Get help from the best in academic writing.

BUSI 740 LUO The Hewlett Packard Universal Power Supply Response Discussion

BUSI 740 LUO The Hewlett Packard Universal Power Supply Response Discussion.

I’m working on a business discussion question and need an explanation to help me learn.

I am looking for 2 300 word replies to the two student papers that are
attached. Very simple and straight forward. (one reply per paper that is
attached). Please let me know if you need anymore guidance. Reply Instructions:

Submit replies of at least 300 words each to at least 2 other students. Each
reply must demonstrate a substantive discussion that meets grading rubric
specification. Support your reply by citing at least 1 peer-reviewed journal
articles. References are to be no more than 5 years old.Requirements: 300 words per reply
BUSI 740 LUO The Hewlett Packard Universal Power Supply Response Discussion

Fast Connected Components Labeling Algorithm Psychology Essay. Connected components labeling is an important operation in pattern recognition and computer vision [1], which is commonly adopted to detect connected regions in binary digital images. The labeling algorithm scans an image and groups its pixels into components based on pixel connectivity, and each component is assigned a unique label. According to the symbolic image, many features (numbers, areas, perimeters, centroid, enclosing rectangles, etc.) of objects in the image can be obtained in the later analysis. Therefore, labeling algorithm is widely used in target recognition and tracking, biological identification (such as fingerprint and face), automated inspection, character extraction, medical image analysis and other applications [2, 3]. Many labeling algorithms have been developed in the literature since the 1966[4], all of which yield to the same result: the number of labeled connected components is the same and consecutive labels can be achieved with a common enumeration procedure [5]. In this paper, we do not consider the parallel algorithms designed for specific processors, but the algorithms for ordinary computer architectures. Taking into account the number of scanning the input image while labeling, the labeling algorithms can be divided into the following three categories. Multi-pass algorithms [6, 7] scan the image in the forward and backward raster directions alternately and propagate the labels until no label is changed. Though simple, the main drawback of these methods is the number of scans depends on the geometrical complexity of connected components. And they may require a large number of image scans before reaching the final labels, which leads to a long execution time. To reduce the passing times, Suzuki [8] proposed a linear-time labeling algorithm based on sequential local operations and demonstrated by experimentation that the labeling of almost any arbitrary images is completed by no more than four passes. However, it is still time-consuming when the image is very large in size. Two-pass algorithms [4, 9-12] assign provisional labels to the foreground pixels in the first scan, and at the meanwhile, record the equivalence information among provisional labels. Then the equivalent labels are merged to determine the final labels. Finally, provisional labels are replaced by their representative labels while scanning the image at the second time. The differences between these algorithms lie in the way in which the image is scanned, the neighborhood is checked, and the equivalence information is resolved. Consequently, many approaches distinguished only in efficiency have been proposed, among which Wu’s search plus array-based union-find (SAUF) algorithm [13, 14], He’s fast connected component labeling (FCL) algorithm [15] and efficient first-scan (EFS) method [16] and Grana’s block-based decision tree labeling (BBDT) algorithm [5] are most efficient ones. One-pass algorithms label the objects pixels by scanning the image only once. They search out an unlabeled object pixel, assign the same label to it and all the object pixels connected to it. Whereas, unlike the label-equivalence-based algorithms that access the image in a regular way, one-pass algorithms usually process the image in an irregular pattern. Thus they are not suitable for hardware implementation because of many irregular memory accesses. For example, Abu- Baker’s region growing based labeling algorithm [17] usually needs to search the neighbor pixels repeatedly, and the stack data structure results in irregular access of pixels which has slow performance in practice. Chang’s contour tracing (CT) algorithm [18] search and label the pixels of one object contours and propagate the label to the internal pixels. It is the most efficient one-pass algorithm in literature, but is not suitable for images with objects of complicated geometrical shape and still slower than efficient two-pass algorithms, for example, the FCL method. In terms of efficiency, two-pass approaches outperform the others. Therefore, in this paper, we combine the efficient first scan method of He (EFS)[16] with the array-based union-find data structure of Wu (SAUF)[13, 14] to form a new faster two-pass labeling algorithm. Tests have been carried out on different datasets to evaluate the scalability and effectiveness of the hybrid algorithm, and the experimental results indicate that it is faster than the existing approaches when labeling the large images without too much label equivalences. Remaining of this paper is organized as follows. A brief review of three most efficient two-pass labeling algorithms so far is given in the next section. In section 2, the proposed combinative algorithm is described in details. Section 3 outlines the experimental conditions and discusses experimental results of our method as compared with the three algorithms in Section 1. And Section 4 is devoted to the conclusions. 