Data Mining Techniques in Airline Industry

Data Mining Techniques in Airline Industry Purpose and Scope All around the world, the airline industry could be described in few words, which is intensely competitive and dynamic. The airline industry generates billions of dollars every year but still has a cumulative profit margin of less than 1%1. Many Airlines are trying to recover from deep debt. The reasons for these are multifold- fuel prices, high cyclicality and seasonality, fierce competition, high fixed costs and many other issues related to security and passengers safety. To ensure for the best economic outcome, Airline companies are trying with their most creative asset data. Data used in conjunction with data mining techniques allows comprehensive intelligent management and decision-making system. Achieving these benefits in a timely and intelligent manner may help in resulting lower operating costs, better customer service, market competitiveness, increased profit margin and shareholder value gain. This purpose of this paper is to demonstrate the applications of data mining techniques on multiple aspects of airline business. For example, to predict the number of domestic and international airline passengers from a specific city/airport, to dynamically price the tickets depending on seasonality and demand, to explore the frequent flyer database to prepare for CRM implementation, to makes the operational decisions about catering, personnel, and gate traffic flow, to assist the security agencies for secure and safe flights for the passenger specially after 9/11 incident. Predict the Number of Passenger by applying Data Mining Technique Forecasting is critical to any business for planning and revenue management, especially in the Airline industry, where a lot of planning is required to buy/lease new aircrafts, to hire crew members, to find the new slots in busy airports and to get the approvals from many aviation authorities. In the case of Air travel, lot of seasonality and cyclicality involved. Passengers are more likely to fly to some destinations based on the time of the year. Business travelers are likely to travel weekdays than weekends. Early morning and evening flights are desired by business travelers who want to accomplish a days work at their destination and return the same day. To forecast the number of passenger, artificial neural network (ANN) can be used. The purpose of a neural network is to learn to recognize patterns in a given data. Once the neural network has been trained on samples of the given data, it can make predictions by detecting similar patterns in future data. The growth factors which might influence the air travel demand depend on several things. Mauro Calvano2 in his study of transport Canada aviation forecast 2002-2016 considered 12 major socio-economic factors as follows: GDP Personal Disposable income Adult Populations US economic Outlook Airline Yield Fleet/route structure/Average Aircraft Size Passenger Load factors Labor cost and productivity Fuel cost/Fuel efficiency Airline cost other than Fuel and Labor Passenger Traffic Allocation Assumptions New technology Factors 1 to 5 are related demand side of the forecast Factors 6 to 10 are related to operations and supply side Factors 10 and 11 represent the structural changes This historical data is called the estimation set. A fraction of the overall available data is reserved for validating the accuracy of the developed forecast model. This reserved data set is called the forecasting set because no information contained in it is used in any form during the development of the forecast model. The data in the forecasting set are used for testing the true extrapolative properties of the developed forecast model. The estimation set is further divided into a training set and a testing set. Information in the training set is used directly for the determination of the forecast model, whereas information in the testing set is used indirectly for the same purpose. Figure1: Forecasting Process Model For a given ANN architecture and a training set, the basic mechanism behind most supervised learning rules is the updating of the weights and the bias terms, until the mean squared error (MSE) between the output predicted by the network and the desired output (the target) is less than a pre-specified tolerance. Neural networks are can be represented as layers of functional nodes. The most general form of a neural network model used in forecasting can be written as: Y = F [H1 (x), H2 (x), à ¢Ã¢â€š ¬Ã‚ ¦. , Hn (x)]+ u Where, Y is a dependent or output variable, X is a set of input/ influencing variables, F Hs are network functions, and u is a model error. This input layer is connected to a hidden layer. Hs are the hidden layer nodes and represents different nonlinear functions. Each node in a layer receives its input from the preceding layer through link which has weights assigned, which get adjusted using an appropriate learning algorithm and the information contained in the training set. Figure2: ANN Architecture Abdullah Omer BaFail3 did the study to forecast the number of airline passenger in Saudi Arabia. He selected the most influencing factors to forecast the number of domestic passengers in the different cities of Saudi Arabia. For Dhahran he selected factors like: Oil gross domestic product for last 6 years, private non-oil gross domestic product, Import of goods and services for last 10 years, and population size for last 2 years. The domestic and international actual and forecasted number of passengers for the city of Dhahran for the years 1993 through 1998 is shown below. Forecasts underestimated the actual travel. The Mean Absolute Percentage Error (MAPE) for domestic travel is about 10%, while for international travel is about 3%. Figure3: Forecasting results from Abdullah Omer BaFail3 The take away from the Abdullah Omer BaFail3 for me is that the efficient forecasting model can be invented using ANN if we using the right influencing indicators. In this study some indicators which influence are oil gross domestic product and per capita income in the domestic and international sectors. In view of the fluctuating nature of the passenger usage of airline services in Saudi Arabia, certain suggestions were made. Most of these recommendations were in order to improve the flexibility of the system to the fluctuations in demand and supply. Hub and spike model was also suggested as solutions in certain sectors to increase the flexibility in adjusting their capacity allocations across markets as new information about demand conditions become available. Application of Data Mining technique to predict the Airline Passengers No-show Rates Airlines overbook the flights based on the expectation that some percentage of booked passengers will not show for each flight. Accurate forecasts of the expected number of no-shows for each flight can increase airline revenue by reducing the number of perishable seats (empty seats that might otherwise have been sold) and the number of involuntary denied boardings at the departure gate. Typically, the simplest way is to go for average no-show rates of historically similar flights, without the use of passenger-specific information. Lawernce, Hong, Cherrier4 in their research paper predicted the no-show rates using specific information on the individual passengers booked on each flight. The Airlines offer multiple fares in different booking class. The number of seats allocated to each booking class is driven by demand for each class, such that revenue is maximized. For example, few seats can be kept on hold for the last-minute travelers with high fares and number of seats sold in lower-fare classes earlier in the booking process. Terms and conditions of cancellation and no-show also vary in each class. The no-shows results in lost revenue if the flight departs with empty seats that might otherwise have been sold. Near accurate forecasts of the expected number of no-shows for each flight are very much desirable because the under-prediction of no-shows leads to loss of potential revenue from empty seats, while over-prediction can produce a significant cost penalty associated with denied boardings at the departure gate and also create customer dissatisfaction. In the simplest model, the overbooking limit is taken as the capacity plus the estimated number of no-shows. Bookings are offered up to this level. No-shows numbers are predicted using time-series methods such as taking the seasonally weighted moving average of no-shows for previous instances of the same flight. Figure4: No-show trend over days to departure Source: Lawernce, Hong, Cherrier4 The simple model does