EEOC and Affirmative Action in the Workplace

About The Deadly Tangshan Earthquake

About The Deadly Tangshan Earthquake At 3:42 a.m. on July 28, 1976, a magnitude 7.8 earthquake hit the sleeping city of Tangshan, in northeastern China. The very large earthquake, striking an area where it was totally unexpected, obliterated the city of Tangshan and killed over 240,000 people - making it the deadliest earthquake of the twentieth century. Fireballs and Animals Give Warning Though scientific earthquake prediction is in its nascent stages, nature often gives some advance warning of an impending earthquake. In a village outside of Tangshan, well water reportedly rose and fell three times the day before the earthquake. In another village, gas began to spout out the water well on July 12 and then increased on July 25th and 26th. Other wells throughout the area showed signs of cracking. Animals also gave a warning that something was about to happen. One thousand chickens in Baiguantuan refused to eat and ran around excitedly chirping. Mice and yellow weasels were seen running around looking for a place to hide. In one household in the city of Tangshan, a goldfish began jumping wildly in its bowl. At 2 a.m. on July 28, shortly before the earthquake struck, the goldfish jumped out of its bowl. Once its owner had returned him to his bowl, the goldfish continued to jump out of its bowl until the earthquake hit.1 Strange? Indeed. These were isolated incidents, spread across a city of a million people and a countryside scattered with villages. But nature gave additional warnings. The night preceding the earthquake, July 27-28, many people reported seeing strange lights as well as loud sounds. The lights were seen in a multitude of hues. Some people saw flashes of light; others witnessed fireballs flying across the sky. Loud, roaring noises followed the lights and fireballs. Workers at the Tangshan airport described the noises as louder than that of an airplane.2 The Earthquake Strikes When the 7.8 magnitude earthquake struck Tangshan at 3:42 a.m. on July 28, over a million people lay sleeping, unaware of the disaster that was to befall them. As the earth began to shake, a few people who were awake had the forethought to dive under a table or other heavy piece of furniture, but most were asleep and did not have time. The entire earthquake lasted approximately 14 to 16 seconds. Once the quake was over, the people who could, scrambled out into the open, only to see the entire city levelled. After an initial period of shock, the survivors began to dig through debris to answer the muffled calls for help as well as find loved ones still under rubble. As injured people were saved from under the rubble, they were lain on the side of the road. Many of the medical personnel were also trapped under debris or killed by the earthquake. The medical centers were destroyed as well as the roads to get there. Survivors were faced with no water, no food, and no electricity. All but one of the roads into Tangshan was undrivable. Unfortunately, relief workers accidentally clogged the one remaining road, leaving them and their supplies stuck for hours in the traffic jam. People needed help immediately; survivors could not wait for help to arrive. Survivors formed groups to dig for others. They set up medical areas where emergency procedures were conducted with the minimum of supplies. They searched for food and set up temporary shelters. Though 80 percent of the people trapped under rubble were saved, a 7.1 magnitude aftershock that hit in the afternoon of July 28 sealed the fate for many who had been waiting under the rubble for help. After the earthquake hit, 242,419 people lay dead or dying, along with another 164,581 people who were severely injured. In 7,218 households, all members of the family were killed by the earthquake. Corpses were buried quickly, usually close to the residences in which they perished. This later caused health problems, especially after it rained and the bodies were again exposed. Workers had to find these impromptu graves, dig up the bodies, and then move and rebury the corpses outside of the city.3 Damage and Recovery Before the 1976 earthquake, scientists didnt think Tangshan was susceptible to a large earthquake; thus, the area was zoned an intensity level of VI on the Chinese intensity scale (similar to the Mercalli scale). The 7.8 earthquake that hit Tangshan was given an intensity level of XI (out of XII). The buildings in Tangshan were not built to withstand such a large earthquake. Ninety-three percent of residential buildings and 78 percent of industrial buildings were completely destroyed. Eighty percent of the water pumping stations were seriously damaged and the water pipes were damaged throughout the city. Fourteen percent of the sewage pipes were severely damaged. The foundations of bridges gave way, causing the bridges to collapse. Railroad lines bent. Roads were covered with debris as well as riddled with fissures. With so much damage, recovery was not easy. Food was a high priority. Some food was parachuted in, but the distribution was uneven. Water, even just for drinking, was extremely scarce. Many people drank out of pools or other locations that had become contaminated during the earthquake. Relief workers eventually got water trucks and others to transport clean drinking water into the affected areas. After the emergency care was given, the rebuilding of Tangshan began almost immediately. Though it took time, the entire city was rebuilt and is again home to over a million people, earning Tangshan the name Brave City of China. Notes 1. Chen Yong, et al, The Great Tangshan Earthquake of 1976: An Anatomy of Disaster (New York: Pergamon Press, 1988) 53.2. Yong, Great Tangshan 53.3. Yong, Great Tangshan 70. Bibliography Ash, Russell. The Top 10 of Everything, 1999. New York: DK Publishing, Inc., 1998. Yong, Chen, et al. The Great Tangshan Earthquake of 1976: An Anatomy of Disaster. New York: Pergamon Press, 1988.

