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North Carolina Central University Plastic Shopping Bags Legally Banned Essay

North Carolina Central University Plastic Shopping Bags Legally Banned Essay.

For this argumentation-persuasion essay you will be required to research a topic of interest and present an informed analytical argument that takes into consideration the research you have completed. You will need to choose a very specific topic that allows you to develop unique and interesting ideas.  However, please do not write about the abortion issue because what can you say that will change anyone’s mind?  Also, local issues usually make for the best essays rather than national issues.
Your final draft must contain these components:

A strong debatable thesis (main argument)
Claims/Evidence (to support your main argument)
Research (to support your claims)
A well-developed conclusion 

(This directly relates to your argument because it takes into account the evidence that you have considered and it reinforces your main argument by asserting that your opinion has not changed in light of the research you have done.) 
Other things you should keep in mind:

Your sources should be academic sources from the library databases.  Good websites to go to for scholarly articles and newspaper articles would be and  Please avoid the Opposing Viewpoints database. 
Your essay should demonstrate an awareness of audience that suggests that you are aware of other/opposing perspectives.
Your strongest claim should be one of the last things your audience reads (except for the conclusion, of course).
Your topic should be controversial and significant and very specific.
Your claims should be based on logos, ethos, and pathos – mostly appealing to logic.

North Carolina Central University Plastic Shopping Bags Legally Banned Essay

Share this: Facebook Twitter Reddit LinkedIn WhatsApp Clostridioides difficile also known as C. difficile or C. diff is a Gram-positive, anaerobic bacterium and is the most prevalent pathogen among all healthcare-associated infections (, 2017). This bacterium causes liquid diarrhea and or life-threatening inflammation of the colon (CDC, 2018). There are about half a million infections every year in the United States (CDC, 2015). “In recent years the infection has become more frequent, severe and difficult to treat” (, 2016). According to the Center for disease control, Cdiff infection is responsible for about 15,000 deaths in the United States with 1 in 11 being 65 yrs and over (2015). C. diff can easily spread from person to person through contact and infection of the bacteria (CDC, 2018). Symptoms can vary from mild to severe and can start as shortly as a few days taking antibiotics. The host may develop frequent diarrhea including loose, smelly, watery stools or frequent bowel movements for several days. Other symptoms may include blood or pus in the stool, fever, stomach tenderness, cramping or pain, loss of appetite, nausea, weight loss, and rapid heart rate (, 2016). Complications may include dehydration, elevated white cells, toxic megacolon, bowel perforation, renal failure, and death. Depending on the affected individual, treatments for cdiff may include stopping the disturbing antibiotic, treat with new antibiotics, probiotics, fluids, hydration, good nutrition, surgery or fecal transplant (, 2016). Caregivers in health care facilities must follow contact plus protocol to keep the disease from spreading. In addition to standard precautions, patients must be placed in a private room. After caring for them, staff must remove gown, gloves, wash their hands with soap and water and clean care areas with an approved sporadically product. Visitors must be educated and encouraged to adhere to the same protocol (, n. d). Determinants of Health More than 80% of C. diff deaths occur in people 65 and older (CDC, 2015). People with chronic disease comorbidity, weakened immune system, and under the age of two are other determinants (, 2016). People who take antibiotics are 7 to 10 times more likely to get C. diff; the use of acid suppressants is also a contributing factor (Bloomfield
Broward Community College Impact of Disease on Family Members Discussion.

Click here to read the following article from the South University Online Library on the impact of disease on family members:Golics, C. J., Basra, M. K. A., Finlay, A. Y., & Salek, S. (2013). The impact of disease on family members: A critical aspect of medical care. Journal of the Royal Society of Medicine, 106(10), 399–407.After reviewing the article, respond to the following questions.Which factor do you feel has the most impact on family members?Support your response with examples from readings.What are some of the reasons it is important to include the support persons in the plan of care?
Broward Community College Impact of Disease on Family Members Discussion

CIS 510 Strayer Week 3 Case Diagram Activity Diagram and ERD Discussion.

I’m working on a computer science question and need an explanation to help me study.