not take account of specific characteristics of the passengers. Lawernce, Hong, Cherrier4 in his study used classification method, similarly Kalka and Weber5 at Lufthansa used induction trees to compute passenger-level no-show probabilities, and compared their accuracy with conventional, historical-based methods. I tried to summarize Lawernce, Hong, Cherrier4 approach and results briefly below. Whenever a ticket is booked the Passenger Name Records (PNRs) is generated and all the passenger information is recorded. The PNR data includes, for each passenger, specifics of all flights in the itinerary, the booking class, and passenger specific information such as frequent-flier membership, ticketing status, and the agent or channel through which the booking originated. Each PNR is also specified whether the passenger was a no-show for the specified flight. In the simplest model the mean no-show rate over a group of similar historical flights is computed. The mean in turn used to predict the number of no-shows over all booking classes. The passenger-level model given by can be implemented using any classification method capable of generating the normalized probabilities. The PNR records are partitioned into segments, and separate predictive models are developed for each segment. In the passenger-level modeling we characterize each using the PNR details. Let Xi; i = 1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦..I denote I features associated with each passenger. Combining all features yields the feature vector X = [X1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Xi ] Each passenger, n = 1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦.N, booked on flight m is represented by the vector of feature values xmn = [xmn, 1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦ xmn, ià ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦.. xmn, I ] We know the predicted no-show rate from the historical model; it is assumed the passenger inherits the no-show rate. The passenger level predictive model is then stated as follows: given a set of class labels cmn a set of feature vectors xmn and a cabin level historical prediction  µmhist predict the output class of passenger n on flight m: P(C = cmn |  µmhist , X= xmn ) We are specifically interested in the no-show probability, cmn = NS, and write this probability in the simplified form P(NS |  µmhist , xmn ) The number of no-shows in the cabin is estimated as à ¢Ã‹â€ Ã¢â‚¬Ëœ P(NS |  µmhist , xmn ) The summing of probabilities for each passenger in the cabin, gives no-show rate for the cabin. An analogous approach can also be used to predict no-show rates at the fare-class level. Lawernce, Hong, Cherrier4 compare results computed using the historical, passenger-level, and cabin-level models. The models were built using approximately 880,000 PNRs booked on 10,931 flights, and evaluated against 374,900 PNRs booked on 4088 flights. The figure shows a conventional lift curve computed using the three different implementations of the passenger-level model. Figure 5: Gain Charts Source: Lawernce, Hong, Cherrier4 Each point on the lift curve shows the fraction of actual no-shows observed in a sample of PNRs selected in order of decreasing no-show probability. The diagonal line shows the baseline case in which it is assumed that the probabilities are drawn from a random distribution. The three implementations of the passenger-level model identify approximately 52% of the actual no-shows in the first 10% of the sorted PNRs. This is one of the way the Airlines can incorporate data mining models incorporating specific information on individual passengers can produce more accurate predictions of no-show rates than conventional, historical based, statistical methods. Application of Data Mining technique to Strategies Customer Relationship Management In the current time most of the industries using frequency marketing programs as a strategy for retaining customer loyalty in the form of points, miles, dollars, beans and so on. Airlines are a big fan of this Kingfishers Kingmiles, Jet Airways Jet Privilege, American Airlines AAdvantage, Japan Airlines Mileage Bank, KrisFlyer Miles etc. they all seemed to have carved their own identities. Frequent Flyer Program presents an invaluable opportunity to gather customer information. It helps to understand the behavioral patterns, unveil new opportunities, customer acquisition and retention opportunities. This helps Airlines to identify the most valuable and the appropriate strategies to use in developing one-to-one relationships with these customers. The objective of data mining application over the frequent flyer customer data could be many, but ideally it is as follows: Customer segmentation Customer satisfaction analysis Customer activity analysis Customer retention analysis Some of the examples in each category are: Classify the customers into groups based on sectors most frequently flown, class, period of year, time of the day, purpose of the trip. Which types of customers are more valuable? Do most valuable customers receive the value for money? What are the attributes and characteristics of the most valuable customer segments? What type of campaign is appropriate for best use of resources? What are the opportunities to up-selling and cross-selling, for example hotel booking, upgrade to next class, credit card, etc. Design packages or grouping of services Customer acquisition. Yoon6 designed a database knowledge discovery process consisting of five steps: selecting application domain, target data selection, pre-processing data, extracting knowledge, and interpretation and evaluation. This study refers to the Yoon process to deal with three mining phases, including the pre-process, data-mining, and interpretation phases for airlines, as illustrated in figure below. Figure 6: database knowledge discovery process Source: Yoon6 Some straightforward solution can be implemented that can also be scaled-up in future like K-means, Kohonen self-organizing networks and classification trees. In the case of K-means algorithm, it is applied on customer data, assigning each to the closest existing cluster center. The K- means model is run with different cluster number until K-means clusters are well separated. In the case of classification trees (C5.0), we derive a simple rule set to uniquely classify the complete database. Again, we have to generate the attributes, resulting from the sequence of flight segments. The accuracy of the forecast for each segment is provided by balancing the training set according to equally sized clusters. We regulate the number of subsequent rules, while determining a minimal numbers of records given within each subgroup. Maalouf and Mansour7 did the study based on 1,322,409 customer activities transactions and 79,782 passengers for a period of 6 years. They prepared Data based on Z-Score Normalization and ran the multiple queries and transformed the data to create the clustering input records. They used K-means and O-Cluster algorithms. The result generated by clustering provides customer segmentation with respect to important dimensions of customers needs and value. The table below is the result is a summary of the profile produced by k-means clustering that includes: revenue mileage, number of services used, and customer membership period. Figure 7: Clustering result on Airline Customer Data Source: Maalouf and Mansour7 The results generated by k-means clustering are used as a basis for the association rules algorithm. Two different scenarios have been applied. The first scenario is based on Financial, Flight, and Hotel activities with 1,896 records. The second scenario is based on the flight activities especially the sectors, with 1,867 records. Figure 8: Association rules for best customer activities Source: Maalouf and Mansour7 Some of the take way from Meatloaf and Mansour7 study. Clustering using k-means algorithm generated 9 different clusters with specific profile for each one. From the cluster analysis it can be found which are the best customer clusters (higher mileage per passenger) than other clusters. Need a retention strategy for these clusters. Cross Selling strategies can be formulated between the clusters (for example between: 15 and 11; 13 and 17 because they are close in services value. The cluster analysis provides an opportunity for the airline to produce more revenue from a customer. For example, the airline could apply an up-selling strategy by selling a higher fare seat depending on the clusters. From the cluster analysis Airline may adopt an enhanced strategy for customers in clusters in order to increase services usage and revenue mileage