1 Most Efficient Algorithms In this section, we introduce three most efficient labeling algorithms: EFS, SAUF and BBDT. For convenience, we assume that the input images are binary images (foreground pixels and background pixels are represented by 1 and 0, respectively) with zero borders and consider only the eight-connectivity. We useto denote the input image andfor the symbolic image. For the general label-equivalence-based algorithms, the mask used in the first scan is shown in Fig. 1. To speedup the labeling procedure, there are two common strategies. One is to reduce the average number of times for checking the processed neighbor pixels in the first scan, and the other is to resolve the label equivalences quickly by an efficient data structure. The mask used in general label-equivalence-based algorithms (is the current pixel) He’s EFS method process the foreground pixels following a background pixel and those following foreground pixels in a different way, which can reduce the average number of neighbors accessed from 2.25 to 1.75. During the first scan, for each current foreground pixel, we have already known whether the previous pixel is a foreground pixel or not, thus, it can be removed from the mask. Therefore, the mask used here consists of only the three processed neighbor pixels of the current foreground pixel in the row above,, and (that is, , and), as shown in Fig. 2. For processing a current foreground pixel following a background pixel, the same method as proposed in FCL [15] is used. While, for the current foreground pixel following another foreground pixel, the label of its previous foreground pixel is assigned to it, and the left thing needed to do is only to check whether this label is equivalent to the label assigned to pixel . To resolve label equivalences between provisional labels, this algorithm adopts an equivalent label set to hold all the provisional labels assigned to a connected component and the smallest label is taken as the representative label. When a new provisional labelis generated, the corresponding equivalent label set is established as, and the representative label is set to itself, i.e.. Whenever label equivalence occurs, say, andin the mask belong to different setsand separately, these two sets are merged together, and their smallest label is regarded as their representative label. They took three simple one-dimensional arrays to implement this process without using pointers, but the drawback is that when a set with more elements is merged into a set with fewer elements, the resolve procedure will cost much time. Anyway, EFS is a most efficient pixel-based scan strategy so far. Wu et al. [13, 14] proposed two optimization strategies to improve connected components labeling algorithms. The first strategy employs a decision tree (see Fig.3) to minimize the number of neighboring pixels accessed in the scanning phase, and the second one streamlines the Union-Find algorithm to track equivalent labels. In the forward scan mask,, andare all neighbors of , hence if is an object pixel, the other three pixels are not needed to be accessed. When the label equivalence information is recorded, it is possible to derive the The mask used in EFS method (e is the current pixel) correct label of (and therefore that of ) later. If is a background pixel, the order to check other pixels in the mask is given as a decision tree shown in Fig.3. Various tests demonstrated that this decision tree reduces the number of neighboring pixels visited from 4 to 7/3 on average, which is still bigger than EFS method. The decision trees used in scanning for 8-connected neighbors (a, b, c, d are neighbors of current pixels e as shown in Fig.1(a)) Whereas, as to dealing with the label equivalence problem, Wu’s array-based Union-Find data structure is more efficient than He’s label sets method for the images with large amounts of connected components in practice. To reduce the random accesses which are the main reason that affects the performance of most Union-Find algorithms [19], they exploited a single arrayto implement the Union-Find algorithm with path compression, a way of flattening the structure of the tree whenever a Find operation is used on it. For every pair of equivalent labels, a path compression is performed to compute their root, say, keep the element of arrayas the representative label of provisional label. It can be proved that their Union-Find approach takes a linear time (even in the worst case) to perform anyUnion and Find operations in the connected components labeling algorithms. Our experiments reveal its excellent performance in resolving the label equivalence problem. As far as we write, the BBDT algorithm [5] proposed by Grana et al. represents the state of the art for connected components labeling. They proposed an efficient modeling of the problem by means of decision tables. An automatic procedure to synthesize the optimal decision tree from the decision tables is used, providing