The Current Economic Situation Marked by High Unemployment and Low Essay

The Current Economic Situation Marked by High Unemployment and Low Inflation - Essay Example This essay not only describes current economic situation in the United States that was explored as an example. The current economic situation in the United States of America marred by high unemployment and low inflation can ideally be explained with the help of the Philips Curve. As per A W Philips, there existed a relationship between the levels of unemployment and consequently the rate of change in money wages and the rate of change in prices and hence inflation. To put it in simple words, as per the Philips Curve, historically speaking, their lies an inverse relationship between Demand Pull Inflation and the rate of unemployment. The researcher also provides the reader with some steps, that would not only increase the aggregate demand but would also check the expanding unemployment rate. The Salient Factors that have impacts on the US economy rates, such as The Labor Market, Product Markets and Other Factor Markets are explored in details. The researcher od this essay also discuss es and presents some alternate scenarios that say an unemployment rate of 8.8 percent and an inflation rate of 10 percent will not warrant such measures. In such scenario that was discussed, the pragmatic approach on the part of the government will be to pursue a restrained fiscal and monetary policy to contract the supply of money in the markets. In conclusion, it is stated that in the current scenario showing high unemployment and low inflation, it is advisable that the government pursues an expansionary fiscal and monetary policy.

C Programming Essay Example | Topics and Well Written Essays - 750 words

C Programming - Essay Example C truly is much more of a â€Å"programming environment† than just a language. Using this environment, a single developer can quickly create a simple application; a team of developers can create a sophisticated, distributed application. The main reason why C is so popular and powerful is the same reason behind the success of Windows. Microsoft took a complex technology (writing computer programs) and made it easier to use through a graphical interface. Suppose you have to write a program for your company. In a visual programming environment, you can quickly design the windows that the user sees by drawing and arranging them just as you would lay out elements for a newspaper. Arithmetic operators These are the simple operators used in daily mathematics. These include the addition ‘+’ operator, subtraction ‘-’ operator, multiplication ‘*’ operator, division ‘/’ operator and the modulus ‘%’ operator.

Ict Procurement Trends in the Uk Essay Example for Free

Ict Procurement Trends in the Uk Essay This report presents the findings from a survey of 136 UK enterprises regarding their approach to Information and Communication Technology (ICT) procurement. The survey investigates the way that UK enterprises like to purchase technology, as well as the major IT and business objectives influencing their IT investment strategies. Introduction and Landscape Why was the report written? To highlight the criteria on which UK enterprises select their IT providers as well as the roles which have influence while making IT purchasing decisions. What is the current market landscape and what is changing? UK enterprises are set to increase their IT spending in 2013. Kables survey shows that ICT spending in the UK is being driven by investments in core technology areas such as security, enterprise applications, IT systems management, and content management. What are the key drivers behind recent market changes? With enterprises being continuously exposed to malicious attacks on their business critical information, the demand for security solutions is growing. What makes this report unique and essential to read? Kable Global ICT Intelligence has invested significant resources in order to interview CIOs and IT managers about their IT Procurement. Very few IT analyst houses will have interviewed 130+ ICT decision makers in the UK market in H2 2012. Key Features and Benefits Provides insights into UK enterprises preferred buying approaches. Comprehend the business objectives that UK enterprises are looking to achieve through their IT investment strategy. Appreciate the IT objectives that UK enterprises are looking to achieve through their IT investment strategy. Understand the factors that are influencing UK enterprises decision to select an ICT provider. Understand which organisational roles influence IT purchasing decisions and signing off budgets. Key Market Issues Despite the uncertain economic conditions across Europe and the UK governments large scale austerity measures which have impacted public sector ICT spend, the vast majority of respondents from Kables survey indicate that their IT budgets will remain at the same level or will increase in 2013. With regards to the authority over signing off budgets, UK enterprises surprisingly give an equal rating to CEOs, CFOs, and CIOs. UK enterprises rate Improve supplier relationships with an average rating of X, indicating that enterprises focus is weighted more to their own operations, followed by their customers, amidst the difficult global economic outlook. Investments in cloud computing are expected to grow with the penetration of this technology increasing from the current level of X% to Y% in the next twenty-four months, driven by factors such as lowering cost and complexity, and ease of use. According to Kables survey, X% of enterprises have a somewhat complex ICT infrastructure with several hardware manufacturers, operating systems, databases, applications, and other elements. Key Highlights Although on-premise deployment is favoured, the demand for hosted applications is also gaining traction, as enterprises are continuously focused on reducing costs in the current economic climate. Raising efficiency is a primary business objective influencing IT investment strategy amongst UK enterprises with the highest rating of X on a scale of 1 to 4. The recent survey reveals X% of enterprises have rated the objective of meeting internal service level agreements as a highest priority. With an average rating of X on a scale of 1 to 4, UK enterprises consider Financial stability and Price to be the most important criteria in choosing an IT solutions provider. UK enterprises rate the CIO/IT department as the most influential authority when making IT purchasing decisions, with the highest average rating of X on a scale of 1 to 4.

