No description available.

The last half century, there has been a global pressure on increased measuring and presenting of results in the public sector. One of the sectors where the pressure and calls for Results-based management (RBM) has been, and currently is, strong both in Sweden and internationally is within international development cooperation. But, to prove the effectiveness of development cooperation is not a simple task, and hence, the steering signals regarding the management model has been met with some criticism. However, although scholars argue that there have been several waves of pressure for RBM since the early 1970s, the massive amount of criticism seems to have been mainly aimed towards the latest push, in the 2000s. Why is that? This thesis take that question as an analytical starting point, but will not make any causal claims. Instead, it will take on a descriptive design, with an aim to identify any differences between RBM in Swedish development cooperation during the first and the latest push for increased focus on results. The main research question for the thesis is; In what way has the introduction of RBM in Swedish development cooperation been visible over time? The question will be analysed through text analyses to describe both the vision of RBM, as well as the evaluations of Swedish development cooperation during the two pushes for RBM. The study identifies differences on both levels, mostly regarding the aim of the model regarding whom the information is for, as well as significant differences in the ways evaluations have been conducted.

The purpose of this project is to evaluate the performance of a forecasting model based on a multivariate dataset consisting of time series of traffic characteristic performance data from a mobile network. The forecasting is made using machine learning with a deep neural network. The first part of the project involves the adaption of the model design to fit the dataset and is followed by a number of simulations where the aim is to tune the parameters of the model to give the best performance. The simulations show that with well tuned parameters, the neural network performes better than the baseline model, even when using only a univariate dataset. If a multivariate dataset is used, the neural network outperforms the baseline model even when the dataset is small.

I den aktuella studien så har följsamhet av RBM-B inom Sveriges Frivårdskontor undersökts. De begrepp och teorier som avhandlas är verktyget RBM-B, modellen RBM, the central eight, följsamhet, individualprevention och tertiär brottsprevention. De frågeställningar som avsetts att besvaras är hur följsamhet av verktyget RBM-B inom Frivården ser ut, om det finns skillnader i hur risk och behovsområdena kodas, samt vilka skillnader det finns i verkställighetsplaneringen. Av resultatet går att utläsa en övervägande följsamhet utifrån risk och behovsområdena förutom för psykisk ohälsa. Det framgår även en övervägande följsamhet för återfallsrisken inom områdena för generell kriminalitet och sexualbrott, dock visas en mindre följsamhet i området partnervåld. Vidare framkommer att samtliga deltagare haft en överensstämmande insatsplanering, dock utläses mindre skillnader. Studien utgörs av en mix mellan kvalitativ och kvantitativ ansats och fokuserar på överensstämmelse och tillförlitlighet. 10 deltagare som arbetar med verktyget RBM-B inom Frivården har deltagit. / <p>2024-01-12</p>

>Magister Scientiae - MSc / This study investigated the current knowledge, perceptions, and practices of Risk-Based Monitoring (RBM) using written and verbal responses to an ethics review board approved questionnaire. Responses were collected from individuals involved in the practice, oversight, and implementation of clinical trial monitoring in the USA. RBM was viewed as a positive force with a bright future. However the results suggested that a renewed focus on change management strategies is needed to ensure RBM practices penetrate all levels of clinical trial management. The site sponsor/site operational relationship was identified as a key RBM component. Shortcomings in this relationship were identified as significant operational barriers to effective RBM practice. Respondents indicated that current RBM training efforts were lacking. Because RBM is new and its practices deviate significantly from the past total monitoring efforts, both industry and the clinic need to work harder to ensure that everyone involved in clinical trial monitoring understands these differences. Fortunately, overcoming the identified barriers will not require massive changes to current RBM practice. By refocusing efforts on the sponsor/CRO and investigative sites to attain RBM governance, develop quality control plans, institute an optimal RBM platform, and improve training, the true promise of RBM is within reach. Each of these are critical pieces to an effective RBM implementation methodology and correcting initial stumbles in their implementation can assure the RBM future is as promised.

RNA Binding Motif (RBM) domain proteins RBM5 and RBM10 have been shown to influence apoptosis, cell cycle arrest and splicing in transformed cells. In this study, RBM5 and RBM10 were examined in non-transformed cells in order to gain a wider range of knowledge regarding their function. Expression of Rbm5 and Rbm10, as well as select splice variants, was examined at the mRNA and protein level throughout H9c2 skeletal and cardiac myoblast differentiation. Results suggest that Rbm5 and Rbm10 may (a) be involved in regulating cell cycle arrest and apoptosis during skeletal myoblast differentiation and (b) undergo post-transcriptional or translational regulation throughout myoblast differentiation. All in all, the expression profiles obtained in the course of this study will help to suggest a role for Rbm5 and Rbm10 in differentiation, as well as possible differentiation-specific target genes with which they may interact.