Share your thoughts and opinions on Use Case Diagram, Activity Diagram, and Entity Relationship Diagram (ERD) in this week’s discussion.Flesh out your thoughts and interact with your classmates. Post your initial response by the middle of each week and then return on a couple of other days to see what’s going on with the discussions. The more you interact, the more you learn from your peers, and the more you share with them about what you know. You will also be showing your instructor what you have picked up.Click the link to access the discussion. If you need help with completing discussions, please watch this video for more information.
CIS 510 Strayer Week 3 Case Diagram Activity Diagram and ERD Discussion

Colorado Technical University The Future of Health Information Systems Project

Colorado Technical University The Future of Health Information Systems Project.

I’m working on a science question and need support to help me understand better.

ype: Individual ProjectUnit: The Future of Health Information SystemsDue Date: Wed,5/19/21Grading Type: NumericPoints Possible: 100Points Earned: Points Earnednot availableDeliverable Length: 3-4 pagesView objectives for this assignmentGo To:Assignment DetailsLearning MaterialsReading AssignmentMy Work:Online Deliverables: SubmissionsLooking for tutoring? Go to SmarthinkingAssignment DetailsAssignment DescriptionWrite a paper of 3-4 pages, not including the title page and reference page, that addresses the following:As the manager of the CTU Health Care information systems department, the chief information officer (CIO) has asked you to complete the following:Analyze the implications and challenges of cost, quality, and external forces on electronic health record (EHR) or electronic medical record (EMR) selection and implementation within your department.Conduct research on 1 major external threat facing the EHR today.Note: Use APA style and cite at least 2 scholarly references published within the last 5 years.Please submit your assignment.For assistance with your assignment, please use your text, Web resources, and all course material
Colorado Technical University The Future of Health Information Systems Project

ENC 1102 FIU American Crises by Rebecca Solnit Book Report

essay writer free ENC 1102 FIU American Crises by Rebecca Solnit Book Report.

The essay is a 4-5 page assignment based on the book Rebecca Solnit Call them by their true names: American CrisesLinks for Book spark notes- is the format my professor wants-Your book review should be insightful and based on your analysis of the text. I am not looking for a retelling of the book. Rather, I am looking for your thoughts on the book in regard to your understanding of American government and politics. Be specific and clear. It is you job as the writer to ensure the reader understands your thesis. Thus, your links need to be well thought out, clearly explained, and demonstrative of your critical thinking at work.Introduction (1 page): The introduction is the most important part of any well-written paper. It is meant to capture the attention of the reader as well as lay out your arguments. It should not be generic, and it should be free from language such as “in this paper I will discuss.” Make it interesting. Be creative. Follow your introduction with a brief summary of the main points and circumstances involved with the issue. The summary must be succinct, concise and clear. This section will be the most difficult portion of the assignment since you must summarize a great deal of information in no more than half of a page.o Some things to keep in mind: what are your initial impressions of the book? What are your thoughts on the first chapter?Reflection (3 pages): Your analysis/reflection includes two components. The first component includes an the application of concepts. How are concepts/issues such as political power, founding principles, government, (in)equality, civic engagement, activism, voting, identity, American society, disenfranchisement, etc. addressed in the book? You must analyze/highlight at least two course-specific concepts. How do these concepts help you understand American politics? The second component involves your pondering where you think the issue is headed in regard to local, state or federal response. Illustrate your argument by referencing and analyzing a specific passage of the book. What would need to happen in order for the issue be “resolved”? Make sure you define “resolved”. Overall, I will be looking for creative, insightful and thoughtful responses that balance opinion and analysis.Conclusion (1/2 to 1 page): What is the book’s main contribution to our understanding of American government and politics? Select a favorite quote or two and analyze them. How do these quotes illustrate or capture the main thesis of the book?
ENC 1102 FIU American Crises by Rebecca Solnit Book Report