per passenger. Plan for marketing campaign or special offers by analysis through association rules, for example, the customers using the Flight and Financial services never use the Hotel Services and the customers using the Flight and Hotel services never use the Financial Services. By analyzing the services used in different clusters, Airline can characterize services integration. It enables the airline to serve a customer the way the customer wants to be served. Application of Data Mining Application technique to understand the Impacts of Severe Weather Severe weather has major impacts on the air traffic and flight delays. Appropriate proactive strategies for different severe-weather days may result in improvement of delays and cancellations. Thus, understanding en-route weather impacts on flight performance is an important step for improving flight performance. Zohreh and Jianping8 in their study proposed a framework for data mining approach to analysis of weather impacts on Airspace system performance. This approach consists of three phases: data preparation, feature extraction, and data mining. The data preparation phase includes the usual process of selection of data sources, data integration, and data formatting. Figure 9: Framework proposed by Zohreh and Jianping8 He used three data sources: Airline Service Quality Performance (ASQP), Enhanced Traffic Management System (ETMS), and National Convective Weather Forecast (NCWF) supplied by National Center for Atmospheric Research. He used NCWF data from April through September 2000 to represent the severe weather season. These data-sets included the scheduled and actual departure and arrival times of each flight of ten reporting airlines, tail number, wheels off/on times, taxi times, cancellation and diversion information, planned departure and arrival times, actual departure and arrival times, planned flight routes, actual flight routes, and cancellations, flight frequencies between two airports, intended flight routes between two airports, flight delays, flight cancellations, and flight diversions. The image segmentation phase resulted in a set of severe-weather regions. Then for each of these regions, a set of weather features and a set of air traffic features are extracted. A day is described by a set of severe-weather regions, each having a number of weather and traffic features. As a result of this study it was found that there is strong correlation of blocked flights, #of bad weather regions, bad weather airports, blocked distance, bad weather longitude, by pass distance, bad weather latitude, # of bad weather pixels with flight performance. Similarly the clustering algorithms (like K-means) can be applied. The expectation is that the same clusters have similar weather impacts on flight performance. Zohreh and Jianping8 generated clusters for the entire airspace It was found that a cluster with worse weather almost always had bad performance. The clusters with large percentage of blocked flights, bypass distance, and blocked distance had a worse performance. These results were promising and showed that days in a cluster have similar weather impacts on flight performance Other data mining approach which can be applied is Classifications. Application of Classification can help us discover the patterns/rules that have significant impact on the flight performance. Discovered rules may be used to predict if a day is a good or a bad performance day based on its weather. For example Rule for Good: if %BlockedFlights and BypassDistance then Good (n, prob) There can be different ways where we can apply data mining approach to analysis of weather impact on airline performance. It seems to be that results obtained from clustering and classifications were very meaningful for airline and passengers to plan ahead. Application of Data Mining techniques to ensure safety and security of Airlines passenger The reaction of the terrorist attack on 26/9 and 11/9 resultant in increase Security at airports: It ends up allowing only ticketed passengers past the security gates, screen carry-on luggage more carefully for possible weapons. The question is whether these steps could have avoided the attacks, the people involved in the attack had legitimate tickets, and carrying box cutters and razor blades (like in any other normal person would do). The uncommon was the combination of their characteristics, like none were U.S. citizens, all had lived in the U.S. for some period of time, all had connections to a particular foreign country, all had purchased one-way tickets at the gate with cash. With the amount of data available about the passenger during ticketing, the can be reviewed to characterize relevant available passenger information. Given a passengers name, address, and a contact phone number, various data bases (public or private) can identify the social security number (SSN), from which much information will be readily available (credit history, police record, education, employment, age, gender, etc.). Since there is large number of characteristics available on both individual passengers, it will be important to identifying signals within the natural variability or noise. If predicted wrong, this may lead to either falsely detaining an innocent passenger or failing to detain a plane that carries a terrorist. The airlines already collect much data on various flights. When the data come in the form of multiple characteristics on a single item, exploratory tools for multivariate data can be applied, such as classification, regression trees, multivariate adaptive regression splines/trees. The security of the air transportation can be improved substantially through modern, intelligent use of pattern recognition techniques applied to large linked databases. Similarly Data mining techniques can be used for the Safety of the passenger. An air safety office plays a key role in ensuring that an aviation organization operates in a safe manner. Currently, Aviation Safety offices collect and analyze the incident reports by a combination of manual and automated methods.. Data analysis is done by safety officers who are very familiar with the domain. With Data mining one can find interesting and useful information hidden in the data that might not be found by simply tracking and querying the data, or even by using more sophisticated query and reporting tools. In a study done by Zohreh Nazeri, Eric Bloedorn, Paul Ostwald10 it was found that finding associations and distribution patterns in the data, bring important inside. The other finding is Linking the incident reports to other sources of safety related data, such as aircraft maintenance and weather data, could help finding better causal relationships. SumMRry Business Intelligence through efficient and appropriate Data mining application can be very useful in the Airline industry. The Appropriate action plans from the data mining analysis can result in improved customer service, help generating considerable financial lift and set the future strategy.

Multiply and Accumulate Unit using Vedic Multiplier

Multiply and Accumulate Unit using Vedic Multiplier Design  and  Implementation  of  FPGA  based  64  bit  MA Cunitusing  VEDIC  Multiplier  and  Reversible  Logic  gates ABSTRACT: Now a days in VLSI technology size, power, and speed are the main constraints to design any circuits. In normal multipliers delay will be more and the number of computations also will be more. Because of that speed of the circuits designed with the normal multipliers will be low and it will consume more power. This paper describes Multiply and Accumulate Unit using Vedic Multiplier and DKG reversible logic gates. The Vedic multiplier is designed by using Urdhava Triyagbhayam sutra and the adder design is done by using reversible logic to perform high  speed operations. Reversible logic gates are also the  essential constraint for the promising field of Quantum computing. The Urdhava Triyagbhayam multiplier is used for the multiplication function to reduce partial products in the multiplication process and to get high concert and less area .The reversible logic is used to get less power. The MAC is designed using Verilog code, simulation,synthesis is done in both RTL compiler using Xilinx and implemented on Spartan 3e FPGA Board. KeyWords:MAC, Vedic multiplier, Reversible Gates I. INTRODUCTION Multiplication is the key in arithmetic operation and multiplier plays an important role in digital signal Processing. Unfortunately, the major source of power dissipation in digital signal processors is multipliers. In the past decade researchers developed multipliers with the help of CMOS logic which has all the disadvantages