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SUMMARY A classification is a theory of the structure of knowledge. From a discussion of the nature of truth, it is held that scientific knowledge is the only knowledge which can be regarded as true. The method of induction from empirical data is therefore applied to the construction of a classification. Items of knowledge are divided into uniquely definable terms, called isolates, and the relations between them, called operators. It is shown that only four basic operators exist, expressing appurtenance, equivalence, reaction, and causation; using symbols for these operators, all subjects can be analysed in a linear form called an analet. With the addition of the permissible permutations of such analets, formed according to simple rules, alphabetical arrangement of the first terms provides a complete, logical subject index. Examples are given, and possible difficulties are considered. A classification can then be constructed by selection of deductive relations, arranged in hierarchical form. The nature of possible classifications is discussed. It is claimed that such an inductively constructed classification is the only true representation of the structure of knowledge, and that these principles provide a simple technique for accurately and fully indexing and classifying any given set of data, with complete flexibility.

The purpose of this study is, first, to determine which developments have shaped official hotel classification systems over recent years (including the impact of guest-review platforms) and second to establish the future of those systems through the eyes of the people who are actually in charge of operating them.

Semi-structured interviews were chosen as the most suitable method for approaching hotel classification system administrators. This method is in line with previous research on approaching key informants in their respective fields. Sixteen people representing 12 different official national hotel classification systems from across the world as well as one commercial hotel star rating system participated in the online interviews.

The first main conclusion is that hotel classification systems – especially voluntary ones – would not have survived the enormous impact of guest-review platforms without quickly adjusting to the ever-changing hotel industry landscape. The frequent review of classification criteria and procedures has become the main survival strategy of classification systems. The second conclusion is that system operators are strongly optimistic about the future outlook of hotel classification based on their proven flexibility to swiftly adapt to new market conditions.

Research about hotel classification systems is usually based on the views of the systems' users, i.e. hotels or hotel guests, whereas the present paper reflects the perspective of the systems' operators, an angle rarely analyzed in the literature.

Construction plant maintenance practice and its plant operators are inextricably linked. This is because, unlike plant operating within the manufacturing sector, construction plant is largely dependent upon operator skill and competence to maintain the item in a safe, fully operational condition. Research has previously successfully modelled machine breakdown, but revealed that the operator’s impact upon machine breakdown rates can be considerable. A conceptual model methodology with which to assess the maintenance proficiency of individual plant operators is presented. Specifically, an artificial intelligent classification model is proposed as a means of classifying plant operator maintenance proficiency into one of three bandings. These are good, average and poor. The results of such work will form the basis of new prescriptive guidelines, for incorporation into the new certificate of training achievement (CTA) scheme, available to inexperienced construction plant operators. The paper concludes with an indication of the palpable benefits of such research, to plant owners and the construction industry at large.

The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.

In a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.

In order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.

The main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

The chapter presents experiences of volunteers to organise and operate public transport services in Germany. A brief overview of the practical issues and a discussion of the possibilities and limitations is given.

The work is based on the author’s experience as a consultant and researcher in the field.

Approximately 250 Bürgerbus presently exist, mainly in rural areas where everyday operations are carried out by a group of volunteers. Service planning is done in partnership with local authorities and transport providers, and benefits from the volunteers’ local knowledge. The services use small vehicles and have a complementary function in the transport system. They primarily cater to local shopping and leisure journeys and, although available to the general public, are predominantly used by pensioners. In recent years, the original concept has evolved and a much greater variety can now be seen.

Beyond providing mobility, the establishment of a Bürgerbus service is an important community achievement and contributes to social cohesion. Variations in the general popularity of volunteering can also be seen in the differing numbers of Bürgerbus schemes in the German regions.

Civic engagement is an important resource for community life, which can also be used for improving mobility. However, experience shows that the professional public transport industry and transport policy must understand the volunteers’ motivations, provide a suitable framework and support to develop concepts tailored to local needs.

The purpose of this paper is to develop a novel and systematic framework for bridge inspection and management to improve the efficiency in current practice.

A new framework that implements camera-based unmanned aerial systems (UASs) with computer vision algorithms to collect and process inspection data, and Bridge Information Modeling (BrIM) to store and manage all related inspection information is proposed. An illustrative case study was performed using the proposed framework to test its feasibility and efficiency.

The test results of the proposed framework on an existing bridge verified that: high-resolution images captured by an UAS enable to visually identify different types of defects, and detect cracks automatically using computer vision algorithms, the use of BrIM enable assigning defect information on individual model elements, manage all bridge data in a single model across the bridge life cycle. The evaluation by bridge inspectors from 12 states across the USA demonstrated that all of the identified problems, except for being subjective, can be improved using the proposed framework.

The proposed framework enables to: collect and document accurate bridge inspection data, reduce the number of site visits and avoid data overload and facilitate a more efficient, cost-effective and safer bridge inspection process.

This paper contributes a novel and systematic framework for the collection and integration of inspection data for bridge inspection and management. The findings from the case study suggest that the proposed framework should help improve current bridge inspection and management practice. Furthermore, the difficulties experienced during the implementation are evaluated, which should be helpful for improving the efficiency and the degree of automation of the proposed framework further.

