The aim is to investigate models that predicts the potential of pulp and evaluate the relevance of the zero-span tensile index within these. Two chemical pulps made from softwood and eucalyptus were refined in a Voith-beater with different energy input in order to study the change of fiber morphology signals and other pulp and paper properties. Chemical, THP pulp from Södra Värö is also used as an initial analysis for morphological connections to Zero-span tensile index. The L&W Fiber Tester Plus is used in order to study the pulps fiber morphology and Pulmac 2000 for zero span. Handsheets are made for mechanical tests such as tensile properties, ZD-strength and optical properties. Many of the given signals change according to clear patterns with increasing refining energy. Using least square methods, formulas describing the development with high adaptation could be formulated. Many of the measured aspects changes over already known patterns. These are then applied in the models. Three possible models is tested: linear regression, Shear-Lag and Page. Of the three, only the two first ones where able to produce reliable models, whereas the third required data that was difficult to acquire at the same time as the adaptation was very low. The only model that use exclusively morphology data is linear regression.

Research about unmanned ground vehicles (UGVs) has received an increased amount of attention in recent years, partly due to the many applications of UGVs in areas where it is inconvenient or impossible to have human operators, such as in mines or urban search and rescue. Two closely linked problems that arise when developing such vehicles are motion planning and control of the UGV. This thesis explores these subjects for a UGV with an unknown, and possibly time-variant, dynamical model. A framework is developed that includes three components: a machine learning algorithm to estimate the unknown dynamical model of the UGV, a motion planner that plans a feasible path for the vehicle and a controller making the UGV follow the planned path. The motion planner used in the framework is a lattice-based planner based on input sampling. It uses a dynamical model of the UGV together with motion primitives, defined as a sequence of states and control signals, which are concatenated online in order to plan a feasible path between states. Furthermore, the controller that makes the vehicle follow this path is a model predictive control (MPC) controller, capable of taking the time-varying dynamics of the UGV into account as well as imposing constraints on the states and control signals. Since the dynamical model is unknown, the machine learning algorithm Bayesian linear regression (BLR) is used to continuously estimate the model parameters online during a run. The parameter estimates are then used by the MPC controller and the motion planner in order to improve the performance of the UGV. The performance of the proposed motion planning and control framework is evaluated by conducting a series of experiments in a simulation study. Two different simulation environments, containing obstacles, are used in the framework to simulate the UGV, where the performance measures considered are the deviation from the planned path, the average velocity of the UGV and the time to plan the path. The simulations are either performed with a time-invariant model, or a model where the parameters change during the run. The results show that the performance is improved when combining the motion planner and the MPC controller with the estimated model parameters from the BLR algorithm. With an improved model, the vehicle is capable of maintaining a higher average velocity, meaning that the plan can be executed faster. Furthermore, it can also track the path more precisely compared to when using a less accurate model, which is crucial in an environment with many obstacles. Finally, the use of the BLR algorithm to continuously estimate the model parameters allows the vehicle to adapt to changes in its model. This makes it possible for the UGV to stay operational in cases of, e.g., actuator malfunctions.

Purpose- This thesis aims to identify the role of the Balanced Scorecard (BSC) for managing strategy risks as well as the types of strategy risks that can be managed using four perspectives of the BSC in the Iranian Petroleum Industry Equipment Manufacturers (IPIEM). Design/ approach/ methodology- In this thesis cross-sectional design and the deduction approach are used. For collecting data for quantitative analysis, a questionnaire was conducted by the research team. Then the data collected from respondents were then analyzed through running simple linear regression analysis in the SPSS software. Findings- The first research question (RQ) is about BSC’s roles in managing strategy risks in IPIEM. These roles are risk assessment, risk controlling and collecting data for decision making of strategy risks. It was proved by the research team that BSC can play a role of the assessment of strategy risks in IPIEM. This means by using BSC as an RM tool in IPIEM, companies can assess strategy risks through identifying, analysing and evaluating strategy risks. However, the results indicate risk controlling and collecting data for decision making cannot be managed by using BSC. The second Research question is about the types of strategy risks that four perspectives of BSC can manage. The results show that from the 8 strategy risks chosen for this thesis, 6 of them which are “liquidity risk” from the financial perspective; “risk of clients’ opposition to pilot testing of the product” from the customer perspective; “risk of improper design of the product at development stages”, and “risk of improper selection of international partners” from the internal perspective; “risk of incorrect evaluation & selection of technology options” and “the risk of not enough operational experience in similar previous projects” from the learning and growth perspective can be managed through using BSC as an RM tool in IPIEM. Based on the conclusion of RQ1, the effect can now be adjusted into RQ2 findings. This study concludes that IPIEM can use BSC for risk assessment of the above-mentioned six different strategy risks. It can also be concluded that the BSC cannot be a full RM tool for managing strategy risks in the companies, since it only can apply for one of the three processes of RM; risk assessment.

