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Currently, the majority of designed robots are not well‐matched to their applications because designers do not employ a clear and organized design process. Additionally, the high cost of robotic systems makes it difficult to financially justify the use of this technology. The purpose of this paper is to present a new design process that gathers conceptual, kinematic and dynamic design, finite elements method (FEM), functional design and virtual reality control. Furthermore, kinematic and dynamic design can be obtained by traditional theory or standard computer tools (SCT) to accelerate the design. Through SCT fitted mathematical models and non‐mathematical virtual models may be acquired.

This paper investigates the design process of a robot. First, the entire methodology is presented (including two new techniques for solving the kinematic and dynamic questions via SCT). Second, a case study using Autodesk^® Inventor™ has been analysed to assess the feasibility of the method and techniques.

The more stages of the design process are considered, the more successful solutions become. Designers can obtain a mathematical solution for an analytically unsolvable robot fitting a mathematical model by SCT. To obtain a rapid design, designers must consider using SCT and following just in need (JIN) philosophy to find a non‐mathematical virtual model.

This paper presents an innovative guide for robotic engineers and researchers which covers the whole design process and new techniques for obtaining mathematical and non‐mathematical solutions.

Begins by focusing on the rise of formalism in the post‐war period leading to virtual monopoly as far as the method of economic analysis is concerned, and on the main consequences of this process. After mounting criticism of formalism, internal as well as external, it is acknowledged that some signs of renewed interest in a non‐mathematical approach and in its potentialities can be observed, particularly in the 1990s. Moreover, it is postulated that free competition between different methods, as opposed to concentrating on only one, is the best framework for the progress of economics. In particular, it increases the possibilities for dealing with many economic problems in an innovative way. Finally, outlines a possible stage where the two main methods (mathematical and non‐mathematical) have a more balanced and useful role in the course of economic analysis.

In his discussion of early lexical development in children Barrett (1995) notes that utterances can be of the following types: an expression of an internal state, a response to a specific context, a social-pragmatic utterance and a referential utterance. A referential utterance can be thought of in the following way: the thing referred to, a mental representation, a word representation and a word sound. An utterance may refer to an object, an action, an attribute or an event. Some utterances are used as the names of classes of objects while other utterances are used as the proper names of individual objects. Looking at this in abstract we might say that, in the early years of childhood, language is used to refer to elements, sets, functions and relations – in other words to the mathematical structures which were discussed in Chapter 2. Of course although early language is used to refer to mathematical objects, the character of the language itself takes the form of ordinary language.

Volume VI, the last volume of the comprehensive work on Aerodynamic Theory prepared under a grant of the Guggenheim Fund for the Promotion of Aeronautics, is sub‐divided into five divisions (P‐T). Division (P), by Professor Durand, deals with the aeroplane as a complex of four interacting systems—the lifting system, the non‐lifting system, the propulsive system and the control system. These four systems interact mutually, and the purpose of the division is to give in a descriptive and non‐mathematical way a general discussion on the nature of these interactions, reference being made where necessary to the more detailed mathematical treatments given in earlier divisions.

A variety of quantitative specifications, usually in terms of numerical limits, have been developed in industry for the description, prediction and control of product quality. As the theoretical foundations of these specifications are often beyond the working knowledge of many manufacturing engineers and managers, this article gives a non‐mathematical account of some of the limits commonly encountered in discussions related to product design, manufacture and inspection; emphasis is placed on the distinctions in their intended purposes and methods of application.

Much of the discussion, both written and verbal, of management information systems tends to be dominated by accountants and computer people; management scientists and behavioural scientists have not yet made anything like the impact their skills should warrant. My purpose in this paper is to examine a number of important aspects of management information systems from the standpoint of the management scientist. I have deliberately attempted to explain concepts in a non‐mathematical fashion in the hope that the general, non‐technical reader will gain some understanding of their nature and relevance to practical information systems; to this end, no mathematical formulae have been introduced.

It is widely recognized that the use of statistical experimental design techniques, including those advanced by Taguchi, can, when applied correctly, lead to a deep understanding of processes and often to their improvement. The benefit arises out of the methodologies providing a vehicle to allow the application of the scientific method. Teaching this to students and those at work is difficult and there is a tendency to get embroiled in the analysis of results, which can lead to missing many of the practical issues and alienation of non‐mathematical students. Reports ways of giving a practical insight in a classroom situation, focuses on the actual running of an experiment in class and gives some results and observations, and describes methods which have been found to achieve the educational aims of giving understanding, confidence and motivation.

The purpose of this paper is to present a formal framework of vulnerability to climate change, to address the conceptual confusion around vulnerability and related concepts.

The framework was developed using the method of formalisation – making structure explicit. While mathematics as a precise and general language revealed common structures in a large number of vulnerability definitions and assessments, the framework is here presented by diagrams for a non‐mathematical audience.

Vulnerability, in ordinary language, is a measure of possible future harm. Scientific vulnerability definitions from the fields of climate change, poverty, and natural hazards share and refine this structure. While theoretical definitions remain vague, operational definitions, that is, methodologies for assessing vulnerability, occur in three distinct types: evaluate harm for projected future evolutions, evaluate the current capacity to reduce harm, or combine the two. The framework identifies a lack of systematic relationship between theoretical and operational definitions.

While much conceptual literature tries to clarify vulnerability, formalisation is a new method in this interdisciplinary field. The resulting framework is an analytical tool which supports clear communication: it helps when making assumptions explicit. The mismatch between theoretical and operational definitions is not made explicit in previous work.

This article contains suggestions for a practical and non‐mathematical approach to magnetism and electromagnetism for electrical craft students. Like the teaching scheme for the fundamentals of electric circuits discussed in the article in last month's TECHNICAL EDUCATION, the method may be found appropriate as a general introduction for students on other than craft courses.

THERE is no attempt made to be mathematical in tliis article, as it aims to interest the non‐statistical inquirer in the possibilities in a type of undeveloped quality control suited to much of our modern industry. Many of our most promising industries are only in the experimental phase, and thus success in international markets after the war is going to depend on the efficiency of the experiments undertaken. Such a statement applies to most modern light engineering industries, but is particularly true of the aircraft industry.