During 2006 an estimated number of 150 persons are supposed to have been killed in road accidents caused by speed limit violations. Through Automatic traffic security control (ATK) the Swedish road administration (Vägverket) is working towards lowering the number of speed related accidents. By placing the speed cameras on roads they've managed to lower the average speed at those places. The enlargement of the number of speed cameras is based upon knowledge about for example how high the risk is for speed related accidents on the roads. The speed cameras always measure the speed in which every vehicle passes, but aren't constantly activated to register speed violations. Our purpose with this essay is consequently to explore possible relations between the activation of the speed cameras and the speed itself on the roads.We studied the average speed and the number of speed violations during 12 weeks evenly distributed in 2007. To see if the results would differ, we used two different response variables in the analysis.  Multiple linear regression was used to analyse the average speed, while Poisson regression was used in the analysis of the number of speed violations. An activated camera proved to cause a lowered average speed and fewer speed violations in three regions (Skåne, Mälardalen, Norr).To study the effect of maximized camera activation, an experiment in the region of Mälardalen was performed in the beginning of 2008. The result showed that maximized camera activation didn't decelerate the average speed, in stead the region's own activation policy seems to be more important for a lowered average speed. When the traffic flow rises the average speed decelerates while the number of speed violations also rises. During the study of commuter traffic we could see that the average speed is lower and there is fewer speed violations on commuter roads compared to normal traffic roads. / Under 2006 beräknas 150 personer ha omkommit i vägtrafikolyckor på grund av överskridna hastighetsgränser. Vägverket arbetar för att sänka dessa siffror bland annat genom att använda sig av Automatisk trafiksäkerhetskontroll (ATK). Genom att placera trafiksäkerhetskameror på sträckor har medelhastigheten på dessa sänkts. Trafiksäkerhetskamerorna mäter alltid hastigheten hos varje passerande fordon, men är inte konstant aktiverade för att registrera hastighetsöverträdelser. Nu vill man optimera kameraaktiveringen för att minska antalet ärenden utan att hanteringskapaciteten överskrids. Vårt syfte med uppsatsen är därför att undersöka möjliga samband mellan aktivering av trafiksäkerhetskameror och själva hastigheten på vägarna.Medelhastigheten och antalet överträdelser studerades under tolv veckor jämnt fördelade över år 2007. Analyserna gjordes med två olika responsvariabler för att se om resultaten skilde sig åt. Vi använde oss av multipel linjär regression för att analysera medelhastigheten, medan Poissonregression användes för antalet överträdelser. Det visade sig att en aktiv kamera gav upphov till sänkta medelhastigheter och färre hastighetsöverträdelser i tre regioner (Skåne, Mälardalen, Norr).För att studera effekten av maximal kameraaktivering utfördes ett experiment i region Mälardalen under början av 2008. Det visade sig att en maximal aktivering inte gav en sänkning av genomsnittshastigheterna, istället verkar regionens egen aktiverings-strategi ha större betydelse för sänkta genomsnittshastigheter.När fordonsflödet på alla sträckor ökar så minskar medelhastigheten medan antalet överträdelser ökar. För pendeltrafiksträckor är medelhastigheten lägre och det sker färre hastighetsöverträdelser än på normaltrafiksträckor.

