English in product advertisements in non-english speaking countries in western europe: Product image and comprehension of the text

Although English has been shown to be the most frequently used foreign language in product advertisements in countries where it is not the native language, little is known about its effects. This article examines the response to advertisements in English compared to the response to the same ad in the local language in Western Europe on members of the target group for which the ad was intended: 715 young, highly educated female consumers. The use of English in a product ad does not appear to have any impact on image and price of the product, but it does affect text comprehension: the meaning of almost 40% of the English phrases was not understood. These results were the same for all countries involved in the study, irrespective of whether the respondents\u27 (self-) reported proficiency in English is high or low. © Taylor & Francis Group, LLC

Development and evaluation of a multimedia environmental fate and food web model for phthalate esters in False Creek, British Columbia

There is limited information on the environmental fate of di-alkyl phthalate esters (DPEs). To better understand the fate of DPEs and their primary metabolites, mono-alkyl phthalates (MPEs), a steady-state multimedia environmental fate and food-web model was developed and tested. The model suggests that the lower log Kow DPEs mainly flow out of the system and by a smaller extent in the ionized form of MPEs, or further biodegrade into phthalic acid. The higher log Kow DPEs are mainly bound to sediment that is buried and flow out of the system. The model also predicted that in fish, lower log Kow DPEs are mainly eliminated through gills whereas the higher log Kow DPEs undergo fecal route. Biotransformation of DEHP and mixture of C8 isomers are also predicted. This model can be used in preliminary ecological risk assessment to predict exposure concentrations, internal body burdens, and remediation targets in aquatic ecosystems

Deep Learning for Prediction of Falling Blood Pressure During Surgery : Prediction of Falling Blood Pressure

Perioperative hypotension corresponds to critically low blood pressure events during the pre, intra and postoperative periods. It is a common side effect of general anaesthesia and is strongly associated with an increased risk of postoperative complications, such as acute kidney injury, myocardial injury and in the worst case death. Early treatment of hypotension, preferably even before onset, is crucial in order to reduce the risk and severity of its associated complications. This work explores methods for predicting the onset of hypotension which could serve as a warning mechanism for clinicians managing the patient’s hemodynamics. More specifically, we present methods using only the arterial blood pressure curve to predict two different definitions of hypotension. The presented methods are based on a Convolutional Neural Network (CNN) trained on data from patients undergoing high-risk surgery. The experimental results show that our network can predict hypotension with 70% sensitivity and 80% specificity 5 minutes before onset. The prediction performance is then quickly reduced for longer prediction times, resulting in 60% sensitivity and 80% specificity 15 minutes before onset. Perioperativ hypotension motsvarar perioder av kritiskt lågt blodtryck före, under och efter operation. Det är en vanlig bieffekt av generell anestesi och är starkt associerad med ökat risk av postoperativa komplikationer, så som akut leverskada, myokardskada och i värsta fall dödsfall. Tidig behandling av hypotension, helst innan perioden börjar, är avgörande för att minska risken och allvarlighetsgraden av postoperativa komplikationer. Det här arbetet utforskar metoder för att förutspå perioder av hypotension, vilket skulle kunna används för att varna vårdpersonal som ansvarar för patientens hemodynamiska övervakning. Mer specifikt så presenteras metoder som endast använder artärblodtryck för att förutspå två olika definitioner av hypotension. Metoderna som presenteras är baserade på ett Convolutional Neural Network (CNN) som tränats på data från patienter som genomgår högriskoperation. De experementella resultaten visar att våran modell kan förutspå hypotension med 70% sensitivitet och 80% specificitet 5 minuter i förväg. Förmågan att förutspå hypotension avtar sedan snabbt för längre prediktionstider, vilket resulterar i 60% sensitivitet och 80% specificitet 15 minuter i förväg

Deep Learning for Prediction of Falling Blood Pressure During Surgery : Prediction of Falling Blood Pressure

