Impacts of Tourism on National Parks (UK) Essay Example for Free

Impacts of Tourism on National Parks (UK) Essay National parks around the world are increasingly attracting visitors to experience pristine and unique natural environments. National Parks are extensive tracts of the countryside that have been given strong protection under legislation for the conservation and enhancement of their special qualities. According to the Environments Act (1995) National Parks were designated for two statutory purposes: 1- To conserve and enhance the natural beauty, wildlife and cultural heritage and 2- To provide opportunities for the understanding and enjoyment of the Park by the public. The National Park Authorities (NPA) are responsible for the overall management of National Parks their duty is to ensure that the two objectives of National Parks are fulfilled. TOURISM IMPACTS Tourism impacts are the effects that tourists and tourists activities have on a destination. We will be looking at three main types of impacts that tourism can have on a destination. They are environmental, socio-cultural as well as economic impacts. The word impact gives a negative meaning however tourism impacts can be both positive and negative on a destination. Environmental Impacts (physical – what can be seen) – It is not possible to develop tourism without incurring environmental impacts. Tourism development and activities (litter / pollution) can gradually destroy an environment’s resources. Many of these impacts involves the construction of infrastructure e.g. Creation of roads; hotels airports etc. Tourism has the potential to create beneficial effects on the environment by contributing to environmental protection and conservation. Tourism can also raise awareness of environmental values and it can serve as a tool to finance protection of natural areas and increase their economic importance. Economic Impacts – There is no doubt that tourism has a large effect on the  economy of a destination. Tourism contribute to sales, profits, employment opportunities for locals (transportations, accommodations), and generate income for payment of goods and services provided. Socio-Cultural Impacts – The outcome of social relationships that occur between tourists and hosts when in contact. Socio-cultural impacts can have an effect on a destination’s culture to the behaviour of its residents. It is considered to be the changes in the quality of life of residents of tourist destinations. Destinations involved in the tourism business experience socio-cultural changes as a result of tourism activity, an increase in tourist may cause locals to become irritated if they feel that their way of life is being threatened. E.g. Rich tourists who are accustomed to a certain way of life; their attitude towards the quality of service provided at a destination may cause the locals to feel threatened.

Strategic Operational And Tactical Level Information Technology Essay

Strategic Operational And Tactical Level Information Technology Essay It is the combination of Information Technology and people using it to carry out operations and management. In a large sense it is frequently used with in people to process data and technology. It is also refer to Information and Communication Technology (ICT), which is used in organization, where people work with this to support business processes. There is a clear different between information system, ICT and business process. IT is completely different from information system, information system consist of ICT component. Information system helps to control the performance of business process. Information system is a special type of work system, which produces products or services for customer, where human and machines perform work using ICT and resources. Information system purpose is to process information. Information system is a system which represents data and process as a form of social memory. Information system supports human decision making and action. What is the role of Information Systems in todays competitive business environment on Strategic, Operational and Tactical level? Strategic Level: IS (Information system) supports business processes and operations: IS helps managers to execute their daily activities and functions properly, for example, in bank, creation of accounts, statement generation etc take place; and IS helps managers control such activities with greater accuracy and in a timely manner with the help of software. IS supports decision making for managers and employees: IS takes data as input and then processes it to generate information; simply defined as Input Æ’Â ¨ Process Æ’Â ¨ Information. This information is used by the managers for the improvement of their organizations, for example, existing historical data about customers in bank can be analyzed by IS and important information can be generated like bad customers and good customers, etc. This information can be used by managers while deciding whether to approve loan for new customers or not IS supports in making strategic decision for competitive advantage: By analyzing data collected from different sources, IS can provide valuable information such as which items to launch in which location; so that the company can have advantage over their competitors using this information. IS can also help business houses; in carrying out their business processes differently than their competitors. Operational Level: IS promises Operational excellence: In order to achieve higher profitability, businesses improve their operations efficiency. Managers make use of IS in business operations to achieve higher levels of productivity and efficiency. An excellent example is the use of the RetailLink system at Wal-Mart; this system digitally links every one of the Wal-Marts stores with its suppliers; the supplier is monitoring the items and as soon as an item is purchased, the supplier knows that a replacement must be shipped to the shelf. IS helps create new products, business models and services: In firms, Information system is a crucial tool in creating new services and products and new business models. Business models describe how the companies produce, deliver and sells a product or service to be successful. An example is Apple Inc; it transformed an old business model which was based on its iPod technology platform that included iPod, iPhone and the iTunes music service. IS helps monitor increase Supplier/Customer intimacy: When a customer is served well by a business, he usually responds by returning and purchasing more from the business; this raises the profits and revenues of the business. The more the business engages its suppliers, the better the suppliers are able to provide vital inputs; which in turn lowers costs. An example is the high-end hotel, Manhattans The Mandarin Oriental and other such high-end hotels; they illustrate the use of IS and technology to achieve better customer intimacy. They track guests preferences using computers, such as their preferred television programs, check-in times and room temperatures. Tactical Level: IS helps making better decisions: Many managers operate in an information bank and they never have the relevant information at the right moment to make a learned decision; poor outcomes like these loose customers and raise costs. Managers can use real time data while making decisions; IS allow managers to use real-time data from the marketplace while making decisions. An example is the Verizon Corporation, which uses a Web-based digital dashboard that gives managers accurate real-time information on customer complains and network performances. By using this information, managers can rapidly inform customers of the repair work, assign repair resources to the involved areas, and restore service promptly. Competitive advantage: When the firms achieve the business objectives, for example, customer intimacy, operational excellence, improved decision making, new services, products and business models, than it is most like that they have achieved a competitive advantage already. Accomplishing these things finer than their objects, responding to suppliers and customers in real time, charging less for premium products all add up to higher sales and profits. An example is the Toyota Production System which mainly focuses on organizing work to make continuous improvements, eliminating waste; Toyota Production System is based on what the customers actually ordered. How are Information Systems transforming the functional areas of organizations? Computers are used for almost any task. We check email with it, watch media, bank and more. Information is the life of organization, damaged or lost data can cause financial loss, law suits, etc. Information system contains hardware, software, data, applications, communication and people. It helps an organization to manage and secure its critical corporate, customer and employee data. Information system improves work process and gives lot of other benefits. An example is the Transaction Processing System (TPS) which is used in one functional area of a business; it process routine transactions more accurately and efficiently. TPS has many sub-species which are directly linked to their respective functional areas, for example, there is Finance and Accounting Systems for the functional areas of accounts and finance, Human Resource System for the Human Resource