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The paper considers special aspects of technical translation and peculiarities of engineering terminology interpretation in English-Russian language pair correlation. Problems of adequate translation of general and specialized words, vector of compound lexical elements in engineering context, text production and inversion in the English and Russian languages, transformations in technical translation with compression and addition techniques are analysed. Preference of building English engineering terms with common words, mainly the "noun-noun" group is highlighted.

In exploring the epistemology of engineering science, we propose a model of engineering. This model incorporates the goals of engineering, the approach to engineering (also called the engineering method) and the role of experience in engineering. The basis for understanding the nature of engineering science will be explored, and will be contrasted with natural science. To begin, a large-scale engineering project that was successfully completed in Ireland many years ago is discussed - specifically, the development of a megalithic passage tomb as an exemplar of the engineering method in structural design, project management and aesthetics. This exemplar firmly demonstrates that engineering method existed before the development and understanding of the relevant natural science. We next contrast the nature of engineering or engineering science and natural science. This discussion will further develop the engineering model, but will contrast the philosophical differences between engineering and science. We then return to build upon the 'engineering model' through the modern day exemplar of the development of the jet engine, demonstrating that invariably multiple factors, including creative design initiatives from different sources, global, political, economic and cultural circumstance, and the passage of time contribute to the evolution and success (or failure) of large sustainable scientific and engineering projects. In conclusion, the engineering model is mapped to a philosophical model demonstrating that philosophy is as relevant to engineering as it is to other fields.

Abstract for the PAWS Women in Science & Engineering Event at which Sandy Black delivered the lecture: New jobs, new skills and new consumer choices are emerging as governments, employers, organisations and individuals work towards a low carbon future. And further change is needed in manufacturing and lifestyles if the UK is to meet agreed targets for the reduction of CO2 emissions - 80% by 2050. From fashion to farming to forging new materials. the degree to which this new culture will permeate our lives is only just being appreciated. This PAWS (Public Awareness of Science) evening introduced some of the women scientists and engineers working to meet this challenge. It also looked at the challenges involved in communicating the issues surrounding a low carbon future to a wide public.

1 Introduction -- 1.1 What are numerical methods? -- 1.2 Numerical methods versus numerical analysis -- 1.3 Why use numerical methods? -- 1.4 Approximate equations and approximate solutions -- 1.5 The use of numerical methods -- 1.6 Errors -- 1.7 Non-dimensional equations -- 1.8 The use of computers -- 2 The solution of equations -- 2.1 Introduction -- 2.2 Location of initial estimates -- 2.3 Interval halving -- 2.4 Simple iteration -- 2.5 Convergence -- 2.6 Aitken's extrapolation -- 2.7 Damped simple iteration -- 2.8 Newton-Raphson method -- 2.9 Extended Newton's method -- 2.10 Other iterative methods -- 2.11 Polynomial equations -- 2.12 Bairstow's method 56 Worked examples 58 Problems -- 3 Simultaneous equations -- 3.1 Introduction -- 3.2 Elimination methods -- 3.3 Gaussian elimination -- 3.4 Extensions to the basic algorithm -- 3.5 Operation count for the basic algorithm -- 3.6 Tridiagonal systems -- 3.7 Extensions to the Thomas algorithm -- 3.8 Iterative methods for linear systems -- 3.9 Matrix inversion -- 3.10 The method of least squares -- 3.11 The method of differential correction -- 3.12 Simple iteration for non-linear systems -- 3.13 Newton's method for non-linear systems -- Worked examples -- Problems -- 4 Interpolation, differentiation and integration -- 4.1 Introduction -- 4.2 Finite difference operators -- 4.3 Difference tables -- 4.4 Interpolation -- 4.5 Newton's forward formula -- 4.6 Newton's backward formula -- 4.7 Stirling's central difference formula -- 4.8 Numerical differentiation -- 4.9 Truncation errors -- 4.10 Summary of differentiation formulae -- 4.11 Differentiation at non-tabular points: maxima and minima -- 4.12 Numerical integration -- 4.13 Error estimation -- 4.14 Integration using backward differences -- 4.15 Summary of integration formulae -- 4.16 Reducing the truncation error 146 Worked examples 149 Problems -- 5 Ordinary differential equations -- 5.1 Introduction -- 5.2 Euler's method -- 5.3 Solution using Taylor's series -- 5.4 The modified Euler method -- 5.5 Predictor-corrector methods -- 5.6 Milne's method, Adams' method, and Hamming's method -- 5.7 Starting procedure for predictor-corrector methods -- 5.8 Estimation of error of predictor-corrector methods -- 5.9 Runge-Kutta methods -- 5.10 Runge-Kutta-Merson method -- 5.11 Application to higher-order equations and to systems -- 5.12 Two-point boundary value problems -- 5.13 Non-linear two-point boundary value problems 198 Worked examples 199 Problems -- 6 Partial differential equations I — elliptic equations -- 6.1 Introduction -- 6.2 The approximation of elliptic equations -- 6.3 Boundary conditions -- 6.4 Non-dimensional equations again -- 6.5 Method of solution -- 6.6 The accuracy of the solution -- 6.7 Use of Richardson's extrapolation -- 6.8 Other boundary conditions -- 6.9 Relaxation by hand-calculation -- 6.10 Non-rectangular solution regions -- 6.11 Higher-order equations 238 Problems -- 7 Partial differential equations II — parabolic equations -- 7.1 Introduction -- 7.2 The conduction equation -- 7.3 Non-dimensional equations yet again -- 7.4 Notation -- 7.5 An explicit method -- 7.6 Consistency -- 7.7 The Dufort-Frankel method -- 7.8 Convergence -- 7.9 Stability -- 7.10 An unstable finite difference approximation -- 7.11 Richardson's extrapolation 261 Worked examples 262 Problems -- 8 Integral methods for the solution of boundary value problems -- 8.1 Introduction -- 8.2 Integral methods -- 8.3 Implementation of integral methods 271 Worked examples 278 Problems -- Suggestions for further reading.

The article presents a methodological system of equalizing training of computer science students in the preparation of technical specialists in the areas of 09.03.01 "Computer Science and Computer Engineering" and 09.03.04 "Software engineering", built on the basis of a step-by-step model of the methodology of leveling students' knowledge, skills and abilities in the use of information technology due to their different training at the previous stage and including adaptation, leveling and control stages. The analysis of the concept of a methodological system from different positions is carried out. The proposed methodological system of equalizing training of computer science students in the preparation of future IT specialists is determined by the goals, content, forms, methods and means of training. The authors substantiate that the target component is system-forming and determines the functions of the other components of the system, it must be considered as a system of goals at three levels: integrative goal, milestone goal, operational goal. It is shown that the content component of the methodological system of leveling training is aimed at the formation of theoretical and practical knowledge in the discipline, theoretical sections for studying, practical tasks for performing at a laboratory workshop and control work tasks for testing students' knowledge and skills are defined. Examples of control work tasks are given. The procedural component of the methodological system of leveling training is described, including methods, forms and means that contribute to achieving the goals of the methodological system.

For engineering and scientific endeavors to progress there must be generally accepted ethical guidelines in place to which engineers and scientists must adhere. This book explores the various scientific and engineering disciplines, examining the potential for unethical behavior by professionals. Documented examples are presented to show where unethical behavior could have been halted before it became an issue. The authors also look to the future to see what is in store for professionals in the scientific and engineering disciplines and how the potential for unethical behavior can be negated.