Assessment and prediction of surface water vulnerability from non-point source pollution in Midwestern watersheds

Non-point source pollution is the leading cause of impairment in surface water in the Midwest. In this research, we seek to predict which watersheds are most vulnerable to point source pollution without field sampling using publically available GIS databases. Watersheds with higher vulnerability ratings can then be targeted for water quality monitoring, and funds used to improve watershed health can be distributed with greater efficacy. To better understand and target watershed vulnerability, we used three different approaches. In the first project, 35 sub-watersheds were sampled in the Lower Grand Watershed, which is a highly agricultural watershed in northern Missouri/southern Iowa. Statistical analyses were performed to determine which of these parameters were most correlated with water quality, and predictive relationships of water quality were developed. In the second project, a new methodology for watershed vulnerability to non-point source pollution was developed. Using the results from our first study to guide the weighting of different parameters, a weighted overlay and analytical hierarchy method was used to predict the vulnerability (poor water quality) of watersheds. This new vulnerability prediction method was tested on ten sub-watersheds within the Eagle Creek Watershed in central Indiana, which has a mixture of agricultural, forested, and urban land use. In the last project, the robustness of the new watershed vulnerability assessment method was tested using hydrological modeling. The Soil and Water Assessment Tool (SWAT) modeling program was used to model non-point source pollution in the Eagle Creek sub-watersheds. The results of these models provided a second method for verifying the robustness of the newly developed watershed vulnerability assessment method --Abstract, page iv

On Modules with Finite Spanning Isodimension

     فى هذا البحث نقدم المقاسات من النمط  المنتهية الامتداد المتكافئات بعديا (finite spanning isodimension) . ليكن  مقاسا من النمط  و ان  هو مقاس جزئى من  () نقول ان  () هو مقاس جزئى متكافىء  صغريا (isosmall) اذا كان () اى مقاس جزئى من ()بحيث ان () فان ) و نقول ان () مقاس منتهى الامتداد متكافىء بعديا اذا كان لكل متتابعة متناقصة تامة  , من المقاسات الجزئية () من  يوجد عدد صحيح (  ) بحيث ان (  ) يكون متكافىء  صغريا لكل ().      وتمت كذلك تعريف و دراسة بعض الاصناف الاخرى من المقاسات الجزئية و منها المقاسات الجزئية المتكافئة عضما (isomaximal) حيث تمت البرهنة على نتائج عديدة تتعلق بهذا النوع من المقاسات الجزئية.  و من بين النتائج التى تمت البرهنة عليها ايضا فى هذا العمل هى ان حلقة التشاكلات الذاتية(ring of endomorphisms) على مقاس متكافىء ببساطة (isosimple) تكون حلقة محلية (موضعية) (local ring).    We introduce modules with finite spanning isodimension. Let be an module    is called module with finite spanning isodimension, if for every strictly decreasing sequence, there exists a positive integer  such that  is isosmall for each . In the following sense, we define isosmall submodule, a submodule  of an module  is called isosmall, if  , then for any submodule  of . Some other classes are studied for instances isomaximal and many results are proved. On the other hand, we determine that the ring of endomorphisms of an isosimple module is a local ring

Compare Between DCT and DWT for Digital Watermarking in Color Image

In This paper we compare between Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) in the field of image authentication and digital watermarking. Our proposed method depending on the embedding stage and extraction stag that presented in [1] but our scheme embeds the logo bits inside the low frequency domain because DWT gives optimal results with LL domain, while [1] used middle frequency domain with DCT. Our improvement by using a secrete K called user key (int. K=1, K<=N) where N=10, is used to generate a random vectors  for the selected coefficient, this attempt is made to increase the security and robustness for the proposed scheme to compare between DCT that illustrated in [1] with middle frequency domain and DCT, DWT in the existing paper that used low frequency domain, it used block based technique with embedding stage, the development that illustrated in the existing paper by using DWT transform on the recovered of binary watermark for the purpose of image authentication in frequency domain using DWT, and DCT transforms with true color image. The method focused on the objective quality after embedding stage and the recovered watermark after extraction stage. With DWT in the first step, the cover image is decomposed into three levels by DWT transform. Then the hiding site was LL sub band of the DWT coefficients. Furthermore, our proposed method deal with true color image without converting its color space into other color space with various image texture all of them with size of 256*256 Bit map image file format, the proposed scheme deal with three sub-bands (Red, Green, and Blue) at the same time to hide logo bits inside the host by using Patchwork technique with embedding stage, so if one is destroyed the other may survive, it provide optimal security whenever any sub-bands color destroyed. With our proposed method a secret watermark in the form of binary (o, 1) pattern is embedded inside the host under DCT, DWT, one bit from the watermark will be embedded inside the selected coefficient from the selected block of the host. Our proposed method was evaluated with different types of intended attacks such as: salt and pepper noise, Poisson noise, and speckle noise. Moreover, unintended attacks consider by spatial enhancement filter such as median filter that used to improve the quality for the watermarked image after unintended attack. After experiments, it was found that our proposed method provides security and high performance with low computational complexity and good objective quality. Our scheme evaluate the imperceptibility for the watermarked image after embedding stage by using Peak signal to Noise Ratio (PSNR), while the recovered watermark evaluated by some types of metrics such as Mean Square Error (MSE), Normalized Correlation (NC), and correlation factor (SIM). Our proposed method has ability to deal with different image texture and format such as (BMP), Portable Network Graphics (PNG), and Tagged Image File Format (TIFF). Keywords: Authentication, Objective, Subjective

Data Analytics and Techniques: A Review

Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis

Omalizumab, an Anti-IgE mAb, Receives Approval for the Treatment of Chronic Idiopathic/Spontaneous Urticaria

Omalizumab, an anti-IgE mAb, has recently been approved by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) for the treatment of chronic idiopathic urticaria. Saini et al. (2014) (this issue) report on ASTERIA I, a 40-week randomized, double-blinded, placebo-controlled phase III trial evaluating omalizumab for the treatment of this disease

Classification of Images Using Decision Tree

In this paper, the proposed system is based on texture features classification for multi object images by using decision tree (ID3) algorithm. The proposed system uses image segment tile base to reduce the block effect and uses (low low) Wavelet Haar to reduce image size without loss of any important information. The image texture features like (Entropy, Homogeneity, Energy, Inverse Different Moment (IDM), Contrast and Mean) are extracted from image to build database features. All the texture features extracted from the training images are coded into database features code. ID3 algorithm uses database features code for classification of images into different classes. Splitting rules for growing ID3 algorithm are Entropy, Information Gain used to build database rules, which depend on if_then format. The proposed algorithm is experimented on to test image database with 375 images for 5 classes and uses accuracy measure. In the experimental tests 88% of the images are correctly classified and the design of the proposed system in general is enough to allow other classes and extension of the set of classification classes