area, etc. Information System is different form other system because its purpose is to monitor and document the operations of other systems, we can also call it target system. For example, production activities would be the target system for a production scheduling information system, human resources would be the target system of a human resource information system. Every reactive system has a sub system called Information system, whose purpose is to monitor and control system. Task 2 There are many, many types of Information systems, but the most common ones are these: Transaction Processing System (TPS); Decision Support System (DSS); Management Information System (MIS); Office Automation System (OAS); Executive Information System (EIS) and Data Warehouses (DW). Transaction Processing System (TPS): TPS is a basic business system and it handles the tracking and processing of transactions. A transaction is simply an event which is of interest to the organization, for example, a railway booking system in which Booking, Cancellation, etc are all transactions; or a sale at a store. TPS is very useful and has many functions and it Serves the most elementary day-to-day activities of an organization. Is very often crucial to the survival of the organization Usually has high volumes of output and input Needs to be fault-tolerant Supports the operational level of the business Supplies data for higher-level management decisions Can have strategic consequences, for example, airline reservation system Deals with well-structured processes. A Transaction processing system has many sub-species, some of which are: Sales and Marketing Systems: These are systems that support the sales and marketing functions by easing the movement of services and goods from producers to customers. For example, a Stores sale system would automatically record and total purchase transactions and print out a packing list; this would improve customer service and maintain customer data. Manufacturing and Production systems: These systems supply data to operate, control and monitor the production processes, such as shipping, receiving, purchasing, scheduling, engineering, resource management, quality control, etc. For example, a system in factory that measures samples of products and gets information from that; then statistically analyses the samples and shows when the operators should take corrective action. Human Resource System: These systems deal with performance evaluation, compensation, placement, career development and recruitment of the firms employees. Examples of HRS include training and skills, applicant tracking, personnel record keeping, positions, benefits. Finance Accounting System: These systems maintain records which concern the flow of funds in the firm and they also produce financial statements, such as income statements and balance sheets. E.g for General Ledger; Budgeting, Billing: Cost Accounting, Accounts Payable/ Receivable; Funds management systems, payroll. These systems were among the earliest to be computerized. Examples of Financial systems are loan management, cash management, securities trading and check processing. Management Information Systems (MIS): They assist lower management in problem solving and making decisions. MIS usually takes data from the transaction processing system and summarizes it into a series of management reports which are to be used by the middle management and operational supervisors. MIS is a set of information processing functions and queries should be handled by it as quickly as they arrive. Database is an important element of MIS. MIS simply provides managers with feedback on daily operations; it also provides information and support for effective decision making. An example of MIS is an annual budgeting system. Decision Support System (DSS): DSS provides information, models or analysis tools to the senior managers and strategic management staff to help them make decisions. DSS are used for support of unstructured decisions and they are for analytical work mainly, for example, creating of what-if models using spreadsheets databases. An example job for a Decision Support System would be a 6 year operating plan. Office Automation Systems (OAS): They are used to improve the productivity of employees who need to process data information for reducing paper warfare. For example, Microsoft Office XP improves the productivity of employees working in an office or system that allow employees to work from home or whilst on the move. OAS software tools are often integrated and are designed for easy operations (for example, a graph from a spreadsheet can be imported in the Word Processor). Executive Information System (EIS): Also known as Executive Support System (ESS) and it provides information to the executives in a readily accessible, interactive format. EIS (or ESS) usually allow summary of the entire organization and also allows drilling down to specific levels of detail. EIS also use data which is produced by ground level Transaction Processing System so the executives can gain an overview of the entire organization. EIS require extensive staff to operate and are very expensive to run and are used by top level (strategic) management. Data Warehouses System: A Data Warehouse is a place where data is stored analysis, security and archival purposes. A data warehouse is usually either a single computer or a giant computer system formed by tying many computers together. Besides storing large amount of data, they must also possess the systems in place that would make it easy to access the data and use in day-to-day operations. It is also sometimes referred to be a major role player in DSS, or Decision Support System. How you identify the major support systems, and relate them to managerial functions? Support systems are Office Automation, Decisions Support Systems, Management Information system and TPS. Management Information system (MIS) generally takes the data from TPS (Transaction processing system) and summarizes it into a series of management reports, which are used by operational supervisors and also by the middle management. Decision-Support Systems are designed to help management make decisions, when there is uncertainty about the possible outcome. DSS gather relevant information with the help of tools and techniques and analyze the options and alternatives. DSS often create what-if models with the use of databases and spreadsheets. Knowledge Management Systems (KMS) helps business create and share information. This helps employees who creates and shares knowledge and expertise, which is shared in organization to create further commercial opportunities. KMS allows efficient categorization and distribution of knowledge. For example knowledge might be in word documents, spreadsheets, power point or internet etc, to share KMS would use collaboration system like intranet. Transaction Processing Systems (TPS) process routine transaction accurately and efficiently; and businesses may have many TPS, for example Invoices are sent to customers using the Billing systems Systems that calculate monthly and weekly payroll and tax payments Calculations of raw materials using Production and purchasing systems Using Stock control systems to process of all movement within the businesses Office Automation Systems (OAP) improves the productivity of the employees that process data information. For example, Microsoft Office XP improves productivity of employees that work in an office or system that enable employees to work on the move or from home. Task 3 Describe the tools and techniques provided by the Information Systems department and its relationship with end-users to solve the specific problems? The Information system department provides many different tools and techniques to solve problems and the main ones are: Data Warehouses: Their main purpose is to establish a data storehouse that makes operational data accessible in a form which is readily acceptable for analytical processing activities like Decision Support or EIS. Data Warehouses have many different characteristics such as Organization (data are organized), Time Variant (data kept for 5-10 years so it can be used for forecasting and comparisons), Non-Volatility (data are not updated once it has been entered in the warehouse), Consistency (data are coded in a consistent manner) and Client/Server (the data warehouse provides the end user an easy access to the data by using the clients/servers). How it solves problems: The Data in the warehouses is organized for less confusion; The Data is stored for a long time, allows for forecasts and comparisons; Takes raw data and codes it in a consistent matter for easy analysis Provides the end user an easy access to its data The data warehouse uses a relational structure The data are located in one place, allows data to be reached quickly Web browsers can be used to allow end users to reach data easily Data Mining: the process of analyzing data from different views and then summarizing it into useful information. Such information could be used to cut costs, raise revenue or both. For years, companies have used very powerful computers to sift through large volumes of supermarket scanner data and analyze market research reports. Data Mining is currently being used in areas like Retailing and Sales, Banking, Insurance, Airlines, Health Care, Computer Software Hardware, etc. How it