son épaisseur totale pour assurer sa robustesse mécanique. Enfin, les capteurs CMOS nous permettent de concevoir un détecteur fortement pixelisé pour accéder à de nouvelles capacités d'imagerie. Cette thèse a eu pour objectif de développer une version optimisé de la sonde. Pour cela, nous avons imaginé un premier prototype de capteur CMOS et nous avons développé un modèle Monte Carlo pour estimer ses propriétés de détection. Nous avons pu démontrer que ses performances le qualifiait pour l'usage prévu. Notamment en terme de sensibilité, de volume d’isoefficacité et d’énergie déposée. Nous avons également pu explorer plusieurs paramètres d’optimisation, les dimensions des pixels et l’épaisseur de la zone sensible, qui nous permettent de considérer MAPSSIC au delà du premier prototype. Fort de ces bases théoriques nous avons conçu plusieurs exemplaires du capteur. Les développements qui ont été établis durant la thèse se sont ensuite focalisés sur un ensemble d'outils méthodologiques, logiciels et matériels afin de permettre la caractérisation physique du capteur à l'aide de sources radioactives. Nous avons pu établir l'uniformité de la réponse des pixels et la plage de taux d’évènements assurant la linéarité du taux de comptage. Ces éléments nous ont permis de conclure sur la pertinence de ce capteur pour la conception d'un dispositif autonome d'imagerie. Celui-ci est constitué d'un implant fait de deux capteurs dos-à-dos, d'un système électronique assurant le contrôle des capteurs, la lecture du signal et la communication sans fil et d'une station d'acquisition. Dans le cadre de la thèse, nous avons montré son adéquation pour l'évaluation des variations de l'activité d'une source radioactive bêta+ liquide dans laquelle l'implant a été plongé. / Over the last 20 years, many animal models have emerged, allowing the development of new approaches for the preclinical study of the healthy and pathological brain. Rodents have become key players in therapeutic advances. In this context, radioisotope imaging, which quantifies radioactive tracers with excellent sensitivity, is a prime tool for the study of brain processes in vivo. But so far, the most common radioimaging techniques require anesthesia or immobilization of the animal. However, anesthetics affect the biological processes studied. In addition, there is a keen interest in the simultaneous study of the behavior of the animal. The acquisition of a dynamic image of brain processes concomitant with the behavior of the awake and freely moving animal is valuable information for the study of addiction, memory, etc.At IMNC lab, we have approached behavioral neuroimaging with an original method based on intracerebral probes that measure the concentration of the radioactive tracer by direct detection of positrons in situ. The PIXSIC probe, based on a pixelized sensor with silicon diodes, demonstrated their relevance in the context of pharmacological studies with completely freely moving animals. However, PIXSIC has shown some limitations for its longitudinal use: a high level of noise due to electromagnetic perturbations, a high sensitivity to annihilation gamma radiation and a high mechanical fragility of the implant thinned to 200 microns.Based on the advent of CMOS technologies for the detection of charged particles in high energy physics, our ambition is to design MAPSSIC, a probe that responds to the difficulties highlighted by PIXSIC. CMOS sensors allows amplification at the pixel level, thus limiting electromagnetic noise. The sensitive volume can be reduced to a thickness of a few tens of microns, thus greatly reducing the sensitivity to gammas and allowing the increase of its total thickness to ensure its mechanical robustness. Finally, CMOS sensors allows us to design a highly pixelated detector to reach new imaging capabilities. This thesis aims to develop an optimized version of the probe. We imagined a first prototype CMOS sensor and we developed a Monte Carlo model to estimate its detection properties. We were able to show that his performances qualified it for the intended use, in terms of sensitivity, isoefficiency volume and deposited energy. We have also been able to explore several optimization parameters, the pixel dimensions and the thickness of the sensitive area, which allow us to consider MAPSSIC beyond the first prototype. With these theoretical bases we have produced several copies of the sensor. The developments that were established during the thesis then focused on a set of methodological tools, software and hardware to allow the physical characterization of the sensor using radioactive sources. We have been able to establish the uniformity of the pixel response and the event rate range ensuring the linearity of the count rate.These elements allowed us to conclude on the relevance of this sensor for the design of an autonomous imaging device. This consists of an implant made of two back-to-back sensors, an electronic system providing sensor control, signal reading and wireless communication and an acquisition station. In the context of the thesis, we have shown its suitability for the evaluation of the variations of the activity of a liquid beta+ radioactive source in which the implant has been immersed.