as discussed earlier. Therefore multipliers design for digital signal processing applications should be competent. So the proposed method is designed using pass logic principles, which shows improvements over CMOS designs. Pass logic principle based circuits are capable to accomplish superior performance in power, speed and area when implemented in VLSI[1]. Several case studies show that pass logic principle based design implements most functions with fewer transistors which reduces the overall capacitance than static CMOS; thus, resulting in low power and fast switching time. The Pass logic  standard based design is a capable, due to its better performance in power consumption, area and speed. thirty percent of the multiplier space is taken by the Booth encoder and selector logic [1-3]. So a improved design of Booth encoder and selector is essential. The main objective of this work is to design and implement new Booth encoders and selector logics which are hardware efficient and consequently power- aware.Various designs of these logic units are proposed in this work where the number of transistors needed are less when compared to previously designed units.The gate level implementations of these designs were tested for functionality using LoKon software gates (XNOR, XOR , NAND,NOR,AND,XOR-XNOR combination gate) and MUX used in these circuits were simulated and verified for functionality using TopSPICE. Due to the limitation in the numbers of transistor count in the TopSPICE, it was not capable to simulate the entire circuit in the transistor level. Further, these designs were used to build multiplier[2]. Multiplier is the need for higher word width for signal process applications. This design is scalable without any loss of merits. All the pass transistor circuits have been tested for fully restored voltage at the output[3]. Therefore, when these circuits are combined to form the whole multiplier voltage drop will not cause a problem. II. LITERATURE REVIEW Nareshnaik, SivaNagendra Reddy proposed Design of Vedic Multiplier for Digital Signal Processing Applications[1] .In this method design of adders is difficult and design may be complex and also its require more power. Anitha, Sarath Kumar proposed A 32 BIT MAC Unit Design Using Vedic Multiplier and Reversible Logic Gate design.In this paper they designed for 32 bit Multiplier.But most of the multipliers used in Digital signal processing applications 64 bit multipliers. So many researchers proposed many methods to design multipliers and adders.Among all the methods multiplier design with reversible logic gate design is the efficient method.In reversible gates also different reversible gate are available[4].Some researchers used Kogge stone Adders,some one used Toffiligates[5].DKG is the one of the gate used in the MAC design.This proposed method represents 64 bit MAC design using reversible logic gates. III. PROPOSEDMETHOD Multiply Accumulate (MAC) unit is designed by using Multipliers and adders both will be joined by an accumulate unit. The applications of MAC unit are Digital Signal Processors, microprocessors, and logic units and.MAC determines the speed and improves the performance of the entire system[6]. The proficient designs by MAC unit are Fast Fourier Transform(FFT/IFFT) ,Discrete Cosine Transform (DCT). Since, they are normally executed by inflexible application of multiplication and addition, the total system speed and performance depends on the speed of the addition and multiplication process speed in the system[7]. In most cases the delay in the architecture is due to the addition in parallel stages which we have to concentrate more to improve the speed. Finally we are going to compare our Vedic MAC unit with the Conventional MAC unit based on the parameters like Speed,area and power consumption[8]. A multiplying blockfunction can be conceded in threedifferent ways: conventional addition, partial product addition (PPA) and finally partial product Generation (PPG). The two bud vase materials that must be considered are raising the speed of MAC which is accumulator block partial and product reduction[9]. The 64 bit MAC design which will make use of Vedic multiplier and reversible logic gate can be accomplished in two stages. Firstly, multiplier stage, where a usual multiplier is replaced by Vedic multiplier using UrdhavaTriyagbhayam sutra from Vedic Mathematics.Multiplication is the primary operation of MAC unit. Speed, area, Power dissipation, consumptionand latency are the major concerns in the multiplier stage. So, to evade them, we will go for fast multipliers in different applications of DSP, networking, etc. There are mostly two major criterions that can possibly improve speed of the MAC units are sinking the partial products and because of that accumulator yoke is getting d ecreased. To perform the multiplication of N*N it requires approximately 2N-1 cross products of different widths and (log2N + 1) partial products. The partial products are obtained from Urdhava sutra is by Criss Cross Method. The maximum number of bits in partial products will lead to Critical path. The second part of MAC is Reversible logic gate. Loss of every bit of information in the computations that are not reversible is kT*log2 joules of heat energy are generated, where k is Boltzmanns constant and T the absolute temperature at which computation is performed. IV. DESIGN OF MAC ARCHITECTURE Fig 1: MAC Architecture The design of MAC architecture consists of 3 sub designs. Design of 64 X 64 bit Vedic Multiplier. Design of 128 bit DKG adder Design of Accumulator which integrates both multiplier and adder stages. Vedic Multiplier Vedic Mathematics is part of four Vedas  (books of wisdom). It is part of Sthapatya- Veda (book on civil engineering and architecture), which is an upa- veda (supplement) of Atharva Veda. Vedic Mathematics existed in ancient India and was revived by a popular mathematician, Sri Bharati Krishna Tirthaji. He divided Vedic mathematics into sixteen formulae(sutras). These formulae deal with Algebra, Analytical Geometry, Algebra, Trigonometry, Geometry etc. The ease in the Vedic mathematics sutras covers way for its application in several prominent domains of engineering like Signal Processing, VLSI and Control Engineering . 1) (Anurupye) Shunyamanyat 2) ChalanaKalanabyham 3) EkadhikinaPurvena 4) EkanyunenaPurvena 5) Gunakasamuchyah 6) Gunitasamuchyah 7) NikhilamNavatashcaramamDashatah 8) ParaavartyaYojayet 9) Puranapuranabyham 10) Sankalana- vyavakalanabhyam 11) ShesanyankenaCharamena 12) ShunyamSaamyasamuccaye 13) Sopaantyadvayamantyam 14) Urdhva-tiryakbhyam 15) Vyashtisamanstih 16) Yaavadunam Vedic Maths can be divided into sixteen different sutras to perform mathematical operations. Among these surtras the Urdhwa Tiryakbhyam Sutra is one of the most highly preferred algorithms for performing multiplication[11-14]. The algorithm is competent enough to be employed for the multiplication of integers as well as binary numbers. The term UrdhwaTiryakbhyam originated from 2 Sanskrit words Urdhwa and Tiryakbhyam which mean vertically and crosswise respectively.The mainadvantage of utilizing this algorithm in comparison with the existing multiplication techniques, is the fact that it utilizes only logical AND operations, half adders and full adders to complete the multiplication operation. Also, the partial products required for multiplication are generated in parallel and apriority to the actual addition thus saving a lot of processing time[15-17]. UrdhwaTiryakbhyam Algorithm Let us consider two eight bit numbers X(7:0) and Y(7:0) , where 7 signify Most Significant Bit and 0 represent Least Significant Bit. P0 to P15 signify each bit of the final computed product. It can be seen from equation (1) to (15), that P0 to P15 are calculated by adding partial products, which are calculated previously using the logical AND operation. The individual bits obtained from equations (1) to equation (15), in turn when concatenated produce the final product of multiplication which is represented in equation (16).The carry bits generated during the computation of the individual bits of the final product are represented from C(1) to C(30). The carry bits generated in (14) and (15) are ignored since they are redundant. Fig 2 : Pictorial Illustration of UrdhwaTiryakbhyam Sutra Graphically exemplifies the step by step procedure of multiplying two eight bit numbers using the Urdhwa Tiryakbyam Vedic Multiplication Sutra[20]. The black circles specify the bits of the multiplier and multiplicand, and the two-way arrows specify the bits to be multiplied in order to arrive at the individual bits of the final product. The hardware