Exam questions Coursework Example | Topics and Well Written Essays - 3250 words

Exam questions - Coursework Example Since collective bargaining is a process directly associated with the trade unions, it can be rightly concluded that a fall in trade union membership would reduce the collective number of workers the unions represents, and therefore would lead to a decline in the collective bargaining practice itself. Union membership in the UK was in a consistent decline period from 1979 to 1998, and this can be attributed to a certain set of factors, both direct and underlying reasons. The direct causes of the dramatic decline of union membership are as below (Millward et al., 2000): Unions failed to gain a bargaining presence where it was necessary in the newly established workplaces, as the British manufacturing industry declined, which led to the establishment of new workplaces The union membership in places where they were previously recognized led to people leaving the trade unions as a follow up to other. The decline however is not due to these simple factors only, there are certain underlyin g factors which must be identified and critically analyzed in order to complete the study. Firstly, the macroeconomic conditions of UK underwent a change during the decline era, there was massive unemployment which led to a weakened status of the employees who did not want to lose their jobs by undertaking the risk of contradicting the employers, since the number of layoffs were high, this consciously instigated the workers to leave trade unions, thus leading to decline in membership and collective bargaining. Secondly, the legal and institutional policy framework established by the state government led to an automatic decline, since the policies were anti-union and unfriendly, the unions could not reach agreements since they no longer held a powerful position, thus resulting in declining membership. Thirdly, the policies instated by the management itself focused on meeting individual needs rather than coordinating with the trade union’s representative to reach a consensus, t his change in policy abolished the need of having joined any trade unions, thereby reducing the membership number by dramatic numbers. And lastly, according to Metcalf, the aggregate number of union membership is not just a function of environmental factors, but it also accounts for the way in which the trade union responds to the environmental changes, and in UK, the trade unions did not respond diligently, while making the recruitment plan more strenuous, and by ignoring the environmental changes leading to no major changes in the union’s agendas, thereby resulting in further decline (Metcalf, 1991, 22). In light of all the reasons stated and explained above, it can be rightly concluded that there were major underlying reasons which led to a decline in union membership which simultaneously caused a decline in the collective bargaining process and practices in the United Kingdom, since they are directly proportional to each other. The decline of one will inevitably result in the decline of other, because collective bargaining is a practice undertaken by trade unions to meet their objectives, and striking agreements

