Effect of Video Games on Society

Effect of Video Games on Society Sam Hawkwood Over the past several years, games have received a lot of attention because of their content. Ever since the advent of the truly interactive video game – especially first person shooter games – people have been looking for signs that such media has a negative effect on. Games that are seen as violent have come under a lot of scrutiny and never more so when some tragedy happens and the perpetrator is known to play violent video games. In this paper, I will be looking at the effects that video games have had on society and I will come to the conclusion if they are good or bad for us. Gaming has gone from something that only nerds do to an activity that more enjoy than not. Rather than something that was regarded as the domain of computer geeks, its become a common activity that people of all ages are finding not only fun but acceptable. During the 80s and 90s, most games were generally all child-friendly and puzzle based, with little in the way of variety and realism, however over the past twenty years, gaming has taken a big leap into more mature games. This can be easily seen in the leap from games like Pac-Man and Mario Bros to Halo and Grand Theft Auto; such a change in little under a generation has had a big impact on society. The largest impact more modern games have had are in how they are perceived by the public. With the rate of tragedies such as shootings rising, the media has often blamed video games. There have been several shootings that have been blamed on video games in the past decade; for example, the 2012 Sandy Hook shooting in Connecticut was initially blamed on the popular game series Mass Effect. As stated some time after the shooting, in which 28 people lost their lives, 24 year old Ryan Lanza was named as the killer (US News). In reality, it was his brother who had stolen his ID card on his way to the school. As soon as Ryan Lanza was wrongfully named the shooter, Facebook users found his profile and learned that he was a fan of the massive hit from Bioware, the previously mentioned Mass Effect. Immediately, thousands of people went to the games Facebook page and blamed the shooting on the game. Even after Ryan was cleared as the killer, Facebook users continued to blame the game, despite the fact that the true killer, Adam Lanza, had had a history of obsessive-compulsive behavior and a fascination with mass shootings, and had not even been a serious player of video games and didnt consider them as particularly interesting. When it comes to a situation like this, video games were wrongly attacked. Although we could assume something else would be blamed if the wasnt any gaming involved. In the case of the Sandy Hook shooting, when Adam was 16, his mother took him out of school and supposedly started to homeschool him, although it was later found that he was not getting any education after his removal from school (US News). In 2011 the Valve game Portal 2 was accused of making fun of adopted children (Pcr). With no real violence, guns, swearing or nudity to speak of, you would have thought that this game would fly under the radar of the media. But unfortunately for Valve, this was not the case. Part way through the game, the primary antagonist taunts the player and says â€Å"fatty, fatty no parents†. This is clearly meant to be a joke and Valve has said that no offense was meant. This is very understandable since the voice actor for this character is the English comedian Stephen Merchant. According to WBTV News, the adopted daughter and father play the game often and when the father heard the line, he immediately turned off the game, hoping his adopted daughter didnt hear it. In a game that is very education and rated E For Everyone. WBTV News even contacted Sony, the distributors of the game. Sony told WBTV News to contact Valve, the developers of the game. But WBTV has said that Valve has not responded (Pcr). With most people of the gaming community blaming the father for over reacting to a joke, it is hard to say how this has effected society. As small of an event as this is, who knows how this could effect the future of the Portal series. In January of 2010, the British Medical Journal published an article about how video games are causing rickets to effect UK children (Cnet). According to the research, more children are staying inside and lacking vitamin D. The inactivity causes their bones to soften, leading to a physical deformity. â€Å"Kids tend to stay in more these days and play on their computers instead of enjoying the fresh air. This means that their vitamin D levels are worse than in previous years,† (Cnet). According to an article by online information technology website www.cnet.com, video game developers should make their protagonists â€Å"losers† (Cnet). Matyszczyk asked, â€Å"How about we talk to the makers of Grand Theft Auto, World of Warcraft, and the rest into making versions in which characters with vitamin D deficiency, in one way or another, losers?† While there is no real evidence to support this idea being fake, there isnt any evidence to disprove it. So while this idea could be true, most of the gaming community brushed it off as an excuse from parents who dont know how to teach their child. This effect of society has the potential to be big, but it was a small story to begin with and unless this can be proven, we will likely never hear of this again. So far, I have given examples of times where games have effected society, in a negative way (regardless of a storys popularity). There have still been multiple articles written about how video games are good for society and even your health. According to a Forbes article from November 2013 (Shapiro, J., 4 Reasons Video Games Are Good For Your Health (According To American Psychological Association), p.01), there are at least four ways that video games can improve your health. The article written by Jordan Shapiro states that playing â€Å"First Person Shooters† can improve your cognitive skills. In controlled tests, people who played â€Å"First Person Shooters† showed faster and more accurate attention allocation, higher spatial resolution in visual processing, and enhanced mental rotation. Apparently, the improvement in spacial skills that game players develop are comparable to those developed in formal courses designed to teach the same skills. There also seems the be convincing evidence that playing enhances problem solving skills, and improves creativity. â€Å"Among a sample of almost 500 12 year-old students, video game playing was positively associated with creativity.† (Forbes) Another point made by Shapiro is that â€Å"Contrast to stereotypes, the average gamer is not a socially isolated, inept nerd.† (Forbes) Many of todays gamers are not loners at all. In fact, gaming brings people together. The whole point of online gaming is working with other people to accomplish a unified goal. The Mario Bros. games have a strong co-op mode for people to play together and work together. There are other parts of online gaming that put you and your friends up against other players. When it comes to violence in gaming, there is no scientific evidence that proves that playing violent games makes you violent. In fact, there is a lot of proof showing how playing violent video games can make you a better person. A study from the University of Buffalo led by professor Matthew Gizzard, PhD, states â€Å"Rather than leading players to become less moral, this research suggests that violent video game play may actually lead to increased moral sensitivity.† (TheDailyBeast) Gizzard points out that when the players gaming session was over, he worked to identify and assess players feelings of guilt. â€Å"The research with video games is so polarizing,† Gizzard said. â€Å"You see people arguing that video games are the worst thing that ever happened to society. You have some people arguing that it it leads to school shootings. On the other side of the equation, you have people saying video games are going to be this cure for all sorts of societal ills.