Perioperative hypotension corresponds to critically low blood pressure events during the pre, intra and postoperative periods. It is a common side effect of general anaesthesia and is strongly associated with an increased risk of postoperative complications, such as acute kidney injury, myocardial injury and in the worst case death. Early treatment of hypotension, preferably even before onset, is crucial in order to reduce the risk and severity of its associated complications. This work explores methods for predicting the onset of hypotension which could serve as a warning mechanism for clinicians managing the patient’s hemodynamics. More specifically, we present methods using only the arterial blood pressure curve to predict two different definitions of hypotension. The presented methods are based on a Convolutional Neural Network (CNN) trained on data from patients undergoing high-risk surgery. The experimental results show that our network can predict hypotension with 70% sensitivity and 80% specificity 5 minutes before onset. The prediction performance is then quickly reduced for longer prediction times, resulting in 60% sensitivity and 80% specificity 15 minutes before onset. Perioperativ hypotension motsvarar perioder av kritiskt lågt blodtryck före, under och efter operation. Det är en vanlig bieffekt av generell anestesi och är starkt associerad med ökat risk av postoperativa komplikationer, så som akut leverskada, myokardskada och i värsta fall dödsfall. Tidig behandling av hypotension, helst innan perioden börjar, är avgörande för att minska risken och allvarlighetsgraden av postoperativa komplikationer. Det här arbetet utforskar metoder för att förutspå perioder av hypotension, vilket skulle kunna används för att varna vårdpersonal som ansvarar för patientens hemodynamiska övervakning. Mer specifikt så presenteras metoder som endast använder artärblodtryck för att förutspå två olika definitioner av hypotension. Metoderna som presenteras är baserade på ett Convolutional Neural Network (CNN) som tränats på data från patienter som genomgår högriskoperation. De experementella resultaten visar att våran modell kan förutspå hypotension med 70% sensitivitet och 80% specificitet 5 minuter i förväg. Förmågan att förutspå hypotension avtar sedan snabbt för längre prediktionstider, vilket resulterar i 60% sensitivitet och 80% specificitet 15 minuter i förväg

Automatic Image Segmentation for Hair Masking: two Methods

We propose two different methods for image segmentation with the objective of marking contaminated regions in images from biochemical tests. The contaminated regions consists of thin hair or fibers and the purpose of this thesis is to eliminate the tedious task of masking the contaminated regions by hand by implementing automatic hair masking. Initially an algorithm based on Morphological Image Processing is presented, followed by solving the problem of pixelwise classification using a Convolutional Neural Network (CNN). Finally, the performance of each implementation is measured by comparing the segmented images with labelled images which are considered to be the ground truth. The result shows that both implementations have strong potential at successfully performing semantic segmentation on the images from the biochemical tests

Automatic Image Segmentation for Hair Masking: two Methods

We propose two different methods for image segmentation with the objective of marking contaminated regions in images from biochemical tests. The contaminated regions consists of thin hair or fibers and the purpose of this thesis is to eliminate the tedious task of masking the contaminated regions by hand by implementing automatic hair masking. Initially an algorithm based on Morphological Image Processing is presented, followed by solving the problem of pixelwise classification using a Convolutional Neural Network (CNN). Finally, the performance of each implementation is measured by comparing the segmented images with labelled images which are considered to be the ground truth. The result shows that both implementations have strong potential at successfully performing semantic segmentation on the images from the biochemical tests

Automatic Image Segmentation for Hair Masking: two Methods

We propose two different methods for image segmentation with the objective of marking contaminated regions in images from biochemical tests. The contaminated regions consists of thin hair or fibers and the purpose of this thesis is to eliminate the tedious task of masking the contaminated regions by hand by implementing automatic hair masking. Initially an algorithm based on Morphological Image Processing is presented, followed by solving the problem of pixelwise classification using a Convolutional Neural Network (CNN). Finally, the performance of each implementation is measured by comparing the segmented images with labelled images which are considered to be the ground truth. The result shows that both implementations have strong potential at successfully performing semantic segmentation on the images from the biochemical tests