the most effective conditions’ evaluation order. With the automatically generated decision tree, this approach scans the image using 2Ã-2 pixel blocks, which lead to significant performance improvements of the neighborhood exploration in terms of memory access times. The mask used in this paper is shown in Fig.4. The advantage of this scanning procedure is that four pixels can be labeled at the same time and much fewer union operations are needed. At the meanwhile, the decision tree algorithm can optimize the problem of increasing computational time caused by the standard procedure. However, this scanning method may check the same pixel multiple times, and a second scan is required to check which pixels in the block should be labeled. These additional works bring extra execution time. In addition, the program of this algorithm is more complex because of multiple alternative actions. The Mask used in 2Ã-2 BBDT algorithm. (a) Gives the identifiers of the single pixels employed in the algorithm, while (b) provides the blocks identifiers. 2 The Proposed Hybrid Algorithm On the basis of the analysis above and our own experiments, we find that He’s EFS method is faster than other pixel-based scanning procedure and Wu’s array-based union-find data structure is more efficient in the label equivalence resolution. Therefore, we combine these two fast strategies to form a new labeling algorithm. We use the mask shown in Fig.2 in this paper. In the first scan, for the current object pixel following a background pixel, its provisional label can be assigned by the following procedure: Procedure 1: if (b= =1) L[e]=L[b]; else if (a= =1) { L[e]=L[a]; if (c= =1 and L[e]!=L[c]) L[e]=union(L[e], L[c]); } else if (c= =1) { L[e]= =L[c]; } else { L[e]=NewLabel; R[NewLabel]=NewLabel; NewLabel ; } While for the current object pixelfollowing another object pixel, is assigned simply by this procedure: Procedure 2: L[e]=L[d]; if (b= =0 and c!=0 and L[e]!=L[c]) { L[e]=union(P, L[e], L[c]); } In these two procedures above, function is the same as proposed by Wu [15]. It always sets the root of the combined tree with the smallest label and changes all nodes on the find path to point the new root directly, i.e., the array element is the representative label of provisional label . Thus, the number of random memory access is greatly reduced and we can invoke the procedure to obtain successive final labels. After the first scan, all the equivalences between the provisional labels have already been solved. And then we can relabel the image through a second scan to get the final symbolic image. 3 Experiments and Discussions 3.1 Experimental conditions To evaluate the performance of the new algorithm, we make comparisons with most efficient labeling algorithms (BBTD, EFS, SAUF) described in Section 1. We got the programs of the BBDT algorithm from the authors’ web site, and wrote the others according to the original articles. All the algorithms’ codes implemented in C language are integrated into one project based on the program of BBDT algorithm, and compiled on Windows using Visual Studio 2008. The tests have been performed on a notebook (Intel Pentium Dual-Core T4300 processor (2.1GHz), 2GB Memory, Windows XP SP3 OS), using a single core for the processing. All algorithms produced the same consecutive labels on all images. Images used in this paper are composed of 80 digital photographs of 3264Ã-2448 pixels, 110 satellite images of different sizes collected from Google Earth, 720 random noise sequence images as described in [5], 109 Brodatz texture images of 640Ã-640 pixels, 80 fingerprint images of 256×364 pixels, and some other images obtained form the Standard Image Database (SIDBA), CCITT, and the USC-SIPI Image Database, as in [15]. All the images were binarized by means of Otsu’s threshold selection method. 3.2 Results and discussions Since all the labeling algorithms yield to the same result, we take the efficiency as the only criterion to measure their performance. All the data of execution time are average of 1000 runs. Firstly, we carried out the same comparison as [5] on the noise image dataset to evaluate the performance of the four algorithms. We can see from Fig.5 (a) that these four labeling algorithms are all linear versus image size, and that with the image size increases, the proposed algorithm runs more steadily than others. Fig.5 (b) indicates that our algorithm is superior to the rest when the noise density is low or very high (i.e. the geometrical shape of connected component is not very complex). Performance of four labeling algorithms on random noise images. (a) Gives result for noise images of eight sizes (32Ã-32, 64Ã-64,…, 4096Ã-4096 pixels) and for each size, 80 images with different densities (from 0.1 to 0.9) are used; and (b) shows the average execution time for the images of 4096Ã-4096 pixels with different foreground pixel densities. Various kinds of natural images with different sizes were used to test the performance of the four labeling algorithms, as shown in Fig.6 and Table 1. Fig.6 illustrates the performance for the images of different types, that is, fingerprints, texture images, misc images from USC, CCITT, digital photos and remote sensing images. It can be seen from Fig.6 that for the small images with many small connected components, these four algorithms have similar performance, and the new algorithm is just a little slower than BBDT