Support Vector Machine Based Model

Support Vector Machine Based Model Support Vector Machine based model for Host Overload Detection in Clouds Abstract. Recently increased demand in computational power resulted in establishing large-scale data centers. The developments in virtualization tech-nology have resulted in increased resources utilization across data centers, but energy efficient resource utilization becomes a challenge. It has been predicted that by 2015 data center facilities costs would contribute about 75%, whereas IT would contribute the remaining 25% to the overall operating cost of the data center. The Server consolidation concept has been evolved for improving the energy efficiency of the data centers. The paper focuses on support vector machine based novel approach to predict the overload and underload pattern of the servers for better data center reconfiguration. Keywords: Support vector machine, energy efficiency. 1 Introduction Virtualization plays an important role in cloud computing, since it permits appropriate degree of customization, security, isolation, and manageability that are fundamental for delivering IT services on demand. One of its striking features is the ability to utilize compute power more proficiently. Particularly, virtualization provides an opportunity to consolidate multiple virtual machine (VM) instances on fewer hosts depending on the host utilization, enabling many of computers to be turned-off, and thereby resulting in substantial energy savings. In fact, commercial products such as the VMware vSphere Distributed Resource Scheduler (DRS), Microsoft System Center Virtual Machine Manager (VMM), and Citirix XenServer offer VM consolidation as their chief functionality[1]. But with the rapid growth in computing demand, the number of datacenters grows with the need which leads to more number of servers active at a time. The high active servers’ ratio leads to more energy emission and production of Carbon dioxide (CO2). According to data centers’ study, the data centers are not utilized up to their maximum utilization level which leads to more active servers, everyone utilized to less than their total capacity. With this in mind, it is worthwhile to attempt to minimize energy consumption through any means available. Various research agencies and universities have contributed into the research and design of heat dissipation and control in the data center. Virtualization is a technology that contributes to the maximum u tilization of the servers by virtual machine (VM) consolidation and VM Migration. The decision of reallocation of virtual machine for VM consolidation depends on the host utilization behavior. The VMs from the under-utilized and over-utilized hosts are relocated to other hosts by packing the VMs on minimum number of hosts. The hosts having no virtual machine are shifted to the passive mode so that the total energy consumption can be reduced. Statistical methods played a great role in predicting the behavior of the host in dynamic manner. The author [3] has proposed various statistical methods for host overload and underload behavior of the hosts in his thesis. These algorithms take input as the previous or current utilization of the hosts and predict the future based on the previous or current state of the system. He has proposed Local Regression, Median Absolute Deviation, Robust Local Regression and Markov Chain model for predicting the overloaded hosts [3]. All statistical models cannot be applied to all the environments. The choice of the statistical methods d epends on the input data, because every statistical model is based on some assumptions. Markov chain model assumes that the data will be stationary but complex and dynamic environment like cloud, experience highly variable non-stationary workload. The author [3] in his thesis modified his model by using multisize sliding window workload estimation method so that it can be suitable for the cloud environment. In this paper we have proposed a prediction based model i.e. Support Vector Machine (SVM) to predict the host utilization to forecast the host overload and underload behavior of the host. The rest of the paper is organized as follows. Section 2 explains the basic concepts and modeling approaches of the Support Vector Machine. In section 3, the literature review related to Support Vector Machine is presented. In section 4 the model is applied to time series forecasting and its performance is compared with those of other forecasting models. Section 5 contains the concluding remarks. 