solves problems: Data mining automates the process of discovering predictive information in very large databases; Data mining tools identify previous hidden patterns in just one step Can operate on unprocessed or even unstructured information. Text Web Mining: Text mining is the application of data mining to less structured text files. Web Mining are mining tools which can be used to analyze large amounts of data on the Web (like what customers are doing on the Internet). How it solves problems: Groups documents by common themes, making them easier to locate sort Finds the hidden content of documents and also additional useful relationships Geographic Information System (GIS): this is a computer-based system which is used for capturing, checking, storing, manipulating, integrating and displaying data using digitalized maps. How it solves problems: Every digital object or record has an identified geographical location Branch performances can be monitored, analyzed and compared Volume and traffic patterns of business activities can be monitored Geographical area served by each Bank branch can be observed, to plan if new banks are needed or not. Data Visualization: This is the presentation of data by technologies like digital images, graphical user interface, 3-d presentations and animations, geographical information systems, virtual reality, multidimensional graphs tables. How it solves problems: Presents many different kinds of data into a common, understandable way for better more accurate analysis; reduces errors too. On-Line Analytic Processing: this is the technology that allows users of multidimensional databases to generate on-line comparative summaries of data and other analytical enquiries; OLAP can also be integrated into corporate databases systems. How it solves problems: OLAP facilities allow managers and analysts to monitor the performance of the business or market. The end-results of OLAP technologies can be very simple (frequency tables, etc) to make the data much easier to understand and analyze. How the Executives may use any alternative data/ information processing techniques to support the decision making This is a crucial step in making an effective decision. The more good options that you consider, the more inclusive your final decision will be. You force yourself to reach deeper and you look at problems via different angles, when you generate alternatives. By using the mindset there must be other solutions out there, you have more chance to make the best possible decision. If you havent got other reasonable alternatives, then theres not much of a decision to make! Below is a summary of some of the important techniques and tools to help teams develop good alternatives. Generating Ideas Brainstorming is possibly the most well known method for generating ideas. Reverse Brainstorming works in the same manner. But, it works by asking people to brainstorm of how to achieve the opposite result from the one required, and then simply reversing those actions. The Charette Procedure is a systematic process and it gathers and develops ideas from many stakeholders. Crawford Slip Writing Technique generates ideas from a large number of people. This method is extremely effective and ensures that ideas from everyone are heard and weighed equally irrespective of the persons power in the organization. Explore the Alternatives You need to evaluate the risks and implications of each choice once you are completely satisfied that you have a good selection of realistic alternatives. Risk Theres almost always some degree of uncertainty in decision making process that may lead to risks, inevitably. You can determine if the risk is manageable of not simply by evaluating the risks involved with the options. Risk Analysis allows you to look at risks impartially. It assess threats and evaluates the probability of events taking place using a structured approach their management costs. Implications You can consider the potential consequences of each of your options Six Thinking Hats, after viewing the alternatives from 6 different perspectives, you can assess the consequences of a decision. Impact Analysis, useful technique for brainstorming the unexpected outcomes that could mount from a decision. Choose the Best Alternative After assessing the different alternatives, the following step is choosing between them. The choice could be very obvious, but if it is not, then the tools below will help: Grid Analysis (or decision matrix), is a very useful tool for this type of evaluation. It helps bring different factors in the process of decision making in a reliable way, therefore this tool is invaluable. Paired Comparison Analysis help decide the importance of differing factors and helps you compare factors that are unlike and decide which ones should influence your decision the most. Decision Trees are useful in deciding between options. These bring the probability of project failure/success in the decision making process and help you lay out the various options available to you. Task 4 Information systems are made out of components that can be assembled in many different con ¬Ã‚ gurations resulting in a variety of information systems and applications, much as construction materials can be assembled to build different homes. The size and cost of a home depend on the purpose of the building, the availability of money, and constraints such as ecological and environmental legal requirements. Just as there are many different types of houses, so there are many different types of information systems. We classify houses as single-family homes, apartments, townhouses, and cottages. Similarly, it is useful to classify information systems into groups that share similar characteristics. A classi ¬Ã‚ cation such as this may help in identifying systems, analyzing them, planning new Information Systems Con ¬Ã‚ gurations Organizations are made up of components such as divisions, departments, and work units, organized in hierarchical levels. For example, most organizations have functional departments, such as production and accounting, which report to plant management, which report to a division head. The divisions report to the corporate headquarters. Although some organizations have restructured themselves in innovative ways, such as those based on cross-functional teams, today the vast majority of organizations still have a traditional hierarchical structure. Thus, we can  ¬Ã‚ nd information systems built for headquarters, for divisions, for the functional departments, for operating units, and even for individual employees. Such systems can stand alone, but usually they are interconnected. Typical information systems that follow the organizational structure are functional (departmental), enterprise-wide, and inter-organizational. These systems are organized in a hierarchy in which each higher-le vel system consists of several (even many) systems from the level below it. A departmental system supports the functional areas in each company. At a higher level, the enterprise-wide system supports the entire company, and inter-organizational systems connect different companies. The major functional information systems are organized around the traditional departments- Finance IS Finance IS Accounting IS Accounting IS Human Resources Human Resources Corporate a System Electronic Market Electronic Market Corporate B System Marketing IS Marketing IS Production IS Production IS Administrative IS Administrative IS Corporate C System

Advantages And Disadvantages Of Smart Antenna

Advantages And Disadvantages Of Smart Antenna The Direction of Arrival (DOA) estimation algorithm which may take various forms generally follows from the homogeneous solution of the wave equation. The models of interest in this dissertation may equally apply to an EM wave as well as to an acoustic wave. Assuming that the propagation model is fundamentally the same, we will, for analytical expediency, show that it can follow from the solution of Maxwells equations, which clearly are only valid for EM waves. In empty space the equation can be written as: =0 (3.1) =0 (3.2) (3.3) (3.4) where . and ÃÆ'-, respectively, denote the divergence and curl. Furthermore, B is the magnetic induction. E denotes the electric field, whereas and are the magnetic and dielectric constants respectively. Invoking 3.1 the following curl property results as: (3.5) (3.6) (3.7) The constant c is generally referred to as the speed of propagation. For EM waves in free space, it follows from the derivation c = 1 / = 3 x m / s. The homogeneous wave equation (3.7) constitutes the physical motivation for our assumed data model, regardless of the type of wave or medium. In some applications, the underlying physics are irrelevant, and it is merely the mathematical structure of the data model that counts. 3.2 Plane wave In the physics of wave propagation, a plane wave is a constant-frequency wave whose wave fronts are infinite parallel planes of constant peak-to-peak amplitude normal to the phase velocity vector[]. Actually, it is impossible to have a rare plane wave in practice, and only a plane wave of infinite extent can propagate as a plane wave. Actually, many waves are approximately regarded as plane waves in a localized region of space, e.g., a localized source such as an antenna produces a field which is approximately a plane wave far enough from the antenna in its far-field region. Likely, we can treat the waves as light rays which correspond locally to plane waves, when the length scales are much longer than the waves wavelength, as is often appearing of light in the field of optics. 