Throughout history stock splits have only been seen as a cosmetic change on how a firm express its market value of equity. This study investigates if abnormal return occurs in connection with stock split announcements on Nasdaq Stockholm and how the variations may be explained by selected factors. An event study is performed on 83 stock splits during the time period 2010-2020 to establish if abnormal return is present. With a multivariate linear regression, split quota, firm size and trading volume are the selected factors which may explain the variations in abnormal return. The results from the event study establish abnormal return one day prior to the announcement and the event day itself. Further, the regression confirms at a statistically significant level the negative relationship between firm size and abnormal return. For trading volume, the regression finds no statistically significant result and thereby it does not explain the variations in abnormal return. As for split quota, no conclusion can be drawn whether it affects abnormal return or not. The study concludes the occurrence of abnormal return in connection with stock split announcements on Nasdaq Stockholm and firm size as one of the factors explaining the variations.

Diese Dissertation untersucht einen möglichen Zusammenhang zwischen dem beschriebenen klinischen Verlauf bei Patienten mit konstitutiv aktivierenden TSH-Rezeptormutationen und der gemessenen in vitro Aktivität. Konstitutiv aktivierende Mutationen finden sich als somatische Mutationen in autonomen Adenomen und als Keimbahnmutationen bei Patienten mit sporadischer bzw. familiärer nicht-autoimmuner Hyperthyreose. Die in vitro Aktivität der zu Grunde liegenden TSH-Rezeptormutationen wird mit Hilfe der Linearen Regressions-Analyse bestimmt. Dies ist ein Verfahren, welches die basale Produktion des second messenger cAMP (Cyclo-Adenosinmonophosphat) misst, unter Berücksichtigung der Expression des TSH-Rezeptors. Die Analyse der Krankheitsverläufe der sporadischen nicht-autoimmunen Hyperthyreose zeigt keinen eindeutigen Bezug zur gemessenen in vitro Aktivität. Es besteht jedoch eine höhere in vitro Aktivität bei Mutationen, die sowohl bei der nicht-autoimmunen sporadischen Hyperthyreose und in autonomen Adenomen zu finden sind, im Vergleich zu ausschließlich familiären Mutationen. Dies entspricht auch dem klinischen Eindruck. Für die wenigen bekannten Fälle der sporadischen nicht-autoimmunen Hyperthyreose wurden dramatische Verläufe mit häufigen Rückfällen unter medikamentöser Therapie und zahlreichen Komplikationen (z.B. mentale Retardierung, Kraniosynostose, zerebrale Ventrikulomegalie, beschleunigte Knochenreifung) beschrieben.

The loss of 6 million U.S. manufacturing jobs since 2000 has severely affected communities that have lost a vital source of employment. Voluntary employee turnover has compounded the problem. The purpose of this correlational study was to examine the relationship between employee turnover and destructive leadership behaviors of managers in small and medium enterprise (SME) manufacturing businesses in Warren County, New Jersey. The constructive-destructive leadership model formed the theoretical framework for the study. A random sample of 96 SME manufacturing firm employees completed the destructive leadership scale (DLS), multifactor leadership questionnaire (MLQ), and the turnover intention scale (TIS-6) via an online survey. Multiple linear regression analyses and Pearson-product correlation coefficients were used to predict employee turnover. Tyrannical leadership and laissez-faire leadership were the only significant contributors to the regression model. Implications for social change include providing business managers with information needed to maintain or increase employee retention levels, which may improve employee morale, increase job satisfaction, and enhance customer satisfaction in the communities served.

No description available.

No description available.

In this thesis an analysis is performed on operation times for different sized products in a manufacturing company. The thesis will introduce and summarise most of the theory needed to perform regression and also cover a worked example where three different regression models are learned, evaluated and analysed. Conformal prediction, which at the moment is a hot topic in machine learning, will also be introduced and will be used in the worked example.