† (TheDailyBeast) It is still hard to say whether or not video games have been good or bad for society. There have been many many moments where games have done some bad, but there are also many times where games have been great for people. The effect video games have on people can vary. Most people would see it as a fake world where nothing in there is related to the real world. Some people see video games as simulator of real life. When people wrongly blame video games for a shooting, it is almost just like saying that television would melt your brain in the 40s and 50s. It seems to be pinning the blame on something that you dont fully understand. I do not believe that video games have been bad for society. In fact, I believe that they have allowed us to go into a new age of technology. But if people continue to see video games as evil, then maybe it shows that we arent ready for that type of technology. My conclusion is that I believe video games have been, and, for a while to come, will be beneficial for human survival. I have given several examples of how video gaming has been given a hard rap for many negative aspects of society, and how studies seem to show this is in fact not the case. We must realize we are leaving the analog age of entertainment and entering the digital age. Video gaming is a very big part of this digital age we must be careful not to quickly put blame onto something in society simply because it is not fully understood or appreciated. References Fox, L. (2013, Nov. 25). Report: Sandy Hook Shooter Adam Lanza was Obsessed with Mass Shootings. U.S. News. Retrieved from URL http://www.usnews.com/ Magnusson, H. (2014, Mar. 02). Report: 5 Riediculous Things the Media Blamed Video Games for. Cracked. Retrieved from URL http://www.cracked.com/ Ashcraft, B. (2012, Dec. 15). Report: Mob Blames Mass Effect For School Shooting, Is Embarrassingly Wrong. Kotaku. Retrieved from URL http://www.kotaku.com Nolan, H. (2012, Dec. 14) Report: The Sandy Hook Elementary Shooting: Everything We Know About the Shooter. Gawker. Retrieved from URL http://www.gawker.com (2012, Dec. 16) Report: Mass Effect Video Game Wrongfully Attacked By Facebook Group Following Sandy Hook Elementary Shooting [Op-Ed]. Inquisitr. Retrieved from URL http://www.inquisitr.com Cohen, J. (2014, Dec. 14) Report: Two Years Later, Still Learning From Sandy Hook. Npr. Retrieved from URL http://www.npr.org Wooden, A. (2011, May. 19) Report: Portal 2 slammed as insensitive by US media. Pcr. Retrieved from URL http://www.pcr-online.biz Matyszczyk, C. (2010, Jan. 21) Report: Video games blamed for return of rickets. Cnet. Retrived from URL http://www.cnet.com Shapiro, J. (2013, Nov. 27) Report: 4 Reasons Video Games Are Good For Your Health (According to American Psychological Association). Forbes. Retrieved from URL http://www.forbes.com Zawacki, K. (2014, Jul. 04) Report: Playing Violent Video Games Makes You a Better Person, Study Says. TheDailyBeast. Retrieved from URL http://www.thedailybeast.com

Impact of Urban Living on Biodiversity and Ecosystems

Impact of Urban Living on Biodiversity and Ecosystems It is estimated that almost more than one half of the worlds population is presently living in urban areas (Sakieh et al. 2016; Weigi et al. 2014). In many parts of the world, increasing urban lands has caused changing land use and land cover (LULC) (Wu 2014). Biodiversity, ecosystem processes and functions and human habitants in an urban environment are influenced by the speed and spatiotemporal pattern of urbanization (Wu et al. 2011; Asgarian et al. 2015; Sangani et al. 2015; Wu 2014; Jaafari et al. 2015). Landscape beauty is being affected by LULC changes and urbanization has led to the destruction of aesthetic values in many parts of the world. Scenic landscapes, as one of the ecosystem services, are elements of the environment with the potential for human enjoyment and in some cases they are considered as valuable parameters for nature conservation and management (Bishop and Hulse 1994). The landscape is continually changing due to human activities but its aesthetics usually su ffers from poor quantification and inclusion in management plans. According to Naveh (1995), scenic landscapes are products of interactivity between humans and natural systems where natural landscapes become inhabited, influenced or altered by mutual relationships between ecological and socioeconomic processes. Such interrelated feedbacks can lead to physical modifications of the environment that ultimately can be seen, so landscape aesthetic assessment seem to be essential in land use planning. Understanding, analysis, monitoring and modeling of urban growth is crucial for the management of current urban systems as well as for the planning of future growth (Zhou et al. 2014). Geospatial predictive models and change detection methods can provide a further level of understanding of the causes and impacts of urban growth mechanisms (Sakieh et al. 2014a). In the process of decision making, land managers need to examine the consequences of the urban development process. Regarding the progress in computing power, easy access to spatial data sets and development of functional computer-based models, now there is a possibility in which land use managers and decision makers can evaluate the outcome of their decisions under different alternatives and at the minimum possible cost (Sakieh et al. 2014b). Inclusion of new methodologies such as spatial multi-criteria evaluation (SMCE) can further improve representation and modelling of urban growth patterns, which finally provide spatial d ecision support systems (SDSS) for better planning and management of urban areas (Dai et al. 2001; Jie et al. 2010; Youssef et al. 2011; Xu et al. 2011; Pourebrahim et al. 2011; Yuechen et al. 2011; Bagheri et al. 2013; Bathrellos et al. 2012; Sheng et al. 2012; Jeong et al. 2013, Sakieh et al. 2014b). Since 2000, there have been noticeable efforts for developing microsimulation LULC change methods such as cellular automata (CA) and agent-based models (Goodarzi et al.2016). CA-based models have a natural compatibility to raster geographic information system (GIS) and remote sensing (RS) data and are appropriate for detail resolution modeling and simulating dynamic spatial processes (Sullivan and Torrens 2000). In recent years, there have been developed some CA-based models such as SLEUTH (Slope, Landuse, Exclusion, Urban, Transportation, and Hillshade) (Clarke et al. 1997), CLUE-S (the Conversion of Land Use and its Effects at Small regional extent) (Verburg et al. 2002), iCity (Stevens et al. 2007) and DINAMICA (Soares-Filho et al. 2002). Compared with the above mentioned spatial models, the SLEUTH model requires fewer input layers and also offers various alternatives for future urban growth prediction (Norman et al. 2012). These characteristics of SLEUTH model have made it as one of the most-implemented and popular methods for land use simulation at different scales (e.g. regional, national, and even binational) (Maithani 2010; Norman et al. 2012; Chaudhuri and Clarke 2013). However, there are a number of limitations with SLEUTH. The first of these is that it is computationally expensive. It requires a high number of model runs using a multi-stage calibration process to narrow down the coefficient value for each input parameter (Goldstein 2003). The second drawback is related to non-linearity of the model for the combination of the coefficients. The Brute Force method[1] which is used in this paper for calibrating the SLEUTH can fall in local maxima and may miss the better coefficient set (Goldstein 2003, Jafarnejad et