algorithm, such as Fig.6 (a), (b) and (c). While for large images with many large components, our approach is superior to others, such as Fig.6 (d), (e) and (f). The time listed in Table 1 is the total execution time for each kind of images. We can see that proposed method gets a higher performance boost (15%) over the BBDT algorithm for large images with larger connected components, for example, remote sensing images and digital natural photos. While for the small images with many small complex components, BBDT algorithm is still the excellent. Fig.7 gives six selected images in our test, and the foreground pixels are displayed in white. Based on the results in Table 2, our algorithm is fastest for labeling large images. Performance for natural images of different types and sizes, expressed by millionsecond. For the image of different sizes, images with larger index are larger in size. Total execution time of labeling different natural images The main reason for the results mentioned above is that for large connected components, the advantage of the scan procedure is brought into full play. Because there are many long runs in large components, and the Procedure 2 in Section 2 is implemented more frequently than Procedure 1, which saves a lot of execution time by reducing the times of neighbor accesses. However, for the small images with many small complex components, Procedure 1 will be carried out more often, and Example images tested in this paper. Test results for images illustrated in Fig.7. (ms) much more neighbor pixel checks are needed in the scan stage, which slows down the new method. Furthermore, the path compression technique employed in the single array-based Union-Find algorithm works efficiently for merging equivalent temporary labels. 4 Conclusion In this paper, we study the most efficient connected component labeling algorithms and propose a new fast two-pass labeling approach. In the first scan, foreground pixels are assigned provisional labels using He’s efficient scan procedure, and once a label equivalence occurs, it will be resolved by Wu’s array-based union-find algorithm. In the second scan, all the provisional labels are replaced by their representative labels. Large amounts of experiments on various images of different sizes indicate that our algorithm is more efficient than existing most efficient approaches for images with high resolution and many big connected components. References¼š A. Rosenfeld and A.C. Kak, Digital Picture Processing, Academic Press, 2nd ed, New York, 1982. Yapa, R. D. and H. Koichi, “A connected component labeling algorithm for grayscale images and application of the algorithm on mammograms,” Proceedings of the 2007 ACM symposium on Applied computing Korea, ACM New York, NY, USA, 2007, 146-152. Yun-fang, Z., “Background Subtraction and Color Clustering Based Moving Objects Detection,” International Conference on Information Engineering and Computer Science, 2009, ICIECS 2009, 2009, 1-5. A. Rosenfeld, J.L. Pfalts, “Sequential operations in digital picture processing, ” J. ACM, 1966, 13(4): 471-494. Costantino Grana, Daniele Borghesani and Rita Cucchiara, “Optimized Block-Based Connected Components Labeling With Decision Trees,” IEEE Trans. On Image Processing, 2010, 19(6): 1596 – 1609. R. M. Haralick, “Some neighborhood operations,” in Real Time/Parallel Computing Image Analysis, New York: Plenum, 1981, 11-35. A. Hashizume et al., “An algorithm of automated RBC classi¬cation and its evaluation,” Bio Med. Eng., 1990 28(1): 25-32. Suzuki K, Horiba I, Sugie N., “Linear-time connected-component labeling based on sequential local operations,” Comput Vis Image Underst, 2003, 89(1): 1-23. R. Lumia, L. Shapiro, O. Zungia, “A new connected components algorithm for virtual memory computers,” Comput. Vision Graphics Image Process, 1983, 22(22): 287-300. R.M. Haralick, L.G. Shapiro, Computer and Robot Vision, vol. I, Addison-Wesley, Reading, MA, 1992, 28-48. S. Naoi, “High-speed labeling method using adaptive variable window size for character shape feature,” in: IEEE Asian Conference on Computer Vision, 1995, 1, 408-411. L. He, Y. Chao, and K. Suzuki, “A run-based two-scan labeling algorithm,” IEEE Trans. Image Process, 2008, 17(5): 749-756. K. Wu, E. Otoo, and A. Shoshani, “Optimizing connected component labeling algorithms,” in Proc. SPIE Conf. Med. Imag., 2005, 5747: 1965-1976. K. Wu, E. Otoo, and K. Suzuki, “Optimizing two-pass connected-component labeling algorithms,” Pattern Anal. Applic, 2009, 12(2): 117-135. Lifeng He, Yuyan Chao, Kenji Suzuki, and Kesheng Wu, “Fast connected-component labeling,” Pattern Recognition, 2009, 42(9):1977-1987. Lifeng He, Yuyan Chao, and Kenji Suzuki, “An efficient first-scan method for label-equivalence-based labeling algorithms,” Pattern Recognition Letters, 2010, 31(1): 28-35. Ayman AbuBaker, Rami Qahwaji, Stan Ipson, and Mohmmad Saleh. “One scan connected component labeling technique,” IEEE International Conference on Signal Processing and Communications (ICSPC2007), 1283-1286. F. Chang and C. Chen, “A component-labeling algorithm using contour tracing technique,” in Proc. Int. Conf. Document Anal. Recog., 2003, 741-745. C. Fiorio and J. Gustedt, “Memory management for union-find algorithms,” in Proceedings of 14th Symposium on Theoretical Aspects of Computer Science. Springer-Verlag, 1997, pp. 67-79. Fast Connected Components Labeling Algorithm Psychology Essay
Need help with Exam.