Content Based Image Retrieval System Project

An Efficient Content-based Image Retrieval System Integrating Wavelet-based Image Sub-blocks with Dominant Colors and Texture Analysis ABSTRACT Multimedia information retrieval is a part of computer science and it is used for extracting semantic information from multimedia data sources such as image, audio, video and text. Automatic image annotation is called as automatic image tagging or automatic linguistic indexing. It is the process in which a computer system automatically designates metadata in the form of keywords or captioning to a digital image. This application is widely used in image retrieval systems to locate and organize images from database. In this paper we have proposed efficient content based image retrieval (CBIR) systems due to the availability of large image database. The image retrieval system is used to retrieve the images based on color and texture features. Firstly, the image is partition into equal sized non-overlapping tiles. For partitioning images we are applying methods like, Gray level co-occurrence matrix (GLCM), HSV color feature, dominant color descriptor (DCD), cumulative color histogram and discrete wavelet transform. An integrated matching scheme can be used to compare the query images and database images based on the Most Similar Highest Priority (MSHP). Using the sub-blocks of query image and the images in database, the adjacency matrix of a bipartite graph is formed. INTRODUCTION: Automatic image annotation is known as automatic image tagging or automatic linguistic indexing. It is the process in which a computer system automatically designates metadata in the form of keywords or captioning to a digital image. This application is widely used in image retrieval systems to locate and organize images from database. This method can be considered as multi class image classification with a large number of classes. The advantage of automatic image annotation is that the queries that can be specified by the user. Content based image retrieval requires users to search by images based on the color and texture and also is used to find example queries. The traditional methods of image retrieval are used to retrieve annotated images from large image database manually and which is an expensive, laborious and time consuming in existence. Animage retrieval system is a computer system for searching, browsing and retrieving images from a largecollectionofdigital images. Most common and traditional methods of image retrieval use some methods of adding metadata such as captioning or descriptions and keywords to the images so that the retrieval can be performed over the annotation words. Image searchis used to find images from database and a user will provide a query terms as image file/link, keywords or click on some image and the system will return images similar to that query image. The similarity matching is done by using the Meta tags, color distribution in images and region/shape attributes. Image Meta Search: – searching the images based on associated metadata such as text, keywords. Content-Based Image Retrieval (CBIR):- This is the main application of computer vision to retrieve the images from image database. The aim of CBIR is used to retrieve images based on the similarities in their contents such as color, texture and shape instead of textual descriptions and comparing a user-specified image features or user-supplied query image. CBIR Engine List: – This is used to search images based on image visual contents as color, texture, and shape/object. Image Collection Exploration: – It is used to find images using novel exploration paradigms. Content Based Image Retrieval: Content based image retrieval is known asquery by image content(QBIC) andcontent-based visual information retrieval(CBVIR) and it is the application ofcomputer vision techniques to retrieve the images from digital image database. This is the image retrieval problem of finding for images in large image database. Content-based image retrieval is to provide more accuracy as compared to traditionalconcept-based approaches. Content-based is the search that analyzes the contents of the image instead of metadata such as keywords, tags, or descriptions associated with that image. The term “content” in this context means textures, shapes, colors or any other information about image can be derived from the image itself. CBIR is popular because of its searches are purely dependent on metadata, annotation quality and completeness. If the images are annotated manually by entering the metadata or keywords in a large database can be a time consuming and sometime it cannot be capture the keywords preferred to describe its images. The CBIR method overcomes with the concept based image annotation or textual based image annotation. This is done by automatically. Content Based Image Retrieval Using Image Distance Measures:- In this the image distance measure method is used to compare the two images such as a query image and an image from database. An image distance measure method is used to compare the matching of two images in various dimensions as color, shape, texture and others. Finally these matching results can be sorted based of the distance to the queried image. Color This is used to compute image distance measures based on color similarity. This is achieved by computing the color histogramfor each image and that is used to identify the proportion of each pixel within an image which is holding a specific values. Finally examine the images based on the colors, which contains most widely used techniques and it can be completed without consider to image size or orientation. It is used to segment color by spatial relationship