The principles of the author's theory of classification are summarized, and the necessity of expressing true relations between concepts in a classification is stressed. The logical faults in existing classifications (especially U.D.C., Bliss, and Colon) are discussed in comparison. The psychological and logical bases of the author's theory are considered in greater detail than before, especially as regards the derivation of the operators. In this connexion a change has been found necessary in the writing of the reaction operator, being A/—B, for B acts on A (instead of A—/B). Four new operators are introduced, being ‘dimensional’ (time and space, &c.), ‘comparison’, ‘association’, and ‘concurrence’, the last three on a basis of learning theory and work on conditioned responses in psychology. Examples are given of their uses. Operators represent logical relations, and their meanings, in everyday language, are discussed. The selection of a preferred order for the construction of a classification is shown to be possible on a logical basis, being the fully deductive order. The problem of notation is then dealt with in detail. It is shown that a fully elastic ‘deductive’ notation, allowing extrapolation and interpolation in all ways, not achieved in other classifications, is possible, but still does not meet the requirements of inductive classification. A notation is developed which provides arbitrary symbols for isolates, connected by operator symbols, and this is shown to be the only solution which meets all the requirements for expressing an inductive classification according to the author's theory.

Hiring, selecting or assessing plant operatives' proficiency in the UK construction industry is an increasingly difficult task. A number of plant operator certification schemes are available to practitioners and each scheme trains to a myriad of bespoke standards. Consequently, the decision to employ a candidate often rests upon the employer's intuition and judgement and creates an unnecessary dilemma. To address this aforementioned problem, findings of research work that modelled plant operators' maintenance proficiency is presented. A UK nationwide survey was conducted to elicit plant professional opinion on what ‘training and educational’ (T&E) attributes constitute ‘good’ operator proficiency. The data was then arranged into three categories of operator maintenance proficiency: good, average and poor Multivariate Discriminant Analysis (MDA) was used on 75 percent of a simulated data set. The model utilised five T&E attributes, namely: duration of training provided, operator holder of alternative training card (not Certificate of Training Achievement (CTA) or Scottish/National Vocational Qualifications (S/NVQ)), operator's oral communication skills, operator's planning skills and operator's mechanical knowledge. Performance analysis revealed that model classification accuracy was 89.10 percent. The remaining 25 percent hold out sample was then modelled for validation purposes using the derived MDA model. Accuracy of the sub‐sample model was high at 77.60 percent whilst a paired sample T‐tests for the 75 percent and 25 percent sample data established that there was no significant statistical difference between actual and predicted classifications. Future work is proposed that aims to model other factors that influence operator maintenance proficiency; namely, work situational, motivational management and personal factors.

The time‐discretization process of transient equation systems is an important concern in computational heat transfer applications. As such, the present paper describes a formal basis towards providing the theoretical concepts, evolution and development, and characterization of a wide class of time discretized operators for transient heat transfer computations. Therein, emanating from a common family tree and explained via a generalized time weighted philosophy, the paper addresses the development and evolution of time integral operators [IO], and leading to integration operators [InO] in time encompassing single‐step integration operators [SSInO], multi‐step integration operators [MSInO], and a class of finite element in time integration operators [FETInO] including the relationships and the resulting consequences. Also depicted are those termed as discrete numerically assigned [DNA] algorithmic markers essentially comprising of both: the weighted time fields, and the corresponding conditions imposed upon the dependent variable approximation, to uniquely characterize a wide class of transient algorithms. Thereby, providing a plausible standardized formal ideology when referring to and/or relating time discretized operators applicable to transient heat transfer computations.

Research suggests that personal motivation is a critical internal driving force that, if harnessed, can significantly improve an operator's productivity rate when working mobile plant and machinery. The purpose of this paper is to investigate the impact of personal motivation upon plant operator productivity (and examine those variables that stimulate personal motivational forces).

To achieve this, an artificial neural network (ANN) was developed. The ANN's topology comprised a multilayer perceptron, with one hidden layer and three output classifications of “good”, “average”, and “poor” (productivity performance). During development of this model, a non‐linear dynamic mapping function and metric were used to improve its classification accuracy. The model initially utilised 32 independent (input) variables identified from the literature such as: pay bonuses; relationships between work colleagues; promotion prospects; and job satisfaction.

Subsequent analyses condensed these variables down to the five most significant (i.e. best motivational classifiers of operative productivity), these being: (v₂) receipt of payment for overtime, (v₁₁) job promotion potential, (v₂₂) a safe working environment, (v₂₄) variety of work activities, and (v₃₁) availability of flexible work patterns. Model accuracy when employing these most significant predictors was high at 87.67 per cent. By testing on a hold out sample of original data, the developed model was validated as being reliable and robust.

The main conclusion of the work is that operators' personal motivation can best be encouraged by paying attention to “personal satisfiers” and “security” aspects, with particular emphasis being given to work flexibility and variety, a safe work environment, and appropriate operator remuneration. By delivering and exploiting these variables, employers can improve plant productivity rates and, as a consequence, company profitability.