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Increasing use of rock materials like shale in building, roofing, embankment filling, brick manufacturing, and in other civil structure application makes it an important rock to consider in construction engineering. Knowledge of thermal and physical properties of shale as building material is required to predict the rock's strength and permanence against weathering. Inconsistent heat capacity of anisotropic rock can result in differential heat flow. This tendency can expand the building materials leading to reduction in strength and initiate disintegration. Authors have studied various thermo-physical properties of anisotropic shale from Tennessee, which is commonly used as building stones and bricks. Experiment was designed to measure the basic thermal property, 'heat capacity' of shale. Series of laboratory tests including durability, strength, specific gravity, moisture content, and porosity were conducted to determine the physical and mechanical behavior of the samples. Results indicated that properties like porosity, strength and heat capacity varied significantly within samples, where as specific gravity and moisture content yielded steady values. Multivariate regression analysis was performed to evaluate possible correlations among the tested properties. Strong positive relationship was evident between heat capacity, and porosity. Heat capacity and Unconfined Compressive Strength of shale were inversely related. This study emphasized that physical and thermal properties of shale are directly linked with strength and durability of the rock mass.

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This thesis aimed to investigate and describe the influence that certain socioeconomical, demographical, and geographical variables had on the election results for the three parliamentary party groups in the 2018 Swedish parliamentary election on the municipal level. The study also aimed to compare the difference in effect of the variables between two different geographical study areas: Norrland and the Greater Stockholm area. The study has been conducted via a regression analysis.  The results indicated that income, education, population density and average age all have a noticeable influence on the election results for the different party blocks. Income was the factor with the overall largest influence on the election result. There was a difference in influence from different variables between the three different party blocks. The study also found that there was a difference in effect between Norrland and Greater Stockholm. Certain variables had more of an effect in Norrland, and vice-versa. Most notably, income and average age had the opposite effect in Norrland compared to Greater Stockholm. The reason for this is not clear, but differences in culture between the study areas may provide an explanation.

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Individual and environmental risk factors in hand eczema have been investigated in a prospective cohort study of 2452 newly employed hospital workers with a follow-up time of 20 months. Current hand eczema was analyzed in 142 wet hospital workers from this cohort with respect to the etiologic importance of irritants, allergens and contact urticants. The density of the microflora and the effect on the microflora of topical treatment with a potent corticosteroid were studied in 20 patients with hand eczema. ’Wet’ hospital work was found to increase the odds of developing hand eczema only twice compared to 'dry' office work. Nursing children under four years old and the lack of a dish-washing machine significantly increased the risk of contracting hand eczema. Unfavourable combinations of these domestic factors increased the risk as much as wet work. A history of atopic dermatitis approximately tripled the odds both in wet as well as in dry work. Histories of earlier hand eczema (HHE), metal dermatitis (HMD) and of atopy were analyzed as risk factors for hand eczema in 1857 women in wet work. HHE increased the odds by a factor of 12.9 and created a subdivision of the population into high risk individuals and normal risk individuals. HHE was found in half of the subjects with atopic dermatitis, in one quarter of the subjects with atopic mucosal symptoms and in one fifth of the non-atopics. A HMD increased the odds by a factor of 1.8. This increase was seen as a high risk level in subjects with HHE and as a normal risk level in subjects with no HHE. A history of atopic disease as a complement to information about HHE and HMD increased the odds by another 1.3 times. The predicted probability of developing hand eczema ranged from 91 % in subjects with a combination of HHE, HMD and atopy to 24% in subjects with none of these risk factors. Subjects with AD were found to suffer a more severe form of hand eczema with significantly higher figures for medical consultation, sick- leave, termination due to hand eczema, early debut, permanent symtoms and vesicular lesions. Amongst the patients investigated for current hand eczema high risk individuals were overrepresented. It was claimed in 92.3% of the cases that trivial irritant factors had elicited the current episodes of hand eczema. In 35% of the cases the exposure to the irritant took place largely at home. Although contact sensitivity and contact urticaria were fairly common, they mostly seemed to be of minor importance in the etiology of the current hand eczema. Staphylococcus aureus colonized eczematous lesions of the hands in 18/20 patients. The density exceeded 105 colony forming units/cm2 in 15/20 patients. Only three of these patients showed signs of clinical infection. Successful topical treatment with a potent corticosteroid significantly reduced the colonization of S. aureus. / <p>Härtill 4 uppsatser</p> / digitalisering@umu

One of the challenges in the structural biological community is processing the wealth of protein data being produced today; therefore, the use of computational tools has been incorporated to speed up and help understand the structures of proteins, hence the functions of proteins. In this thesis, protein structure investigations were made through the use of Multivariate Analysis (MVA), and Fourier Transformed Infrared (FTIR), a form of vibrational spectroscopy. FTIR has been shown to identify the chemical bonds in a protein in solution and it is rapid and easy to use; the spectra produced from FTIR are then analysed qualitatively and quantitatively by using MVA methods, and this produces non-redundant but important information from the FTIR spectra. High resolution techniques such as X-ray crystallography and NMR are not always applicable and Fourier Transform Infrared (FTIR) spectroscopy, a widely applicable analytical technique, has great potential to assist structure analysis for a wide range of proteins. FTIR spectral shape and band positions in the Amide I (which contains the most intense absorption region), Amide II, and Amide III regions, can be analysed computationally, using multivariate regression, to extract structural information. In this thesis Partial least squares (PLS), a form of MVA, was used to correlate a matrix of FTIR spectra and their known secondary structure motifs, in order to determine their structures (in terms of "helix", "sheet", “310-helix”, “turns” and "other" contents) for a selection of 84 non-redundant proteins. Analysis of the spectral wavelength range between 1480 and 1900 cm-1 (Amide I and Amide II regions) results in high accuracies of prediction, as high as R2 = 0.96 for α-helix, 0.95 for β-sheet, 0.92 for 310-helix, 0.94 for turns and 0.90 for other; their Root Mean Square Error for Calibration (RMSEC) values are between 0.01 to 0.05, and their Root Mean Square Error for Prediction (RMSEP) values are between 0.02 to 0.12. The Amide II region also gave results comparable to that of Amide I, especially for predictions of helix content. We also used Principal Component Analysis (PCA) to classify FTIR protein spectra into their natural groupings as proteins of mainly α-helical structure, or protein of mainly β-sheet structure or proteins of some mixed variations of α-helix and β-sheet. We have also been able to differentiate between parallel and anti-parallel β-sheet. The developed methods were applied to characterize the secondary structure conformational changes of an unfolding protein as a function of pH and also to determine the limit of Quantitation (LoQ).Our structural analyses compare highly favourably to those in the literature using machine learning techniques. Our work proves that FTIR spectra in combination with multivariate regression analysis like PCA and PLS, can accurately identify and quantify protein secondary structure. The developed models in this research are especially important in the pharmaceutical industry where the therapeutic effect of drugs strongly depends on the stability of the physical or chemical structure of their proteins targets; therefore, understanding the structure of proteins is very important in the biopharmaceutical world for drugs production and formulation. There is a new class of drugs that are proteins themselves used to treat infectious and autoimmune diseases. The use of spectroscopy and multivariate regression analysis in the medical industry to identify biomarkers in diseases has also brought new challenges to the bioinformatics field. These methods may be applicable in food science and academia in general, for the investigation and elucidation of protein structure.

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.