2 Support Vector Machine Support vector machine is a novel technique based on neural network invented by Vapnik and his co-workers at AT T Bell Laboratories in 1995. The objective of SVM is to find a generalized decision rule through selecting some particular subset of training data, called support vectors. Training SVMs is equivalent to solving a linearly constrained quadratic programming problem. The quadratic equation is solved such that the solution of SVM is globally optimal and the quality complexity of the solution does not directly depend on the input space. Another key advantage of SVM is that SVMs tend to be resistant to over-fitting, even in cases where the number of attributes is greater than the number of observations. According to Vapnik there are three main problems in machine learning, e.g. Density Estimation Classification and Regression. In every case the main goal is to learn a function (or hypothesis) from the training data using a learning machine and then conclude general results base d on this knowledge. Time series is a series of data points S t à ¯Ã†â€™Ã… ½ R usually ordered in time. Time series analysis comprises the methods for analyzing the time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting models predicts future values based on the previously observed values. The main focus of this paper is to predict the overload and underload behavior of the hosts in cloud data centers based on the previous load pattern of the hosts in the datacenter. The time series prediction is affected by various factors like data is linearly separable or follows non-linear patterns, the learning is supervised learning or unsupervised learning and on support vector kernels. In Euclidean geometry linear separability is a geometric property of a pair of sets of points. The points are linearly separable or not are decided by visualizing the points in two dimensions plane by taking one set of points as being colored green and the other set of points as red. These two sets are linearly separable if there exists at least one line in the plane with all of the green points on one side of the line and all the red points on the other side. Usually in practical problems the data points are mapped to the high dimensional plane and the optimal separating hyper plane is constructed with the help of some special functions known as support vector Kernels in this new feature space. This method also resolves the problem where the training points are not separab le by a linear decision boundary. Because by using an appropriate transformation the training data points can be made linearly separable in the feature space. Figure1 (a) Linearly separable data1(b) non-linear patterns of data In supervisory learning, the training data is composed of input as well as the output vector (also called supervisory signals) whereas in un-supervisory learning the training data is composed of only input vectors. Supervisory learning produces better results because the output vector is already known and the predicted values by the SVM are compared with the output to learn better for the next step. In un-supervisory learning the output data points are not known and the training depends on the probability to drive better results out of it. SVM comes in the supervisory learning category and the kernel function makes the technique applicable for the linear as well as non-linear approximation. 3 Related Works In various practical domains time series modeling and forecasting has essential importance. A lot of research works is going on in this subject during several years. Many models have been proposed in literature for improving the accuracy and efficiency of time series modeling and forecasting. The author [1] has compared various time series prediction methods widely used these days. This paper investigated the application of SVM in financial forecasting. The autoregressive integrated moving average model(ARIMA), ANN, and SVM models were fitted to Al-Quds Index of the Palestinian Stock Exchange Market time series data and two-month future points were forecast. The results of applying SVM methods and the accuracy of forecasting were assessed and compared to those of the ARIMA and ANN methods through the minimum root-mean-square error of the natural logarithms of the data. Results proved that svm is better method of modeling and outperformed ARIMA and ANN. The author of [2] explains the time series concept and the various methods of predicting the future values based on ARIMA model, Seasonal ARIMA model, ANN model, time lagged ANN, seasonal ANN, SVM for regression, SVM for forecasting etc. they have also explained the forecast performance measure MFE (Mean Forecast Error), MAE (Mean Absolute error), MAPE (Mean absolute percentage error), MPE (Mean percentage error), MSE (Means squared error) etc. In paper [5], a model based on least squares support vector machine is proposed to forecast the daily peak loads of electricity in a month. In [5] the time series prediction was first used to forecast electricity load .In paper [4] the author has improved the method presented in [5] to derive more accurate results. The author has proposed dynamic least square support vector machine (DLS-SVM) to track the dynamics of nonlinear time-varying systems. The dynamic least square method works dynamically by replacing the first vector by the new input vector to obtain more accurate result.. The author in paper [9] has proposed the modified