architecture of the 88 Urdhwa multiplier has been designed and shown in Fig 2. à ¯Ã¢â‚¬Å¡Ã‚ · Lowest quantum cost DKG Gate A 4 X 4 reversible DKG gate that preserve work singly as a reversible full adder and parallel adder is shown in below fig 5. If input A is zero, the DKG gate performed Full adder operation, and if input A is 1 then reversible logic gate performed Full subtractor operation. It has been confirmed that a reversible full- adder circuit requires at least two or three garbage outputs to make the distinctive[10][19]. output combinations Fig 3: 32 ÃÆ'-32 Vedic multiplier using 16 ÃÆ'- 16 Vedic multiplier Fig 4: 64ÃÆ'- 64 Vedic multiplier using 32x32Vedic multiplier V. DESIGN OF ADDERUSING Fig. 5a DKG gate Fig. 5b Parallel adder using DKG gate AccumulatorStage: Accumulator has an significant role in the DSP applications in different ranges. The register designed REVERSIBLE LOGICDKGGATE in the accumulator is used to add the multiplied Reversible logic is a distinct method diverse from other logic). Loss of information is not probable numbers. Multiplier, adder and an accumulator are forming the vital establishment for the MAC unit. The conventional MAC unit has a multiplic and and here. In this logic, the numbers of outputs are identical multiplier to do the basic multiplication and some to the number of inputs. General consideration for reversible logicgate parallel adders to add the partial products generated in  the previous step. To get the final multiplication output A Boolean function is reversible if and only if  we add the partial product to these results. Vedic all the values in the input set can be mapped with a single value in the output position. Landauer and Multiplier has put forward to intensify the action of the MAC Unit. Bennet both demonstrated that conventional irreversible circuits will  the usage of construct us to VI. RESULTS AND DISCUSSION power dissipation a circuit consisting of only reversible gates does not dissipate power. The following points necessity be reserved in mind to realize an optimized circuit: à ¯Ã¢â‚¬Å¡Ã‚ · Loops are not authorized à ¯Ã¢â‚¬Å¡Ã‚ · Minimum delay à ¯Ã¢â‚¬Å¡Ã‚ · Zero energy dissipation Fig 6: RTL Schematic of MAC Unit à ¯Ã¢â‚¬Å¡Ã‚ · Fan-out is not authorized The modified 64 bit  multiplier using Vedic à ¯Ã¢â‚¬Å¡Ã‚ · Garbage outputs must be small  multiplier and DKG adder is fast and design of MAC done using Xilinx. The above fig 7shows comparison between Verilog code using Xilinx. The below fig 6 shows the RTL Schematic of the proposed design. Logic Utilization 70000 No.of Slice FlipFlops 60000 No.of 4 input LUTs 50000 MAC design unit using different Adders. The number of LUTs and utilization of logic blocks in MAC design using CSA, RCA, KSA will be greater than DKG and speed is also more in MAC design using DKG. But it will take more area. Compare to array multipliers, baugh wooley multipliers and booths multipliers Vedic multipliers requires less area and performs operations at high speed. The below fig 8 shows the statistics results of MAC design Vedic Multiplier with different adders. In which DKG Adders has moderate delay. But it consumes very less power and it can be designed in small area. 40000 Number of occupied Slices Number of Slices containing only related logic 1000 900 800 30000 20000 10000 Number of Slices containing unrelated logic Total Number of 4 input LUTs Number used as logic Number used as Shift Registers 700 600 500 400 300 200 100 MAC Design using RCA MAC Design using CSA MAC Design using KSA MAC Design using DKG 0 Number of nonded 0 IOBs Number of BUFGMUXs Average Fanout of non-Clock Nets Fig7: Synthesis report of 64-bit MAC using Vedic Multiplier using RCA,DKG and KSA Reversible logic gates Fig8: Delay Analysis report of 64-bit MAC using Vedic Multiplier using RCA,DKG and KSA Reversible logic gates in table 2. By Combining the Vedic and reversible logic will direct to new and competent attainments in developing various fields of digital signal processing Applications. Fig 9: Simulation result of Adder The above fig 9 shows that simulation result of DKG adder. It is a 32 bit adder. In this design we used two 64 bit adders. This adder has two inputs a and b ,two outputs sum and carry. For adder a =19997091 and b= 0001fffdapplied.Which results sum is 0199b708e and carry is 0. Fig 10: Vedic Multiplier result of 64 bit MAC unit The above fig 10 shows the simulation result of 64 bit MAC design unit. For this design we applied two inputs. In which values are a=12345678 and b=78945612 and it will give result of 55bed11b057ec60. Fig 11: Vedic Multiplier result of 64 bit MAC unit on FPGA CONCLUSION AND FUTURE SCOPE The results of this proposed 64 bit Urdhava Triyagbhayam Vedic multiplier with DKG adder are quite good. Design of MAC unit structure and its performance has been scrutinize for all the blocks. Therefore, the 64-bit Urdhava Triyagbhayam sutra Multiplier and reversible logic is the best in all aspects like speed power product ,delay, area and complication as compared to all other architectures which are shown

THROAT CANCER Essay -- essays research papers

Throat Cancer Each year, smoking kills more people than AIDS, alcohol, drug abuse, car accidents, murder, suicide, and fires---combined! The use of tobacco increases the risk of contracting throat cancer. Throat cancer is one of the most prevalent cancers within the United States. Cancer of the larynx (or voice box) is a disease in which cancer (malignant) cells are found in the tissues of the larynx. The larynx is a short passageway shaped like a triangle that is just below the pharynx in the neck. The pharynx is a hollow tube about 5 inches long that begins behind the nose and goes down to the neck to become part of the esophagus (the tube that goes to the stomach). Air passes through the pharynx and then the larynx on the way to the windpipe (trachea) and into the lungs. Food passes through the pharynx on the way to the esophagus. The larynx has a small piece of tissue over it, called the epiglottis which keeps food from going into it or the air passages. Throat cancer occurs when cells in the pharynx or larynx begin to divide abnormally and out of control. Most throat cancer begins on the vocal cords, later spreading to other parts of the larynx and pharynx. One of the most reliable and earliest symptom of throat cancer is sudden, unexplained hoarseness in the voice. About 10,000 new cases of throat cancer occur in the United States per year, generally affecting older males who use chewing tobacco, or those who have been heavy smokers (cigarettes, cigars and/or pipe smokers), or alcohol abusers. Studies indicate that the incidence of throat cancer in women and young people has increased due to the greater use of tobacco and alcohol products in this bracket. Throat cancer is more common in men, because smoking is a more common habit among males. Only one-fifth of the patients diagnosed with throat cancer will be women. Throat cancer is also closely associated with other cancers. Fifteen percent of throat-cancer patients also are diagnosed with cancer of the mouth, esophagus, or lung. Surprisingly another ten to twenty percent of these patients develop these cancers later on. Other people at risk include those who consume large quantities of alcohol, especially in combination with smoking. The most affective way to prevent throat cancer is to cease the use tobacco products entirely and cut back on alcohol. When throat cancer affects the vocal cords, the ... ...sed back up to produce sound for speech. This is a very difficult procedure to master for the common person. A popular method of speech production following laryngectomy is the voice restoration procedure. A puncture is placed through the back wall of the trachea windpipe into the esophagus and held open with a one-way valved prosthesis. By occluding the opening in the neck (tracheostoma), air is directed through the voice prosthesis into the throat causing vibration of the soft tissue and generating voice. Fluent speech that is usually readily understandable is produced (See picture #3). In conclusion, the evidence of the rise in the number of people being diagnosed with cancers caused by the use of tobacco products should be a sufficient warning to people to avoid the use of these products entirely. It is important to pay attention to symptoms that may indicate a potential problem and to seek medical attention as soon as possible. Family medical histories should be considered as well when reporting the development of unusual symptoms to the physician. As always, a healthy diet and regular exercise are the best preventative measures to take ensure a long and productive life.

