Denna studie syftar till att undersöka i vilken utsträckning kvinnor påverkas av öletikettens utseende. För att undersöka detta genomfördes en visuell innehållsanalys för att ta fram de vanligaste designelementen från makro- och mikrobryggeriers öletiketter. Därefter skapades typetiketter baserat på de vanligaste designelementen för respektive bryggerityp som sedan bedömdes i en webbenkät. Webbenkäten omfattade 130 kvinnor som dels fick bedöma typetiketterna efter egen preferens, dels svara på frågor som berörde öletiketter ur ett genusperspektiv. Avslutningsvis genomfördes fyra semistrukturerade djupintervjuer med respondenter aktiva inom ölbranschen för att få ett bredare perspektiv på ämnet. Respondenternas svar användes till att besvara frågeställningarna i studien. Det visade sig att mikro- och makrobryggerierna har vissa skillnader i sin design. Mikrobryggeriernas öletiketter var färggladare och hade ett mer lekfullt designmanér gentemot makrobryggerierna som använde en mer traditionell design. Studien visade bland annat att könsnormer och smakfaktorer påverkar kvinnors ölintresse i större utsträckning än själva öletiketten. Studien visade även att kvinnor är intresserade av lokalproducerad öl, smaker och fler alkoholfria sorter. Resultaten antyder att kvinnor upplever att vissa öletiketter har en maskulin framtoning och att det finns ett intresse för mer färgglada, ljusare och mindre maskulina öletiketter. / This study aims at investigating the influence of the beer label on women's beer interest in order to find out if women are affected by the appearance of the beer label. To investigate this, a visual content analysis was conducted to obtain the most common design elements from the macro and micro breweries' labels. Then, typefaces were created based on the most common design elements for the respective brewery types, then evaluated in a web site. The web browser consisted of 130 women who, on the one hand, had to assess the type labels according to their own preferences, but also answered questions relating to labels from a gender perspective. Finally, four semi-structured deep interviews were conducted with respondents active in the beer industry to gain a broader perspective on the subject. Respondents' answers were used to answer the questions in the study. The results of this study show that the micro and macrobreweries have some differences in their design. The micro-brewer's beer labels were more colorful and had a more playful design over the macrobreweries that used a more traditional design. The study showed that gender norms and taste factors affect women's beer interest to a greater extent than the actual beer label. The study also shows that women are interested in locally produced beer, flavors and more non-alcoholic varieties. The results presented in the study suggest that women find that some labels have a masculine appearance and that there is an interest in more colorful, lighter and less masculine labels.

The object of this work is identification with the private label offer of selective chain stores in the czech market and make the analyse of consumer behaviour in relation to private label products.

The aim of this thesis is to analyse past and present development of market of private labels in the Czech republic. Pursuant to this purpose of possible future trends with recommendation for individual market subjects. The first theoretical part is based on introduction to retail management. There are defined important concepts related to private labels. The next part is dedicated to summary of market of private labels in the world. This part is followed by analysis of past and present development of market of private labels in the Czech republic. Based on this statement it is proposed own consumer research. Research is focused on students behaviour. The final part is summary of research and recommendation for retail practise.

Indiana University-Purdue University Indianapolis (IUPUI) / Being in the midst of a pandemic with patients having minor symptoms that quickly become fatal to patients with situations like a stemi heart attack, a fatal accident injury, and so on, the importance of medical research to improve speed and efficiency in patient care, has increased. As researchers in the computer domain work hard to use automation in technology in assisting the first responders in the work they do, decreasing the cognitive load on the field crew, time taken for documentation of each patient case and improving accuracy in details of a report has been a priority. This paper presents an information extraction algorithm that custom engineers certain existing extraction techniques that work on the principles of natural language processing like metamap along with syntactic dependency parser like spacy for analyzing the sentence structure and regular expressions to recurring patterns, to retrieve patient-specific information from medical narratives. These concept value pairs automatically populates the fields of an EMR form which could be reviewed and modified manually if needed. This report can then be reused for various medical and billing purposes related to the patient.

Recognition and comparison of image is one of the main problems and area of the field of computer vision. This thesis adds to these two issues the third, the recognition image semantics, so called annotations or labels. This work uses the knowledge of methods of recognizing the similarity of images to create a tool that is able based on training dataset of images and annotations, create a group most likely annotation for the test set of images. This work presents several types of test datasets suitable for the detection of annotation information for images. Subsequently, best set with the necessary training dataset size and enough information about annotations is selected. Based on this training dataset algorithm is designed for easy loading test set without large demands on computer performance. Evaluation of annotation information is done based on different similarity algorithms. At the beginning of this work was to use a simple, but not very effective method of MSE and comparison of color histograms, but gradually it was necessary to move to using more advanced methods (such as Tamura, Gabor, CEDD nebo různé druhy hostistogramů). The results of this comparison are then taken to evaluate the likelihood of the annotation for the image specified test set. The last part is an evaluation of the accuracy of annotation based on information from the test set.