al. 2015). Urban expansion is a complicated event which mainly occurs because of increasing population and the need for more construction. Consequently, vast lands of valuable ecosystems such as agricultural lands, forests and pastures are consumed and converted to urban areas (Sakieh et al. 2014b). Therefore it is essential to understand and recognize this process in order to implement effective management and avoid reducing the aesthetic value of landscapes. To achieve this goal, areas with high aesthetic value should be recognized at the first step. In this regard, there are various approaches to determine the aesthetic impacts on different areas of the city including expert-based methods such as Multi-Criteria Evaluation (MCE), statistical approaches such Logistic Regression (LR) and Artificial Intelligence-based methods such as Multi-Layer Perceptron (MLP) Neural Networks (Riveira and Maseda 2006). These methods are repeatedly being implemented for suitability mapping of utilities such as urbanization (Pijanowski et al. 2002; Hu and Lo 2007; Pao 2008; Mahiny and Clarke 2012; Sakieh et al. 2015), environmental conservation (Singh and Kushwaha 2011; Mehri et al. 2014; Sakieh et al. 2015) and agricultural activities (Mozumder and Tripathi 2014; Bodaghabadi et al. 2015) but are less implemented for mapping aesthetic values. The MLP neural network approach has a remarkable ability to derive meaning from complicated or imprecise data and detect trends that are too complex for either humans or other computer techniques. MLP is a more accurate modelling method compared to the others (Saeidi and Salmanmahiny 2014) and has been used in this research. Accordingly, creating a suitable model to predict the landscape scenic value could provide a basis for explicit, quick and accurate integration of aesthetic evaluation into land-use planning efforts. Therefore, the main objective of this study is to evaluate the landscape aesthetic suitability and predict the spatial patterns of u rbanized lands in an effort to preserve landscapes of high aesthetic value. The following section describes how a directed modeling framework can be employed to introduce urban growth scenarios with regarding landscape aesthetic suitability, and finally to develop a city without considerable impact on its aesthetic suitability. Materials and methods Study area Gorgan is one of the cities in the northeast of Iran and the capital of Golestan province, located in 36 °, 49 ´ N and 54 °, 24 ´ E (Fig. 1). Gorgan has a mild and humid climate though summers are very hot and humid. The regional topography is very diverse and includes mountains, forests and grasslands, steppes and plains, desert and barren, rivers, wetlands and agricultural lands. Lush Hyrcanian temperate forests are located in the south, while flat areas with farmlands and rangelands make the main structure of the landscape in the north part of the case study. the region is also a destination for about two millions of tourists each year because of its aesthetic values and touristic environment, (Mehrnews 2015).Regarding the nomination of the area as a new province of Iran, rapid population growth has occurred that caused the increasing of built-up surfaces and consequently has made a series of conflicts between land developers and conservation agencies. These disagreements emphasise the importance of LULC planning in this area (Sakieh et al. 2016 b). Figure (1) Scenario based urban growth modelling In this research the SLEUTH cellular automata urban growth model was used to predict dynamics of Gorgan City developing under three different scenarios including historical, managed and aesthetically sound urban growth up to year 2030. The Historical Urban Growth (HUG) scenario assumes that the present pattern of urban growth will be maintained in the future. At the Managed Urban Growth (MUG) scenario, we tried to dictate an infill form of urban development with the aim of protecting the immediate environment of the city against urbanization. In the Aesthetically sound Urban Growth (AUG) scenario, an aesthetic suitability layer was used as the extra excluded layer in SLEUTH model to protect patches of high scenic value. Fig. 2 depicts a research flowchart of the study. The procedure for determining the aesthetic suitability map which was used as the excluded layer is explained in the following section. Figure (2) Aesthetic suitability mapping using MLP MLP is a feed forward artificial neural network model that maps different sets of input data toward a set of applicable and meaningful outputs (Rumelhart 1986). In a feed forward neural network, the information moves only in forward direction, from the input nodes, over the hidden nodes and to the output nodes. A node is considered to be a connection point that can receive, create, store or send data along distributed network routes (Ciresan et al. 2012). Exclusive of the input nodes, each node is a neuron or processing element with a nonlinear transfer function (Fig. 3). There are no cycles or loops in the network. MLP utilizes a supervised learning technique called back propagation for training the network (Rosenblatt et al. 1961; Rumelhart 1986). Learning process conducts in the perceptron by changing the connection weights after the processing of each part of data. Figure (3) Back propagation includes two main stages, forward and backward propagation, to achieve its modification of the neural status. During model training, each sample (e.g. a feature vector related to a single pixel) is entered into the input layer and the receiving node sums the weighted signals from all nodes to which it is connected in the former layer. In this regard, the input to a single node is weighted based on the following equation: Eq. (1) given: wij indicates the weight between node i and node j and o is the output from node i. The result from a given node is j is then computed from: Eq. (2) Function f is often a non-linear sigmoidal transformation that is used to weight the sum of inputs before it sends a signal to the next node. When the forward pass is finished, the performance of the resultant nodes are compared with their corresponding expected values. When a pattern is given to the network, each output node will differ from the preferred results, the difference is linked to the error in the network as well. This error is then propagated backward with weights for corresponding connections modified using a relation known as the delta rule: Eq. (3) Given: ÃŽÂ · is the learning rate of the model; ÃŽÂ ´ is the computed error; and ÃŽÂ ± is the momentum factor. This factor intends to avoid oscillation problems during the search for the minimum value on the error surface and is used to speed up the convergence procedure (Richards et al. 1999). The forward and backward passes continue until the network is properly trained for the characteristics of the targeted utility which in this research is the scenic beauty. Model training is aimed to retrieve the correct weights both for the connections between the input and hidden layers, and between the hidden and the output layer for the categorization of the unknown pixels. The input pattern is categorized to a class that possesses the node with the greatest activation level. The two training elements, automatic training and dynamic, can be employed to automatically execute the MLP. If one or both are used, the training procedure automatically restarts when the algorithm is highly oscillated or become trapped in a local minimum error surface. For each automatic restart of the model training procedure, one of the following items occurs to either learning rate or the sample used in the training procedure, or both. If only automatic training is selected and the first occurrence that the training procedure restarts, the starting weights are randomized. Through the next restarts, the