Presentation of the ProblemDoe is a 17 year old, Caucasian male from a lower middle class rural area. He was arrested for attempted murder of two police officers. Doe’s attorney has requested an evaluation of Doe’s mental state at the time of the offense. Due to the seriousness of Doe’s offense he has been transferred to adult court. Doe has no prior arrest history or legal involvement in the juvenile or adult legal system. In addition, Doe has no previous medical or mental health history; nor a history of substance abuse or addiction. Doe’s mother described him as being a “smart, outgoing and friendly young boy in elementary school and mid-school.” However, during high school Doe became withdrawn and detached per his parent’s observations. He stopped socializing with others and by his junior year he was failing all of his classes.Per Doe’s parents eight months prior to the crime Doe began to exhibit bizarre behavior in the home. For example, Doe was observed watching television and chanting “circa mal te, cira mal te, cira mal te…” Doe’s mother stated when she asked Doe what was he chanting Doe stated “I have to talk in code so they want understand me, you know they can hear us you know and then pointed at the television. We can’t let them know my plan to fight them.” Doe’s father stated a few days before the crime he observed Doe having a running commentary with himself and when he asked Doe who was he talking to Doe yelled out “ …their coming, their coming…” Due to Doe’s parent becoming increasing concerned about Doe’s bizarre behavior they scheduled an appointment at an outpatient clinic for a psychiatric evaluation.During the evaluation, Doe communicated marked delusional beliefs about policemen who were “non-human.” Doe stated he had seen the “non-human, alien policemen,” but would not elaborate further. Doe reported the “alien policemen” were placed on earth to facilitate the “takeover of the world and seize all humans.” He elaborated that this world “takeover” would involve the murder of “all government leaders” and “millions of innocents.” When asked, Doe said he did not have any thoughts of hurting himself or others nor did he exhibit an inability to take care of himself. Doe was released to his parent’s care with a prescription of an atypical antipsychotic with a follow-up appointment scheduled in five days. Per Doe’s parents Doe was non-compliant in taking his prescribed medication. Two days later, Doe walked into his local police station and began firing his father’s .357 magnum and chanting “circa mal te, cira mal te, cira mal te…” Two policemen were seriously injured. Based on the vignette provided, please compose a well-written and organized response to each of the following questions. When writing your responses, please: Use APA (6th edition) Style, with 1-inch margins, double-spaced, 12 font, with a reference list at the end. Write clearly and concisely. Cite appropriate, and especially current, literature (empirical and/or theoretical). Avoid all sexist idioms and allusions. Remember to demonstrate your multicultural competence where appropriate. Psychological Theory and Practice What assessments would you conduct to enhance your understanding of the problems of the person in the vignette and how would your choice of assessment(s) inform your diagnostic formation and treatment planning? Assessments may include structured or unstructured interviews, valid and reliable assessment measures, and/or formalized assessment procedures that may be conducted by yourself or by someone else referred by you. Provide your diagnostic impressions (based on the DSM-5) for this individual. In narrative form, please describe how the individual meets the diagnostic criteria for the disorder(s) chosen in addition to the differential diagnostic thought process that you used to reach your hypotheses. Be sure to include any additional (missing) information that is needed to either rule out or confirm your differential diagnoses impressions. Legal Theory and Application Explain the background, current presentation, and behavior of the person in the vignette utilizing biological, learning, and social theories on offenders to support your position. Do not simply restate the background information from the vignette. Instead, provide a theoretically-based discussion to understand the criminal behaviors of the person in the vignette. Consider the type of crime in the vignette and discuss how that type of crime generally impacts a victim of it. Do not limit yourself to discussing just the victim in this vignette. Instead obtain scholarly sources for information on how this type of crime can affect any victim, their family members, and other members of society. Describe the psycholegal standards and/or definitions for each of the following: competence to stand trial, duty to warn, and insanity. Identify and describe one or more landmark case(s) for each standard (at least three cases total). Describe the elements or issues that a mental health professional usually focuses on when assessing a person’s adjudicative competence, risk and insanity, and any additional items that might be especially important to focus on in the provided vignette. Assessment, Research and Evaluation Describe tests or assessment procedures you would employ to address the psycholegal issues of (competence to stand trial, risk of dangerousness, and insanity). You may refer to these from the Psychological Theory and Assessment Section “A” if you already covered them there. Discuss what the anticipated conclusions would be based upon information provided in the vignette. Develop a research question and a testable research hypothesis regarding offenders or the type of crime that is discussed in the vignette (such as, addiction, recidivism, criminal behavior, etc.). Explain the variables in your question and the type of research study that could answer your question as well as why that research would make a contribution to the field of forensic psychology. Leadership, Consultation, and EthicsWhat are the ethical and legal dilemmas this vignette introduced? What would be your immediate steps and why? Please be specific and make sure that you describe your process of ethical decision making and the solutions/consequences to which this process might lead. Your discussion should be informed by the American Psychological Association’s Ethics Code as well as the Specialty Guidelines for Forensic Psychologists.Interpersonal EffectivenessWhat diversity factors, cultural considerations, or other demographic variables pertaining to the person in the vignette would you take into account in rendering diagnoses, choosing assessment measures, forming case conceptualizations, and designing the treatment plan? Be sure to discuss cultural/diversity factors that could apply even if they are not explicitly mentioned in the vignette.Your writing, use of citations, ability to form a logical argument, and proper APA Style, including the use of paraphrasing, will be evaluated as a measure of your interpersonal effectiveness. No response is required for “B”. There is an example of what the paper should look like. I just need assistance for the most part
Need help with Exam