This research delves into idiopathic autism spectrum disorder (ASD), investigating the role of TrkB signaling pathways and BDNF regulation in the cortex. Additionally, it explores offering insights into maternal influences on mouse models. / Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by challenges in social interactions and repetitive behaviors. Prevalence of ASD is estimated to be 1 in 54 globally and is rising recently in many countries including Canada. ASD affects individuals differently, making diagnosis challenging. At present, no molecular diagnosis of ASD is available. Further, available medications only manage some symptoms of the disease and have adverse side effects in children. Therefore, there is a need for accurate molecular diagnostic tools to aid in molecular detection and treatment of ASD. To this end, a better understanding of the underlying molecular mechanisms that link ASD etiology to ASD-related behavior is crucial. While genetic factors contribute to syndromic ASD, most cases of ASD are idiopathic with unknown causes, influenced by a combination of epigenetic and environmental factors. TrkB and its downstream signaling pathways, such as Akt and Erk, are hyper-activated in syndromic ASD and hypo-activated in idiopathic cases. Therefore, drugs like rapamycin that inhibit the mTOR pathway downstream of TrkB are beneficial for syndromic ASD but not idiopathic cases. Additionally, insulin-like growth factor 1 (IGF-1), which mitigates ASD-related synaptic disruptions via Akt and Erk signaling, shows unchanged mRNA and protein levels along with its receptor in the idiopathic ASD fusiform gyrus. In ASD with either genetic or epigenetic/environmental causes, disruptions in synaptic connectivity are observed. Synaptic function is regulated by signaling pathways involving brain-derived neurotrophic factor (BDNF) and its receptor, tropomyosin-related kinase B (TrkB), as well as their downstream signaling cascades such as MAPK and Akt. The existing literature suggests that there is an association between BDNF and TrkB signaling pathways and ASD. However, a serious gap in knowledge about the precise molecular role of TrkB in ASD pathology is that our current understanding is correlational in nature and based on observational studies that lack causal experiments. This underscores the importance of further research to understand the causative role of TrkB and its related molecular events in idiopathic ASD. The present work aims to provide a deeper understanding about the causative role of molecular mechanisms underlying TrkB signaling in ASD. ASD mouse models exhibit behaviors and molecular features resembling those observed in human ASD. Therefore, these mouse models are helpful tools for studying ASD. However, understudied physiological confounding factors, such as maternal age and parity, can introduce biases and add to data variability, thus negatively impacting the reproducibility and translational value of ASD mouse models. To achieve a reliable mouse model of ASD, we conducted our first study that examines the impact of maternal age and parity on pregnancy complications, neurodevelopment, and social behavior in mice. Results demonstrate that older maternal age and prior motherhood interact to ensure a normal, steady developmental rate and provide protective effects against anxiety, social impairment, and olfactory deficits. Given the current lack of clarity regarding the causative impact of TrkB on ASD pathology, our subsequent investigation sought to establish a causal relationship between TrkB signaling and ASD. We used the TrkB agonist, LM22A-4 treatment in a validated ASD mouse model. Our results demonstrate that treatment with LM22A-4 effectively rescues the core symptoms associated with ASD (social impairment and repetitive behavior). These findings indicate that impaired TrkB signaling is responsible for ASD-like behavior of valproic acid (VPA)-exposed mice. However, unlike TrkB-related molecular events occurring in the fusiform gyrus of idiopathic ASD, TrkB isoform protein levels, BDNF species, Akt, and Erk total protein levels and activation remained unchanged in VPA-exposed cortices compared to healthy control mice. Since our VPA mouse model does not replicate human idiopathic ASD, our study cannot draw a conclusion on how disruptions in these signaling pathways may contribute to the development and manifestation of ASD symptoms. Cortex is responsible for various aspects of social behavior that are impaired in ASD. However, regulatory mechanisms that are involved in ASD upstream of cortical TrkB and BDNF are not well known. BDNF expression is highly cell-and tissue-specific and is regulated by different sets of transcription factors in specific tissues. While NURR1, the BDNF regulator in midbrain neurons, is associated with ASD pathology, its specific role in regulation of cortical BDNF is not yet well-established. Our third study aimed to understand the role of NURR1 in regulating BDNF specifically in the cortex. We showed that in resting and depolarized neurons, when NURR1 is knocked down, BDNF mRNA levels remained unchanged, suggesting that NURR1 does not regulate BDNF in cortical neurons and highlighting the tissue-specificity of BDNF regulation. In summary, we address the understudied effects of maternal factors on mouse models, which enhances the reliability of ASD research. Further, our studies significantly enhance the understanding of ASD by elucidating the role of TrkB and its downstream signaling