weights are randomized and the learning rate is halved. If both automatic training and dynamic learning rate are chosen and the training restarts automatically, new samples are selected, the weights are randomized, and the learning rate splits in half. If only dynamic rate is chosen, and the learning rate is progressively lowered based on the number of iterations assigned and the start and end learning rates. For instance, if 10,000 iterations are specified and the model is configured with start rate of 0.1 and end rate of 0.001, it will divide 0.009 by 10,000 and lower the learning rate by the result at each iteration (Civco 1993). The acceptable error rate is related to the learning of the network and it is assessed based on the Root Mean Square (RMS error). Lower values of RMS error and higher values of total r2 shows the better fit of model. The MLP algorithm can produce both a hard and soft classifier. The hard classification output generates a discrete layer in which each cell belongs to a definitive category. Activation level maps, however, unlike the output of the hard classifier, are a series of images depicting a degree of membership for each pixel to each possible category. The output is set of images (one per class). Unlike the probability map, the sum of values for any location will not necessarily sum to 1. This is because the results from the neural network are acquired through standardizing the signal values in the range of 0-1 with the activation equation. Larger values imply a higher membership degree of the membership belonging to that corresponding category. The computation of the hard classification result is on the basis of the activation level maps. Data used for aesthetic suitability modelling through MLP method In the MLP analysis of the targeted area, multiple of factors were considered as input layers. Due to the characteristics of Gorgan City, a set of urban and natural criteria was used to model its landscape aesthetic values. These criteria were outlined during previous studies performed in our research area included gardens and agricultural lands (Othman et al, 2015; Mobargheie and Torbati 2014), tree type diversity, vegetation density (Aminzadeh et al, 2014; Weiqi et al, 2014; Chen et al, 2014; Kremer et al, 2016; Martina et al, 2016), topographic diversity (Arrowsmith 2001), buildings height and density (Weiqi et al. 2014; Chen et al. 2014), forest and urban parks (Ayad 2005; Weiqi et al. 2014), ancient sites and squares, refuges and boulevards (Bahrainy 1999; Aminzadeh et al. 2014). The GIS layers of gardens and agricultural lands, parks, squares, boulevards and refuges were obtained from the land use map maintained by the Gorgan municipality. The values of cells within these layers, together with ancient sites, were standardized using a user-defined function and based on the experts opinions. Whereas the relationship between the map value and fuzzy membership did not follow a certain function (e.g. linear, J-shaped or Sigmoidal), the user-defined function was the most applicable function and the user could reclass the map in the standard range. The ancient sites layer was obtained from Department of Cultural Heritage, Crafts and Tourism of Golestan province. The tree type for the study area consisted of six categories of tree communities (scale 1:25,000). Pattern analysis (with window size of 3 ÃÆ'- 3 pixels = 8100 m2) was applied as a filter to count the number of various classes inner a square vicinity of the central cell. Those pixels with three or more different categories in their vicinity were chosen to represent the diversity of a given location for its tree types. By using a Landsat TM image for the study area for 2012, vegetation density was calculated using the Normalized Difference Vegetation Index (NDVI) formula: Eq (4) NDVI is a widely used graphical indicator that can be used for detecting vegetative land cover. This index can be calculated based on red and near-infra-red (Xred, Xnir) spectral bands of Landsat image as equation 4 (McFeeters 1996). The layer was standardized using a symmetrical linear function having inflection values as: a=2879, b=5795, c=7595 and d=9545 (Fig. 4). Figure.(4) As the graph shows, by increasing the vegetation density (that is increasing NDVI values) to the point b, scenic value of landscape increases, then in a specific area remains constant (point c) then over increasing of the vegetation density due to restrictions in visibility detracts from scenic value of landscape. The building height and density layer was produced using the current status map of building density provided by the Gorgan municipality. Using a monotonically decreasing linear function in order to determine classification, this layer was standardized. The landscape aesthetic value was therefore decreased by increasing the building height and density, due to the viewshed being blocked. In the spatial input factors, a topographic diversity layer was also included. To determine this layer, a Digital Elevation Model (DEM) of the research area was acquired from National Cartographic Centre of Iran. A surface shape categorisation was performed on a DEM layer, which consisted of multiple topographic features: peak, flat, ravine, pit, ridge, saddle, slope hillside, saddle hillside, convex hillside, concave hillside and inflection hillside. The categorized layer was then analysed using a mode filter (window size of 3 ÃÆ'- 3 pixels) to specify a new score to the central cell based on most frequent values within the window. Then, a filter size of 7 ÃÆ'- 7 pixels was used to count the number of various categories within the neighbourhood of a central pixel to achieve the final map. This layer demonstrates the most diversified locations in terms of topographic features. The layer was standardized using a monotonically increasing linear function, whereby categories with highe r diversity got the higher score in the standardised value. Fig. 5 portrays factor layers used for aesthetic suitability mapping in this study. Figure (5) After preparing required inputs, the MLP model was configured according to the following data: Input variables: number of input variables = 8 (standardized factor maps) Input specifications: training points file = a raster map of 164 points, which retains the location of 164 attractive (99) and non-attractive (65) spots | maximum training pixels used = 200 | maximum testing pixels used = 200 Network topology: input layer nodes: 8 (equals to the number of input data) | output layer nodes = 1 (continuous surface of aesthetic suitability) | hidden layers =1 | hidden layer nodes = 16 Training parameters: the dynamic learning rate was employed | start learning rate = 0.01 | end learning rate = 0.001 | Momentum factor = 0. 