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This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked‐output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson‐Sparck Jones F4 retrospective relevance weight. Extended experiments with seven document test collections show that the ga can often find weights that are slightly superior to those produced by the deterministic weighting scheme. That said, there are many cases where the two approaches give the same results, and a few cases where the F4 weights are superior to the ga weights. Since the ga has been designed to identify weights yielding the best possible level of retrospective performance, these results indicate that the F4 weights provide an excellent and practicable alternative. Evidence is presented to suggest that negative weights may play an important role in retrospective relevance weighting.

The purpose of this paper is to present a novel method to design and manufacture rapid prototyping (RP) lightweight photopolymer‐resin models for wind‐tunnel tests. This method can ensure the structural configuration similarity considering model deformation under aerodynamic loads.

Photopolymer‐resin based on RP technique was used to fabricate DLR‐F4 models. Testing in a subsonic and transonic wind tunnel was carried out and the test results were compared to analyze performance predictions.

RP photopolymer‐resin wind‐tunnel models fabricated by the design methods yielded satisfactory aerodynamic performance. The methods can decrease the model's weight and prevent resonance occurrence among the models, wind‐tunnel, and support system, shorten the processing period, and lead to decrease in manufacturing period and cost.

Stiffness shortage of the thin components, such as wing tip, often leads to deformation occurrence under aerodynamic loads in transonic wind‐tunnel tests, which has significant influence on aerodynamic characteristics of the test models. Therefore, model deformation should be taken into account in the design process.

This design and manufacture method, aerodynamic and structural combination design and structural optimization, can obtain RP lightweight photopolymer‐resin wind‐tunnel models for satisfactory aerodynamic performance, which makes RP techniques more practical for manufacturing transonic wind‐tunnel test models, considering deformation induced by aerodynamic forces such as lift force. The methods also present an inexpensive way to test and evaluate preliminary aircraft designs, in both academia and industry.

This paper aims to explore the growth of the South African additive manufacturing (AM) industry over the past 31 years through the lens of the innovation system (IS) perspective, examining the actor dynamics and mechanisms that facilitated or hindered the industry’s development.

The study used a case study research approach, analysing semi-structured interviews with eight South African AM experts and documentary evidence. The IS framework and the realist evaluation perspective were used, using a context-intervention-mechanism-outcome (CIMO)-based event history analysis (EHA) framework to explore the actor dynamics and mechanisms of the case study.

The study used a case study research approach, analysing semi-structured interviews with eight South African AM experts and documentary evidence. The IS framework and the realist evaluation perspective were used, using a CIMO-based EHA framework to explore the actor dynamics and mechanisms of the case study.

This paper contributes to the South African AM industry literature by providing an overview of the industry events over the past three decades and analysing the industry through the IS framework. The study is among the first to analyse the development of the South African AM industry, presenting innovation scholars and managers with valuable decision-making support by providing insights into the innovation activities performed during each stage of the industry’s development, who performed them, the sequence in which they were performed and the outcomes they delivered.

The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain.

The paper presents a method for adapting semantic web technologies on multiple criteria decision-making algorithms to endow to them dynamic characteristics. It also showcases the enhancement of the decision-making process by visual analytics.

The semantic enhanced ranking method enables the reproducibility and transparency of ranking results, while the visual representation of this information further benefits decision makers into making well-informed and insightful deductions about the problem.

This approach is suitable for application domains that are ranked on the basis of multiple criteria.

The discussed approach provides a dynamic ranking methodology, instead of focusing only on one application field, or one multiple criteria decision-making method. It proposes a framework that allows integration of multidimensional, domain-specific information and produces complex ranking results in both textual and visual form.

The purpose of this paper is to know how do early-phase planning of real estate (RE) and facilities management (FM) create value for owners and users of commercial and public sector buildings.

The data were collected in Norway in 2015 through a national online survey (N = 837). The sample gives a good picture of Norwegian owners’ and even users on tactical-level (customer) perspectives on RE and FM. The data have been analysed through descriptive statistics and exploratory factor analysis. The hypotheses have been tested through analyses of correlations and ordinary least square (OLS) linear regressions.

Exploratory factor analysis made it possible to establish seven composite variables (constructs). Based on these seven constructs, six hypotheses were derived and tested. Obstacles and financials have no significant effect on buildings’ perceived usability. The most important factors during early-phase planning that influence buildings’ perceived usability and lifetime value creation are measures promoting environment and life-cycle costs (LCC), FM, adaptability and image.

Further empirical and preferably, comparative studies are needed to establish whether the findings can be generalized. The study has shown that a building’s usability and lifetime value creation is largely determined by decisions made during early phase planning.

Well-founded early-phase planning of RE and FM may actually provide very high return on the investments and significantly improve the buildings’ lifetime value creation for owners and users. Early-phase planning is also of great importance both for buildings’ physical design, as well as for successful FM during the buildings’ use phase, and may prevent irreversible blunders.

This is a large N empirical study in Norway. The findings indicate what owner and users of buildings should emphasize during early phase planning.

The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.

IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).

The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.

The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.

The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

Aims to identify factors which affect the location decisions of companies in the Greek metal industry. Attempts to determine what these factors are, how important they are relative to each other and how important taxes and inducements (financial factor) are in the process of location decision making. The methodology employs: a point allocation system to evaluate the relative importance of the various location factors, experts′ opinion and mail questionnaire for data collection, experience in undertaking new investment for drawing the sample firms, a prospective approach, and non‐parametric statistics. The results are: 16 factors are identified; labour is the first, and financial factor is the second in importance; and the difference in importance between financial factor and other remaining factors is not statistically significant. Concludes that, given the evidence there would be nothing to lose and everything to gain from a much more critical approach to the use of the financial factor as an instrument of regional policy.

Attempts by Greek governments to attract manufacturing industry to problem regions have been shaped by a large number of development laws. Location policy works principally by influencing the decisions of individual firms. The article examines the relevance of Greek government location policy and the local criteria actually used by firms operating in the food industry. Raw materials were the most important determinant and the results otherwise suggest that the use of financial factors as an instrument of regional policy needs re‐examination.

The purpose of this study is to identify organisational homeostasis factors in the context of healthcare organisations and to develop a conceptual model for green transformation.

The organisational homeostasis factors were determined by review of literature study and the opinions of healthcare experts. Scheduled interviews and closed-ended questionnaires are employed to collect data for this research. This study employed “TISM methodology” and “MICMAC analysis” to better comprehend how the components interact with one another and prioritise them based on their driving and dependence power.

This study identified 10 factors of organisational homeostasis in healthcare organisation. Recognition of interdependence, hormesis, strategic coalignment, consciousness on dependence of healthcare resources and cybernetic principle of regulations are the driving or key factors of this study.

The study's primary focus was on the organisational homeostasis factors in healthcare organisations. The methodological approach and structural model are used in a healthcare organisation; in the future, these approaches can be applied to other industries as well.

The key drivers of organisational homeostasis and the identified factors will be better comprehended and understood by academic and important stakeholders in healthcare organisations. Prioritizing the factors helps the policymakers to comprehend the organisational homeostasis for green transformation in healthcare.

In this study, the TISM and MICMAC analysis for healthcare is proposed as an innovative approach to address the organisational homeostasis concept in the context of green transformation in healthcare organisations.