A popular type of investments are financial investments. Even though the Swedish society aspires for equality, there are still financial differences between the sexes. This thesis project focuses on what factors that affect the amount that women and men invest in stocks. The aim is to obtain a deeper knowledge about the investment market and how it appears for women and men. The reason for this is to raise awareness about the inequality issue when it comes to the low representation of females in the investment market.  The objective for this project is to produce two models where one of them is for women and the other one for men. Those models have equal regressors: inflation, GDP growth, OMX Stockholm Price Index, average income per year and eight different age groups. The response variable is the average portfolio value for a specific age group at respective model. The data is taken mainly from Swedish Statistical Central Bureau, but is also gathered from NASDAQ, as well as the World Bank. The data is collected between the years 2000 to 2020.  The models are firstly evaluated at their full model, meaning that all regressors are included. The women's model shows a considerably good fitting, since almost all regressors are significant with low p-values along with a Multiple R-squared at 0.773 and Adjusted R-squared at 0.757. However, improvements can be made since outliers need to be removed, and the regressor income hold multicollinearity. Men's full model has a poor performance with fewer significant regressors and lower Multiple and Adjusted R-squared. Both models are then transformed, with applied square root of inflation at both models, and the square of income at men's model.  To determine the reduced model, variable selection is implemented. With Best Subset Selection, women's model includes OMX, GDP, income, age group 1, age group 6, age group 7 and age group 8. On the other hand, men's model has the regressors GDP, income, age group 1, age group 2, age group 3, age group 4, age group 5 and age group 7. Both these models perform much better where all the regressors had significant p-values and satisfactory Model Assumptions plots. In addition, no multicollinearity exists in the models. In conclusion, both the reduced models are chosen for final models.  Proposed further research within this topic is to include more appropriate regressors that may affect the portfolio value as well as to compare with other countries and include real investment instead of just financial investments. / En vanlig typ av investeringar är finansiella investeringar. I Sverige råder finansiella skillnader mellan könen trots att Sverige eftersträvar jämställdhet. Detta projekt fokuserar på vilka faktorer som påverkar den mängd kvinnor och män investerar i finansiella investeringar, däribland aktier. Syftet är att öka medvetenheten kring jämställdhetsfrågan när det kommer till den låga representationen av kvinnor på investeringsmarknaden.  Projektets mål är att skapa två modeller där ena är en modell för kvinnor och den andra är en modell för män. Dessa två modeller har likvärdiga regressorer vilket är inflation, BNP, OMX Stockholm Price Index, medelinkomst per år, samt åtta olika åldersgrupper. Responsvariabeln är medelportföljvärde för respektive åldersgrupp för varje modell. Datainsamlingen är främst från Sveriges Statistiska Centralbyrå, men också NASDAQ och Världsbanken. Datat är insamlat från år 2000 till år 2020.  Först, utvärderas de fulla modellerna som inkluderar alla möjliga regressorer. Modellen för kvinnor visar sig vara hyfsad eftersom nästan alla regressorer är signifikanta med låga p-värden samt ett Multiple R-squared på 0.773 och ett Adjusted R-squared på 0.757. Emellertid, behöver förbättringar åstadkommas eftersom extremvärden ska tas bort samt att multikollinearitet upptäcks för regressorn inkomst. Den initiala modellen för män presterar ogynnsamt med låga Multiple och Adjusted R-squared och därtill färre signifikanta regressorer. Båda modellerna behöver transformationer. För kvinnomodellen appliceras roten ur inflation och för mäns modell används roten ur inflation men också inkomst upphöjt med två.  Vidare, bestäms de reducerade modellerna genom så kallad variable selection. Med utövandet av metoden Best Subset Selection har modellen för kvinnor regressorerna: OMX, BNP, inkomst, åldersgrupp 1, åldersgrupp 6, åldersgrupp 7 och åldersgrupp 8. Modellen för män får kombinationen: BNP, inkomst, åldersgrupp 2, åldersgrupp 3, åldersgrupp 4, åldersgrupp 5 och åldersgrupp 7. Bägge dessa modeller presterar bättre då alla regressorer har signifikanta p-värden och visar lämpliga grafer som granskar modelluppbyggnadsantaganden. Därför är båda dessa modellerna fördelaktiga och väljs som finala modeller.  För vidare forskning inom detta ämne skulle det vara relevant att inkludera fler lämpliga regressorer i modellerna som kan påverka portföljvärdet samt att jämföra med andra länder. Dessutom, kan det vara intressant att inberäkna reala investeringar istället för endast finansiella investeringar.