5 | sigmoid constant a = 1.0 Stopping criteria: root mean square (RMS) error = 0.01 | iterations = 10,000 Output function = sigmoidal Once the model was trained, its performance was evaluated by plotting training RMS versus testing RMS during 10,000 model iterations. Lower values for testing error during iterations indicates proper training of the model, and therefore, it can be used to produce aesthetic suitability surface. Data processing for SLEUTH modeling For the SLEUTH modeling undertaken in this study, four urban extent years depicting the distribution of manmade features over time, two layers of the transportation network for two different time periods, one excluded aesthetics layer from urbanization, slope and hillshade layers were used. These input data layers were prepared by the integrated application of geographic information systems and remote sensing. As a model requirement, all binary urban/non-urban layers were stretched linearly and converted into a GIF format. The urban and transportation layers were created based on Landsat MSS and TM images for the years 1987, 1992, 2000 and 2010. These were then used to predict the expansion of Gorgan in 2030. Using a 30-m digital elevation model (DEM), slope percent and hillshade layers were derived. For the first and second modeled scenarios, hydrographical networks (rivers, dams and wetlands), dense forests and roads were used as excluded layers from urban growth. For the third sce nario, the aesthetic layer was added as an exclusory layer. These are shown in Fig. 6. Figure (6) Model calibration SLEUTH is a CA-based model in which five coefficients (diffusion, breed, spread, slope and road gravity) control four types of growth rules including new spreading center growth, spontaneous growth, edge growth and road gravity growth (Jantz et al. 2014). In addition, the straightforward calibration method applied by SLEUTH makes it adaptable to any particular geographic area over time (Clarke et al. 1996). In order to show the relative importance, each coefficient has a dimensionless value ranging between 0 (least important) to 100 (most important). During the calibration process, the form of urban expansion was detected via the four growth rules. The prediction of the model was based on the best range of refined coefficients derived from the calibration step. Table (1) shows the relationships between growth types and growth coefficients. Table (1) The main assumption of the SLEUTH model is based on the inherent pattern of urban dynamics whereby the city will witness the same growth in the future based on its historical trend in the past (Clarke et al. 1997). During the calibration process, the model seeks to derive the best range for each coefficient to enable better simulation based on local historical data (Silva and Clarke 2002). SLEUTH model benefits from a stochastic computation algorithm known as the Monte Carlo method. The model utilizes Monte Carlo iterations stochastically to generate multiple simulations of urban growth so parameters are standardized in a range between 0 and 100. These inputs reflect the relative contribution of each parameter to the dynamics of urban growth in the study area (Sakieh et al. 2014b). Finally, by using the best set of derived coefficients from three steps (coarse, fine and final) of calibration, the model was executed for the simulation of the historical data set. The number of Monte Ca rlo iterations support the robustness of final coefficients to run the prediction part of the model (Candau 2002; Jantz et al. 2004; Sakieh et al. 2014b). For the coarse calibration step, the default parameter values from the sample calibration scenario were employed. Five Monte Carlo iterations were specified for the coarse calibration phase, and growth parameters were set at their widest range of 0 25 100 as START, STEP and STOP values, respectively. A goodness of fit metric, known as the Optimal SLEUTH Metric (OSM) will provide the most robust results for SLEUTH calibration. The OSM is the product of the compare, population, edges, clusters, slope, X-mean, and Y-mean metrics (Dietzel and Clarke 2007). These seven metrics range between 0 and 1 and are multiplied together to calculate the OSM. The iterations are then sorted based on this metric and the best ranges of performing coefficients are chosen for the subsequent calibration stage. Applying the OSM metrics of the best perf orming iterations, the five multipliers were refined and reduced for use in the fine calibration step. The fine calibration step was executed through full resolution input layers in eight Monte Carlo iterations. Based on OSM values, the ranges for the five growth parameters in SLEUTH were further narrowed for the final phase of the calibration mode, which used 10 Monte Carlo iterations. Finally, the ranges for averaging values of the five coefficients of urban development in SLEUTH were set and the averaging was run for 100 Monte Carlo iterations. Model prediction After the calibration and performance validation of the model, the prediction step was executed using the entire data coverage and 100 Monte Carlo iterations. Prediction of the model was based on the initial seed year of the current urban pattern, using those refined values of coefficients. The output of the SLEUTH model is a continues surface in which each cell has a probability value to become an urbanized space in the future. This map is produced for every year including the first year (1987) to the last year (2030). There are three different methods used to simulate the expansion of urban area under different scenarios in the SLEUTH model. In the first method, best-à ¯Ã‚ ¬Ã‚ t multipliers derived from the calibration phases can be altered (Leao et al. 2004; Rafiee et al. 2009) and consequently the growth rules will change. In the second method, the excluded layer is weighted through a continuous range of resistance values against urbanization to show that even cells within the excluded layer have the potential to be urbanized under different probabilities (Oguz et al. 2007; Jantz et al. 2010; Mahiny and Clarke 2012, 2013). In the third method, the constraints of self-organization can be modià ¯Ã‚ ¬Ã‚ ed (Yang and Lo 2003; Xi et al. 2009). In this study, the first and the second methods were applied for two scenarios. The coefficients were altered in the MUG and AUG scenarios and an aesthetic suitability map of the study area was also used as an extra excluded layer in the AUG scenario. In th is case, the historical trend of the urban growth and two different scenarios were forecasted (Table 2). The adopted scenarios in this study used additional information regarding the study area and its development in the past. In addition, it was acknowledged that land use plans are mostly controlled by master plans for cities derived from regional land use planning (Makhdoum 2001; Dezhkam et al. 2014). The adopted scenarios were set up according to assumptions of uncontrolled and controlled growth, which allows decision makers to construct a quantitative comparative basis for evaluation of different growth alternatives. After calibration of the model, scenarios were introduced to model urban growth to the year 2030 by using two methods of parameter modification and the inclusion of the hydrology, dense forest and transportation exclusion layers in the first two scenarios as well as aesthetic exclusion layers in the third scenario. Table (2) The first scenario assumed that the present pattern of urban growth will be maintained in the future, and therefore, the originally derived parameters were used. The first exclusion layer including hydrographical networks, dense forests and roads were used for this. The prediction was conducted by means of the same resolution data and 100 Monte Carlos iterations. The second scenario used the same exclusion layer as the HUG scenario, but spread and breed coefficients were reduced (from 30 and 59 to 20 and 40 respectively) to dictate an infill urban development with the aim of protecting the immediate environment of the city against urbanization. The slope resistance coefficient was decreased to one-half of its original value, to reflect the current status of urbanization in Gorgan City which shows increasing development on steeper slopes. The third scenario (AUG) used the same coefficient values as those used for the MUG simulation, but the aesthetic suitability layer was used as an extra excluded layer to protect areas of high aesthetic value. The output from the SLEUTH model is a probability map, which shows the probability of each single pixel becoming urbanized. In order to produce a clear map that indicates future urbanized areas, a 90 % value was taken as a threshold to depict those cells which were considered most probable ones to become urbanized by 2030. [1] Brute Force refers to any of several problem-solving methods involving the evaluation of multiple possible answers (urban growth patterns) for model fitness.

