A Study of Refusal Strategies by American and International Students at an American University

Refusals are delicate speech acts for non-native speakers to negotiate because they require negative responses to an interlocutor\u27s invitation or request. In addition to cultural variation, variables such as gender and modes of communication (e.g., emails) add dimensions to the complexity when performing refusals. The main objective of this study is to investigate the difference in refusal strategies between American and international college students as well as gender variation. Using a written Discourse Completion Task, six situations were developed and grouped in two stimulus types eliciting refusals to an invitation and a request. Each stimulus type involved an email refusal to professors, friends, and a staff member of an academic department. The refusals of sixteen undergraduate American students and thirty-two international students were analyzed in terms of frequency, order, and content of semantic formulas. The results of this study suggest that when using email, all groups demonstrated preference for direct refusal. American females preferred expressions of gratitude and stating positive opinions, whereas American male provided reasons and alternatives. The international students used a greater variety of semantic formulas; however, they lacked positive opinions and providing alternatives. Additionally, the international students tended to use more regret than the American students. The international students (both male and female) also tended to use more specific excuses as compared to more general excuses used by the Americans

Performance Analysis of Solar Walls in Minnesota: Final Project Report

A study of unglazed transpired solar collectors (referred to as solar walls for this report), has been undertaken. Several installations in the Twin Cities, MN region have been identified, researched, and studied. A combination of weather stations, data logging systems, and building energy management systems were used to collect experimental data on four buildings. Performance calculations were then performed for these buildings and compared with various performance/modeling tools

Technical and Economic Analysis of Solar Cooling Systems in a Hot and Humid Climate

The aim of this paper is to promote efficient and cost effective implementation of advanced solar cooling systems and techniques for the hot and humid climates cities in the United States. After an introduction of basic principles, the development history and recent progress in solar cooling technologies are reported. Nevertheless, the economics of solar energy systems are particularly complex with much inevitable uncertainty due to several factors. In this paper, a simplified comprehensive economic optimization model is developed to determine whether a particular solar system is economically advantageous for a particular project. This model explains and illustrates with simple, but realistic examples the use of life-cycle cost analysis and benefit-cost analysis to evaluate and compare the economic efficiency of the solar cooling system. Consequently, under appropriate conditions, solar or solar-assisted air conditioning systems may be reasonable alternatives to conventional air-conditioning systems in a hot and humid climate.Florida Solar Energy Center, University of Central Florid

Advanced image analysis and techniques for degradation characterization of aggregates

Morphological or shape properties of virgin and recycled aggregate sources are known to affect pavement and railroad track mechanistic behavior and performance significantly in terms of strength, modulus and permanent deformation. Under repeated traffic loading aggregate particles used in construction of pavement and railroad track are routinely subjected to degradation through attrition, impact, grinding and polishing type mechanisms, which result in altering their shape and size properties. The recent advances in digital image acquisition and processing techniques have the potential to be used for objective and accurate measurement of aggregate particle size and shape properties in a rapid, reliable and automated fashion both in the laboratory and in the field. The primary focus of this dissertation includes the design, manufacturing, calibration and validation of different hardware and software components of an Enhanced-University of Illinois Aggregate Image Analyzer (E-UIAIA) with many improvements over the first generation device. A new fully automated color image segmentation algorithm was developed as part of this research which showed excellent performance in detecting aggregate particles with different sizes and natural colors. Customized Look Up Tables (LUTs) were developed to enhance the Hue (H) and Saturation (S) representations of dark and bright aggregate images which improved the thresholding results. The different binary image processing modules available in the original UIAIA device for computing size and shape properties of aggregate particles were updated and merged into a single user friendly interface. Moreover, a new processing algorithm for image arithmetic operations and thresholding was developed and validated for computing the percentages of asphalt coating on Reclaimed Asphalt Pavement (RAP) aggregates. The research findings presented in this dissertation include the implementation of newly developed E-UIAIA in capturing the rate and magnitude of changes in shape and size properties of aggregate particles caused by abrasion, polishing and breakage actions at different degradation levels. The standard laboratory degradation test results including Los Angeles Abrasion (LAA) and Micro-Deval (MD) were combined with imaging based particle shape indices to successfully classify different aggregate sources according to their resistance to degradation. As a step forward for bringing the advances in aggregate imaging methods to project sites and quarries, this dissertation introduces a field aggregate image acquisition and processing procedure. Advanced image analysis and segmentation techniques that combine a Markov Random Field (MRF) approach for image modeling, graph cut for optimization and user interaction for enforcing hard constraints were used. The developed algorithm was utilized for extraction and analyses of individual aggregate particle size and shape properties from 2D field images of multi-aggregate particles captured in a single frame using a Digital Single Lens Reflex (DSLR) camera. The developed field imaging and segmentation methodology showed satisfactory performance in two case studies involving quantification of size and shape properties of large size aggregate sources as well as railroad ballast samples collected from various ballast depths in a mainline freight railroad track. The image acquisition and processing methodologies presented in this dissertation hold the potential to provide optimized aggregate resource selection, better aggregate quality control and quality assurance (QC/QA) as well as improved material specifications