and by region among several color region. Texture Textures are represented as texels and are then located into a number of sets based on a lot of textures and are detected in the images. These sets are used to define texture and also detect where the textures are located in images. Texture measures are used to define visual patterns in images. By using texture such as a two- dimensional gray level variation is to identify specific textures in an image is achieved. Using texture, the relative intensity of pairs of pixels is estimated such as contrast, regularity, coarseness and directionality.Identifying co-pixel variation patterns and grouping them with particular classes of textures like silky, orrough. Different methods of classifying textures are:- Co-occurrence matrix. Laws texture energy. Wavelet transforms. LITERATURE SURVEY: In this paper a multscale context dependent classification algorithm is developed for segmenting collection of images into four classes. They are background, photograph, text, and graph. Here, features are used for categorization based on the distribution patterns of wavelet coefficients in high frequency bands. The important attribute of this algorithm is multscale nature and is used to classifies an image at different resolutions adaptively and enabling accurate classification at class boundaries. The collected context information is used for improving classification accuracy. In this two features are defined for distinguishing local image types in image database according to the distribution patterns of wavelet coefficients rather than the moments of wavelet coefficients as features for classification. The first feature is defined for matching between the empirical distribution of wavelet coefficients in high frequency bands and the Laplacian distribution. The second feature is defined for measuring the wavelet coefficients in high frequency bands at a few discrete values. This algorithm was developed to calculate the feature efficiently. The multscale structure collects context information from low resolutions to high resolutions. Classification is done on large blocks at the starting resolution to avoid over-localization. Here, only the blocks with extreme features are classified to ensure that the blocks of mixed classes are left to be classified at higher resolutions and the unclassified blocks are divided into smaller blocks at the higher resolution. These smaller blocks are classified based on the context information achieved at the lower resolution. Finally simulations shows that the classification accuracy is significantly improved based on the context information. Multiscale algorithm is also provides both lower classification error rates and better visual results [1]. This paper proposed content based image retrieval technique that can be derived in a number of different domains as Medical Imaging, Data Mining, Weather forecasting, Education, Remote Sensing and Management of Earth Resources, Education. The content based image retrieval technique is used to annotate images automatically based on the features like color and texture known as WBCHIR (Wavelet Based Color Histogram Image Retrieval). Here, color and texture features are extracted using the color histogram and wavelet transformation and the mixture of these two features are strong to scaling and translation of objects in an image. In this, the proposed system i.e. CBIR has demonstrated a WANG image database containing 1000 general-purpose color images for a faster retrieval method. Here, the computational steps are effectively reduced based on the Wavelet transformation. The retrieval speed is increases by using the CBIR technique even though the time taken for retrieving images from 1000 of images in database is only a 5-6 minutes [2]. This paper presents content based image retrieval scheme for medical images. This is an efficient method of retrieving medical images based on the similarity of their visual contents. CBIR-MD system is used to facilitate doctors in retrieving related medical images from the image database to diagnose the disease efficiently. In this a CBIR system is proposed by which a query image is divided into identical sized sub-blocks and the feature extraction of each sub-block is conceded based on Haar wavelet and Fourier descriptor. Finally, matching the image process is provided using the Most Similar Highest Priority (MSHP) principle and by using the sub-blocks of query and target image, an adjacency matrix of bipartite graph partitioning (BGP) created [3]. In this paper a content based image retrieval (CBIR) system is proposed using the local and global color, texture, and shape features of selected image sub-blocks. These image sub-blocks are approximately identified by segmenting the image into small number of partitions of different patterns. Finding edge density and corner density in each image partition using edge thresholding, morphological dilation. The texture and color features of the identified regions are calculated using the histograms of the quantized HSV color space and Gray Level Co- occurrence Matrix (GLCM) and the combination of color and texture feature vector is evaluated for each region. The shape features are computed using the Edge Histogram Descriptor (EHD). The distance between the characteristics of the query image and target image is computed using the Euclidean distance measure. Finally the experimental results of this proposed method provides a improved retrieving result than retrieval using some of the existing methods [4]. An efficient content based image retrieval system plays an important role due to the availability of large image database. The Color-Texture and Dominant Color