Hur samverkan mellan hem och förskola kom att påverkas av Covid-19 utbrottet har ännu inteforskats om i särskilt hög grad. Denna studies syfte grundar sig därför i att undersökahur samverkan mellan hem och förskola kom att påverkas av Covid-19-utbrottet. Med hjälp avatt se på situationer i förskolan där vårdnadshavare och förskollärare möts har vi kunnat skapafrågeställningar för att undersöka syftet av studien, de frågeställningar är: På vilket sätt harinskolningen av nya barn förändrats till följd av pandemin?, Hur har hämtning och lämningorganiserats under pandemin och hur har detta upplevts av vårdnadshavare respektiveförskollärare?, Vilka erfarenheter och upplevelser har nya rutiner för hemskickande av barnmed luftvägssymtom gett upphov till?, och Hur har det dagliga respektive organiserade samtaletmellan förskolans personal och vårdnadshavare upplevts under pandemin?. Som metod har vihaft en kvalitativ metod där vi haft intervjuer med vårdnadshavare och förskollärare med öppnafrågor. Den teori som ligger till grund för analys och resultat är systemteorin och dess centralabegrepp unfreezing, moving, refreezing, ekvifinalitet, jämvikt och störning av jämvikt. Resultatet av hur samverkan mellan hem och förskola påverkats av Covid-19 utbrottet förståsoch uppfattas olika av respondenterna. Vissa menar att det dagliga samtalet förändrats och attinformationsutbytet inte håller samma standard som förut, andra menar att det är likadant sominnan utbrottet men att organiseringen av hämtning och lämning är annorlunda. Det framkommeralltså i resultatet olika erfarenheter och känslor om hur samverkan påverkats eller inte, det lyftsockså fram olika sammanhang där hem och förskola möts. Nyckelord: Förskola, Covid-19, samverkan, kommunikation, unfreezing, moving och refreezing.

This thesis investigates how a closure of a maternity ward affects the moves intoand out of a municipality. The policy focus of a centralization of maternal care in Sweden may bring unseen consequences that matter for society in a larger sense than only births. By treating closeness to a maternity ward as a positive amenity for a municipality, these closures introduce an exogenous variation that is used to estimate how moves change over time. With the lack of data availability, utilizing Sweden’s administrative areas, the lack of data over amenities as well as other factors such as employment possibilities are compensated for, revealing the possibility of using similar areas as potential substitutes for amenities. The results indicate that closures are likely to introduce a lower mobility for the regions as both fewer moves in and fewer moves out are revealed in the estimates. The results are strongly significant, a one minute increase of travel time would be associated with a decrease of around 3 moves in and 2.7 moves out. These results are mostly robust, and findings suggest north and south of Sweden are fundamentally different.

The future is constantly growing as a more and more significant matter of interest, both in the academic fields and in everyday conversations. This thesis focuses on a specific perspective of the future: what if cities were no longer static by nature, but cruising across the world? By analysing three works of literary fiction, the aim is to illustrate how fiction, and more specifically ‘anticipation fiction’, can participate in shaping our social imaginaries, the understanding we have of the world, both when it comes to our present and our future. If the influence of future fiction has been shown before, this thesis will focus on the specific imaginary of moving cities in the future, to highlight how those authors imagine the future, covering the historical and geographical contexts of those futures, the urban logic of those moving cities, the social implications that follow, and finally the value given to humanity in those ‘realities’. Because of the fundamentally indeterminate nature of the future, future studies such as this one do not aim for a fixed answer, but rather for a deeper understanding of the relation of influence between present and future, of how the future shapes us just as we shape it.

Mitt syfte med detta arbete har varit att belysa det problem som det kan innebära för en skola atthantera elever som flyttat in under grundskoletiden och undersöka hur dessa elevers resultatpåverkas av det faktum att de har flyttat in från en annan skola.Den teori jag har använt är huvudsakligen systemteorin som den beskrivs i Peter Senges ”Denfemte disciplinen”. Jag valde denna teori eftersom frågeställningen i detta arbete förutsätter attman intar ett övergripande perspektiv på frågor som rör hela den svenska grundskolan – helasystemet.I undersökningen används till största delen statistiska metoder för att ta fram och analysera hurstor andel av eleverna i tre svenska grundskolor i en kommun som har flyttat under singrundskoletid och hur det har påverkat deras resultat, mätt som meritvärde i slutet av årskurs nio.Undersökningen visar att deras resultat, mätt som genomsnittet av meritvärdena i årskurs nio ärsignifikant lägre än genomsnittet för den aktuella årskullen. Mer studier kommer att behövas föratt säkerställa reliabiliteten i undersökningsresultatet och framför allt för att kunna diskuteralämpliga åtgärder för att hantera dessa resultat. / How the moving of the family affect student outcome in Swedish secondary school. I found that students in a community that have moved at least once during their nine years in the Swedish compulsory school get significantly lower grades than others.