Limitations of Wacc as a Method of Investment Appraisal Essay

Limitations of Wacc as a Method of Investment Appraisal - Essay Example The present paper has identified that, despite its use in investment appraisal, there are various assumptions that are made when using WACC, hence, limiting its use in appraising investment. The first restrictive assumption that has to be met is that the investment project should be small compared to the investing organization. This, therefore, limits the use of WACC in appraising small investment but cannot be used when a small organization wants to acquire a large organization. The second limitation in the use of WACC is that the business activities of the investment project should be similar to those presently being undertaken by the investing organization. In the light of this, a company cannot use WACC when appraising an investment to diversify its business activities. Student Account describes that when using the weighted average cost of the capital method in appraising an investment, the financing mix adopted to undertake the investment project must be similar to the present c apital structure being used in the investment company. This, therefore, means that if a company uses shareholders money to finance its capital investment, it cannot rely on borrowed capital to make an investment. This, therefore, limits those companies that do not have adequate resources from using this method to appraise investments. Student Account further describes that when using a WACC, the existing providers of finance to the investing company will not change their required rates of return following the investment project that is being undertaken. This is, however, not possible since the rates keep changing following changes in the rates of inflation. In addition, the rates of return on borrowed capital from banks change from time to time due to changes in the rate of interest. This also limits companies that may agree to reduce the number of dividends paid to shareholders from what is paid during the previous years to use these resources in an investment.

How Dangerous Nuclear Reactors Are Essay Example | Topics and Well Written Essays - 1000 words

How Dangerous Nuclear Reactors Are - Essay Example The most important thing is to have a thorough understanding of exactly what happens inside of a nuclear reactor. The kind of physical process that occurs within a nuclear reactor is called nuclear fission, and it is one of two common forms of nuclear reaction. The other is called nuclear fusion, and that is what is used in most modern atomic bombs, and won’t be discussed extensively here. Each kind of reaction revolves around changing a single atom – fission is the splitting of one atom into two, whereas fusion is the combining of two atoms into a new one. Every atom is made of three parts: neutrons, protons, and electrons. Each of these has a charge – protons are positively charged, and attract negatively charged electrons. Neutrons have no charge. Neutrons are naturally occurring parts of atoms that exist in every element – usually, there are just as many neutrons as there are protons in any given element. They are very heavy and give most elements about half of their total weight. If you look at your body in terms of atomic weight, about half of it would be made of neutrons. The three parts of atoms, protons, electrons, and neutrons, exist in a balance of atoms that make up the things that we see around us. They exist in a balance because whenever they become unbalanced, they break down or recombine in a more balanced form. Think of a coin standing on its edge – in small change will cause it to fall so that one of the two sides is flat against the ground, but once it falls it won’t move or change as easily as it originally did. Atoms are like this – they are in a stable situation because if they weren’t, they couldn’t last very long. Changing one of the elements, either protons, neutrons or electrons, however, creates an unbalanced atom that wants to change to become balanced again.