Microcredit Essay -- Economy, Loans, Microloans

Microcredit can be defined as small loans, or microloans, for people around the world in extreme poverty to help spur entrepreneurship. The issue of microcredit is extremely important in the world’s economy. Poverty alleviation and economic development are the primary goals of microcredit programs, that is why they began in the developing countries of Asia and Latin America, economist Muhammad Yunus and his Grameen Bank in Bangladesh are credited of pioneering this financial innovation (Smith, Thurman, 2007). After acquiring a loan, impoverished people get involved in self-employment projects that help them to start a business and begin generating income and in many cases leave poverty. Microcredit offers loans to poor people without requesting any financial history from them. These loans help to improve the quality of life of individuals and communities through commitment. In recent years, the idea of giving small loans to poor people became the darling of the development wor ld, giving a way to propel even the poorest people into better lives (Jolis, 2011). Since its emergence, microcredit has been viewed as a very important tool for development. Many around the world believe microcredit is the antidote for global poverty. Although the Grameen Bank focuses only on people from Bangladesh, different microfinance institutions had been established around the world. Accion International is one example of these institutions in Latin America, which started providing loans in 1973 (The history of microfinance, 2005). These financial institutions started to grow rapidly due to high demands of small loans. Poor people around the world started to lose faith to their countries’ authorities to provide for their well being and started to tur... ...e of the challenges that the Grameen Bank has faced in the last years is that the government believed that citizens from Bangladesh are just growing a big dept that will only damage their lives in the future. However, as stated before, 98% of the loans have been repaid. Overall, microcredit has helped millions of people around the world and it continues to have a great impact on poor people, informing them that all they need is a little ‘push’ or start-up money to begin creating a better life and subsequently a better community. Each organization has its own goals and purposes depending on the country where they reside as well as different challenges that have appeared. Microcredit is helping poor people and small business owners to better themselves as well as to their families and have their time, skills, and ideas utilized in an effective and positive way.

Silk - The Queen of Fibers Essay -- Textiles

Silk, sometimes affectionately referred to as the â€Å"queen of fibers,† is the strongest natural fiber in the world, and it is used to make expensive cloth. There’s more to silk, though, than being great to make fine garments. Did you know that a thread of silk can be stronger than some kinds of steel? Probably not. We hope to give you more insight into the wonders of silk in our report. THE DISCOVERY OF SILK One of the only – if not the only – documentation on the discovery of silk is an ancient Chinese legend. According to this legend, silk was discovered in the garden of Emperor Huangdi around 2700 B.C.E. The mulberry trees in his garden were being destroyed, and he ordered his wife, Xilingshi, to go out there and see what was the cause of the damage done to his trees. When Xilingshi went out to examine the trees, she found white worms eating the leaves of the mulberry leaves and spinning shiny cocoons. She then accidentally dropped one of the cocoons into some hot water. And when she started playing with the cocoon in the water, long white strings disentangled themselves from the cocoon. It is said that this was how silk was discovered. Xilingshi then went to Emperor Huangdi to ask him to give her a grove of mulberry trees, in order for her to breed thousands of worms that would spin these beautiful cocoons. The king then obliged. Some accounts claim that she was the person who invented the silk reel, which is a device used to join fine silk filaments into a thread thick enough to be used for weaving. Others also credit her for being the maker of the first silk loom. How true these stories are still remain uncertain with historians. One thing they are sure about, though, is that silk was first used in China. The Ch... ...pired designs (like leaves or cherry blossom trees) or of animals (like dragons or phoenixes). Pictures of Chinese silk are on the last page of the report. Works Cited "History of Silk." Silkroad Foundation. Silkroad Foundation, 2000. Web. 16 Feb. 2011. . Hong, Lily Toy. The Empress and the Silkworm. Morton Grove, Illinois: Albert Whitman, 1995. Print. "Silk." World Book S-Sn Volume 17. 2004. Print. Textile Fabric Consultants Inc., Amy Willbanks. "Silk." www.fabrics.net/ amysilk.asp. fabrics INC, 19 Feb. 2011. Web. 19 Feb. 2011. . "Who smuggled the silkworm into japan + broke the silk monopoly of the Chinese?" Yahoo! Answers. Yahoo! Inc., 2008. Web. 16 Feb. 2011. .