Based Image Retrieval System (CTDCIRS) is used to retrieve images based on the three features such as Dynamic Dominant Color (DDC), Motif Co-Occurrence Matrix (MCM) and Difference between Pixels of Scan Pattern (DBPSP). By using the fast color quantization algorithm, we can divide the image into eight partitions. From these eight partitions we obtained eight dominant colors. The texture of the image is obtained by using the MCM and DBPSP methods. MCM is derived based on the motif transformed image. It is related to color co-occurrence matrix (CCM) and it is the conventional pattern co-occurrence matrix and is used to calculate the possibility of the occurrence of same pixel color between each pixel and its nearby ones in each image, which is the attribute of the image. The drawback of MCM is used to capture the way of textures but not the difficulty of texture. To overcome this, we use DBPSP as texture feature. The combination of dominant color, MCM and DBPSP features are used in image retrieval system. This approach is efficient in retrieving the user interested images [5]. In this paper content based image retrieval approach is used. It consists of two features such as high level and low level features and these features includes color, texture and shape which are present in each image. By extracting these features we can retrieve the images from image database. To obtain better results, RGB space is converted into HSV space and YCbCr space is used for low level features. The low level features are to be used based upon the applications. Color feature in case of natural images and co-occurrence matrix in case of textured images yields better results [6]. OBJECTIVE: To retrieve images more efficiently or accurately. To improve the efficiency and accuracy by using the multi features for image retrieval (discrete wavelet transform). Image classification and accuracy analysis. Time saving. Robustness. METHODOLOGY: Discrete Wavelet Transform. Conversion to HSV Color Space. Color Histogram Generation. Dominant Color Descriptor. Gray-level Co-occurrence Matrix (GLCM). ARCHITECTURE: This architecture consists of two phases: Training phase Testing phase These two phases of the proposed system consists of many blocks like image database, image partitioning, wavelet transform of image sub-blocks, RGB to HSV, non uniform quantization, histogram generation, dominant color description, textual analysis, query feature, similarity matching, feature database, returned images. In training phase, the input image is retrieved from image database and then the image is being partitioned into equal sized sub-blocks. Further, for each sub-block of the partitioned image, wavelet transform is being applied. Then the conversion from RGB to HSV taken place preceded with non uniform quantization, inputted to histogram generation block where a color histogram is generated for the sub-blocks of the image. Then the dominant color descriptors are extracted and texture analysis of each sub-block of the image is done. Finally the image features from the feature database and the input image features are compared for the similarity matching using MSHP principle. Then the matched image is being returned. In testing phase, the processing steps are same as training phase, except the input image is given as the query image by the user not collected from the image database. OUTCOMES: It provides accurate image retrieving. Comparative analysis and graph. Provides better efficiency. CONCLUSION: To retrieve images from image database, we can use discrete wavelet transform method based on color and texture features. The color feature of the pixels in an image can be described using HSV, color histogram and DCD methods, similarly texture distribution can be described using GLCM method. By using these methods we can achieve accurate retrieval of images. REFERENCES: [1] Jia Li, Member, IEEE, and Robert M. Gray, Fellow, IEEE, “Context-Based Multiscale Classification of Document Images Using Wavelet Coefficient Distributions”, IEEE Transactions on Image Processing, Vol. 9, No. 9, September 2000. [2] Manimala Singha and K.Hemachandran, “Content Based Image Retrieval using Color and Texture”, Signal

Effect of Age Stereotypes on Balance Performance

Effect of Age Stereotypes on Balance Performance. Question 1: An important aspect of physical functioning is the ability to stay balanced. How may expectations generated by age stereotypes influence older adults’ balance performance? Critically review psychological theory and research relevant to this issue, and discuss broader implications for interventions that may support healthy physical functioning of older persons. Loh Qiu Yan Melissa Abstract Older adults face wide range of age stereotypes as they age into their golden years. Such life cycles made people question their cognitive ability and physical functions. The effect of age stereotypes led to one facing both positive and negative aspect of life. These constant stereotyping had negative impacts on health and physical function. But with the help of social interactions, it helped older folks have a choice in leading a more balanced life. The use of social networks helped maintain their physical and cognitive functioning, giving them the room to have independence as well as learning more about their bodily functions. Importance and interventions in maintaining balance performance in physical functioning of older adults. Aging is an inevitable process in living beings where the condition of the body deteriorates resulting in decline of functioning. This challenges the physical abilities and cognitive functioning of older people (Wulf, ChiviacowskyEffect of Age Stereotypes on Balance Performance