The use of remote sensing technology has made it possible for the non-contact measurement of soil moisture content (SMC). Many remote sensing techniques can be used such as microwave sensors, electromagnetic waves sensors, capacitance, and thermal infrared sensors. Some of those techniques are constrained by their high fabrication cost, operation cost, size, or complexity. In this study, a thermal infrared technique was used to predict soil moisture content with the aid of using weather meteorological variables. <p>The measured variables in the experiment were soil moisture content (%SMC), soil surface temperature (Ts) measured using thermocouples, air temperature (Ta), relative humidity (RH), solar radiation (SR), and wind speed (WS). The experiment was carried out for a total of 12 soil samples of two soil types (clay/sand) and two compaction levels (compacted/non-compacted). After data analysis, calibration models relating soil moisture content (SMC) to differential temperature (Td), relative humidity (RH), solar radiation (SR), and wind speed (WS) were generated using stepwise multiple linear regression of the calibration data set. The performance of the models was evaluated using validation data. Four mathematical models of predicting soil moisture content were generated for each soil type and configuration using the calibration data set. Among the four models, the best model for each soil type and configuration was determined by comparing root mean of squared errors of calibration (RMSEC) and root mean of squared errors of validation (RMSEV) values. Furthermore, a calibration model for the thermal infrared sensor was developed to determine the corrected soil surface temperature as measured by the sensor (Tir) instead of using the thermocouples. The performance of the thermal infrared sensor to predict soil moisture content was then tested for sand compacted and sand non-compacted soils and compared to the predictive performance of the thermocouples. This was achieved by using the measured soil surface temperature by the sensor (Tir), instead of the measured soil surface temperature using the thermocouples to determine the soil-minus-air temperature (Td). The sensor showed comparable prediction performance, relative to thermocouples. <p>Overall, the models developed in this study showed high prediction performance when tested with the validation data set. The best models to predict SMC for compacted clay soil, non-compacted clay soil, and compacted sandy soil were three-variable models containing three predictive variables; Td, RH, and SR. On the other hand, the best model to predict SMC for compacted sandy soil was a two-variable model containing Td, and RH. The results showed that the prediction performance of models for predicting SMC for the sandy soils was superior to those of clay soils.

The use of remote sensing technology has made it possible for the non-contact measurement of soil moisture content (SMC). Many remote sensing techniques can be used such as microwave sensors, electromagnetic waves sensors, capacitance, and thermal infrared sensors. Some of those techniques are constrained by their high fabrication cost, operation cost, size, or complexity. In this study, a thermal infrared technique was used to predict soil moisture content with the aid of using weather meteorological variables. <p>The measured variables in the experiment were soil moisture content (%SMC), soil surface temperature (Ts) measured using thermocouples, air temperature (Ta), relative humidity (RH), solar radiation (SR), and wind speed (WS). The experiment was carried out for a total of 12 soil samples of two soil types (clay/sand) and two compaction levels (compacted/non-compacted). After data analysis, calibration models relating soil moisture content (SMC) to differential temperature (Td), relative humidity (RH), solar radiation (SR), and wind speed (WS) were generated using stepwise multiple linear regression of the calibration data set. The performance of the models was evaluated using validation data. Four mathematical models of predicting soil moisture content were generated for each soil type and configuration using the calibration data set. Among the four models, the best model for each soil type and configuration was determined by comparing root mean of squared errors of calibration (RMSEC) and root mean of squared errors of validation (RMSEV) values. Furthermore, a calibration model for the thermal infrared sensor was developed to determine the corrected soil surface temperature as measured by the sensor (Tir) instead of using the thermocouples. The performance of the thermal infrared sensor to predict soil moisture content was then tested for sand compacted and sand non-compacted soils and compared to the predictive performance of the thermocouples. This was achieved by using the measured soil surface temperature by the sensor (Tir), instead of the measured soil surface temperature using the thermocouples to determine the soil-minus-air temperature (Td). The sensor showed comparable prediction performance, relative to thermocouples. <p>Overall, the models developed in this study showed high prediction performance when tested with the validation data set. The best models to predict SMC for compacted clay soil, non-compacted clay soil, and compacted sandy soil were three-variable models containing three predictive variables; Td, RH, and SR. On the other hand, the best model to predict SMC for compacted sandy soil was a two-variable model containing Td, and RH. The results showed that the prediction performance of models for predicting SMC for the sandy soils was superior to those of clay soils.