version on svm for time series forecasting. The algorithm performs the forecasting in phases. In the first phase, self-organizing map (SOM) is used to partition the whole input space into several disjointed regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second phase, multiple SVMs, also called SVM experts, are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs. 4 Support Vector Machine Regression Formulations for Forecasting Host Overload Detection Host overload and underload detection is based on current utilization patterns of the host. The host utilization is a univariate time series. In univariate time series the future values are entirely based on past observations. The goal of the SVM regression is to find a function that presents the most deviation from the target values so the maximum allowed error is. The future values are predicted by splitting the time series data into training inputs and the training outputs. Given training data sets of N points, with input data and output data . Assume a non-linear function as given below: (1) w = weight vector, b=bias and is a non-linear mapping to a higher dimensional space. The optimization problem can be defined as: : (2) is a user defined maximum error allowed. The above equation (2) can be rewrite as: : (3) To solve the above equation slack variables needs to be introduced to handle the infeasible optimization problem. After introducing the slack variables the above equations takes the form as given below: : (4) The slack variables defines the size of the upper and the lower deviation as shown in the figure 2(a). Figure 2 (a) The Accurate points inside Tube 2(b) Slope decided by C For simplicity and for avoiding the case of infinite dimensionality of the weight vector w the optimization operation are performed in the dual space[4] the Lagrangian for the problem(a) is given by [2] (3) Here, where are the Lagrange multiples. Applying the conditions of the optimality, one can compute the partial derivatives of L with respect to equate them to zero and finally eliminating w and obtain the following linear system of equations (4) Here, and with is the kernel matrix. The LS-SVM decision function is thus given by [4] (5) The dynamic least square support vector machine is modified so that it is best suitable for the real world problems. The key feature of DLS-SVM is that it can track the dynamics of the non-linear time varying system by deleting one existing data point whenever a new observation is added, thus maintaining the constant window size. 4 Experiments We have used CloudSim for retrieving the utilization of the host based on the workload defined in the PlanetLab folder in CloudSim. It contains the daily virtual machine requirement and the utilization of the host is calculated based on the daily requirement of the virtual machines. After retrieving the utilization of the hosts LSSVMLabv1 toolbox is used for support vector regression and the results are compared with [10] and [5]. The comparison is based on MAPE (mean absolute percentage error) and Maximal error (ME). The chart shows that DLS-SVM produce better forecast for the load pattern of the hosts in the data centers. Figure2: Comparison of errors References Okasha, M. K.,Using Support Vector Machines in Financial Time Series Forecasting.International Journal of Statistics and Applications 2014, 4(1): 28-392. Adhikari, R., Agrawal, R. K. (2013). An Introductory Study on Time Series Modeling and Forecasting.arXiv preprint arXiv:1302.6613. Beloglazov, Anton. Energy-efficient management of virtual machines in data centers for cloud computing. (2013). Niu, D. X., Li, W., Cheng, L. M., Gu, X. H. (2008, July). Mid-term load forecasting based on dynamic least squares SVMs. InMachine Learning and Cybernetics, 2008 International Conference on(Vol. 2, pp. 800-804). IEEE. Bo-Jeun Chen, Ming-Wei Chang, and Chih-Jen LIN, â€Å"Load forecasting using support vector machines: A study on EUNITE competition 2001†, IEEE Trans. Power Syst., vol. 19, no. 4, pp. 1821-1830, Nov. 2004. Fan, Y., Li, P., Song, Z. (2006, June). Dynamic least squares support vector machine. InIntelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on(Vol. 1, pp. 4886-4889). IEEE. Kim, K. J. (2003). Financial time series forecasting using support vector machines.Neurocomputing,55(1), 307-319. Gui, B., Wei, X., Shen, Q., Qi, J., Guo, L. (2014, November). Financial Time Series Forecasting Using Support Vector Machine. InComputational Intelligence and Security (CIS), 2014 Tenth International Conference on(pp. 39-43). IEEE. Cao, L. (2003). Support vector machines experts for time series forecasting.Neurocomputing,51, 321-339. Haishan Wu, Xiaoling Chang. â€Å"Power load forecasting with least square support vector machines and Chaos Theory†, Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23, 2006. Rà ¼ping, S. (2001).SVM kernels for time series analysis(No. 2001, 43). Technical Report, SFB 475: Komplexità ¤tsreduktion in Multivariaten Datenstrukturen, Università ¤t Dortmund.