Gaussian Mixture Model

Gaussian Mixture Model Many computer related vision technology, it is critical to identify moving objects from a sequence of videos frames. In order to achieve this, background subtraction is applied which mainly identifies moving objects from each portion of video frames. Background subtraction or segmentation is a widely used technique in video surveillance, target recognitions and banks. By using the Gaussian Mixture Model background model, frame pixels are deleted from the required video to achieve the desired results. The application of background subtraction involves various factors which involve developing an algorithm which is able to detect the required object robustly, it should also be able to react to various changes like illumination, starting and stopping of moving objects. Surveillance is the monitoring of the behaviour, activities or other changing information usually of people and often ina surreptitiousmanner. Video surveillance is commonly used for event detection and human identification. But it is not easy as think to detect the event or tracking the object. There are many techniques and papers introduced by many scientists for the backend process in the video surveillance. Different automated softwares are used for the analysis of the video footage. It tracks large body movements and objects. In this report we discuss the application of mixture of Gaussian (M.O.G) which is used as a background subtraction model in order to achieve object/target recognition; this method involves calculation the mean and standard deviation of each pixel frame which will be discussed more in detailed as the report progresses. 2. Background Subtraction (Literature Review) Figure 1 Background subtraction Flow Diagram [1] According to [1], the above figure shows the basic flow diagram on how background subtraction algorithm is based. The four important steps described by [1] and which play an important role in background subtraction are described below and include: 2.1 Preprocessing Temporal or spatial smoothing is used in the early pre processing stage to eliminate device noise which can be a factor under different light intensity. Smoothing technique also includes removing various other elements like environment such as rain and snow.   In real-time systems, frame size and frame rate are commonly adopted to reduce the data processing rate. Another key factor in preprocessing technique is the data format used by the background subtraction model. Most algorithms can handle luminance intensity which is one scalar value per each pixel.   As shown in the examples below [2]: Figure 2 Image on the left shows snowing and image on the right is a resultant of smoothing effect In the figure 2, shown are two images which shows snow on the left and whereas with the application of spatial and temporal smoothing on right image results in the elimination of snow producing an more clear and effective image for background subtraction. 2.2 Background Modeling This step uses the new video frame in order to calculate and update the background model. The main aim of developing a background model is that it should be robust against environmental changes in the background, but sensitive enough to identify all moving objects of interest. 2.3 Foreground Detection In this step, it identifies the pixels in the frame. Foreground detection compares the video frame with the background model, and identify candidate foreground pixels from the frame. Commonly- used approach for foreground detection is to check whether the pixel is significantly different from the corresponding background estimate as show below: A different foreground detection scheme is normalised threshold based statistics as shown below: Where,  µd and are the mean and standard deviation of the for all spatial locations (x,y). 2.4 Data Validation Finally, this step eliminated any pixels which are not connected to the image. It involves the process of improving the foreground mask based on the information obtained from the outside background model. Most background models lack three main points: 1. Ignoring any correlation between neighbouring pixels 2. The rate of adaption may not match the moving speed of the foreground object. 3. Non-stationary pixels, from moving leavers or shadow cast by moving objects are at times mistaken for true foreground objects. 3. Background Reading There are different types of background modelling which are as follows: 3.1 Simple Frame Difference Frame difference is considered the simplest form of background subtraction. The method involves the subtraction of previous pixel frame with current pixel frame and if the value obtained from this greater than a set threshold then it is considered as the foreground. Advantages: Less complex The method can easily and quickly adapt to changes Able to distinguish the background from foreground much affectively. Disadvantages: Not Robust, object has to continuously move. Only applicable when with fixed camera. Fails to handle illumination changes. 3.2 Moving Average Filtering Referring from [3], Moving Average filtering is a method in which an estimate value of a particular background pixel value is weighted as average of the previous values. Pixels in the far part are weighted at Zero and the weights increase smoothly.  Ã‚   3.3 Online Gaussian Model In this method, we assume the background as a Gaussian distribution rather than a single value. To calculate Gaussian for 1-D is as follows[4]: Where, x is the current pixel value,  µ is the mean of each pixel and sigma is the standard deviation of the pixel. Finally, the following equations are used in online updated of Gaussian mean and variance: In the above equations[4]:  µ(t) is the mean for each pixel where ÃŽÂ ± is the learning rate, t is the time of the previous pixel and x is the current pixel value. à Ã†â€™2(t) is the standard deviation of the each pixels. The foreground decision rule applied here is when the distance between each pixel value and Gaussian model is larger than a certain threshold, it is considered as the Foreground. 3.3 GMM Gaussian Mixture Model In order to use GMM the following equations are considered: In the above equations [4]:  µ(t) is the mean for each pixel where ÃŽÂ ± is the learning rate, t is the time of the previous pixel and x is the current pixel value. à Ã†â€™2(t) is the standard deviation of the each pixels. The description and implementation of GMM model will be discussed more in detail in the later part of the report 3.4 Codebook based Technique It is a method where a quantized and clustering technique is motivated by Kohonen to construct the background model from long observation sequences, without making any parameter assumptions. For each pixels, a codebook contains one or more codewords and mixed backgrounds can be modelled by using multiple codewords. Samples at each pixel are clustered into the set of codewords based on a color distortion metric together with a brightness ratio. [4] 4.0 Gaussian Mixture Model In this report we implement the Gaussian Mixture Model for background subtraction. This background subtraction model is more robust than other models discussed in previous section. Most importantly it can handle multi-modal situations e.g. trees and sky which is more effectively filtered by the GMM model. Each pixel value is modelled by a mixture of Gaussian rather than a