The Swedish Transport Administration has, and manages, a database containing information of the status of road condition on all paved and governmental operated Swedish roads. The purpose of the database is to support the Pavement Management System (PMS). The PMS is used to identify sections of roads where there is a need for treatment, how to allocate resources and to get a general picture of the state of the road network condition. All major treatments should be reported which has not always been done. The road condition is measured using a number of indicators on e.g. the roads unevenness. Rut depth is an indicator of the roads transverse unevenness. When a treatment has been done the condition drastically changes, which is also reflected by these indicators. The purpose of this master thesis is to; by using existing indicators make predictions to find points in time when a road has been treated. We have created a SAS-program based on simple linear regression to analyze rut depth changes over time. The function of the program is to find levels changes in the rut depth trend. A drastic negative change means that a treatment has been made. The proportion of roads with an alleged date for the latest treatment earlier than the programs latest detected date was 37 percent. It turned out that there are differences in the proportions of possible treatments found by the software and actually reported roads between different regions. The regions North and Central have the highest proportion of differences. There are also differences between the road groups with various amount of traffic. The differences between the regions do not depend entirely on the fact that the proportion of heavily trafficked roads is greater for some regions.

In some classification problems where human judgments, qualitative and imprecise data exist, uncertainty comes from fuzziness rather than randomness. Limited number of fuzzy classification approaches is available for use for these classification problems to capture the effect of fuzzy uncertainty imbedded in data. The scope of this study mainly comprises two parts: new fuzzy classification approaches based on Tanaka&rsquo / s Fuzzy Linear Regression (FLR) approach, and an improvement of an existing one, Improved Fuzzy Classifier Functions (IFCF). Tanaka&rsquo / s FLR approach is a well known fuzzy regression technique used for the prediction problems including fuzzy type of uncertainty. In the first part of the study, three alternative approaches are presented, which utilize the FLR approach for a particular customer satisfaction classification problem. A comparison of their performances and their applicability in other cases are discussed. In the second part of the study, the improved IFCF method, Nonparametric Improved Fuzzy Classifier Functions (NIFCF), is presented, which proposes to use a nonparametric method, Multivariate Adaptive Regression Splines (MARS), in clustering phase of the IFCF method. NIFCF method is applied on three data sets, and compared with Fuzzy Classifier Function (FCF) and Logistic Regression (LR) methods.

<p>During 2006 an estimated number of 150 persons are supposed to have been killed in road accidents caused by speed limit violations. Through Automatic traffic security control (ATK) the Swedish road administration (Vägverket) is working towards lowering the number of speed related accidents. By placing the speed cameras on roads they've managed to lower the average speed at those places. The enlargement of the number of speed cameras is based upon knowledge about for example how high the risk is for speed related accidents on the roads. The speed cameras always measure the speed in which every vehicle passes, but aren't constantly activated to register speed violations. Our purpose with this essay is consequently to explore possible relations between the activation of the speed cameras and the speed itself on the roads.We studied the average speed and the number of speed violations during 12 weeks evenly distributed in 2007. To see if the results would differ, we used two different response variables in the analysis.  Multiple linear regression was used to analyse the average speed, while Poisson regression was used in the analysis of the number of speed violations. An activated camera proved to cause a lowered average speed and fewer speed violations in three regions (Skåne, Mälardalen, Norr).To study the effect of maximized camera activation, an experiment in the region of Mälardalen was performed in the beginning of 2008. The result showed that maximized camera activation didn't decelerate the average speed, in stead the region's own activation policy seems to be more important for a lowered average speed. When the traffic flow rises the average speed decelerates while the number of speed violations also rises. During the study of commuter traffic we could see that the average speed is lower and there is fewer speed violations on commuter roads compared to normal traffic roads.</p> / <p>Under 2006 beräknas 150 personer ha omkommit i vägtrafikolyckor på grund av överskridna hastighetsgränser. Vägverket arbetar för att sänka dessa siffror bland annat genom att använda sig av Automatisk trafiksäkerhetskontroll (ATK). Genom att placera trafiksäkerhetskameror på sträckor har medelhastigheten på dessa sänkts. Trafiksäkerhetskamerorna mäter alltid hastigheten hos varje passerande fordon, men är inte konstant aktiverade för att registrera hastighetsöverträdelser. Nu vill man optimera kameraaktiveringen för att minska antalet ärenden utan att hanteringskapaciteten överskrids. Vårt syfte med uppsatsen är därför att undersöka möjliga samband mellan aktivering av trafiksäkerhetskameror och själva hastigheten på vägarna.Medelhastigheten och antalet överträdelser studerades under tolv veckor jämnt fördelade över år 2007. Analyserna gjordes med två olika responsvariabler för att se om resultaten skilde sig åt. Vi använde oss av multipel linjär regression för att analysera medelhastigheten, medan Poissonregression användes för antalet överträdelser. Det visade sig att en aktiv kamera gav upphov till sänkta medelhastigheter och färre hastighetsöverträdelser i tre regioner (Skåne, Mälardalen, Norr).För att studera effekten av maximal kameraaktivering utfördes ett experiment i region Mälardalen under början av 2008. Det visade sig att en maximal aktivering inte gav en sänkning av genomsnittshastigheterna, istället verkar regionens egen aktiverings-strategi ha större betydelse för sänkta genomsnittshastigheter.När fordonsflödet på alla sträckor ökar så minskar medelhastigheten medan antalet överträdelser ökar. För pendeltrafiksträckor är medelhastigheten lägre och det sker färre hastighetsöverträdelser än på normaltrafiksträckor.</p>