Enzyme Experiments :: Free Essays

Enzyme Experiments Central Experiment: The Action Of An Enzyme, e.g., amylase converting starch to reducing sugar. 1. Substrate: starch - use the same volume of the same starch solution each time 2. Enzyme: amylase - use the same volume of the same amylase solution each time 3. Only use starch and amylase solutions that test negative for reducing sugar with Benedict's Reagent 4. Four test tubes individually one third full of (a) starch solution, (b) amylase solution, (c) starch solution and (d) water 5. Warm all to 37Â °C in a heated water bath at 37Â °C 6. Mix (a) and (b) - the experiment. Mix (c) and (d) - the control 7. Use a buffer to maintain the pH at a constant suitable value: pH 8 8. Every minute test a small sample of each for starch using iodine 9. Control Results: always blue-black - starch always present, starch not broken down 10. Experiment Result: eventually a yellow-brown colour - starch is not present, it has been broken down 11. Now test the experiment solution for reducing sugar using Benedict's Reagent. Result: brick-red colour formed - reducing sugar is now present 12. Repeat the entire procedure many times to verify the results 13. Conclusion: amylase converts starch to reducing sugar. Only when amylase is present does starch disappear and reducing sugar appear Note: Fehlings can be used to test for reducing sugar. Experiment: To Determine the Effect of Enzyme Concentration on the Rate of Enzyme Action. Same as the central experiment, at different amylase concentrations, as far as 10. Make up different dilutions of the amylase solution by adding increasing amounts of water.

Adoption of E-Prescribing in Healthcare Organizations Essay -- Health

Electronic prescribing (e-prescribing) has the ability to improve the quality, safety and cost effectiveness of health care. Experts have predicted that e-prescribing could avoid over 2 million adverse drugs events annually, of which 130,000 are life-threatening. This report defines e-prescribing and the challenges that may be faced in adopting this system. E-Prescribing The eHealth Initiative defines e-prescribing as â€Å"the use of computing devices to enter, modify, review, and output or communicate drug prescriptions.† (Electronic Prescribing, n.d.) There are varying levels of e-prescribing systems ranging from Level 1 through Level 6. Level 2, which is a â€Å"stand-alone† application, enables providers to order medication electronically but does not include medical patient information. Level 6, which is the most sophisticated, is an integrated system which uses other electronic databases such as the Electronic Health Record (EHR), pharmacy, and Pharmacy Benefit Manager (PBM) systems, which provide greater efficiency, patient safety and the largest return on the investment. The Medicare Part D prescription drug program more formally defines e-prescribing as: E-prescribing means the transmission, using electronic media, of prescription or prescription-related information between a prescriber, dispenser, pharmacy benefit manager or health plan, either directly or through an intermediary, including an e-prescribing network. E-prescribing includes, but is not limited to, two way transmissions between the point of care and the dispenser. This allows the physician to electronically access information regarding a patient’s drug benefit coverage, medication history (including adverse drug events), and to submit the pr... ...ful use incentives. HealthImaging. Retrieved January 23, 2012 from http://www.healthimaging.com/index.php?view=article&id=30707:nchs-more-physicians-applying-for Leavitt, M.O. (2007). Pilot Testing of Initial Electronic Prescribing Standards. Health Information Technology. U.S. Department of Health & Human Services. Retrieved January 23, 2012 from http://healthit.ahrq.gov/portal/server.pt?open=514&objID=5554&mode=2&holderDispla yURL=http://wci-p Popovich, M. (2011, November). e-Prescribing Essentials: Get the Most Out of E-Prescribing. Practical Dermatology. Retrieved January 23, 2012 from http://bmctoday.net/practicaldermatology/2011/11/article.asp?f=e-prescribing-essentials-get-the-mo Wolper, L.F. (2011). Health Care Administration: Planning, Implementing, and Managing Organized Delivery Systems (5th ed.). Sudbury: Jones & Bartlett Publishers.