Table of Contents Introduction Insights Conclusion Reference List Introduction This paper is an individual report of my role as a marketing manager for the simulated company (2). In this report, I evaluate the company’s performance across different developmental stages, which are divided into eight periods. In each one of them, information relating to the company’s performance in previous periods, factors influencing strategic and operational decisions, internal and external organisational dynamics, and the key figures used in decision-making are provided. Based on the broader evaluation of the company’s performance, an assessment of its economic growth and crises that affected each period is provided. The assessment is provided in the log below. Insights Period Insights P0 The situation of the company in P0 was neutral because the business was in the inception stage. The strategic and operational decisions made at this stage of development were premised on the need to increase sales, market share, and the total revenue for the company. Here, group synergy was sought. The quest to realise group synergy aligns with the recommendations of Roome and Louche (2016), which suggest that business models should be fashioned to create synergy by fostering group cohesion among stakeholders. This vision is partly supported by the resource-based and market-based views, which presuppose that companies could use their internal resources to achieve maximum external leverage (Aghazadeh 2015; Droli et al. 2014; Dassler 2016; Xie, Wang

RSCH 5800 JWU Measuring Actionability of Evidence for Evidence Based MGT Discussion

RSCH 5800 JWU Measuring Actionability of Evidence for Evidence Based MGT Discussion.