Under tillverkningsprocessen i massa- och pappersindustrin bildas avloppsflöden i flera olika steg. Ett av de miljöbelastande utsläppen utgörs av organiskt material. Organiskt material kan mätas som biokemisk syreförbrukning (BOD), kemisk syreförbrukning (COD) eller som totalt organiskt kol (TOC). Analys av COD inkluderar ofta miljöfarliga ämnen (till exempel kvicksilver). Naturvårdsverket har under flera år aviserat att analysen kan komma att förbjudas och därmed blir analys av TOC allt mer vanligt. EU:s referensgränsvärden anges dock i COD och design av reningsanläggningar utgår oftast från COD. Detta leder till att båda parametrarna kommer att fortsätta att vara aktuella. Med syftet att förbättra kunskapen om sambanden mellan COD och TOC för olika typer av avlopp inom massa- och pappersindustrin samlades jämförande serier över COD och TOC in från olika svenska bruk. Serierna analyserades med hjälp av regressions- och korrelationsanalys, för att sedan jämföras med medelvärdet av brukens COD/TOC faktorer (omvandlingsfaktorer). Studien innehöll även en del där information om analyser, förbehandlingar och övergången från COD till TOC samlades in genom intervjuer med personer ute på bruken. Som en avslutande del av projektet genomfördes ett laborativt arbete i mindre skala med främsta syfte att se hur konstant omvandlingsfaktorn var över tiden. Resultaten visade att korrelationen mellan COD och TOC ofta var hög men att omvandlingsfaktorn var beroende av typ av tillverkningsprocess och rening. Värdet på omvandlingsfaktorn varierade därför mycket mellan olika bruk och delströmmar. Bruk med tillverkningsprocesser som använder få kemikalier hade en mer lika omvandlingsfaktor sinsemellan än bruk med kemikaliekrävande processer. Dessutom gav en högre reningsgrad en lägre omvandlingsfaktor. Omvandlingsfaktorn föreföll varken påverkas av halten suspenderat material eller vara årstidsberoende. Dessutomvar den också relativt konstant över tiden. / The industrial production of pulp and paper generates wastewater in several different steps in the production chain. Organic material from the wastewater will have an impacton the surrounding environment. Biological oxygen demand (BOD), chemical oxygendemand (COD) and total organic carbon (TOC) are three ways to measure organicmaterial. When analyzing COD, harmful substances such as mercury are often used. The Swedish Environmental Agency has for several years forecasted that this analysis might be banned, and therefore analysis of TOC has become more and more common in Sweden. However, COD is often used as a design parameter when designing new wastewater treatment plants and the European Union’s reference values are given as COD. Hence, both parameters will continue to be used and both need to be taken into consideration. With the aim to improve the knowledge of the relationship between COD and TOC, data series with TOC and COD were collected. The data originated from several different Swedish pulp and paper mills, as well as from different types of waste water streams within each mill. The data series were analyzed using linear regression and correlation analysis, and then compared with the companies’ mean value of the ratio between COD and TOC (the conversion factor). Information about the shift from COD to TOC, and the analyzing methods and pretreatment methods was gathered by conducting interviews with employees at the different mills. The final part of the project was a laboratory study, with the main focus to examine if the conversion factor changed over time. The results showed that the correlation between COD and TOC often was high, but that the conversion factor depended on type of manufacturing process and waste water treatment. Mills with a low use of chemicals had a more similar conversion factor than mills with a more chemical demanding process. Furthermore, a high degree of purification resulted in a lower conversion factor. The conversion factor did not seem to depend on neither the content of suspended solids nor the time of the year. Additionally, the conversion factor was relatively constant over time.