Math Coursework - The Fencing Problem :: Math Coursework Mathematics

The Fencing Problem Aim - to investigate which geometrical enclosed shape would give the largest area when given a set perimeter. In the following shapes I will use a perimeter of 1000m. I will start with the simplest polygon, a triangle. Since in a triangle there are 3 variables i.e. three sides which can be different. There is no way in linking all three together, by this I mean if one side is 200m then the other sides can be a range of things. I am going to fix a base and then draw numerous triangles off this base. I can tell that all the triangles will have the same perimeter because using a setsquare and two points can draw the same shape. If the setsquare had to touch these two points and a point was drawn at the 90 angle then a circle would be its locus. Since the size of the set square never changes the perimeter must remain the same. [IMAGE] The area of a triangle depends on two things: the height and the base. The base is fixed in this example so the triangle that has the biggest height, i.e. the middle triangle, will have the biggest area. The middle triangle turns out to be an icosoles triangle. I am going to focus only on icosoles triangles. I have constructed a formula linking all three sides in and icosoles triangle. [IMAGE] X X X=any number which is greater than 250 and less than 500 ======================================================== 1000 - 2X Using Pythagoras theorem I can find and equation linking a side to the area. ====================================================================== ÂÂ ½(1000 - 2X)ÂÂ ² + HÂÂ ² = XÂÂ ² HÂÂ ² = XÂÂ ² + (X -500)ÂÂ ² H = height X 500 - X XÂÂ ² - (500-X)ÂÂ ² H Area 251 249 1000 31.6 7874.1 300 200 50000 223.61 44721.0 333.33

Revenue-Recognition Problems in the Communications Equipment Industry Essay

1) In late 2000, Lucent announced that revenues would be adjusted downwards by $679m as a result of revenue recognition problems. Yet the firm’s market capitalization plummeted by $24.7bn. Why do you think the market reacted so negatively to Lucent’s announcements of the problems? The large drop in market capitalization is probably due to several factors. Historically, Lucent had successfully met analysts’ projections for 15 consecutive quarters before announcing, in January 2000, a major shortfall in profits relative to previous expectations. In June, the quarterly balance sheet reported an operating loss of $301m (for the first time since 1998) while warning of weaker profits in Q4. In addition, the revenue recognition issues announced by the new CEO appointed in October were surely perceived as an indication that Lucent’s management was managing revenues and therefore a possible cause of a future fall in revenues. This led investors to modify their earnings expectations in light of the revenue-recognition problems faced by the firm. Since a company’s share price reflects forecasts of future cash flows, and Lucent’s Q3 and Q4 revenues were substantially written-down, investors would rationally expect future earnings to be affected as well. In an efficient market environment, the $24.7bn in lost market capitalization would equal the discounted value of these expected cash flows. However, it is also likely that the repeated missed expectations caused an overreaction by investors, as the company was forced to revise its revenues downward two times over the span of two quarters. This probably raised fears in the market of more widespread problems with the firm’s accounting practices. It should also be kept in mind that the Internet bubble had just burst and a technology related company announcing an operating loss and lower revenues could easily cause a panic selloff among investors, as typically happens when a speculative bubble bursts. 2) What are the specific revenue recognition problems faced by Lucent? On December 22, 2000, Lucent announced a $679m downward adjustment in revenues  of their fourth-quarter financial statement from September 2000. There were four different reasons for the adjustment. First of all, Lucent stated $125m of recorded sales that did not meet the company’s revenuerecognition rules. These revenues were included in the financial statement due to â€Å"misleading documentation and incomplete communications between a sales team and the financial organization†. Additionally, Lucent sold $452m worth of equipment to system integrators and distributors and recorded them as revenues. In fact, the products were not passed on to the customers, because of their weakened financial condition, and Lucent had already verbally agreed to take back the equipment. Therefore, the sales could not be accounted as revenues. Thirdly, sales teams had verbally offered credits to customers worth $74m and booked them as revenue in order to boost the fourth-quarter numbers. As the credits were meant for use at a later date without an actual sale of equipment taking place, these could not be accounted as revenues in the fourth-quarter. Finally, sold equipment worth $28m had not been completely shipped, leaving the service incomplete. Since this violated the first revenue recognition criteria â€Å"The firm has performed all the services or conveyed the asset to the buyer†, recognition of these revenues is not in line with regulation. 3) What financial statement adjustments will Lucent have to make to correct the revenue recognition problems announced in late 2000? In our treatment of the accounting figures we found it necessary to make assumptions relating to tax rates and COGS, as the information is not given directly. In deciding which tax rate to use for the adjustments we have two obvious alternatives; either assume a corporate tax rate of 35%, or calculate the average tax rate based on the presented financial statement. However, due to certain revenues and expenses being non-taxable we have opted to discard the average tax rate as a suitable estimate, and assumed a corporate tax rate of 35%. In relation to the Cost of Goods Sold, Lucent faces the problem that some of  their goods are tangible (communications equipment) while some are intangible (software licenses, services etc.). We are aware of the fact that Lucent’s intangible assets are subject to different costs as its tangible assets, and therefore have to be restated differently. However, we do not know the costs of neither intangible nor tangible assets due to a lack of information and thus assume a representative cost mix that is proportional to total revenues. Hence, we use the average COGS (69% of revenues in Q4, 2000) when we calculate the restatements. In the balance sheet, we treat the physical goods as â€Å"inventory†, and intangible goods as â€Å"other current assets†. When readjusting the income statement and balance sheets we need to reduce the revenues by a total of $679mn, with a corresponding reduction in accounts receivable. The cost of goods sold is reduced by $470mn, as per our assumption above relating to the average cost of goods sold. On the balance sheet this is reflected in the increase of inventories for tangible sales, and other current assets for intangible sales. This leads to a reduction of pretax income of $209mn, and subsequently a reduction in income taxes of $73mn. In the balance sheet this is represented by a reduction in the deferred tax liability (current liabilities in Lucent’s balance sheet), and finally a reduction in stockholders retained equity by $136mn. 4) How would you judge whether a firm is likely to face revenue recognition problems? Revenue-recognition problems in Lucent’s case emerge from mismanagement of the financial statements by all parties involved in compiling them. For instance, the initial $125 million adjustment was due to miscommunication between the sales team and the financial organization. The lack of a proper internal reporting organization or of efficient external auditors therefore is a sign of increased risk of revenue misrepresentation. It is also important to mention that the events described in the case occurred before the Sarbanes-Oxley Act was enacted. This means that, at the time, financial statements did not require a seal of approval from top management in order to be published. The fact that these reports were approved and published suggests awareness and involvement of the board of administrators in the revenue-recognition problems. Making CEOs accountable  for the financial statements was an important step toward prevention of unwanted accounting practices. From a broader perspective, companies are constantly subject to the need of reaching – and beating – the market’s profitability expectations. Missing these targets may result in a steep share price fall, especially considering the â€Å"herd mentality† that is prevalent during market bubbles. Investors will typically overreact at the first sign of negative news from a company, triggering sharp sell offs in stock, as was the case with Lucent, during the height of the dotcom bubble. Further revenue misrepresentation drivers we can deduce from Lucent’s case are: firstly, firms providing financing solutions to customers may fall into the temptation of using these tools in order to boost their quarterly