3.2.1 Mathematical definition Two functions which meet the criteria of having a constant frequency and constant amplitude are defined as the sine or cosine functions. One of the simplest ways to use such a sinusoid involves defining it along the direction of the x axis. As the equation shown below, it uses the cosine function to express a plane wave travelling in the positive x direction. (3.8) Where A(x,t) is the magnitude of the shown wave at a given point in space and time. is the amplitude of the wave which is the peak magnitude of the oscillation. k is the waves wave number or more specifically the angular wave number and equals 2à Ã¢â€š ¬/ÃŽÂ », where ÃŽÂ » is the wavelength of the wave. k has the units of radians per unit distance and is a standard of how rapidly the disturbance changes over a given distance at a particular point in time. x is a point along the x axis. y and z are not considered in the equation because the waves magnitude and phase are the same at every point on any given y-z plane. This equation defines what that magnitude and phase are. is the waves angular frequency which equals 2à Ã¢â€š ¬/T, and T is the period of the wave. In detail, omega, has the units of radians per unit time and is also a standard of how rapid the disturbance changing in a given length of time at a particular point in space. is a given particular point in time, and varphi , is the wave phase shift with the units of radians. It must make clear that a positive phase shift will shifts the wave along the negative x axis direction at a given point of time. A phase shift of 2à Ã¢â€š ¬ radians means shifting it one wavelength exactly. Other formulations which directly use the waves wavelength, period T, frequency f and velocity c, are shown as follows: A=A_o cos[2pi(x/lambda- t/T) + varphi], (3.9) A=A_o cos[2pi(x/lambda- ft) + varphi], (3.10) A=A_o cos[(2pi/lambda)(x- ct) + varphi], (3.11) To appreciate the equivalence of the above set of equations denote that f=1/T,! and c=lambda/T=omega/k,! 3.2.2 Application Plane waves are solutions for a scalar wave equation in the homogeneous medium. As for vector wave equations, e.g., waves in an elastic solid or the ones describing electromagnetic radiation, the solution for the homogeneous medium is similar. In vector wave equations, the scalar amplitude is replaced by a constant vector. e.g., in electromagnetism is the vector of the electric field, magnetic field, or vector potential. The transverse wave is a kind of wave in which the amplitude vector is perpendicular to k, which is the case for electromagnetic waves in an isotropic space. On the contrast, the longitudinal wave is a kind of wave in which the amplitude vector is parallel to k, typically, such as for acoustic waves in a gas or fluid. The plane wave equation is true for arbitrary combinations of à Ã¢â‚¬ ° and k. However, all real physical mediums will only allow such waves to propagate for these combinations of à Ã¢â‚¬ ° and k that satisfy the dispersion relation of the mediums. The dispersion relation is often demonstrated as a function, à Ã¢â‚¬ °(k), where ratio à Ã¢â‚¬ °/|k| gives the magnitude of the phase velocity and dà Ã¢â‚¬ °/dk denotes the group velocity. As for electromagnetism in an isotropic case with index of refraction coefficient n, the phase velocity is c/n, which equals the group velocity on condition that the index is frequency independent. In linear uniform case, a wave equation solution can be demonstrated as a superposition of plane waves. This method is known as the Angular Spectrum method. Actually, the solution form of the plane wave is the general consequence of translational symmetry. And in the more general case, for periodic structures with discrete translational symmetry, the solution takes the form of Bloch waves, which is most famous in crystalline atomic materials, in the photonic crystals and other periodic wave equations. 3.3 Propagation Many physical phenomena are either a result of waves propagating through a medium or exhibit a wave like physical manifestation. Though 3.7 is a vector equation, we only consider one of its components, say E(r,t) where r is the radius vector. It will later be assumed that the measured sensor outputs are proportional to E(r,t). Interestingly enough, any field of the form E(r,t) = , which satisfies 3.7, provided with T denoting transposition. Through its dependence on only, the solution can be interpreted as a wave traveling in the direction, with the speed of propagation. For the latter reason, ÃŽÂ ± is referred to as the slowness vector. The chief interest herein is in narrowband forcing functions. The details of generating such a forcing function can be found in the classic book by Jordan [59]. In complex notation [63] and taking the origin as a reference, a narrowband transmitted waveform can be expressed as: (3.12) where s(t) is slowly time varying compared to the carrier . For, where B is the bandwidth of s(t), we can write: (3.13) In the last equation 3.13, the so-called wave vector was introduced, and its magnitude is the wavenumber. One can also write, where is the wavelength. Make sure that k also points in the direction of propagation, e.g., in the x-y plane we can get: (3.14) where is the direction of propagation, defined counter clockwise relative the x axis. It should be noted that 3.12 implicitly assumed far-field conditions, since an isotropic, which refers to uniform propagation/transmission in all directions, point source gives rise to a spherical traveling wave whose amplitude is inversely proportional to the distance to the source. All points lying on the surface of a sphere of radius R will then share a common phase and are referred to as a wave front. This indicates that the distance between the emitters and the receiving antenna array determines whether the spherical degree of the wave should be taken into account. The reader is referred to e.g., [10, 24] for treatments of near field reception. Far field receiving conditions imply that the radius of propagation is so large that a flat plane of constant phase can be considered, thus resulting in a plane wave as indicated in Eq. 8. Though not necessary, the latter will be our assumed working mode l for convenience of exposition. Note that a linear medium implies the validity of the superposition principle, and thus allows for more than one traveling wave. Equation 8 carries both spatial and temporal information and represents an adequate model for distinguishing signals with distinct spatial-temporal parameters. These may come in various forms, such as DOA, in general azimuth and elevation, signal polarization, transmitted waveforms, temporal frequency etc. Each emitter is generally associated with a set of such characteristics. The interest in unfolding the signal parameters forms the essence of sensor array signal processing as presented herein, and continues to be an important and active topic of research. 3.4 Smart antenna Smart antennas are devices which adapt their radiation pattern to achieve improved performance either range or capacity or some combination of these [1]. The rapid growth in demand for mobile communications services has encouraged research into the design of wireless systems to improve spectrum efficiency, and increase link quality [7]. Using existing methods more effective, the smart antenna technology has the potential to significantly increase the wireless. With intelligent control of signal transmission and reception, capacity and coverage of the mobile wireless network, communications applications can be significantly improved [2]. In the communication system, the ability to distinguish different users is essential. The smart antenna can be used to add increased spatial diversity, which is referred to as Space Division Multiple Access (SDMA). Conventionally, employment of the most common multiple access scheme is a frequency division multiple access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA). These independent users of the program, frequency, time and code domain were given three different levels of diversity. Potential benefits of the smart antenna show in many ways, such as anti-multipath fading, reducing the delay extended to support smart antenna holding high data rate, interference suppression, reducing the distance effect, reducing the outage probability, to improve the BER (Bit Error Rate)performance, increasing system capacity, to improve spectral efficiency, supporting flexible and efficient handoff to expand cell coverage, flexible management of the district, to extend the battery life of mobile station, as well as lower maintenance and operating costs. 3.4.1 Types of Smart Antennas The environment and the systems requirements decide the type of Smart Antennas. There are two main types of Smart Antennas. They are as follows: Phased Array Antenna In this type of smart antenna, there will be a number of fixed beams between which the beam will be turned on or steered to the target signal. This can be done, only in the first stage of adjustment to help. In other words, as wanted by the moving target, the beam will be the Steering [2]. Adaptive Array Antenna Integrated with adaptive digital signal processing technology, the smart antenna uses digital signal processing algorithm to measure the signal strength of the beam, so that the antenna can dynamically change the beam which transmit power concentrated, as figure 3.2 shows. The application of spatial processing can enhance the signal capacity, so that multiple users share a channel. Adaptive antenna array is a closed-loop feedback control system consisting of an antenna array and real-time adaptive signal receiver processor, which uses the feedback control method for automatic alignment of the antenna array pattern. It formed nulling interference signal offset in the direction of the interference, and can strengthen a useful signal, so as to achieve the purpose of anti-jamming [3]. Figure 2 click for text version Figure 3.2 3.4.2 Advantages and disadvantages of smart antenna Advantages First of all, a high level of efficiency and power are provided by the smart antenna for the target signal. Smart antennas generate narrow pencil beams, when a big number of antenna elements are used in a high frequency condition. Thus, in the direction of the target signal, the efficiency is significantly high. With the help of adaptive array antennas, the same amount times the power gain will be produce, on condition that a fixed number of antenna elements are used. Another improvement is in the amount of interference which is suppressed. Phased array antennas suppress the interference with the narrow beam and adaptive array antennas suppress by adjusting the beam pattern [2]. Disadvantages The main disadvantage is the cost. Actually, the cost of such devices will be more than before, not only in the electronics section, but in the energy. That is to say the device is too expensive, and will also decrease the life of other devices. The receiver chains which are used must be decreased in order to reduce the cost. Also, because of the use of the RF electronics and A/D converter for each antenna, the costs are increasing. Moreover, the size of the antenna is another problem. Large base stations are needed to make this method to be efficient and it will increase the size, apart from this multiple external antennas needed on each terminal. Then, when the diversity is concerned, disadvantages are occurred. When mitigation is needed, diversity becomes a serious problem. The terminals and base stations must equip with multiple antennas. 3.5 White noise White noise is a random signal with a flat power spectral density []. In another word, the signal contains the equal power within a particular bandwidth at the centre frequency. White noise draws its name from white light where the power spectral density of the light is distributed in the visible band. In this way, the eyes three colour receptors are approximately equally stimulated []. In statistical case, a time series can be characterized as having weak white noise on condition that {} is a sequence of serially uncorrelated random vibrations with zero mean and finite variance. Especially, strong white noise has the quality to be independent and identically distributed, which means no autocorrelation. In particular, the series is called the Gaussian white noise [1], if is normally distributed and it has zero mean and standard deviation. Actually, an infinite bandwidth white noise signal is just a theoretical construction which cannot be reached. In practice, the bandwidth of white noise is restricted by the transmission medium, the mechanism of noise generation, and finite observation capabilities. If a random signal is observed with a flat spectrum in a mediums widest possible bandwidth, we will refer it as white noise. 3.5.1 Mathematical definition White random vector A random vector W is a white random vector only if its mean vector and autocorrelation matrix are corresponding to the follows: mu_w = mathbb{E}{ mathbf{w} } = 0 (3.15) R_{ww} = mathbb{E}{ mathbf{w} mathbf{w}^T} = sigma^2 mathbf{I} . (3.16) That is to say, it is a zero mean random vector, and its autocorrelation matrix is a multiple of the identity matrix. When the autocorrelation matrix is a multiple of the identity, we can regard it as spherical correlation. White random process A time continuous random process where is a white noise signal only if its mean function and autocorrelation function satisfy the following equation: mu_w(t) = mathbb{E}{ w(t)} = 0 (3.17) R_{ww}(t_1, t_2) = mathbb{E}{ w(t_1) w(t_2)} = (N_{0}/2)delta(t_1 t_2). (3.18) That is to say, it is zero mean for all time and has infinite power at zero time shift since its autocorrelation function is the Dirac delta function. The above autocorrelation function implies the following power spectral density. Since the Fourier transform of the delta function is equal to 1, we can imply: S_{ww}(omega) = N_{0}/2 ,! (3.19) Since this power spectral density is the same at all frequencies, we define it white as an analogy to the frequency spectrum of white light. A generalization to random elements on infinite dimensional spaces, e.g. random fields, is the white noise measure. 3.5.2 Statistical properties The white noise is uncorrelated in time and does not restrict the values a signal can take. Any distribution of values about the white noise is possible. Even a so-called binary signal that can only take the values of 1 or -1 will be white on condition that the sequence is statistically uncorrelated. Any noise with a continuous distribution, like a normal distribution, can be white noise certainly. It is often incorrectly assumed that Gaussian noise is necessarily white noise, yet neither property implies the other. Gaussianity refers to the probability distribution with respect to the value, in this context the probability of the signal reaching amplitude, while the term white refers to the way the signal power is distributed over time or among frequencies. Spectrogram of pink noise (left) and white noise (right), showed with linear frequency axis (vertical). We can therefore find Gaussian white noise, but also Poisson, Cauchy, etc. white noises. Thus, the two words Gaussian and white are often both specified in mathematical models of systems. Gaussian white noise is a good approximation of many real-world situations and generates mathematically tractable models. These models are used so frequently that the term additive white Gaussian noise has a standard abbreviation: AWGN. White noise is the generalized mean-square derivative of the Wiener process or Brownian motion. 3.6 Normal Distribution In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution that has a bell-shaped probability density function, known as the Gaussian function or informally as the bell curve[1]. f(x;mu,sigma^2) = frac{1}{sigmasqrt{2pi}} e^{ -frac{1}{2}left(frac{x-mu}{sigma}right)^2 } The parameter ÃŽÂ ¼ is the mean or expectation (location of the peak) and à Ã†â€™Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °2 is the variance. à Ã†â€™ is known as the standard deviation. The distribution with ÃŽÂ ¼ = 0 and à Ã†â€™Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °2 = 1 is called the standard normal distribution or the unit normal distribution. A normal distribution is often used as a first approximation to describe real-valued random variables that cluster around a single mean value. http://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Standard_deviation_diagram.svg/325px-Standard_deviation_diagram.svg.png The normal distribution is considered the most prominent probability distribution in statistics. There are several reasons for this:[1] First, the normal distribution arises from the central limit theorem, which states that under mild conditions, the mean of a large number of random variables drawn from the same distribution is distributed approximately normally, irrespective of the form of the original distribution. This gives it exceptionally wide application in, for example, sampling. Secondly, the normal distribution is very tractable analytically, that is, a large number of results involving this distribution can be derived in explicit form. For these reasons, the normal distribution is commonly encountered in practice, and is used throughout statistics, natural sciences, and social sciences [2] as a simple model for complex phenomena. For example, the observational error in an experiment is usually assumed to follow a normal distribution, and the propagation of uncertainty is computed using this assumption. Note that a normally distributed variable has a symmetric distribution about its mean. Quantities that grow exponentially, such as prices, incomes or populations, are often skewed to the right, and hence may be better described by other distributions, such as the log-normal distribution or Pareto distribution. In addition, the probability of seeing a normally distributed value that is far (i.e. more than a few standard deviations) from the mean drops off extremely rapidly. As a result, statistical inference using a normal distribution is not robust to the presence of outliers (data that are unexpectedly far from the mean, due to exceptional circumstances, observational error, etc.). When outliers are expected, data may be better described using a heavy-tailed distribution such as the Students t-distribution. 3.6.1 Mathematical Definition The simplest case of a normal distribution is known as the standard normal distribution, described by the probability density function phi(x) = frac{1}{sqrt{2pi}}, e^{- frac{scriptscriptstyle 1}{scriptscriptstyle 2} x^2}. The factor scriptstyle 1/sqrt{2pi} in this expression ensures that the total area under the curve à Ã¢â‚¬ ¢(x) is equal to one[proof], and 12 in the exponent makes the width of the curve (measured as half the distance between the inflection points) also equal to one. It is traditional in statistics to denote this function with the Greek letter à Ã¢â‚¬ ¢ (phi), whereas density functions for all other distributions are usually denoted with letters f or p.[5] The alternative glyph à Ã¢â‚¬   is also used quite often, however within this article à Ã¢â‚¬   is reserved to denote characteristic functions. Every normal distribution is the result of exponentiating a quadratic function (just as an exponential distribution results from exponentiating a linear function): f(x) = e^{a x^2 + b x + c}. , This yields the classic bell curve shape, provided that a 0 everywhere. One can adjust a to control the width of the bell, then adjust b to move the central peak of the bell along the x-axis, and finally one must choose c such that scriptstyleint_{-infty}^infty f(x),dx = 1 (which is only possible when a Rather than using a, b, and c, it is far more common to describe a normal distribution by its mean ÃŽÂ ¼ = à ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °b2a and variance à Ã†â€™2 = à ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °12a. Changing to these new parameters allows one to rewrite the probability density function in a convenient standard form, f(x) = frac{1}{sqrt{2pisigma^2}}, e^{frac{-(x-mu)^2}{2sigma^2}} = frac{1}{sigma}, phi!left(frac{x-mu}{sigma}right). For a standard normal distribution, ÃŽÂ ¼ = 0 and à Ã†â€™2 = 1. The last part of the equation above shows that any other normal distribution can be regarded as a version of the standard normal distribution that has been stretched horizontally by a factor à Ã†â€™ and then translated rightward by a distance ÃŽÂ ¼. Thus, ÃŽÂ ¼ specifies the position of the bell curves central peak, and à Ã†â€™ specifies the width of the bell curve. The parameter ÃŽÂ ¼ is at the same time the mean, the median and the mode of the normal distribution. The parameter à Ã†â€™2 is called the variance; as for any random variable, it describes how concentrated the distribution is around its mean. The square root of à Ã†â€™2 is called the standard deviation and is the width of the density function. The normal distribution is usually denoted by N(ÃŽÂ ¼,à ¢Ã¢â€š ¬Ã¢â‚¬ °Ãƒ Ã†â€™2).[6] Thus when a random variable X is distributed normally with mean ÃŽÂ ¼ and variance à Ã†â€™2, we write X sim mathcal{N}(mu,,sigma^2). , 3.6.2 Alternative formulations Some authors advocate using the precision instead of the variance. The precision is normally defined as the reciprocal of the variance (à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢2), although it is occasionally defined as the reciprocal of the standard deviation (à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢1).[7] This parameterization has an advantage in numerical applications where à Ã†â€™2 is very close to zero and is more convenient to work with in analysis as à Ã¢â‚¬Å¾ is a natural parameter of the normal distribution. This parameterization is common in Bayesian statistics, as it simplifies the Bayesian analysis of the normal distribution. Another advantage of using this parameterization is in the study of conditional distributions in the multivariate normal case. The form of the normal distribution with the more common definition à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢2 is as follows: f(x;,mu,tau) = sqrt{frac{tau}{2pi}}, e^{frac{-tau(x-mu)^2}{2}}. The question of which normal distribution should be called the standard one is also answered differently by various authors. Starting from the works of Gauss the standard normal was considered to be the one with variance à Ã†â€™2 = 12 : f(x) = frac{1}{sqrtpi},e^{-x^2} Stigler (1982) goes even further and insists the standard normal to be with the variance à Ã†â€™2 = 12à Ã¢â€š ¬ : f(x) = e^{-pi x^2} According to the author, this formulation is advantageous because of a much simpler and easier-to-remember formula, the fact that the pdf has unit height at zero, and simple approximate formulas for the quintiles of the distribution. 3.7 Cramer-Rao Bound In estimation theory and statistics, the Cramà ©r-Rao bound (CRB) or Cramà ©r-Rao lower bound (CRLB), named in honor of Harald Cramer and Calyampudi Radhakrishna Rao who were among the first to derive it,[1][2][3] expresses a lower bound on the variance of estimators of a deterministic parameter. The bound is also known as the Cramà ©r-Rao inequality or the information inequality. In its simplest form, the bound states that the variance of any unbiased estimator is at least as high as the inverse of the Fisher information. An unbiased estimator which achieves this lower bound is said to be (fully) efficient. Such a solution achieves the lowest possible mean squared error among all unbiased methods, and is therefore the minimum variance unbiased (MVU) estimator. However, in some cases, no unbiased technique exists which achieves the bound. This may occur even when an MVU estimator exists. The Cramà ©r-Rao bound can also be used to bound the variance of biased estimators of given bias. In some cases, a biased approach can result in both a variance and a mean squared error that are below the unbiased Cramà ©r-Rao lower bound; see estimator bias. statement The Cramà ©r-Rao bound is stated in this section for several increasingly general cases, beginning with the case in which the parameter is a scalar and its estimator is unbiased. All versions of the bound require certain regularity conditions, which hold for most well-behaved distributions. These conditions are listed later in this section. Scalar unbiased case Suppose theta is an unknown deterministic parameter which is to be estimated from measurements x, distributed according to some probability density function f(x;theta). The variance of any unbiased estimator hat{theta} of theta is then bounded by the reciprocal of the Fisher information I(theta): mathrm{var}(hat{theta}) geq frac{1}{I(theta)} where the Fisher information I(theta) is defined by I(theta) = mathrm{E} left[ left( frac{partial ell(x;theta)}{partialtheta} right)^2 right] = -mathrm{E}left[ frac{partial^2 ell(x;theta)}{partialtheta^2} right] and ell(x;theta)=log f(x;theta) is the natural logarithm of the likelihood function and mathrm{E} denotes the expected value. The efficiency of an unbiased estimator hat{theta} measures how close this estimators variance comes to this lower bound; estimator efficiency is defined as e(hat{theta}) = frac{I(theta)^{-1}}{{rm var}(hat{theta})} or the minimum possible variance for an unbiased estimator divided by its actual variance. The Cramà ©r-Rao lower bound thus gives e(hat{theta}) le 1. General scalar case A more general form of the bound can be obtained by considering an unbiased estimator T(X) of a function psi(theta) of the parameter theta. Here, unbiasedness is understood as stating that E{T(X)} = psi(theta). In this case, the bound is given by mathrm{var}(T) geq frac{[psi'(theta)]^2}{I(theta)} where psi'(theta) is the derivative of psi(theta) (by theta), and I(theta) is the Fisher information defined above. Bound on the variance of biased estimators Apart from being a bound on estimators of functions of the parameter, this approach can be used to derive a bound on the variance of biased estimators with a given bias, as follows. Consider an estimator hat{theta} with biasb(theta) = E{hat{theta}} theta, and let psi(theta) = b(theta) + theta. By the result above, any unbiased estimator whose expectation is psi(theta) has variance greater than or equal to (psi'(theta))^2/I(theta). Thus, any estimator hat{theta} whose bias is given by a function b(theta) satisfies mathrm{var} left(hat{theta}right) geq frac{[1+b'(theta)]^2}{I(theta)}. The unbiased version of the bound is a special case of this result, with b(theta)=0. Its trivial to have a small variance à ¢Ã‹â€ Ã¢â‚¬â„¢ an estimator that is constant has a variance of zero. But from the above equation we find that the mean squared errorof a biased estimator is bounded by mathrm{E}left((hat{theta}-theta)^2right)geqfrac{[1+b'(theta)]^2}{I(theta)}+b(theta)^2, using the standard decomposition of the MSE. Note, however, that this bound can be less than the unbiased Cramà ©r-Rao bound 1/I(ÃŽÂ ¸). See the example of estimating variance below. Multivariate case Extending the Cramà ©r-Rao bound to multiple parameters, define a parameter column vector boldsymbol{theta} = left[ theta_1, theta_2, dots, theta_d right]^T in mathbb{R}^d with probability density function f(x; boldsymbol{theta}) which satisfies the two regularity conditions below. The Fisher information matrix is a d times d matrix with element I_{m, k} defined as I_{m, k} = mathrm{E} left[ frac{d}{dtheta_m} log fleft(x; boldsymbol{theta}right) frac{d}{dtheta_k} log fleft(x; boldsymbol{theta}right) right]. Let boldsymbol{T}(X) be an estimator of any vector function of parameters, boldsymbol{T}(X) = (T_1(X), ldots, T_n(X))^T, and denote its expectation vector mathrm{E}[boldsymbol{T}(X)] by boldsymbol{psi}(boldsymbol{theta}). The Cramà ©r-Rao bound then states that the covariance matrix of boldsymbol{T}(X) satisfies mathrm{cov}_{boldsymbol{theta}}left(boldsymbol{T}(X)right) geq frac {partial boldsymbol{psi} left(boldsymbol{theta}right)} {partial boldsymbol{theta}} [Ileft(boldsymbol{theta}right)]^{-1} left( frac {partial boldsymbol{psi}left(boldsymbol{theta}right)} {partial boldsymbol{theta}} right)^T where The matrix inequality A ge B is understood to mean that the matrix A-B is positive semi definite, and partial boldsymbol{psi}(boldsymbol{theta})/partial boldsymbol{theta} is the Jacobian matrix whose ijth element is given by partial psi_i(boldsymbol{theta})/partial theta_j. If boldsymbol{T}(X) is an unbiased estimator of boldsymbol{theta} (i.e., boldsymbol{psi}left(boldsymbol{theta}rig

Genetic Engineering: There is No Genetic Definition of Humanity Essay

With advances in genetics and the decryption of the human genome, many people are taking the time to sit back and ponder the questions of what humanity is and where it comes from.1 Will techniques such as gene therapy eventually create people who aren't quite human? If humanity is a flexible and ever-changing concept, then how do people know if they are human? Does some standard measure of humanity seem likely in our future, and is it even ethically proper to impose such a standard? Philosophy offers the most satisfying definition of humanity: a human person is a conscious individual who interacts with an outside world. The details of the various philosophical debates on the exact nature of personhood would be enough to fill a library, but the main ideas can be summarized as follows: a person is self-aware, having the ability to think about thinking. Nothing in this definition of humanity involves matters of genetics or quantitative analyses of specific traits, which makes this definition applicable to people who may not be human in the way science tries to define the term. Defining humanity in a scientific sense, however, is a nettled endeavor. Many "strictly human" traits can be found in animals. Wolves have a complex social structure. Bonobos, a subspecies of chimpanzee, can learn an abstract symbol-language and show the ability to understand grammar and syntax.2 In other experiments dolphins-who are genetically more distant from humans than bonobos-learned a type of sign language showing that they, too, are able to grasp complex rules of language.3 One only has to yell at the family dog to see that animals can express emotion and empathy. What, then, is left to humans? Many point to our advanced technology as proof... ... 1. This paper was originally written for the course, "Human Genetics, Society, and Ethics," held at Washington College, Chestertown, Maryland. 2. Robert A. Baron, Psychology 5th ed. (Boston: Allyn and Bacon, 2000). 3. Baron. 4. N. A. Campbell, J. B. Reece, and L. G. Mitchell, Biology 5th ed., (New York: Addison Wesley Longman, 1999). 5. Matt Ridley, Genome: The Autobiography of a Species in 23 Chapters (New York: HarperCollins Publishers, 1999) 24. 6. Ibid. 7. Baron. 8. Ridley, 24. 9. Ibid. 10. Campbell et al., 446. Bibliography Baron, Robert. A. Psychology. 5th ed. Boston: Allyn and Bacon, 2000. Campbell, N. A., J. B. Reece, and L. G. Mitchell. Biology. 5th ed. New York: Addison Wesley Longman, 1999. Ridley, Matt. Genome: The Autobiography of a Species in 23 Chapters. New York: HarperCollins Publishers, 1999.

United NAtions :: essays research papers

United Nations â€Å"5 W’s† What: The political organization established 1945 by the allied powers who were later joined by other nations Who: Today there are 191 nations in the United Nations but It was originally started by those who were fighting against the axis powers. Where: It was decided to have it located in the Eastern United States, they bought land with money given by John D. Rockefeller Jr. along the East River in NYC. When: The United Nations was officially coined such in 1941 by President Franklin D. Roosevelt. The term was not used officially until Jan. 1, 1942, when 26 states joined in the Declaration by the United Nations. Why: The war effort needed to be joined so that they didn’t make peace separately. The need for an international organization to replace the League of Nations was not stated until Oct. 30, 1943. Six Bodies: General Assembly: It meets in regular yearly sessions under a president elected from among the representatives. The regular session usually begins on the third Tuesday in September and ends in mid-December. Special sessions can be convened at the request of the Security Council, of a majority of UN members, or, if the majority concurs, of a single member Security Council: It is charged with maintaining peace and security between nations. While other organs of the UN only make recommendations to member governments, the Security Council has the power to make decisions which member governments must carry out under the United Nations Charter. Economic and Social Council: It assists the General Assembly in promoting international economic and social cooperation and development. Trusteeship Council: It was established to help ensure that non-self-governing territories were administered in the best interests of the inhabitants and of international peace and security. They suspended operation in 1994. Secretariat: It provides studies, information, and facilities needed by United Nations bodies for their meetings.

Jesus And Law of the Prophets Essay

During the time of Jesus many did not believe in Him for what He teaches are a contradiction to their beliefs. Pharisees are the ones who are considered the righteous persons during that time. They and the people thought that Jesus’ testimonies are not true because what He teaches and did are against the Law of the Prophets, He opened the eye of a blind during Sabbath Day which is a sin for them. On Matthew 5:17-20, Jesus said that He cone not to abolish the Law but to fulfill them. Anyone who breaks one of the least of these commandments and teaches others to do the same will be called least in the kingdom of heaven, but whoever practices and teaches these commands will be called great in the kingdom of heaven (Matthew 5:19). With that account, Jesus is trying to say that He did not come to abolish the Law; in fact He is more concerned with the Law. The rest of Matthew 5 gives us a clear thought that Jesus is in favor of the Law. He said on Matthew 5:21-22 that anyone who is angry of his brother will be subject to judgment (note that in Matthew 5:21 Jesus said: You have heard that it was said to people long ago, ‘Do not murder, and anyone who murders will be subject to judgment.’). With that account we can say that the Law is not what the Pharisees think and do. If the Law is like what the Pharisees have thought He would have not said: For I tell you that unless your righteousness surpasses that of the Pharisees and the teachers of the law, you will certainly not enter the kingdom of heaven. Why Jesus did say this? This is because no one can obey the law all the time. No who is perfect enough to not commit sin against the Law. That was also the purpose of Christ coming; to save the world. That why in Ephesians 2:8-9 it says: For it is by grace that you have been saved, through faith – and this is not from yourselves, it is the gift of God- not by works, so that no one can boast. Actually, Matthew is does not contain a Jewish and anti Jewish in gospel. The matter is not because of the people itself but with the belief and practices that they have. Jesus is not imposing a new law but rather fulfilling the law as what He said. God is not against of the people but He is against of what the people (sinners) have done. Work cited: The Holy Bible (1988). The New International Version. Broadman & Holman Publishers: Nashville

Scientific Literacy in the Philippines

Module 6: Science Education in the Philippine Society Lesson 13: Scientific Literacy Science Literacy Science is frequently perceived to be of great importance because of its links to technology and industry which, from a national perspective, may be areas with high priority for development. Countries wanting to improve their people’s quality of life cannot escape the need to harness their science and technology capability as a way of developing competitiveness. Consequently, science is included as a core element in elementary and secondary levels despite conceptual complexity and high cost of implementation.Another justification for the inclusion of science in high school curricular is that all citizens need to achieve a degree of â€Å"scientific literacy† to enable them to participate effectively as citizens in modern societies. It is, therefore, important to be guided by past and present experiences in science education to be able to recognize the turning points for the country’s future which we need to decide now. Studies indicate however, that many of our Filipino learners are not attaining functional literacy, without which they find it too difficult to meet the challenges posed by our rapid changing world.Scientific literacy is a related concept to issue of cultural and technological literacy (a term used in recognition of the relationship between science and technology in everyday life). Some scientific educators have attempted to define or analyze it. The term ‘scientific literacy’ has been used in the literature for more than four decades (Gullagher and Harsch, 1997) although not always with the same meaning (Bybee, 1997). Benjamin Shen (1983) distinguishes 3 types of scientific literacy: practical, civic, and scientific cultural literacy.Practical scientific literacy is that kind of scientific and technical knowledge that can be put to use to help solve practical problems. The example given is that of the reduction in the dependence on infant formulae. Also, the use of alternative medicines like herbal plants instead of synthetic ones to prevent the cause of side effects that are harmful to one’s health. Civic scientific literacy enables the citizens to become more aware of science and science related issues so that he can face these issues with common sense. Shen defines civic scientific literacy as â€Å"a level of understanding of scientific terms and constructs sufficient to . . understand the essence of competing arguments on a given dispute on controversy†. An example common in many countries these days is the growing concern about the environment particularly the pollution of air, water and land. Media have contributed much to such awareness by bringing to public attention the activities of active environmental groups. However such concerns are more vigorous and numerous in developed countries than in developing countries. It is time that citizens of developing countries b ecame more aware and attentive to such matters. The third form is cultural scientific literacy.People who seek this form of scientific literacy desire to know something about science as a major human achievement. This group would come mostly from the intellectual community, those who watch television programs like Nova, Invention and similar discovery documentaries. The widely publicized subjects are based on the notion that scientific literacy has 3 components (Hodson) 1. substantive concepts with science 2. the nature of scientific activity 3. role of science in society and culture Norris and Philips(2003) argue that the term â€Å"scientific literacy† has been used to include various components from the following: a.   Ã‚  Ã‚   Knowledge of the substantive content of science and the ability to distinguish from non-science; b. )  Ã‚  Ã‚   Understanding science and its applications; c. )  Ã‚  Ã‚   Knowledge of what counts as science; d. )  Ã‚  Ã‚   Independence in le arning science; e. )  Ã‚  Ã‚   Ability to think scientifically; f. )  Ã‚  Ã‚  Ã‚   Ability to use scientific knowledge in problem solving; g. )  Ã‚  Ã‚   Knowledge needed for intelligent participation in science-based issues; h. )  Ã‚  Ã‚   Understanding the nature of science, including its relationship with culture; i. )  Ã‚  Ã‚  Ã‚  Ã‚   Appreciation of and comfort with science, including its wonder and curiosity; j.   Ã‚  Ã‚  Ã‚  Ã‚   Knowledge of the risks and benefits of science; and k. )  Ã‚  Ã‚   Ability to think critically about science and to deal with scientific expertise. They cite references to illustrate this. The confusion as to a precise meaning has led to a call to remove such term as a goal for school science literacy for future adult life through a longitudinal international study (OECD, 2007), although this has been criticized, not least because its measures are through written tests and questionnaires, which generally show developing countries to be in poor shape to meet such a goal. Philippine SettingThe Philippines established the National Science Development Board, in 1958 and Philippine Science High Schools or schools with science and tech-oriented classes were established because there is no streaming, or grouping of students according to their intellectual capacity at the higher levels of secondary school. Aims and Objectives The government recognizes the importance of science and technology capability for the development of our industry and country. The education sector collaborates with other government agencies to contribute to the success of government goals.As such, DECS (now DepEd) has focused its efforts towards programs and projects aimed at improving English, Science and Mathematics education in basic education. The objectives of elementary and secondary school science: †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   At the end of grade VI, the student is expected to apply his scientific knowledge and skills in recogniz ing and solving problems in relation to health and sanitation, nutrition, food production, preparation and storage, environment and the conservation of its resources, and evolving better ways and means of doing things. Bureau of Elementary Education, 1998) †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The Secondary Science Education Programme aims to develop understanding of concepts and key principles of science, science processes, skills and desirable values to make the students scientifically literate, productive and effective citizens (Bureau of Secondary Education, 1998). Education for three types of literacy can come from both formal and informal sources. In developing countries like the Philippines, informal sources are not as easily accessible as they are in affluent and developed countries.Much of such learning can be derived from museums, science centers, and botanical gardens, zoos, well-ordinate programme of lectures and experiments, visits to manufacturing companies and indus trial sites, science fair and camps, media, clubs and science-related organizations. With a minimum of such resources, most developing countries rely on formal education (generally up to elementary levels only) for the development of scientific literacy of their citizens. ProblemsThe Survey of Outcomes of Elementary Education (SOUTEL) reported the poor performance of elementary school pupils and the lack of difference in the achievement of 5th and 6th grade. Third International Mathematics and science study (1915) reported also that Philippines ranked among the lowest scoring countries. Problems are encountered in curriculum, learning materials, teachers and students performance. Factors of low achievement in science and mathematics (Ibe, M) ? Absence of a science culture ?  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Teacher training, the school curriculum   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Instructional material ?  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Teacher-learning process ?  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Languag e instruction ?  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Governance of education Reforms I. Improvements are foreign-assisted projects implemented in the country. Among these are: †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The Science and Mathematics Education Manpower Development Program (SMEMDP) of the Japan Bank †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Project in Basic Education (ProBE) funded by Australian Agency for International Development (AusAID) †¢ National Science Teaching and Instrumentation Center, a project with German governmentII. Information and Communication Technology (ICT) Enhancing scientific literacy through science education is developing an ability to creatively utilize appropriate evidence-based scientific knowledge and skills, particularly with relevance for everyday life and a career, in solving personally challenging yet meaningful scientific problems as well as making responsible socio-scientific decisions. But it is necessary to recognize that enhancing scientific literacy is also dependent on the need to:Develop collective interaction skills, personal development and suitable communication approaches as well as the need to exhibit sound and persuasive reasoning in putting forward socio-scientific arguments. The emphasis on enhancing scientific literacy is placed on an appreciation of science; the development of personal attributes and be acquisition of socio-scientific skills and values. (Holbrook and Rannikmae, 2007) The government has a lot to do to improve the quality of science and technology education in the country.The Department of Education, Culture and Sports (DECS) should implement and develop the programs created. The school should nurture the talents and skills of students to develop their scientific literacy as well as appliying the knowledge in their lives. References: †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Batomalaque, A. Basic Science Development Program of the Philippines for International Cooperation. University of San Car los, Cebu City, Philippines. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Hernandez, D. History and Philosophy of Science Education. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Holbrook, J. and Rannikmae, M. 2009. â€Å"The Meaning of Science Literacy† in Coll, R. nd Taylor, N. (Eds. ), Special Issue on Scientific Literacy. International Journal of Environmental and Science Education. Vol. 4 No. 3. July, 2009. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Ibe, M. and Ogena, E. â€Å"Science Education in the Philippines: An Overview. † Presented at the Science Education Congress, ISMED, November 27-28, 1998. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   http://www. ibe. unesco. org/fileadmin/user upload/ archive/ curriculum/China/Pdf/beijingrep. pdf †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   http://www. suite101. com/article. cfm/mass communication/ 95438 †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   www. sensepublishers. com/catalog/files/9789087905071. pdf