Crude Oil Price Volatility Economics Essay

Energy plays the cardinal function in the universe economic system. In malice of considerable disposition to alternative renewable beginnings of energy like air current, H2O, atomic and solar power, the function of rough oil in macroeconomic motions has non waned yet ( Mehrara & A ; Mohaghegh, 2011 ) . Since the find and production of oil Nigeria in 1958, the subsector has continued to play a major and dominant function in the Nigerian economic system. The extractive sector in the Nigerian economic system is big and extended, with oil playing a critical function. With about 37.2 billion barrels in militias and 2.13 % of planetary production, Nigeria has the universe ‘s 10th largest proven militias ( 3.1 % of planetary militias ) , and is among the top 10 oil manufacturers ( Akinlo, 2012 ) . Nigeria has been a member of the Organization of the Petroleum Exporting Countries ( OPEC ) since July 1971. Presently, there are 12 member states including Nigeria in this international organisation whose purpose of the organisation, harmonizing to its Statute, is the finding of the best agencies for safeguarding their involvements, separately and jointly ; inventing ways and agencies of guaranting the stabilisation of monetary values in international oil markets with a position to extinguishing harmful and unneeded fluctuations ; giving due respect at all times to the involvements of the bring forthing states and to the necessity of procuring a steady income to the bring forthing states ; an efficient, economic and regular supply of crude oil to devouring states, and a just return on their capital to those puting in the crude oil industry ( OPEC, 2012 ) . Crude Oil is an of import trade good non merely because it is of course happening in the Earth but because of the many fractions that are derived from it after the refinement procedure. Those changing fractions such as fuel oil, jet fuel, kerosine etc. are extracted at different temperatures during the refinement procedure. The demand for rough oil is derived from the demand for its fractions and it depends on a big figure of factors such as its class, sulfur content and its locations. Crude Oil is classified in the international market harmonizing to changing molecular features by giving pricing mentions to the countries where such barrels are produced. Such pricing mentions include the North American West Texas Intermediate, Nigerian Bonny Light, Brent etc. In most oil exporting states, authorities which is well big in comparing with little private sector, straight receives the oil gross. Spending this gross, authorities ‘s behaviour becomes the most of import feature of the economic system. In other words, the financess needed for authorities ‘s outgo come from oil gross. So, financial and pecuniary policies depend upon oil monetary value ( Rosser & A ; Sheehan, 1995 ) . Oil monetary value is extremely variable ; even more than any other trade good ( Dehn, 2001 ) . Crude oil monetary values have increased on norm from US $ 25 per barrel in 2002 to US $ 55 per barrel in 2005. There has been a steep upward tendency in the monetary value of rough oil in recent old ages, making a record nominal high of US $ 147 in mid-2008 and a crisp bead to US $ 46 a barrel ( Akpan, 2009 ) . The transmittal mechanisms through which oil monetary values have impact on existent economic activity include both supply and demand channels. The supply side effects are related to the fact that rough oil is a basic input to production, and accordingly an addition in oil monetary value leads to a rise in production costs that induces houses to take down end product. Oil monetary value alterations besides entail demand-side effects on ingestion and investing. Consumption is affected indirectly through its positive relation with disposable income. Oil monetary value rises cut down the consumers passing power. Investing may besides be affected if the oil monetary value daze encourages manufacturers to replace less energy intensive capital for more energy-intensive capital. The magnitude of this consequence is in bend stronger the more the daze is perceived to be durable. For this ground, the theoretical literature has been of a general equilibrium nature, with different writers delega ting different weights to the supply and demand channels ( Olomola & A ; Adejumo, 2006 ) . When oil monetary value depreciates, big public sector expectedly can non cut down its disbursement instantly and proportionally ; so faces immense shortages. The financial instabilities followed by an oil monetary value lessening can be lay waste toing if the state is extremely dependent on oil grosss ; which is the instance in most oil exporting states. More disappointingly such falls are normally unpredictable. Several uncomplete undertakings and immense debts are the chief heritages of this period for the undermentioned fruitful epoch. After some rough experiences, presents, insulating the existent sectors of economic system from oil monetary value volatility is accepted as one of the most of import functions of authorities ( Mehrara & A ; Mohaghegh, 2011 ) . Presently, one-year budgets are formulated in Nigeria based on oil monetary values. The 2009 budget was revised in line with the predominating market monetary value of oil declarative mood of immense dependance of Nigeria on oil returns. In 2012, there was an intense argument refering the benchmark monetary value of Crude Oil that would be the footing of gross and outgo computations in the passing of the 2013 Appropriation Act ( budget ) through the Nigerian National Assembly. Huge influx of oil grosss in Nigeria are more frequently associated with enlargement in the degree of Government passing while periods of dwindling oil grosss are normally accompanied by budget shortages. There is no addition stating that Nigeria relies so much on gross from oil exports, but, it every bit massively imports refined crude oil and other related merchandises. Evidence, for case, shows that Government disbursement, which hitherto, before 1999 remained good below N0.5 trillion, hit N1.02 trillion grade in 2001 and N1.5 trillion in 2004. The figures for 2006 and 2007 stood at N2.04 and N2.45 trillion severally ( Aliyu, 2009 ) . Oil monetary value dazes are preponderantly defined with regard to monetary value fluctuations ensuing from alterations in either the demand or supply side of the international oil market ( Hamilton, 1983 ) . These alterations have been traditionally traced to provide side breaks such as OPEC supply quotas, political turbulences in the oil-rich Middle East and activities of hawkish groups in the Niger Delta part of Nigeria. The dazes could be positive ( a rise ) or negative ( a autumn ) . Two issues are identified sing the dazes ; foremost is the magnitude of the monetary value addition which can be quantified in absolute footings or as per centum alterations, second is the timing of the daze, that is, the velocity and continuity of the monetary value addition ( Akpan, 2009 ) . Traveling by the foregoing, four oil dazes can be observed in Nigeria. Each of the dazes had connexions with some motions in cardinal macroeconomic variables in Nigeria. For case, the 1973-74, 1979-80, and 2003-2006 periods were associated with monetary value additions while the oil market prostration of 1986 is an episode of monetary value lessening. During the first oil daze in Nigeria ( 1973-74 ) , the value of Nigeria ‘s export measured in US dollars rose by about 600 per cent with the footings of trade lifting from 18.9 in 1974 to 65.3 by 1982. Government gross which stood at 8 per cent of GDP in 1972 rose to about 20 per cent in 1975. This resulted in increased authorities outgo owing mostly from the demand to monetise the rough oil grosss. Investing was mostly in favor of instruction, public wellness, conveyance, and import replacing industries ( Nnnanna & A ; Masha, 2003 ) . During the oil monetary value daze of 2003-2006, Nigeria recorded addition in the portion of oil in GDP from approximately 80 per cent in 2003 to 82.6 per cent in 2005. The daze was gradual and persisted for a piece. This could be regarded as a lasting daze. The consequence of the daze was a favorable investing clime, increased national income within the period although a little diminution was observed in the growing rate of the GDP. Despite this sensed benefit of oil monetary value alteration, the macroeconomic environment in Nigeria during the roars was unwanted. For case, rising prices was largely dual figure in the 1970s ; money supply grew steeply, while immense financial shortages were besides recorded ( Akpan, 2009 ) . The growing way of the state has been really unsmooth over the old ages. During the oil roar epoch, approximately 1970-78, GDP grew positively by 6.2 per centum yearly – a singular growing. However, in the 1980s, GDP had negative growing rates. In the period 1988-1997 which constitutes the period of Structural Adjustment Programme ( SAP ) , which entails economic liberalisation, the GDP responded to economic accommodation policies and grew at a positive rate of 4.0 per centum. Agriculture, industry and fabrication, oil and gas sectors had greater laterality in