particular type of distribution. Based on the variance of each of the Gaussian of the mixture, we determine which Gaussians may correspond to background colors. Pixel values that do not fix the background distributions are considered foreground until there is a Gaussian that includes them with sufficient consistent evidence supporting it [5]. This method is very adaptable with lighting changes, repetitive motions and slow moving objects. This method contains 2 main significant parameters -Alpha, the learning constant and T, the proportion of the data that should be accounted for by the background The Gaussian mixture components for a pixel have normalized weights calculated from the past observations. The likelihood that a pixel has a value of Xt is assumed to be:    [5] The parameters of the mixture components are updates with the new frames. A retrieved pixel value is compared with all the components of the mixture assigned to that pixel to find out if there is a match. A match is said to happen when the retrieved pixel value is within 2.5 times standard deviation of a mixture component. The update procedure is different for the matching component and other components. The mean values and the covariance matrices are updated for only the matching component. The value formulas for the matching components are: And the weights are updated with given formula    [6] In case there is not a match between the current pixel value and the mixture components related to that pixel .the component having the smallest likelihood with respect to the current pixel value is discarded. A new Gaussian component is created in place of the discarded one, having a mean value equal to the current pixel value, and a variance equal to a predetermined constant. To retrieve the foreground the following equation is used. Where, T is the threshold value for the sum of the weights.[6] 5.0 Algorithm In order to give a better understanding of the algorithm used for background subtraction the following steps were adopted to achieve the desired results: Firstly, we compare each input pixels to the mean mu of the associated components. If the value of a pixel is close enough to a chosen components mean, then that component is considered as the matched component. In order to be a matched component, the difference between the pixel and mean must be less than compared to the components standard deviation scaled by factor D in the algorithm. Secondly, update the Gaussian weight, mean and standard deviation (variance) to reflect the new obtained pixel value. In relation to non-matched components the weights w decreases whereas the mean and standard deviation stay the same. It is dependent upon the learning component p in relation to how fast they change. Thirdly, here we identify which components are parts of the background model. To do this a threshold value is applied to the component weights w. Fourthly, in the final step we determine the foreground pixels. Here the pixels that are identified as foreground dont match with any components determined to be the background. 6.0 Experimental Results For better results, median filter was used where the main purpose was to filter out unconnected pixels from the large connected pixels (e.g. people, vehicles) so that it was easy to distinguish between the background and foreground. With median filter the value of the output pixel is determined by the median of the neighboring pixels instead of the mean. Median has a much smaller insensitivity compared with mean to extreme values. The function used was medfilt2(A,[m n]). Figure 3 GMM Background Subtraction with high light intensity In the above image, the picture on the right shows the output of background subtraction using the GMM model. There is still false negative foreground detection which produces the disturbances in the image. Although it still shows the objects moving hence, making it suitable for object tracking. The reason for these disturbances can be narrowed down to illumination changes. Figure 4 GMM Background Subtraction with Low light intensity For the above, a short 30 second video was recorded where light intensity was moderate and the camera was still. It can be observed that the hand is visible and false negative detection is low which suggests that the model works robustly in low light intensities. From the above results, we can say that there are still improvements to be made in the algorithm mostly to work with illumination which increases the false negative background subtraction. 7.0 Further Improvements The GMM model used in this paper could be further tweaked to provide better results in terms of zero disturbances/noises. The use of morphological filters could be implemented into the algorithm to reduce these unconnected pixels which were seen in the figures above and make it more robust in object tracking. The algorithm has the capabilities of further improvements to run large size videos and importantly the ability to process more frames per seconds using larger number of Gaussians in the mixture model. Apart from the above improvements, we further plan to research the model using PTZ (Pan Tilt Zoom) technique to study the processing rate of frame/second and observe the robustness of the algorithm in terms of disturbances/noise. There are possibilities of false positive results where background could be recognised as foreground. 8.0 Conclusions In this project we implemented a very strong and widely used background subtraction method according to the paper Adaptive background mixture models for real-time tracking. This method is very adaptable for lighting changes and shadow removals. Also it finds the repetitive actions as well with the use of mixture of Gaussians. Alpha and T are the key parameters in this paper. The values of these parameters changes with the different cameras and different environment. So it is very important to get the best values for them to work with for different videos. Also we tried to improve the output quality by using some filtration methods like median filter. The guassain model is not perfect with the result since there are some disturbances after the modelling also. But there are many good post processing techniques are introduced and we referenced two of them. A new region Gaussian background model for video surveillance by Xun Cai and Long Jiang, and Improved Post-Processing for GMM based Adaptive Background Modeling by Deniz Turdu,Hakan Erdogan. But the 1st paper doesnt give any clear information about the techniques failed to explain the equations as well. Second paper introduces a very good technique and we couldnt able to get the result properly. But this assignment was very challenging for us and helped us to get a very good knowledge about different background subtraction methods used in Video surveillance. 9.0 References [1] Robust techniques for background subtraction in urban traffic video Sen-Ching S. Cheung and Chandrika Kamath, 2006/07 [2] Background Subtractions of Moving Objects https://computation.llnl.gov/casc/sapphire/background/background.html [3] Page444, Computer vision A modern approach, David A. Forsyth [4] Dr J. Zhang, CSC7006 Lecture 2 Slides, Queens University Belfast. [5] Adaptive background mixture models for real-time tracking, Chris Stafer, W.E.L Grimson [6] Improved Post processing for GMM based adaptive background modelling by Deniz Turdu, Hakan Erdogan