In this study, I examine whether the mandatory adoption of International Financial Reporting Standards (IFRS) in a country where local Generally Accepted Accounting Principles (GAAP) is of similar quality to IFRS is associated with changes in the comparability of financial statements. I also investigate the sources of any changes in the comparability of financial statements. I use data from South Africa, where, word for word, prior to the mandatory adoption of IFRS, local GAAP was the same as IFRS, and enforcement remained unchanged. I use two different measures of comparability, one based on accounting data (accruals-cash flow measure) and the other based on both accounting data and market data (earnings-return measure). I compare South African firms with two different groups, namely other mandatory IFRS adopters and non-adopters. My data show evidence of an increase in the comparability of the financial statements of South African firms with those of both adopters (both measures) and non-adopters (the earnings-return measure) following the mandatory adoption of IFRS. In additional analysis, I found a global increase in the comparability of firms' financial statements that is consistent with market changes unrelated to IFRS adoption as one of the sources of the increase in comparability. Moreover, an incremental increase in the comparability of the financial statements of South African firms after the mandatory adoption of IFRS, relative to the increase in the comparability of the financial statements of non-adopting firms, is consistent with benefits from using the IFRS "label" and with the expanded IFRS network as sources of increased comparability. / Thesis (PhD)--University of Pretoria, 2017. / Accounting / PhD / Unrestricted

In the past decade, the field of biosensing has experienced an incredible pace of development, due to the compelling need for accurate and reliable tools for characterization of biomolecular kinetics. Specifically, label-free kinetic measurements are the most direct method for studying molecular binding, for example to establish the efficacy of drug-receptor interactions. For this reason, researchers in the pharmaceutical industry rely heavily on label-free detection for drug and antibody screening. Meanwhile, in the biosafety industry and healthcare, there is great demand for screening tools that can target biothreats, in order to accurately recognize the presence of toxins and pathogens with high sensitivity in diverse samples, such as bodily fluids, food and drinking water. This research topic has become particularly relevant during the recent pandemic, where vaccine development was carried out side by side with quantification and characterization of single viral particles. Here, we introduce a versatile biosensing platform capable of characterizing virtually any type of target compound, down to the single molecule level. For this work, we have improved the Interferometric Reflectance Imaging Sensor (IRIS) to perform accurate measurements of the binding kinetics of analytes ranging in molecular weight from less than 1kDa (small molecules) to more than 1MDa (biological nanoparticles). For the first time, we demonstrate multiplexed kinetic binding characterization of small molecules to surface immobilized antibody probes, as well as detection and phenotyping of large and complex analytes, on the same platform. The IRIS platform utilizes the optical interference signal produced by thinly layered substrates in order to precisely measure the thickness of a transparent film atop a silicon chip. In the context of this work, dynamic characterization of a wide range of biomolecular and nanoparticle targets was made possible by a multidimensional optimization, in order to improve both the sensitivity and the dynamic range of the instrument. Analysis of low molecular weight compounds required a significant increase in signal to noise ratio, which was achieved through averaging, as well as complete elimination of background solution effects ('bulk effect’). Additionally, the best surface chemistry for each application was identified by a new technique which consists of immobilizing capture probes on a multiplexed array of active polymers functionalized on the same sensor surface, allowing for simultaneous side-by-side comparison of their performance. Surface chemistry plays a huge role in kinetic measurements, in terms of probe functionality, steric hindrance, charge distribution and diffusion effects. Finally, imaging optics, illumination wavelength, and thickness of the silicon dioxide film were optimized to perform detection and phenotyping of large analytes, such as extracellular vesicles (EVs) and antibody-conjugated gold nanoparticles (mAb-GNPs). Results obtained from numerical simulations allowed for selection of the best experimental parameters for each application. Experimentally, mAb-GNPs were utilized to produce a real-time sandwich lateral flow assay. In this context, we demonstrated how the improved IRIS platform can bridge the gap between single-particle detection ('digital’ configuration) and bulk reflectance measurements ('analog’ configuration), creating a new 'hybrid' system (h-IRIS), which only requires minimal hardware adjustments to easily switch from one modality to the other. This brought a substantial improvement in sensitivity, improving the limit of detection by three orders of magnitude and enabling single-molecule level measurements. Finally, future system optimization ideas are presented to achieve even higher accuracy and further extend the range of target analytes.

The use of dietary supplements occurs in the majority of American adults >20 years old and has been reported to be on the rise by the National Health and Nutrition Examination Survey. People are not only taking more dietary supplements, but taking them without the advice of a health care provider. With the lack of education time and focus on this topic, physicians need to know some evidence regarding the most common supplements used, how to understand dietary supplement labels, and where to find reputable information about dietary supplements. Vitamin D, Omega-3, and Glucosamine/ Chondroitin are reviewed here. Also, a description of the various components of a dietary supplement label is explained. Finally, a brief description of independent companies (Consumerlabs.com®, US Pharmacopedial Convention, Natural Medicine Comprehensive Database, and Natural Standard) as well as the federally run Office of Dietary Supplements are presented.

No description available.

Many real world problems require multi-label classification, in which each training instance is associated with a set of labels. There are many existing learning algorithms for multi-label classification; however, these algorithms assume implicit negativity, where missing labels in the training data are automatically assumed to be negative. Additionally, many of the existing algorithms do not handle incremental learning in which new labels could be encountered later in the learning process. A novel multi-label adaptation of the backpropagation algorithm is proposed that does not assume implicit negativity. In addition, this algorithm can, using a naive Bayesian approach, infer missing labels in the training data. This algorithm can also be trained incrementally as it dynamically considers new labels. This solution is compared with existing multi-label algorithms using data sets from multiple domains and the performance is measured with standard multi-label evaluation metrics. It is shown that our algorithm improves classification performance for all metrics by an overall average of 7.4% when at least 40% of the labels are missing from the training data, and improves by 18.4% when at least 90% of the labels are missing.