As a reminder, Graduate level work requires that you be able to interpret, synthesize and communicate your own views on a subject. Consequently, material plagiarized from other sources (e.g., cut and pasted from a website) will not receive credit.Why EBMgt?What are the indicators of the credibility of evidence?Describe the four elements that need be present in order to conclude that one variable influences another?In broad (but clear) terms, describe the role of concomitant variation in establishing “cause-and-effect” (e.g., that one variable is likely to influence another).What role do ‘statistics’ play in the establishment of credibility
RSCH 5800 JWU Measuring Actionability of Evidence for Evidence Based MGT Discussion

Your consulting work with the physician’s group in Module Three earned you rave reviews and now your boss has Essay

i need help writing an essay Your consulting work with the physician’s group in Module Three earned you rave reviews and now your boss has asked you to put your skills to work in support of a local community health center. As the Assistant to the CFO you have been tasked with preparing a public statement on the justification of purchasing the electronic medical record. After explaining the benefits of the EMR and making suggestions for ways the benefits can be determined, write a memo to the community health center’s Board of Directors that uses your determinations to justify the purchase of the EMR. For additional details, please refer to the Module Five Journal Guidelines and Rubric document.

Florida National University Black Markets Ticket Selling Discussion

Florida National University Black Markets Ticket Selling Discussion.

Tickets to athletic events, concerts and other venues are often resold at higher than original prices. In some states, scalping is outright illegal, while in other states, scalping is legal under certain circumstances.You may have your own opinion about scalping, and it may be related to moral issues. I would like you to use some of the concepts you have learned in this course to analyze the issue and answer the following questions:1. Is the original ticket price necessarily an accurate measure of the equilibrium price?2. Who are the winners and losers in scalping?
Florida National University Black Markets Ticket Selling Discussion