pathways in the behavioral aspects of the disorder. We also contribute to the knowledge of BDNF regulation in the cortex, a brain tissue with crucial roles in various aspects of social behavior. In a forward-looking approach, the results of our studies provide valuable insights into mouse modeling of idiopathic ASD and the potential role of TrkB in ASD behavioral symptoms. / Thesis / Candidate in Philosophy / Autism spectrum disorder (ASD) is a condition that is accompanied by challenges in social interaction and repetitive behaviors. ASD is a complicated condition because we do not fully understand all the details of how it works in the body. Studying ASD is important as it is the most challenging condition in children and it is becoming more common, especially in the last two decades. While scientists are developing molecular tools to improve ASD diagnosis and understand its biology, these tools are not widely used in clinics for ASD diagnosis yet. Also, the approved medications available can only help with managing some of the behavioral symptoms like self-harming behavior. Despite the pressing need to find a solution, our recent advancements have not yet brought us closer to a cure for ASD, mainly because of the complexity of the disorder. Therefore, identifying the specific ASD-related mechanisms at the molecular level that contribute to ASD-related behaviors is crucial for gaining a deeper understanding of the disease. In ASD, there are problems with how brain cells communicate with each other. This communication is controlled by certain molecules in the brain, such as brain-derived neurotrophic factor (BDNF) and its receptor, tropomyosin-related kinase B (TrkB), along with other molecules. There is evidence suggesting a link between these molecules and ASD, but we have not fully understood their precise roles because most of the current knowledge is based on observations and correlations, rather than on establishing cause-and-effect relationships. To bridge this gap, our research focused on understanding TrkB's role in ASD. We required reliable mouse models. Since we aimed to induce ASD-like behaviors in mice using an ASD-causing chemical, it was crucial to ensure they were healthy beforehand. We needed to confirm that any social deficits or repetitive behaviors were not due to other factors, such as adverse infancy experiences or impaired interactions between mother and infant. We discovered that sexually mature dams aged between 3 to 6 months, with a history of previous pregnancies and motherhood, give birth to healthier litters. These litters can serve as a more dependable source for our animal behavioral studies. Many cases of ASD in humans are caused by non-genetic factors such as environmental influences like pesticides, air pollution, and the use of certain drugs during pregnancy. In cases of human ASD triggered by non-genetic factors, there is an increase in proBDNF, the precursor of BDNF. However, this proBDNF does not efficiently convert to BDNF. With insufficient BDNF and TrkB receptors, molecules like Akt (protein kinase B, also PKB) and Erk (Extracellular Signal-Regulated Kinase), which are crucial for neuron communication, are also less active downstream. This imbalance disrupts neuron connections, leading to ASD behaviors. In our research, the ASD-causing chemical which we used is valproic acid. It is originally an anti-seizure medication. When pregnant women took valproic acid, the chance of their child having ASD increased. Scientists used this information to inject pregnant mice with valproic acid, and as a result, all the offspring showed ASD-like behaviors. We anticipated that by isolating the brains of these offspring and measuring protein levels of BDNF, TrkB, Akt, and Erk, we would observe a similar pattern to that seen in humans with non-genetic ASD cases. We focused on studying the cortex, a region of the brain responsible for regulating social behaviors in both mice and humans. Since ASD is associated with challenges in social behaviors, we isolated the cortex from mouse brains to analyze protein levels. A chemical known as LM22A-4 with a structure resembling BDNF can bind to TrkB and activate it. We expected that the offspring of pregnant dams injected with valproic acid, which led to reduced TrkB axis activation in their brains, would show improvement in ASD behavior. This anticipation stems from the understanding that LM22A-4 activates the TrkB axis, thus compensating for its reduction, which is thought to be causing ASD-like behaviors. The offspring of mothers injected with valproic acid exhibited ASD-like behaviors, unlike the control mice. Control mice were offspring of pregnant dams injected with a solution containing only the substances used to dissolve valproic acid, typically water and salt (saline). Mice prenatally exposed to valproic acid (VPA) exhibited ASD-like behaviors, but treatment with LM22A-4 helped alleviate these behaviors, promoting more typical behavior patterns. LM22A-4, by activating TrkB receptors, helped to protect the brain from harm caused by exposure to valproic acid before birth. This could mean that valproic acid-induced changes in TrkB-related molecular mechanisms are involved in social behavior difficulties and increased repetitive behaviors seen in autism. Nevertheless, the levels of TrkB, BDNF, proBDNF, Akt, and Erk in the cortex of offspring from mothers injected with valproic acid were like those in the offspring from mothers injected with the saline