This dissertation investigates the interactive or joint influence of autocorrelative processes (autoregressive-AR, moving average-MA, and autoregressive moving average-ARMA) and sample weights present in a longitudinal panel data set. Specifically, to what extent are the sample estimates influenced when autocorrelation (which is usually present in a panel data having correlated observations and errors) and sample weights (complex sample design feature used in longitudinal data having multi-stage sampling design) are modeled versus when they are not modeled or either one of them is taken into account. The current study utilized a Monte Carlo simulation design to vary the type and magnitude of autocorrelative processes and sample weights as factors incorporated in growth or latent curve models to evaluate the effect on sample latent curve estimates (mean intercept, mean slope, intercept variance, slope variance, and intercept slope correlation). Various latent curve models with weights or without weights were specified with an autocorrelative process and then fitted to data sets having either the AR, MA or ARMA process. The relevance and practical importance of the simulation results were ascertained by testing the joint influence of autocorrelation and weights on the Early Childhood Longitudinal Study for Kindergartens (ECLS-K) data set which is a panel data set having complex sample design features. The results indicate that autocorrelative processes and weights interact with each other as sources of error to a statistically significant degree. Accounting for just the autocorrelative process without weights or utilizing weights while ignoring the autocorrelative process may lead to bias in the sample estimates particularly in large-scale datasets in which these two sources of error are inherently embedded. The mean intercept and mean slope of latent curve models without weights was consistently underestimated when fitted to data sets having AR, MA or ARMA process. On the other hand, the intercept variance, intercept slope, and intercept slope correlation were overestimated for latent curve models with weights. However, these three estimates were not accurate as the standard errors associated with them were high. In addition, fit indices, AR and MA estimates, parsimony of the model, behavior of sample latent curve estimates, and interaction effects between autocorrelative processes and sample weights should be assessed for all the models before a particular model is deemed as most appropriate. If the AR estimate is high and MA estimate is low for a LCAR model than the other models that are fitted to a data set having sample weights and the fit indices are in the acceptable cut-off range, then the data set has a higher likelihood of having an AR process between the observations. If the MA estimate is high and AR estimate is low for a LCMA model than the other models that are fitted to a data set having sample weights and the fit indices are in the acceptable cut-off range, then the data set has a higher likelihood of having an MA process between the observations. If both AR and MA estimates are high for a LCARMA model than the other models that are fitted to a data set having sample weights and the fit indices are in the acceptable cut-off range, then the data set has a higher likelihood of having an ARMA process between the observations. The results from the current study recommends that biases from both autocorrelation and sample weights needs to be simultaneously modeled to obtain accurate estimates. The type of autocorrelation (AR, MA or ARMA), magnitude of autocorrelation, and sample weights influences the behavior of estimates and all the three facets should be carefully considered to correctly interpret the estimates especially in the context of measuring growth or change in the variable(s) of interest over time in large-scale longitudinal panel data sets.

Anomaly detection aims to identify the data samples that do not conform to a known normal (regular) behavior. As the definition of an anomaly is often ambiguous, unsupervised and semi-supervised deep learning (DL) algorithms that primarily use unlabeled datasets to model normal (regular) behaviors, are popularly studied in this context. The unmanned aerial system (UAS) can use contextual anomaly detection algorithms to identify interesting objects of concern in applications like search and rescue, disaster management, public security etc. This thesis presents a novel multi-stage framework that supports detection of frames with unknown anomalies, localization of anomalies in the detected frames, and validation of detected frames for incremental semi-supervised learning, with the help of a human operator. The proposed architecture is tested on two new datasets collected for a UAV-based system. In order to detect and localize anomalies, it is important to both model the normal data distribution accurately as well as formulate powerful discriminant (anomaly scoring) techniques. We implement a generative adversarial network (GAN)-based anomaly detection architecture to study the effect of loss terms and regularization on the modeling of normal (regular) data and arrive at the most effective anomaly scoring method for the given application. Following this, we use incremental semi-supervised learning techniques that utilize a small set of labeled data (obtained through validation from a human operator), with large unlabeled datasets to improve the knowledge-base of the anomaly detection system. / Master of Science / Anomaly detection aims to identify the data samples that do not conform to a known normal (regular) behavior. As the definition of an anomaly is often ambiguous, most techniques use unlabeled datasets, to model normal (regular) behaviors. The availability of large unlabeled datasets combined with novel applications in various domains, has led to an increasing interest in the study of anomaly detection. In particular, the unmanned aerial system (UAS) can use contextual anomaly detection algorithms to identify interesting objects of concern in applications like search and rescue (SAR), disaster management, public security etc. This thesis presents a novel multi-stage framework that supports detection and localization of unknown anomalies, as well as the validation of detected anomalies, for incremental learning, with the help of a human operator. The proposed architecture is tested on two new datasets collected for a UAV-based system. In order to detect and localize anomalies, it is important to both model the normal data distribution accurately and formulate powerful discriminant (anomaly scoring) techniques. To this end, we study the state-of-the-art generative adversarial networks (GAN)-based anomaly detection algorithms for modeling of normal (regular) behavior and formulate effective anomaly detection scores. We also propose techniques to incrementally learn the new normal data as well as anomalies, using the validation provided by a human operator. This framework is introduced with the aim to support temporally critical applications that involve human search and rescue, particularly in disaster management.