The capacitance-resistance model (CRM) has been continuously improved and tested on both synthetic and real fields. For a large waterflood, with hundreds of injectors and producers present in a reservoir, tens of thousands of model parameters (gains, time constants, and productivity indices) in a field must be determined to completely define the CRM. In this case obtaining a unique solution in history-matching large reservoirs by nonlinear regression is difficult. Moreover, this approach is more likely to produce parameters that are statistically insignificant. The nonlinear nature of the CRM also makes it difficult to quantify the uncertainty in model parameters. The analytical solutions of the two linear reservoir models, the linearly transformed CRM whose control volume is the drainage volume around each producer (ltCRMP) and integrated capacitance-resistance model (ICRM), are developed in this work. Both models are derived from the governing differential equation of the producer-based representation of CRM (CRMP) that represents an in-situ material balance over the effective pore volume of a producer. The proposed methods use a constrained linear multivariate regression (LMR) to provide information about preferential permeability trends and fractures in a reservoir. The two models’ capabilities are validated with simulated data in several synthetic case studies. The ltCRMP and ICRM have the following advantages over the nonlinear waterflood model (CRMP): (1) convex objective functions, (2) elimination of the use of solver when constraints are ignored, and (3) faster computation time in optimization. In both methods, a unique solution can always be obtained regardless of the number of parameters as long as the number of data points is greater than the number of unknowns (parameters). The methods of establishing the confidence limits on CRMP gains and ICRM parameters are demonstrated in this work. This research also presents a method that uses the ICRM to estimate the gains between newly introduced injectors and existing producers for a homogeneous reservoir without having to do additional simulations or regression on newly simulated data. This procedure can guide geoscientists to decide where to drill new injectors to increase future oil recovery and provide rapid solutions without having to run reservoir simulations for each scenario. / text

To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p><p> </p>

Globally female smoking rates are considerably lower than male smoking rates. However, there is great concern regarding female smoking due to the potential for future increases and the associated harm to health. To gain a better understanding regarding female smoking, this study examines the role of gender equality and economic development in explaining the variability in female smoking rates and female-to-male smoking differentials by examining data from 193 World Health Organization member states. Data on the dependent variables, female smoking prevalence rates and female-to-male smoking prevalence ratio, were obtained from the Tobacco Atlas. Data on independent variables i.e., measures of gender equality and gross national income per capita, proxy measure for economic development, were obtained from the 2005 Human Development Report, Central Intelligence Agency, and the World Bank. A composite gender equality index was constructed from the individual measures of gender equality. Multiple regression analysis showed composite gender equality index and gross national income per capita to be significant positive predictors of relative and absolute female smoking rates, with income being a stronger predicator. Individual measures of gender equality failed to show significance with either dependent variable. The results attest to the need for disentangling smoking from the notion of advancement in gender equality and economic development.