Healthcare Marketing: John Hopkins Hospital Essay

Introduction In 1960’s, a marketer by the name E J McCarthy came up with a marketing mix strategy to be used in business when marketing and making marketing strategies. The name of the strategy is called 4Ps; these are initials for product/service, price, promotion and place. Alongside this marketing mix, a company should choose an appropriate target market for a successful business (Möller, 2006). John Hopkins is a medical facility in Baltimore which has adopted the above marketing strategies as it meets its target customers. This paper analyzes marketing mix (4Ps) and target market of the hospital. Marketing mix (4Ps) Product/service The hospital aims at offering high quality services to its customers. This is facilitated by maintaining high quality medical personnel’s/physicians and undertaking an intensive research on different areas in medicine. Other than resident doctors and medical practitioners, the hospital maintains contacts with experts in certain areas who may be working freelance to offer such services. This is done to both inpatient and outpatient customers. The level of technology is changing daily in all sectors of an economy; the hospital realizes this and embarked on massive technological improvement. This ensures that they can deliver quality and timely services to their patients. Other than the medical part of it, all employees whether they belong to an outsourced partner or they are directly employed in the organisation are required to treat customers with courtesy and guide them to the best of their knowledge. Price The hospital is used by John Hopkins School of medicine for research and development. In a system which seems to be a return to the society, the hospital charges a reduced cost especially when a patient is treated by/with a student. This does not mean that the quality of the hospital is compromised by student but they are just involved to help them have experience. When it comes to professional services, the hospital when using its internal facilities and personnel’s it charges a reduce rate. When it calls for experts from other areas, the patient is expected to cater for the physicians expenses and pay a small fee for the use of hospital facilities. Get your 100% original paper on any topic done in as little as 3 hours Learn More Despite this the hospital has negotiated prices with external experts so the prices for the hospital can be termed as competitive. This move has assisted in making the hospital competitive. Place The hospital is situated in Baltimore, which is the largest city in Maryland and a central city for Maryland. The reason why this hospital is situated here is because there is John Hopkins School of medicine and the population that it is able to reach to at this point. The position is strategic and can be easily accessed by a great population of Maryland. Baltimore is also a central point with offices and other recreation facilities making the hospital strategically placed. The place of the hospital makes its competitive since it is accessible 24 hours in 7days. This is mad possible by good security and transport networks. There are times that the hospital undertake free medical camps in different parts of the country in their effort to market and improve the health of the population. Promotions The hospital doubles as a research institute for John Hopkins School of medicine; this makes the experts to interact with the outside world around the hospital and in other places in the world. After developing something, the first people that it aims at assisting are the locals. They are educated on the development and this forms part of its promotions. On the other hand the hospital calls for medical camps to check the general health of people living in the locality. These moves have made patients be loyal to the hospital. Why is understanding Marketing Mix important When developing an efficient marketing strategy, there is need to understand and put into consideration all the 4Ps of marketing. This will help in offering good service to target customers and ensuring that an organisation understands the needs of its customers. There is no point of producing goods and service which are not consumed by target customers and thus ensuring that quality products are produced, then distributed to appropriate places at an affordable price is important (McNamara, 2009). We will write a custom Essay on Healthcare Marketing: John Hopkins Hospital specifically for you! Get your first paper with 15% OFF Learn More Relationship between organization’s marketing and its partnerships When developing a marketing strategy, it is important for a business to understand the effects that this strategies will have on the outside world. The needed effect is persuasion to potential customers and maintaining the already existing ones. Partnership includes having a good relationship with the outside world; these are people who affect the business for example creditor’s banks and the society. When there is good partnership, the hospital will be self marketed by the people as they refer other to the facility. To have a good relationship involves offering quality and affordable services to the people and meeting ones obligations as people expect them done (Möller, 2006). Explain how and why those partnerships are valuable to the organization Partnerships are valuable to a business since they lead to a good relationship between an organisation and the people the organisation is serving. When there is good relationship, there is customer loyalty which is a valuable asset to a business. Customer loyalty results to an organisation self marketing itself as people are able to refer others to products and services of the said organisations. Target market The hospital is located in Baltimore which is the largest city of Maryland; the metropolitan area has a population of 1.5 million people. The locality is known for a wide range of cultural and recreational facilities (The Johns Hopkins Hospital official website, 2010). The hospital has a wide target market which is not limited to the locality that it is operating in; maternity, dental, in patient, out patients, among others. It offers these services to the old, young, children, female and male patients. Through its website someone can book for a certain service from whichever the country he may be originating from. There are also special programs for women and children. On time bases, there are some medical seminars conducted by the hospital targeting a certain group for example the youth to train them on different medical issues like HIV-Aids. These are part of its social corporate responsibly Explain why understanding target market is vital to a successful marketing plan A target market assists a company to understand the kind of products/services that people it aims to serve require. When the services on demand are known, then the hospital is able to structure its processes to offer exactly that. On the other hand, to have an ethically conducted business, there is need t understand social, political, cultural and economic factors affecting a certain population. Different people have different expectations and thus an understanding of a target market helps in coming up with an efficient marketing plan and strategy. Not sure if you can write a paper on Healthcare Marketing: John Hopkins Hospital by yourself? We can help you for only $16.05 $11/page Learn More Conclusion For an effective marketing policy and strategy, businesses need to understand 4Ps of marketing and ensure that it can meet the need of its target customers. When this is done, then a company gains competitive advantage this understanding of target customer lead to good partnership between an organization and the outside world. References The Johns Hopkins Hospital official website.(2010). McNamara, P. (2009). 5 ‘marketing opportunities’ for hospitals. Network World, 26(20), 34. Retrieved from MasterFILE Premier database. Möller, K. (2006). Marketing Mix Discussion – Is the Mix Misleading Us or is We Misreading the Mix?. Journal of Marketing Management, 22(3/4), 439-450. Retrieved from Business Source Complete database.