A six degree-of-freedom model for a tailless supersonic aircraft (TSA) concept was developed using MATLAB and Simulink. Aerodynamic data was provided through the computational fluid dynamics analysis of Techsburg, Inc. A three degree-of-freedom model of the configuration's longitudinal dynamics was completed first. Elevator control power was derived from the dynamic response requirements for pitch chosen by Techsburg. The propulsion model utilized General Electric F-414-400-like turbofan engines because an engine deck was readily available. Work on the six degree-of-freedom dynamic model began with determining the necessary rolling and yawing moment coefficients necessary to meet the rest of the chosen dynamic response requirements. These coefficients were then used to find the corresponding all-moving tip deflections. The CFD data showed that even at small all-moving tip deflections the rolling moment coefficient produced was much greater than the amount of yawing moment coefficient produced. This result showed that an additional roll effector was needed to counteract excess rolling moment at any given all-moving tip deflection and trim the aircraft. An angle of attack and pitch rate feedback controller was used to improve the longitudinal dynamics of the aircraft. Because this configuration lacked a vertical tail, a lateral-directional stability augmentation system was vital to its success. The lateral-directional dynamics were improved to Level 1 flying qualities through use of a modified roll/yaw damper. The modified controller fed yaw rate back to both the all-moving tips and roll effector. The six degree-of-freedom model was augmented with actuator dynamics for the elevator, roll effector, and all-moving tips. The actuators were modeled as first order lags. The all-moving tip actuator time constant was varied to determine the effect of actuator bandwidth on the lateral-directional flying qualities. After the actuator dynamics were successfully implemented, the six degree-of-freedom model was trimmed for both standard cruise and engine-out situations. The eccentuator concept from the DARPA Smart Wing program was selected as a possible conceptual design for the all-moving tip actuation system. The success of the TSA six degree-of-freedom dynamic model proved that morphing all-moving tips were capable of serving as effective control surfaces for a supersonic tailless aircraft. / Master of Science

The Norrbotnia Line Dance project focuses on the expansion and densification of Piteå, a small city in northern Sweden. To accommodate the expected population growth, the thesis explores interventions to urban sprawl by reorganizing existing buildings along Kyrkbrogatan, a main street in the city. The interventions proposed are: "Repurposing," which integrates a train station within a supermarket; "Addition," involving a shift and addition of a public building to enhance its urban presence; and "Relocation," relocating a villa to a new site and utilizing its space for apartment buildings. Through the exploration of architectural expressions and the integration of dualities, the interventions propose innovative solutions that contribute to the city's future growth and development while preserving its unique identity. By seeing buildings as free of its site the project aims to counter urban sprawl and promote a sustainable urban fabric. Drawing inspiration from Piteå's history, including its Roman-influenced city plan and previous relocations of buildings. From residential, the relocation, my thesis Norrbotnia Line Dance moves along Kyrkbrogatan through Public Offices, shifting and adding towards the beginning and end of the city at the Train Station, with its repurposing, while facing the issues of continuity as we go down the line.

The primary objective of this study is to develop a sliding interface method for simulations involving relative rotational grid motion suitable for unstructured grid topologies. The present method alleviates computationally expensive grid deformation, remeshing, and hole cutting procedures. Rotational motion is accomplished by rigidly rotating a subdomain representing the moving component. At the subdomain interface boundary, the faces along the interfaces are extruded into the adjacent subdomain to create new volume elements and provide a one-cell overlap. These new volume elements close the control volumes for the nodes on the interface surface and allow a flux to be computed across the subdomain interface. An interface flux is computed independently for each subdomain. The values of the solution variables and other quantities for the nodes created by the extrusion process are found by interpolation. The extrusion is done so that the interpolation will maintain information as localized as possible. A parallel implementation of the neighbor search is used to find the extruded points in the adjacent subdomain. The method has been implemented in a parallel, node-centered finite volume, high-resolution viscous flow solver. The method does not impose any restrictions on the subdomain interface aside from the axisymmetric limitation required for rotational motion. In addition, the grid on the subdomain interface is arbitrary. The boundary surfaces between the two subdomains can have independent grids from one another. They do not have to connect in a one-to-one manner and there are no symmetry or pattern restrictions placed on the surface grid. A variety of numerical simulations were performed on several small-scale model problems to examine conservation of the interface flux. Overall flux conservation errors were found to be comparable to that for fully connected and fully conservative simulations. In addition, excellent agreement was obtained with both theoretical and experimental results. Three large-scale applications were also used to validate the method and highlight some of the advantages of the sliding interface method compared to the current state-of- the-art for unstructured grid applications. This sliding interface method requires no geometric modifications and has significantly shorter run times Furthermore, there were no apparent adverse effects on the numerical solutions by not strictly enforcing flux conservation at the subdomain boundary.

No description available.