Dyslexia: Causes and Treatment :: Science Research Disorder Essays

Dyslexia: Causes and Treatment Works Cited Missing The learning disability dyslexia once perplexed scientists who now are beginning to make breakthrough discoveries into its causes. Dyslexia traditionally was vaguely defined as a difficulty in learning to read and write. In the past, dyslexics often were dismissed as lazy, not focused, or unintelligent. With these recent discoveries, scientists may be able to define much more specific disorders. Researchers now are finding out that people with dyslexia use specific brain regions that process written languages differently than those without the disorder. The specific brain regions which are involved, however, remain uncertain. With the knowledge that dyslexia results from differences in the language areas of the brain, it will be possible for researchers to help dyslexics better compensate for their conditions. Scientists also are coming closer to exactly pinpointing what causes those areas of the brain to act differently in dyslexics. Though which regions of the brain most central to dyslexia remain unresolved, technology has made headway to answer this question. Brain imaging, which is a technique of photographing the brain â€Å"in action,† indicates that dyslexics have higher levels of the chemical lactate in certain regions of the brain during language and sound processing. According to neurophysicist and brain specialist, Todd Richards, who heads a research team at the University of Washington, the regions of the brain that show high levels of chemical lactate are mostly in the left anterior quadrant of the brain that includes: the Left Frontal Cortex, Broca’s area, the Inferior Frontal Gyrus, the Middle Frontal Gyrus, and the Striatum (Richards). Dyslexics have to expend more brain energy in these regions to accomplish the same tasks as non-dyslexics, which results in higher levels of chemical lactate. Research also shows that dyslexics have less activity in the angular gyrus (AG) than those without the disability. â€Å"[T]he angular gyrus translates the mass of words and letters we encounter in day-to-day life into language† (Dyslexia par. 8). The AG is located towards the back of the brain and is a key component in normal reading. Many researchers believe that this part of the brain does not function normally in dyslexics. Some scientists are speculating that dyslexics may use certain areas of the brain inadequately, compensating for this by disproportionately using other areas of the brain.

The Gay Science,by Friedrich Nietzsche :: The Gay Science, Friedrich Nietzsche

1) Nietzsche could have written The Gay Science differently. What justifies the style of composition he chose? More importantly, is his style of writing effective? What relation do you see between the style of his writing and the content of thought it expresses? Nietzsche's style of writing was a deliberate stylistic choice meant to hide the meaning of his work and philosophy from those who would not be able to understand it, and through there misunderstanding would abuse it. This writing style was also meant to help support and give meaning to Nietzsche's arguments on the nature of language and how language is, at its root a metaphor describing an object that is disconnected from us. Nietzsche's work broke down language to its metaphorical roots and explored the nature of how our language is disconnected from the objective reality around us. Nietzsche uses the metaphorical roots of our language to show that words and language our fundamentally disconnected because of the subjective nature of language. Nietzsche shows these metaphorical roots by showing how simple words and phrases that we use in our everyday life are really disconnected or at least removed by the barrier of language. Language is a serious of metaphor's all describing ho w an object subjectively appears to the individual. No language can describe what it is like to "be" that object, nor properly describe what it is that makes the object what it is. All language can do is provide a vehicle through which man can communicate what he is subjectively experiencing and relate it via a metaphor to another individual who will only get a idea of what is being described rather than an actual concrete description. 2) In sections 124, 343, and 377, Nietzsche claims that, following the death of God, human beings find themselves "in the horizon of the infinite," on the "open sea," and "homeless." What are the consequences of the death of God? With reference to section 347, discuss the ambiguity of this new found freedom. How might it terrify some people and empower others? The consequences for the death of god are far reaching and and many in Nietzsche's work. Christianity sparked the death of God as most of us know him through the actions of Martin Luther. Luther's desire to give the common man the ability to understand and read the bible brought a end to the churches monopoly on morality and brought the "divine" to the common man making the common man "divine".