the composing of the Nigeria ‘s GDP. The twelvemonth 1989 – 1998 was the most disruptive period in the history of the state ‘s growing form. Real GDP grew merely by an norm of 3.6 per centum, against the population growing rate of 2.8 % during the same period. Inflation, poorness, exchange rate were all at dismaying rates. Foreign direct investing, which is a necessary engine of growing, was stifled be cause of unsupportive enabling environment. Between 1999 and 2008, the state ‘s growing public presentation improved significantly. GDP growing rate averaged 7.8 % during the decennary entirely due to the growing of non-oil sector which grew by 9.5 per centum. In this respect, nevertheless, oil sector constitutes both a retarding force on growing and a beginning of instability in GDP growing form ( Aliyu, 2009 ) . Ongoing financial and pecuniary reforms aim to cut down the degree of rough oil dependence but at the minute, uncertainnesss about the degree of rough oil monetary values has cost the economic system a batch of investing chances over the old ages. In order for Nigeria to achieve balanced, uninterrupted and sustainable economic growing, the consequence of volatility in oil monetary values must be given equal attending.STATEMENT OF THE PROBLEMIn Nigeria, the rate of petroleum oil dependence is high. It accounted for about 82.1 % of entire authorities gross during the oil roar in 1974 before cut downing to a portion of 64.3 % by which was a effect of the rapid diminution in universe market monetary value of rough oil. The portion of oil gross in entire authorities gross still remains significant as evidenced by the attainment of 85.6 % and 86.1 % in 2004 and 2005 severally ( Akpan,2009 ) . This has generated a batch of involvement particularly amongst stakeholders in the Nigerian econom ic system. Of recent, is the argument in the Nigerian National Assembly about the appropriate petroleum oil monetary value benchmark to be included in the 2013 Appropriation Act which has generated a batch of contention. From a planning position, fluctuations in the petroleum oil monetary value have been the beginning of a batch of incomplete authorities undertakings due to the alterations in the gross watercourse and the dependability of cost estimations. The transmittal mechanisms through which rough oil monetary values impact on existent economic activity and analysing the dynamic interrelatedness among the selected macroeconomic variables is of great importance. Variations in rough oil monetary values hinders effectual economic planning and development most particularly for rough oil dependent economic systems like Nigeria. The impact of these fluctuations in footings of magnitude, continuance and way is of great importance if the Nigerian economic system must come on. The following are inquiries refering to the impact of rough oil monetary value volatility in Nigeria: – How significantly can Crude Oil Price fluctuations hinder sustainable economic growing in Nigeria? What magnitude of the alterations in macroeconomic variables is associated with fluctuations in rough oil monetary value?1.3 OBJECTIVES OF THE STUDYThe wide aim of this survey is to analyse the consequence of petroleum oil monetary value volatility on economic activity in Nigeria. The specific aims include: a. To analyse the consequence of oil monetary value volatility on the growing rate of existent GDP, Inflation rate, Money supply and other financial and pecuniary variables. B. To find whether there is a long tally relationship between oil monetary value volatility and existent GDP growing in Nigeria. 1.4 RESEARCH QUESTIONS The inquiries that guide the research are as follows: a. Is there a important relationship between Oil monetary value dazes and economic system activity in Nigeria? B. What is the magnitude of the relationship ( if any ) between Oil monetary value dazes and macroeconomic variables in Nigeria?1.5 HYPOTHESES OF STUDYThe followers are the research hypotheses to be tested during the survey: Holmium: Crude Oil Price has no important consequence on economic activity in Nigeria. H1: Crude Oil Price has a important consequence on economic activity in Nigeria.1.6 SCOPE AND LIMITATIONS OF THE STUDYThis survey on the consequence of volatile economic activity may be relevant to all states in the universe but the overall considerations, analysis and deductions are within the model of the Nigerian economic system. This survey covers the periods from 1975 to 2011. However, restrictions due to unavailability and undependability of informations could originate in the class of the survey and this should non be overlooked.1.7 DEFINITION OF UNFAMILIAR TERMSThe undermentioned footings used during the survey are explained below: a. Volatile: Care to change frequently or widely B. Organization of Petroleum-Exporting Countries ( OPEC ) : An organisation of states formed in 1961 to hold on a common policy for the production and sale of crude oil. Its members include Iraq, Indonesia, Iran, Kuwait, Libya, Angola, Algeria, Nigeria, Qatar, Saudi Arabia, the United Arab Emirates, and Venezuela.1.8 SIGNIFICANCE OF STUDYThis survey is of import to demo the extent to which the Nigerian economic system is affected by rough oil by analyzing macroeconomic variables. It is necessary to understand the way, continuance and magnitude of the relationship.1.9 METHODOLOGYThe variables to be considered include: a. Oil Price Variance B. Real Gross Domestic Product Growth Rate c. Inflation Rate d. Index of Industrial Production e. Money Supply f. Government Outgo The clip period under consideration is 1975-2011. The Ordinary Least Square Method is used because it is the best linear indifferent calculator. Garch ( 1,1 ) theoretical account is used to mensurate rough oil monetary value volatility and the conditional discrepancy series generates the volatility informations. Johansen Co-Integration technique based on vector autoregressive theoretical account determines the long tally relationship between oil monetary value fluctuations and GDP growing. Vector Error Correction Model ( VECM ) specifies convergence or divergency among the variables in the theoretical account. Trials for Stationarity ( being of unit roots ) such as the augmented dickey-fuller and Phillip Perron Tests will be employed. Variance decomposition, impulse response map, farmer causality trial examines consequence of Oil Price volatility on other variables.1.10 DATA BeginningsSecondary informations beginnings from Central Bank Statistical Database, Central Bank Statistical B ulletin, OPEC Statistical Bulletin British Petroleum Statistical Review of World Energy.1.11 OUTLINE OF CHAPTERS

Geography – Problem Solving: Flooding

Problem-solving Flooding I turn over that the best solution from the Environment part is the Upstream Flood Storage. I have in mind this would be the best option because it use up out stop the townsfolksfolkspeople from getting fill but to a fault, it would be better downstream. The body of water will be temporarily stored on fields upstream of the town, on provoke land. There may be animals on the fields but they stub slowly be moved out of the way. This could also lead to new wildlife in the area, although also separate the wildlife already there. The pig out terminus reservoir upstream of the town can pass back flood lampwaters, and so slash the maximum flood hunt through and through the town.A flood storage reservoir system effective so long as it does not become full of water. Although, if a larger flood occurs, the reservoir fills up and the special water will flow over the top. One other psyche is that this will be costly, but could that many businesse s and homes from being flooded. I estimate that the Floodplain Reinstatement (move the town off the flood plain so if the river floods, it wont matter) is a rubber conceit because it would be extremely costly and very nasty to persuade the people living in the town to move away. It would be bad for the economy and people would also doze off their jobs and businesses in the town.Although, by moving the town out the way of floods, this would mean that the town will no longer get flooded. By doing this, it will also disrupt the wildlife where the town could be moved. The Bypass carry (building an extra remove to take roughly of the flood water) also would not be such a good paper because they would just make the flood worse downstream. The water would move very promptly through the town, causing the channel to accumulate the river where the flood will be at the same time. As a result, this saves the town from being flooded but the flood downstream will be worse.Also, the ch annel may not have a lot of water in it only year round so could displace the wildlife in the area. The additional channel faculty (making the channel deeper and wider so it will hold more water) is a good idea as it could stop small-scale floods from victorious place. But, if there was to be a bad flood then the water could try over the top leading the town to flood. The secondary defenses (build flood walls to hold the extra water) could also work well for preventing the small floods, but not for a free flood. As a result, the water could dummy up rise over the top of the walls.