solution. Therefore, the BDNF and TrkB signaling pathways remained unchanged in the cortex of our valproic acid model in this study, and they differ from those observed in human idiopathic ASD. We also speculated that a protein, called NURR1 acting upstream of BDNF and TrkB might be involved in the process. NURR1 acts as a regulatory protein that binds to the BDNF, increasing the production of copies from the BDNF. We also used a small RNA that targets a specific region in the Nurr1 and inhibits its protein production We anticipated a reduction in Nurr1 levels. As NURR1 acts as an upregulator of BDNF, lower levels of Nurr1 would result in decreased BDNF production. Activating NURR1 resulted in increased BDNF mRNA levels. However, when NURR1 was reduced, BDNF mRNA levels remained unaffected. This led us to conclude that if NURR1 levels decrease, other proteins may step in to maintain BDNF mRNA levels. Therefore, in the cortex, unlike in some other brain regions, the presence of NURR1 is not essential for regulating Bdnf. In summary, before inducing ASD-like behavior in mice using valproic acid, it is crucial to ensure the health of the mice. We used sexually mature mothers with prior pregnancy experience to provide a healthy baseline. We showed valproic acid induced ASD-like behaviors in mice offspring. We also observed that LM22A-4 treatment alleviated ASD-like behaviors of offspring. In our study, we demonstrated that the levels of BDNF, TrkB, Erk, and Akt proteins in the cortex of mice exposed to valproic acid were not affected. For this reason, our mouse model does not resemble human non-genetic ASD. Finally, NURR1's role in BDNF regulation varies by brain region. Lowering NURR1 did not affect BDNF mRNA levels, suggesting compensatory mechanisms. Our findings suggest new directions for further research to better understand the roles of TrkB and BDNF in non-genetic ASD. Overall, this study provides valuable knowledge that can contribute to advancing our understanding of idiopathic ASD-related molecular mechanisms.

Quality of Experience (QoE) is the degree of delight or annoyance of the user of an application or service [1]. To ensure a proper level of QoE for end users, networks and service providers have to continuously monitor their systems in terms of technical parameters, which can then be used to estimate QoE. Especially for video streaming services, which consume a large amount of traffic, network problems such as bandwidth fluctuations quickly develop into annoying artefacts visible to the users, which may lead to abandonment of services. Internet Service Providers (ISPs) are therefore continuously monitoring video network streams in order to provide the better QoE. In this regard to conduct the user behavioral studies, the ISPs spend a large amount of money and energy every time. To avoid this, we are using existing user behavioral studies and simulating the user behavior in an automated set-up and try to measure the impact of network conditions. In our current studies based on the user-behavioral model used [5], we can conclude that low upload speeds don’t affect on simulated user behavior unless they are in high download speed networks. Simulated users with the mid-range download and upload bandwidth tend to face more stalling and quality switches compared to both low and high-bandwidth users. Key quality indicators(KQIs) of video QoE also depends on the number of videos we measure in a single session. Reloading of player helps to reduce stalling for mid and high bandwidths. Reloading worsens the situation in low bandwidth scenarios. / Kvalitet av erfarenhet (QoE) definieras som: "Graden av fröjd eller förargelse av användaren av en applikation eller en service. Den resulterar från uppfyllelsen av hans eller hennes förväntningar med hänsyn till hjälpmedlet, och/eller njutning av applikationen eller servicen i ljuset av användarens personlighet och aktuella tillstånd" [1]. Att att se till en riktig nivå av QoE för slut användare, nätverk och tjänstefamiljeförsörjare måste övervakar fortlöpande deras system när det gäller tekniska parametrar, som kan därefter vara den van vid bedömningen QoE. Speciellt för videoen som strömmar service, som konsumerar ett stort belopp av trafik, framkallar nätverksproblem liksom bandbreddväxlingar snabbt in i förargliga artefacts som är synliga till användarena, som kan leda till övergivande av service. Internetleverantörer (ISPs) är därför fortlöpande videopp nätverksströmmar för övervakning för att att ge den bättre QoEen. I detta avseende att föra de beteendestudierna för användaren, spenderar ISPsna en stor mängd pengar och energi varje gång. Att undvika denna, använder simulerar vi beteendestudier för existerande användare och användareuppförandet i en automatiserat aktivering och försök för att mäta inverkan av nätverksvillkor. I våra aktuella studier som baseras på den använda användare-beteendemodellen [5], oss kan avsluta som laddar upp lågt hastigheter inte påverkar på simulerat användareuppförande, om inte de är i höga nedladdninghastighetsnätverk. Simulerade användare med mitt–området nedladdar och laddar upp bandbredd ansar för att vända mot mer avbrottsoch kvalitetsströmbrytare som jämförs till både låga och höga bandbreddanvändare. Nyckelkvalitetsindikatorer (KQIs) av video QoE beror också på numret av video som vi mäter i en enkel period. Tillbakaläggande av spelaren hjälper att förminska avbrott för mittoch höga bandbredder. Tillbakaläggande försämrar läget i scenarion för låg bandbredd.