General Education Essay -- essays research papers fc

With living costs as high as they are in this day and age, it is completely unreasonable to expect the average individual to squander already limited resources. Receiving a bachelor’s degree today requires an assortment of classes that often are not directly related to one’s career objectives. For some, they find this to be an enjoyable adventure, broadening their knowledge and learning about new aspects of life, but for others this is just burdensome. However it is looked upon, the college curriculum still requires a diverse selection of courses to develop well rounded, responsible individuals, but in turn creates added pressure upon students. Is it the job of secondary education to start developing all inclusive students who have been familiarized with a broad range of subjects? Is it fair that some children are able to afford private education and expensive tutoring with a one on one basis? The government needs to step in on this matter because the children who are growing up now are going to be this countries future. The rich are always going to be well educated because they can afford it. There needs to be government programs that provide free tutoring and counseling for the underprivileged. But the way things are going this will never happen because education is almost always one of the first things to be cut. One of the greatest sacrifices of college is the money required to attend. The Education Statistics Quarterly says: One of the biggest concerns for many families is how they are going to pay their children's college expenses. In academic year 2002–03, the average total price for full-time undergraduates to attend 4-year institutions—including tuition, fees, room, board, books, supplies, and other education expenses, as estimated by the institutions—was more than $12,800 at public institutions and almost $28,000 at private institutions (College Board 2003a). Over the past decade, inflation-adjusted tuition prices at public and private 4-year colleges and universities jumped nearly 40 percent, while the median income of families with a head of household 45 to 54 years old (those families most likely to have traditional college-age children) rose only 8 percent (College Board 2003b). Such price increases have made it much more difficult for families from nearly all income levels to pay for college. Researchers have, for many years, wondered how... ...eir general education courses. This mentality is not healthy. Not only are general education classes required, they are necessary for the development of well rounded citizens. It may cost a little more but what is a pretty penny compared to ones future. These courses are also valuable because it allows students to sample other aspects of life that they may find more interesting than his/her initial major. General education is quite valuable for the development of a fully literate society. For the development of cultured, interesting individuals, general education must be viewed as a necessity. Works Cited Kirsziner Laurie G. and Mandell Stephen R. Patterns for College Writing, Bedford/St. Martin’s Boston, New York Kozol Jonathan. â€Å"The Cost of an Illiterate Society.† From Illiterate America by Jonathan Kozol. Copyright 1985 by Jonathan Kozol Zinsser William. â€Å"College Pressures.† From Blair and Ketchum’s County Journal, Vol VI, No. 4, April 1979. Copyright 1979 by William K. Zinsser. National Center for Education Statistics http://nces.ed.gov/programs/quarterly/vol_5/5_2/q2_4.asp 1990 K Street, NW, Washington, DC 20006, USA, Phone: (202) 502-7300