The Hunters: Moonsong Chapter Four

â€Å"Trust Bonnie to meet a cute guy on her first day at col ege,† Elena said. She careful y drew the nail-polish brush over Meredith's toenail, painting it a tannish pink. They'd spent the evening at freshman orientation with the rest of their dormmates, and now al they wanted to do was relax. â€Å"Are you sure this is the color it's supposed to be?† Elena asked Meredith. â€Å"It doesn't look like a summer sunset to me.† â€Å"I like it,† Meredith said, wiggling her toes. â€Å"Careful! I don't want polish on my new bedspread,† Elena warned. â€Å"Zander is just gorgeous,† Bonnie said, stretching out luxuriously on her own bed on the other side of the room. â€Å"Wait til you meet him.† Meredith smiled at Bonnie. â€Å"Isn't it an amazing feeling? When you've just met somebody and you feel like there's something between you, but you're not quite sure what's going to happen?† She gave an exaggerated sigh, rol ing her eyes up in a mock swoon. â€Å"It's al about the anticipation, and you get a thril just seeing him. I love that first part.† Her tone was light, but there was something lonely in her face. Elena was sure that, as composed and calm as Meredith was, she was already missing Alaric. â€Å"Sure,† Bonnie said amiably. â€Å"It's awesome, but I'd like to get to the next stage for once. I want to have a relationship where we know each other real y Well, a serious boyfriend instead of just a crush. Like you guys have. That's even better, isn't it?† â€Å"I think so,† said Meredith. â€Å"But you shouldn't try to hurry through the we-just-met stuff, because you've only got a limited time to enjoy it. Right, Elena?† Elena dabbed a cotton bal around the edges of Meredith's polished toenails and thought about when she had first met Stefan. With al that had happened since then, it was hard to believe it was only a year ago. What she remembered most was her own determination to have Stefan. No matter what had gotten in her way, she had known with a clear, firm purpose that he would be hers. And then, in those early days, once he was hers, it was glorious. It felt as if the missing piece of herself had slotted into place. â€Å"Right,† she said final y, answering Meredith. â€Å"Afterward, things get more complicated.† At first, Stefan had been a prize that Elena wanted to win: sophisticated and mysterious. He was a prize Caroline wanted, too, and Elena would never let Caroline beat her. But then Stefan had let Elena see the pain and passion, the integrity and nobility, he held inside him and she had forgotten the competition and loved Stefan with her whole heart. And now? She stil loved Stefan with everything she had, and he loved her. But she loved Damon, too, and sometimes she understood him – plotting, manipulative, dangerous Damon – better than she did Stefan. Damon was like her in some ways: he, too, would be relentless in pursuing what he wanted. She and Damon connected, she thought, on some deep core instinctive level that Stefan was too good, too honorable to understand. How could you love two people at the same time? â€Å"Complicated,† Bonnie scoffed. â€Å"More complicated than never being sure if somebody likes you or not? More complicated than having to wait by the phone to see if you have a date for Saturday night or not? I'm ready for complicated. Did you know that forty-nine percent of col ege-educated women meet their future husbands on campus?† â€Å"You made that statistic up,† Meredith said, rising and picking her way toward her own bed, careful not to smudge her polish. Bonnie shrugged. â€Å"Okay, maybe I did. But I bet it's a real y high percentage, anyway. Didn't your parents meet right here, Elena?† â€Å"They did,† Elena said. â€Å"I think they had a class together sophomore year.† â€Å"How romantic,† Bonnie said happily. â€Å"Well, if you get married, you have to meet your future spouse somewhere,† Meredith said. â€Å"And there are a lot of possible future spouses at col ege.† She frowned at the silky cover on her bed. â€Å"Do you think I can dry my nails faster if I use the hair dryer, or wil it mess up the polish? I want to go to sleep.† She examined the hair dryer as if it were the focal point of some science experiment, her face intent. Bonnie was watching her upside down, her head tipped back off the end of the bed and her red curls brushing the floor, tapping her feet energetical y against the wal . Elena felt a great sWellof love for both of them. She remembered the countless sleepovers they'd had al through school, back before their lives had gotten †¦ complicated. â€Å"I love having the three of us together,† she said. â€Å"I hope the whole year is going to be just like this.† That was when they first heard the sirens. Meredith peered through the blinds, col ecting facts, trying to analyze what was going on outside Pruitt House. An ambulance and several police cars were parked across the street, their lights silently blinking red and blue. Floodlights lit the quad a ghastly white, and it was crawling with police officers. â€Å"I think we should go out there,† she said. â€Å"Are you kidding me?† Bonnie asked from behind her. â€Å"Why would we want to do that? I'm in my pajamas.† Meredith glanced back. Bonnie was standing, hands on hips, brown eyes indignant. She was indeed wearing cute ice-cream-cone-printed pajamas. â€Å"Well, quick, put on some jeans,† Meredith said. â€Å"But why?† asked Bonnie plaintively. Meredith's eyes met Elena's across the room, and they nodded briskly to each other. â€Å"Bonnie,† Elena said patiently, â€Å"we have a responsibility to check out everything that's going on around here. We might just want to be normal col ege students, but we know the truth about the world – the truth other people don't realize, about vampires and werewolves and monsters – and we need to make sure that what's going on out there isn't part of that truth. If it's a human problem, the police wil deal with it. But if it's something else, it's our responsibility.† â€Å"Honestly,† grumbled Bonnie, already reaching for her clothes, â€Å"you two have a – a saving-people complex or something. After I take psychology, I'm going to diagnose you.† â€Å"And then we'l be sorry,† Meredith said agreeably. On their way out the door, Meredith grabbed the long velvet case that held her fighting stave. The stave was special, designed to fight both human and supernatural adversaries, and was made to specifications handed down through her family for generations. Only a Sulez could have a staff like this. She caressed it through the case, feeling the sharp spikes of different materials that dotted its ends: silver for werewolves, wood for vampires, white ash for Old Ones, iron for al eldritch creatures, tiny hypodermics to fil with poisons. She knew she couldn't take the stave out of its case on the quad, not surrounded by police officers and innocent bystanders, but she felt stronger when she could feel the weight of it in her hand. Outside, the mugginess of the Virginia September day had given way to a chil y night, and the girls walked quickly toward the crowd around the quad. â€Å"Don't look like we're heading straight over there,† Meredith whispered. â€Å"Pretend we're going to one of the buildings. Like the student center.† She angled off slightly, as if she was heading past the quad, and then led them closer, glancing over at the police tape surrounding the grass, pretending to be surprised by the activity next to them. Elena and Bonnie fol owed her lead, looking around wide-eyed. â€Å"Can I help you ladies?† one of the campus security men asked, stepping forward to block their progress. Elena smiled at him appealingly. â€Å"We were just on our way to the student center, and we saw everyone out here. What's going on?† Meredith craned her head to look past him. Al she could see were groups of police officers talking to one another and more campus security. Some officers were on their hands and knees, searching careful y through the grass. Crime scene analysts, she thought vaguely, wishing she knew more about police procedure than what she'd seen on TV. The security officer stepped sideways to block her view. â€Å"Nothing serious, just a girl who ran into a bit of trouble walking out here alone.† He smiled reassuringly. â€Å"What kind of trouble?† Meredith asked, trying to see for herself. He shifted, blocking her line of sight again. â€Å"Nothing to worry about. Everyone's going to be okay this time.† â€Å"This time?† Bonnie asked, frowning. He cleared his throat. â€Å"You girls just stick together at night, okay? Make sure to walk in pairs or groups when you're out around campus, and you'l be fine. Basic safety stuff, right?† â€Å"But what happened to the girl? Where is she?† Meredith asked. â€Å"Nothing to worry about,† he said, more firmly this time. His eyes were on the black velvet case in Meredith's hand. â€Å"What have you got in there?† â€Å"Pool cue,† she lied. â€Å"We're going to play pool in the student center.† â€Å"Have a good time,† he said, in a tone of voice that was clearly a dismissal. â€Å"We wil ,† Elena said sweetly, her hand on Meredith's arm. Meredith opened her mouth to ask another question, but Elena was pul ing her away from the officer and toward the student center. â€Å"Hey,† Meredith objected quietly, when they were out of earshot. â€Å"I wasn't done asking questions.† â€Å"He wasn't going to tel us anything,† Elena said. Her mouth was a grim straight line. â€Å"I bet a lot more happened than someone getting into a little trouble. Did you see the ambulances?† â€Å"We're not real y going to the student center, are we?† Bonnie asked plaintively. â€Å"I'm too tired.† Meredith shook her head. â€Å"We'd better loop back behind the buildings to our dorm, though. It'l look suspicious if we head right back where we came from.† â€Å"That was creepy, right?† Bonnie said. â€Å"Do you think† – she paused, and Meredith could see her swal ow – â€Å"do you think something real y bad happened?† â€Å"I don't know,† Meredith said. â€Å"He said a girl ran into a little bit of trouble. That could mean anything.† â€Å"Do you think someone attacked her?† Elena asked. Meredith shot her a significant look. â€Å"Maybe,† she said. â€Å"Or maybe something did.† â€Å"I hope not,† Bonnie said, shivering. â€Å"I've had enough somethings to last me forever.† They'd crossed behind the science building, down a darker, lonelier path, and circled back toward their dorm, its brightly lit entryway like a beacon before them. Al three sped up, heading for the light. â€Å"I've got my key,† Bonnie said, feeling in her jeans pocket. She opened the door, and she and Elena hurried into the dorm. Meredith paused and glanced back toward the busy quad, then, past it, at the dark sky above campus. Whatever â€Å"trouble† had happened, and whether the cause was human or something else, she knew she needed to be in top condition, ready to fight. She could almost hear her father's voice saying, â€Å"Fun time is over, Meredith.† It was time to focus on her training again, time to work toward her destiny as a protector, as a Sulez, to keep innocent people safe from the darkness.