Klarna uses a logistic regression to estimate the probability that an e-store customer will default on its given credit. The logistic regression is a linear statistical model which cannot detect non-linearities in the data. The aim of this project has been to develop a program which can be used to find suitable non-linear interaction-variables. This can be achieved using a Restricted Boltzmann Machine, an unsupervised neural network, whose hidden nodes can be used to model the distribution of the data. By using the hidden nodes as new variables in the logistic regression it is possible to see which nodes that have the greatest impact on the probability of default estimates. The contents of the hidden nodes, corresponding to different parts of the data distribution, can be used to find suitable interaction-variables which will allow the modelling of non-linearities. It was possible to find the data distribution using the Restricted Boltzmann Machine and adding its hidden nodes to the logistic regression improved the model's ability to predict the probability of default. The hidden nodes could be used to create interaction-variables which improve Klarna's internal models used for credit risk estimates. / Klarna använder en logistisk regression för att estimera sannolikheten att en e-handelskund inte kommer att betala sina fakturor efter att ha givits kredit. Den logistiska regressionen är en linjär modell och kan därför inte upptäcka icke-linjäriteter i datan. Målet med detta projekt har varit att utveckla ett program som kan användas för att hitta lämpliga icke-linjära interaktionsvariabler. Genom att införa dessa i den logistiska regressionen blir det möjligt att upptäcka icke-linjäriteter i datan och därmed förbättra sannolikhetsestimaten. Det utvecklade programmet använder Restricted Boltzmann Machines, en typ av oövervakat neuralt nätverk, vars dolda noder kan användas för att hitta datans distribution. Genom att använda de dolda noderna i den logistiska regressionen är det möjligt att se vilka delar av distributionen som är viktigast i sannolikhetsestimaten. Innehållet i de dolda noderna, som motsvarar olika delar av datadistributionen, kan användas för att hitta lämpliga interaktionsvariabler. Det var möjligt att hitta datans distribution genom att använda en Restricted Boltzmann Machine och dess dolda noder förbättrade sannolikhetsestimaten från den logistiska regressionen. De dolda noderna kunde användas för att skapa interaktionsvariabler som förbättrar Klarnas interna kreditriskmodeller.

Magister Legum - LLM / South Africa

This work focuses on automatic classification of images into semantic classes based on their contentc, especially in using SVM classifiers. The main objective of this work is to improve classification accuracy on large datasets. Both linear and nonlinear SVM classifiers are considered. In addition, the possibility of transforming features by Restricted Boltzmann Machines and using linear SVM is explored as well. All these approaches are compared in terms of accuracy, computational demands, resource utilization, and possibilities for future research.

Forest harvesting has been shown to cause various changes in water quantity and water quality parameters, highlighting the need for comprehensive forest practice rules. Studies show a myriad of impacts to ecosystems as a result of watershed level changes, such as forest harvesting. Being able to better understand the impact that forest harvesting can have on stream temperature is especially critical in locations where federally threatened or endangered fish species are located. The overall goal of this research project is to assess responses in stream temperature to various riparian and forest harvest treatments in a maritime, mountainous environment. The results of this study aim to inform decision makers with additional information pertaining to the effects of forest harvest on water temperature. Modeling is done as a part of the third Caspar Creek Paired Experimental Watershed study. Located in Mendocino County, the site provides a place for California researchers and decision makers to learn about the cumulative watershed effects of forest management operations on peak flows, sediment production, anadromous fish, macro-invertebrate communities, nutrient cycling and more. Historic data was used to calibrate the Distributed Hydrology Soil Vegetation Model (DHSVM) and River Basin Model (RBM) to measured stream temperatures in the South Fork of Caspar Creek (SFC) for hydrologic years 2010-2016. Critical summer time periods, when temperatures are highest and flows are low, are the primary concern for this work. The key modeling scenarios evaluated were (1) varying percentages of Watercourse and Lake Protection Zones (WLPZ) canopy cover, (2) the 2018-2019 SFC forest harvest and (3) an experimental design converting dominant riparian vegetation along 300-yard stream reaches. Modeling results showed that stream temperatures begin to rise above third-growth conditions when canopy cover is reduced to 25% and 0% retention levels. Larger increases in Maximum Weekly Maximum Temperature (MWMT) values, compared to Maximum Weekly Average Temperature (MWAT) values, were seen across all scenarios. There was essentially no difference between altering buffer areas along only class I streams, compared to along all stream classes. At the 0% canopy retention, MWMT values consistently rose above recommended thermal limits for Coho salmon (Oncorhynchus kisutch) and state regulations prohibiting more than a 5 degree F increase in waters. Clearcutting the entire watershed produced less of an effect than simulations clearing on only the riparian area, suggesting that groundwater inflows act to mitigate stream temperature rises in the SFC. The 2018-2019 harvest showed a relatively consistent increase in MWAT values (avg. 0.11 degree C) and more varied increases in MWMT values (avg. 0.32 degree C). Simulations converting dominant riparian vegetation by clearing could not be considered conclusive due to sensitivity analyses suggesting potentially unrealistic tracking of downstream temperatures. Additional sensitivity analyses suggest that tree height and the monthly extinction coefficient (a function of Leaf Area Index) are most influential on stream temperature changes in SFC. This is consistent with other modeling studies and suggests stream temperature management focus on tall, dense buffers as opposed to wider buffer widths.