A High-Sensitivity Hydraulic Load Cell for Small Kitchen Appliances

In this paper we present a hydraulic load cell made from hydroformed metallic bellows. The load cell was designed for a small kitchen appliance with the weighing function integrated into the composite control and protection of the appliance. It is a simple, low-cost solution with small dimensions and represents an alternative to the existing hydraulic load cells in industrial use. A good non-linearity and a small hysteresis were achieved. The influence of temperature leads to an error of 7.5%, which can be compensated for by software to meet the requirements of the target application

Efecto de la aplicación foliar de selenio y zinc para aumentar los rendimientos cuantitativos y cualitativos de colza en diferentes fechas de siembra

The sowing date is an important factor for expanding the cultivated area of rapeseed and affects seed yield, oil content, and fatty acid compounds. Micronutrient elements play an important role in improving the vegetative and reproductive growth of the plant, especially under conditions of biological and environmental stresses. A two-year experiment (2014-2016) was performed to study the response of rapeseed genotypes to foliar application of micronutrients on different sowing dates. The treatments were arranged as a factorial-split plot in a randomized complete block design with three replicates. Three sowing dates of 7 (well-timed sowing date), 17, and 27 (delayed sowing dates) October and two levels of foliar application with pure water (control), selenium (1.5%), zinc (1.5%), and selenium+zinc (1.5%) were factorial in the main plots and five genotypes of SW102, Ahmadi, GKH2624, GK-Gabriella, and Okapi were randomized in the subplots (a total of 30 treatments). Seed yield, oil yield and content, oleic acid, and linoleic acid were reduced when rapeseeds were cultivated on 17 and 27 October, while the contents in palmitic, linolenic, and erucic acids, and glucosinolate increased (p < 0.01). a selenium+zinc treatment improved seed yield, oil content and yield (p < 0.01). The oil quality increased due to increased contents of oleic and linoleic acids under the selenium+zinc treatment (p < 0.01). The GK-Gabriella and GKH2624 genotypes are recommended to be sown on well-timed (7 October) and delayed sowing dates (17 and 27 October) and treated with selenium+zinc due to the higher oil yield, linoleic and oleic acids.La fecha de siembra es un factor importante para expandir el área cultivada de colza que afecta el rendimiento de la semilla, el contenido de aceite y la composición en ácidos grasos. Los micronutrientes juegan un papel importante en la mejora del crecimiento vegetativo y reproductivo de la planta, especialmente en condiciones de estrés biológico y ambiental. Se realizó un experimento de dos años (2014-2016) para estudiar la respuesta de los genotipos de colza a la aplicación foliar de micronutrientes en diferentes fechas de siembra. Los tratamientos se organizaron como una parcela dividida factorial en un diseño de bloques completos al azar con tres repeticiones. Tres fechas de siembra del 7 (fecha de siembra en el momento oportuno), 17 y 27 (fechas de siembra retrasadas) de octubre y dos niveles de aplicación foliar con agua pura (control), selenio (1,5%), zinc (1,5%) y selenio + zinc (1.5%) fueron factoriales en las parcelas principales y cinco genotipos de SW102, Ahmadi, GKH2624, GK-Gabriella y Okapi fueron aleatorizados en las subparcelas (un total de 30 tratamientos). El rendimiento de semilla, el contenido y rendimiento de aceite, los ácidos grasos oleico y linoleico se redujeron cuando se cultivaron semillas de colza los días 17 y 27 de octubre, mientras que los contenidos de los ácidos grasos palmítico, linolénico y erúcico y glucosinolato aumentaron (p <0,01). El tratamiento con selenio + zinc mejoró el rendimiento de semillas, el contenido de aceite y el rendimiento (p <0,01). La calidad del aceite aumentó debido al mayor contenido de ácidos oleico y linoleico bajo tratamiento con selenio + zinc (p <0.01). Se recomiendan los genotipos GK-Gabriella y GKH2624 sembrados en fechas oportunas (7 de octubre) y tardía (17 y 27 de octubre) y tratados con selenio + zinc, respectivamente, debido al mayor rendimiento de aceite y contenido de los ácidos linoleico y oleico

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness