Scientific And Numerical Computing

The work in numerical and scientific computing involves the development, analysis and execution of computational algorithms in solving mathematical problems ranging from the spectrum of science to engineering. On application of these algorithms and techniques to a diverse range of problems an effective set of solutions can be reached. The area of knowledge and skill set required in the effective implementation of these algorithms especially on high-performance computers cannot be overlooked.

The area of knowledge and skill set required in the effective implementation of these algorithms as per still on high-performance computers cannot be overlooked.
Numerical computing is an approach to solve Complex mathematical problems by using simple operations. The approach basically involves of mathematical models i.e. physical situations to be solved with the help of arithmetic operations.

This type of computing requires development, analysis and the use of extensive algorithms. Numerical computation cannot be created with the simplicity of automatic operations; they require fast and efficient computing devices (Dhere, 2018). Personal computers have a profound impact in the application of numerical computing methods in solving scientific problems.Numerical computing is characterized by accuracy, efficiency and numerical stability. The first characteristic accuracy is an important aspect of numerical computing. Every method of numerical computing introduces errors. The errors affect the accuracy of the results hence they need to be considered during the processing stage.

The second characteristic is efficiency: in choosing a numerical method for the solution of a mathematical model it is important to consider efficiency. It generally means the amount of effort required by both human and computer in implementation of the method.
Numerical stability is an important characteristic. Errors introduced into a computation propagate in different ways. In many cases the errors tend to grow exponentially resulting in disasters i.e. computational disasters. The stability of numerical computing plays an Oberwolfach, (2019) important role in this working.

Putting it simply numerical computing is an interconnected combination of computer science and mathematics. It is used to develop and analyze algorithms in the solving of important problems in Science, Engineering, medicine and business for instance in the designing of a bridge for detecting tumors in medical images. Scientific computing is basically the science of solving problems with computers the problems arise from a diverse discipline ranging from mathematics engineering Biology physics chemistry and other natural Sciences consequently scientific computing is inter disciplinary by nature.

Scientific computing is defined as the third pillar of science and it stands right next to theoretical analysis and experiments for scientific Discovery. Scientific computing comes into the picture when numeric computing is not enough. The problem at hand is not able to be solved by traditional experimental or the theoretical means for instance attempting to predict climate change (Nassif & Fayyad, 2016).

It also comes into picture when experimentation is very dangerous for example characterization of toxic materials.Scientific computing is the collection of tools techniques and the theories that are required to solve on the complex computer mathematical models of problems in Science and Engineering. A majority of these tools techniques and theories are originally developed in mathematics. Many of them have their Genesis long before the advent of computers. This set of a mathematical theories and techniques is classified as numerical analysis constituting a major part of scientific computing.

The development of electronic computers has signalled a new era in the approach to the solution of scientific problems. In brief scientific computing draws on mathematics and computer science in developing the best way to use computer systems for solving problems ranging from Science to engineering.Furthermore computer simulations can be embedded in optimization algorithms for building optimal designs. The foremost example is the optimal design of aircrafts in computer instead of experience driven trial and error designs with the support of expensive wind tunnel experiments (Ueberhuber, 2012). The simulation is responsible for saving a lot of time and money in creating experiments and consequently creating a design.

In the past decade large scale computing has become a prevalent means of Discovery in areas of Research and Technology. The research area of numerical analysis and scientific computing is primarily responsible in this evolution of developing numerical methods for advanced simulation. Research in the area of scientific computing involves a variety of methods and techniques ranging from the development and mathematical analysis of numerical algorithms to advanced implementations on parallel supercomputers. Scientific computing is responsible for accurate computing results which is very important in many areas of Science and Engineering (Nassif & Fayyad, 2016). The dependence on actual experiments is much reduced saving money and time. Scientific computing is the new computing.

Ethics, law, and policies for privacy, security and liability
Privacy, trust and security are closely interrelated similar to the relation of law and ethics. Privacy, Preservation and security provisions rely on trust. Violation of privacy is a risk and a threat to security, law is a bridge: it is a resolution, ethics provide context to law. Law is responsible for bringing order into the system for instance law allows trading for the purpose of making a profit but ethics provides input to ensuring trade is conducted fairly. Privacy breaches disturb trust running the risk of diluting for losing security. In other words ethics is a show of respect to the law.

Data privacy mainly concerns itself with the access, use and collection of data. The data subject’s legal right to data concerns itself with the freedom from unauthorized access to Private data, in appropriate or misappropriate use of data and the accuracy and completeness in the collection of data (Guru 99, 2019) about a person or persons by technology.

Data privacy also concerns itself with the costs for instance if data privacy is breached the cost included are the so-called hard cost such as the financial parrot is composed by regulators are the compensation payments in lawsuits and the soft costs such as the reputation all damage and the loss of client trust.

Different cultures would different values on privacy affecting is stability E and universal value hence there is a broad consensus giving data privacy intrinsic Core and social value consequently the privacy approach embraces the law ethical principles and societal and environmental concerns despite the complexity and difficulty in upholding data privacy

Data Privacy Protection
Protecting data privacy is urgent and complex but the protection is very important because of the complexity of Technology driven and information intensive environment. The complexity of technology driven trend among other (Inderscience, 2019) things make the Marketplace more transparent and the consumers better informed. The trade practices are more fair, the downsides majorly include social techno risk originating with technology and its human uses. The created opportunities give space to organized and complex cyber criminals. This risk is majorly responsible for information protection as being propelled to the top of the corporate management agenda.

The requirement for the need of data privacy protection is urgent due to its multidirectional demand information protection is an essential information security function to develop and implement strategies ensuring data privacy policies standards guidelines and processes are property communicated and compiled with the policies. The standards are required to be technically efficient economically and legally sound and justifiable as well as ethically consistent and socially acceptable.

The social problems encountered after the implementation and contract sending are of a technical and ethical nature hence information (Wanbil, Zankl, & Chang, 2016) security decisions need to be logically workout.Data privacy protection is complex due to the socio techno risk. The risk occurs with the abuse and exploitation of technology used to store and process data.

Using technology in a manner not consistent with ethical principles has a high probability of creating ethical risk and another type of risk due to its progression and evolution. In order to regulate the risk associated it is required to take measures effectively. These measures should not be considered as a burden but should be welcomed into the corporate atmosphere considering the importance of Technology.

Though the problem of data privacy is serious and complex it is not unsolvable. A composite approach fine tunes these loopholes or errors in the system making the technology ethically and socially feasible.

The International Data Privacy Principles
Data privacy can be successfully achieved through USENIX, (2019) technical as well as social solutions. Technical Solutions majorly include safeguarding the data from unauthorized for accidental access or loss. The social solutions include the creation of acceptability and awareness among the customers on how the data is being used and doing so in a transparent and confidential way.

Employees are required to comply with the corporate privacy rules and organizations are responsible in instructing them in actively avoiding activities compromising privacy. The third element of achieving privacy is complying with data protection laws and regulations. The first concern with the data protection is the legal regulation. It is slow and unable to keep up with the rapid development of Information Technology. Legal regulations are basically one step behind technological developments. The data privacy by electronic means must not only be based on traditional jurisdiction but also on soft law self-binding policies. Data privacy is inherent (Jenny O’Brien, (2019) to usage and manipulation of data. The regulation of data privacy is equivalent to the management of data.