Spatial and Temporal Study of Heat Transport of Hydrothermal Features in Norris Geyser Basin, Yellowstone National Park

Monitoring the dynamic thermal activity in Yellowstone National Park is required by the United States Congress. The continuous monitoring is important to maintain the safety of the visitors and park service personnel, plan and relocate infrastructure, and study potential impact from nearby geothermal development including oil and gas industry. This dissertation is part of a study initiated in the early 2000s to monitor the thermal activity of dynamic areas within the Park, using airborne remote sensing imagery. This study was focused in Norris Geyser Basin, the hottest geyser basin in the park, located near the northwestern rim of the Yellowstone’s caldera. The study is considered the first long-term comprehensive airborne remote sensing study in the basin which took place between August 2008 and October 2013. In this study, at least one 1-meter resolution thermal infrared image and three-band images (multispectral) were acquired and used to estimate year-to-year changes in radiant temperature, radiant flux, and radiant power from the thermal source in Norris. Presence of residual radiant flux in the ground from absorbed solar radiation and atmospheric longwave radiation was the main challenge to compere year-to-year changes in the thermal activity. This residual flux is included in the total radiant flux calculated through the remote sensing images which gives false estimates of the flux generated from the underling thermal source. Two methods were suggested in Chapters 2 and 4 of this dissertation to estimate the residual radiant flux. A method was developed in Chapter 2 to estimate the residual radiant flux in a bare ground area covered with hydrothermal siliceous sinter deposit. The method compared ground-based measurements with high spatial resolution airborne remote sensing measurements to estimate the residual radiant flux. In Chapter 4, a method was developed to estimate the residual radiant flux in the six surface classes in Norris, including bare ground, bare ground with siliceous sinter deposit, lakes and pools, river, forest, and grass. The assumptions and implications of each method were discussed to suggest a reliable method to estimate the geothermal radiant flux after subtracting the absorbed residual radiant flux. Chapter 3 provides an analysis of the four components of heat flux in the ground surface, including conduction of sensible heat, convection of sensible heat by liquid water and water vapor, and convection of latent heat by water vapor. The main purpose from the analysis was to assess the hypothesis that the convection and latent heat flux are negligible which therefore supported the results obtained from the analysis in Chapters 2 and 4

Mathematical Problem Solving Strategies in Plain English

The mathematics word problem solving in EFL setting of Jordanian undergraduates were investigated through Polya’s (1957) adopted test as accompanied by self-report open questions procedures. In this study, mathematical problems content is used to assess their language and learning strategies. The results indicated that participating undergraduates were identified either as low problem solvers as well as limited English language proficiency students. Key words:problem solving strategies, EFL, math, Jordanian undergraduates

Implementing Pragmatism And John Deweys Educational Philosophy In Jordanian Public Schools

The teachings and writings of John Dewey, an American philosopher and educator, offer insightful influences on contemporary education, not only in the United States but also worldwide. His philosophy of education, commonly referred to as Pragmatism, focused on learning by doing as an alternative to rote knowledge and strict teaching. The purpose of this study is to investigate the extent to which this philosophical thought is implemented in Jordanian public schools according to Jordanian teachers. Both quantitative and qualitative methods were employed in this study. The findings reveal that Jordanian teachers believe Pragmatism is implemented in Jordan to a moderate degree

Sentiment Analysis of Customers' Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Online media has an increasing presence on the restaurants' activities through social media websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers' reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the SupportVector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classi cation technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classiffication techniques in terms of accuracy, F-measure, G-mean and Area Under the Curve (AUC), for different versions of the datasets

Reality of the Industrial Sector in Jordan

The study aims to examine Jordan's industrial sector by using descriptive statistical and econometric methods. For the purposes of analysis, we used a time series data for the Jordanian industrial sector (Mining, manufacturing, and electricity) from 1990 to 2017. The study came out of the importance of the Jordanian industrial sector. During the study period, the industrial sector ranked first among the sectors in terms of relative importance in its contribution to GDP at an annual average rate of 16% . Comparing the industry's contribution to Jordan's GDP with other countries, Jordan is still relatively late compared to semi-industrialized developing countries, like Singapore, Indonesia, South Korea, Thailand, Malaysia, and Egypt. In addition, there was a fluctuating rise in labor productivity in the industrial sector, this may be due to the increase in the amount of fixed capital, as expressed by high capital intensity technology, or to the development of labor’s skills and competencies. The study recommended the need to work to improve the output of the educational system in line with the requirements of revitalization of the industrial sector, and work to increase the efficiency of vocational training institutions in order to raise the productive efficiency of local labor because of the impact of the revitalization of the industrial sector. Keywords: industry , manufacturing, Mining, market , Jordan, electricity, Value added DOI: 10.7176/JESD/10-18-11 Publication date:September 30th 201

Understanding the Molecular and Structural Selectivity of Oxidant-induced Nitration and its Reversal in Sarcoplasmic Reticulum Ca2+ -ATPase SERCA2a vs. SERCA1a

The present study uses existing sequence (SERCA1a & SERCA2a) and structure (SERCA1a) information to extrapolate a tertiary structure construct for SERCA2a using computational modeling software. A comparison of SERCA1a and SERCA2a models could reveal structural anomalies that explain the basis for selective and specific Tyr nitration in SERCA2a but not SERCA1a

Technical Analysis: Exploring MACD in the Lebanese Stock Market

The stock markets have shown a great growth in the financial world that required traders to deal with many quantitative methods to analyze markets in order to predict commodities’ future prices. This study assesses the effect of technical analysis on the Lebanese stock markets by using a tool known as the Moving Average Convergence/Divergence (MACD) oscillator that explores how MACD can be utilized to optimize profits in the Lebanese stock exchange, during the trading process. The study is performed on closing prices of shares of six Lebanese banks and a real estate company, over a time period extending from the beginning of the year 2004 till the end of the year 2014. Results are meant to indicate whether MACD is able to optimize profits and forecast the Lebanese stock prices. It is concluded that the application of MACD in the decision making process for investing in the Lebanese stock market does not significantly contribute to the maximization of profitability on investments

Simultaneous determination of warfarin and 7-hydroxywarfarin in rat plasma by HPLC-FLD

In this study, high-performance liquid chromatography with fluorescence detection (HPLC-FLD) has been used for the first time, for direct determination of warfarin and its major metabolite, 7-hydroxywarfarin, in rat plasma. The simple and sensitive method was developed using Fortis® reversed-phase diphenyl column (150 × 4.6 mm, 3 μm) and a mobile phase composed of phosphate buffer (25 mmol L–1)-methanol-acetonitrile (70:20:10, V/V/V), adjusted to pH 7.4, at a flow rate of 0.8 mL min–1. The diphenyl chemistry of the stationary phase provided a unique selectivity for separating the structurally related aromatic analytes, warfarin and 7-hydroxywarfarin, allowing their successful quantification in the complex plasma matrix. The method was linear over the range 0.01–25 μg mL–1, for warfarin and 7-hydroxywarfarin, and was found to be accurate, precise and selective in accordance with US FDA guidance for bioanalytical method validation. The method was sensitive enough to quantify 0.01 μg mL–1 of warfarin and 7-hydroxywarfarin (LLOQ) using only 100 µL of plasma. The applicability of this method was demonstrated by analyzing samples obtained from rats after oral administration of a single warfarin dose, and studying warfarin and 7-hydroxywarfarin pharmacokinetics

A 37‐Year‐Old Man With Primary Antiphospholipid Syndrome Presenting With Respiratory Distress and Worsening Toe Ischemia

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137728/1/acr23168.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137728/2/acr23168_am.pd

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. TwoWell-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results