revenues by granting credits to clients. In fact, computing Lucent’s Account Receivables / Turnover ratio, it is observable that average collection days increase substantially from 1998 (85 days) to 2000 (119 days). This means that Lucent was selling products extending financing rather than collecting cash. Secondly, when companies rely on a distribution network rather than on direct sale it is easier for them to engineer revenue-boosting activities (e.g. provide distributors with more than what can be sold and take back the equipment later on). Thirdly, relying on big clients accounting for a large percentage of revenues increases may enhance corporate relationships, thus facilitating non-transparent verbal agreements or offbalance-sheet operations (e.g. financing, discounts). In addition, any changes in accounting practices and assumptions accounted for in the income statement should be investigated closer as a possible case of accounting fraud, as in the case of Lucent. In the 1st quarter of fiscal 1999 $1.3bn is booked as a â€Å"cumulative effect of accounting change†. This is enough to say that a revenue recognition problem exists, but certainly warrants further investigation. Finally, incentives of a more general nature to accounting malpractice include regular evaluation of company credit quality by rating agencies, and distorted compensation incentives for management. The former occur at regular intervals, providing incentives for management to â€Å"polish† a firm’s balance sheet prior to evaluations by the agencies, while the latter usually  involves stock options. Since employees are only allowed to sell their options at certain dates, they have an incentive to push the company’s share price up through accounting manipulation, prior to executing their options. 5) Assess whether any of Lucent’s competitors are likely to face revenue recognition problems in the coming quarters. Cisco Systems’ multichannel approach to sales and marketing includes a direct sales force to distributors, value-added resellers and system integrators. This could allow them to boost their revenues by selling excessive amounts to distributors close to the end of a quarter and taking the equipment back afterwards. On the other hand, Cisco does not rely on a single client, but has a diversified client base. In addition, the financing that Cisco provides is clearly reported on the balance sheet as noncurrent long-term lease receivables, which clearly differs to Lucent’s approach concerning verbal agreements about credits to clients. Unlike Cisco, Juniper Networks mainly relies on one large customer, WorldCom, who generated 18% of their revenues in 2000. Thus, they were highly dependent on that client and had most likely build up a close relationship with them, both concerning equipment sales and credit granting. This increases the risk of false revenue recognition due to either channel stuffing or the sale of equipment (meant to be taken back if not sold) close to the end of the quarter. Nortel is mainly a service provider, in fact 82% of its revenues are made up by services. This could be a red flag for revenue-recognition issues as services may have no clear delivery date and thus allow revenue management. In addition, Nortel granted credit to its customers of $5.6bn, of which only $1.5bn had been used. This could mean that Nortel is trying to attract customers by aggressively offering financing. On the other hand, Nortel does not depend on any single client. We did not find any significant pattern in insiders’ dispositions of their stock options to indicate fraudulent activity, neither for Lucent or any of their competitors. We also closely examined the two key ratios â€Å"Account Receivables Turnover† and â€Å"Cash Flow Return† for Lucent and its competitors (Juniper Networks has been excluded due to data absence). As can be seen in  the following graph all cash flow returns recently started to decline, which could raise concerns with regards to their revenue recognition policy. In Cisco’s and Nortel’s case one can see that this change is due to a parallel decline in cash flow from operations as well as an increase in sales. However, this movement by itself is not a red flag and could be due to other factors, which calls for a more detailed investigation. We can see that the suspicious decrease in cash flow return is mainly due to a substantial increase in sales and can also be seen in a substantial increase in accounts receivables. Hence, we looked at â€Å"accounts receivable turnover† or more precisely â€Å"days sales outstanding† and found that the average level over the course of the previous three years stays approximately the same while showing a negative trend for Cisco and even constantly decreased slightly for Nortel. This is a very good sign and means that these two still manage to collect their receivables in a timely manner although sales increase rapidly. Cash Flow Returns should therefore stabilize again in the ne ar future. Lucent’s Account receivables turnover on the other hand, as already elaborated in the previous question, steeply increases. This may indicate Lucent was selling products by extending financing to customers rather than collecting cash since we cannot apply the same argumentation as for Cisco and Nortel in Lucent’s case.

Ethics †morality Essay

When asking people â€Å"what does ethics mean? † we get many different replies. Some relate ethics to feelings, others relate it to religion, others might relate it to the law, others relate it to society and some just do not know. They are all wrong. Ethics refers to well-founded standards of right and wrong. Feelings, religion and the law may misguide people from what is ethical. The majority of people misunderstand what being ethical means. Some think that being ethical means following the law. The law often incorporates ethical standards to which most citizens subscribe. But laws, like feelings, can deviate from what is ethical. Our own pre-Civil War slavery laws and the old apartheid laws of present-day South Africa are grotesquely obvious examples of laws that deviate from what is ethical. Adela Cortina (March, 2000) said, â€Å"A natural law standpoint which, whether in its traditional or ‘post traditional’ version, ultimately takes only what is just from a certain moral conception as ‘valid law’, is not an acceptable basis for legal legislation in a morally pluralist society. † Nor should one identify ethics with religion. Most religions, of course, advocate high ethical standards. Yet if ethics were confined to religion, then ethics would apply only to religious people. But ethics applies as much to the behavior of the atheist as to that of the devout religious person. Religion can set high ethical standards and can provide intense motivations for ethical behavior. Ethics, however, cannot be confined to religion nor is it the same as religion. Praveen Parboteeah, Martin Hoegl and John B. Cullen (June, 2008) mention â€Å"some studies have found no difference between religious and non religious individuals on unethical behaviors such as dishonesty and cheating. Many people tend to equate ethics with their feelings, but being ethical is clearly not a matter of following one’s feelings. A person following his or her feelings may recoil from doing what is right. Feelings frequently deviate from what is ethical. Just like Ken Bowen (August, 1994) said, â€Å"With a set of rules guilt is all too often a conflict between what is said to be wrong and what one feels to be right and can be devastating to an individuals personality. † What, then, is ethics? Ethics is two things. First, ethics refers to well-founded standards of right and wrong that prescribe what humans ought to do, usually in terms of rights, obligations, benefits to society, fairness, or specific virtues. Ethics, for example, refers to those standards that impose the reasonable obligations to refrain from rape, stealing, murder, assault, slander, and fraud. Ethical standards also include those that enjoin virtues of honesty, compassion, and loyalty. And, ethical standards include standards relating to rights, such as the right to life, the right to freedom from injury, and the right to privacy. Such standards are adequate standards of ethics because they are supported by consistent and well-founded reasons. Secondly, ethics refers to the study and development of one’s ethical standards. As mentioned above, feelings, laws, and social norms can deviate from what is ethical. So it is necessary to constantly examine one’s standards to ensure that they are reasonable and well-founded. Ethics also means, then, the continuous effort of studying our own moral beliefs and our moral conduct, and striving to ensure that we, and the institutions we help to shape, live up to standards that are reasonable and solidly-based. Most people do not really know what the meaning of ethics is. They all have a different idea of it. They relate it to things like religion, law and feelings. Well these usually deviate us from what is right and wrong. That is not all there is to ethics. Ethics has to do with standards of right and wrong. They may change through time. One must always examine one’s standards.