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With the increasing spread of ontologies in various domains, units have gradually become an essential part of ontologies and units ontologies have been developed to offer a better expression ability for the practical usage. From the perspectives of architecture, comparison and reuse, the purpose of this paper is to provide a comprehensive survey on four mainstream units ontologies: quantity-unit-dimension-type, quantities, units, dimensions and values, ontology of units of measure and units ontology (UO) of the open biomedical ontologies, in order to address well the state of the art and the reuse strategies of the UO.

An architecture of units ontologies is presented, in which the relations between key factors (i.e. units of measure, quantity and dimension) are discussed. The criteria for comparing units ontologies are developed from the perspectives of organizational structure, pattern design and application scenario. Then, the authors compare four typical units ontologies based on the proposed comparison criteria. Furthermore, how to reuse these units ontologies is discussed in materials science domain by utilizing two reuse strategies of partial reference and complete reference.

Units ontologies have attracted high attention in the scientific domain. Based on the comparison of four popular units ontologies, this paper finds that different units ontologies have different design features from the perspectives of basis structure, units conversion and axioms design; a UO is better to be applied to the application areas that satisfy its design features; and many challenges remain to be done in the future research of the UO.

This paper makes an extensive review on units ontologies, by defining the comparison criteria and discussing the reuse strategies in the materials domain. Based on this investigation, guidelines are summarized for the selection and reuse of units ontologies.

In the recent years, the rapid growth of the grocery retailing industry has created a great heterogeneity in prices across sellers in the market. Online price comparison agents which are key mechanisms to solve this problem by providing prices from different sellers. However, there are many sellers in the grocery industry do not offer online service, and so it is impossible to automatically retrieve price information from such grocery stores. In this manner, crowdsourcing can become an essential source of information by collecting current price data from shoppers. Therefore, this paper aims to propose Kiyaslio, a gamified mobile crowdsourcing application that provides price information of products from different grocery markets.

Kiyaslio has been developed through leveraging the power of crowdsourcing technology. Game elements have also been used to increase the willingness of users to contribute on price data entries. The proposed application is implemented using design science methodology, and it has been evaluated through usability testing by two well-known techniques which are the system usability scale and the net promoter score.

The results of the usability tests indicate that participants find Kiyaslio as functional, useful and easy to use. These findings prove its applicability and user acceptability.

The proposed platform supports crowd sourced data collection and could be effectively used as a tool to support shoppers to easily access current market product prices.

This paper presents a mobile application platform for tracking current prices in the grocery retail market whose strength is based on the crowdsourcing concept and incorporation of game elements.

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes being considered. However, little attention has been applied to deciphering which specification is best supported by the data. This paper highlights the use of existing Bayesian model comparison techniques to investigate the proper specification for endogenous covariates and to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes.

The comparison of large volumes of complex data resulting from numerical modelling in computational electromagnetics is a demanding task, especially when validating the performance of numerical models against experimental results and testing experimental repeatability. “By‐eye” comparisons can lead to inconsistencies and inherent subjectivity. This paper establishes a “visual” benchmark by which comparisons can be made and therefore used to assist in the development of an algorithmic approach to data comparison.

This new method presented here is based on the Cooper‐Harper Rating Scale, which is a test pilot's evaluation‐rating instrument. This has been modified through qualitative research. The assertion that the rating scale will leave the group mean response unaltered but will reduce the variance has been statistically tested.

The proposed rating scale provides a calibration technique by which to benchmark comparisons. The scale also reduces subjectivity by producing an overall quantitative measure of similarity. The paper concludes with an application of the rating scale to assessment of a candidate algorithmic approach against correlation.

The research findings are based on small data sets, which is a limit imposed by the industrial environment in which this scale will be used.

This paper provides a tool to overcome some of the key substantial difficulties in communicating similarity or difference, namely that “similarity” and “difference” have no stand‐alone definition, there is a lack of a shared language for the comparisons and little commonality for a decision‐making framework.

This paper provides modellers and experimentalists in computational electromagnetics (particularly electromagnetic compatibility) with a structured approach to quantifying the quality of comparative results.

The purpose of this paper is to investigate the role of the different circular business models (CBMs) in the manufacturing and service sector and apply this in the context of the food industry (FI), through a systematic literature review of related published journals and articles. The research study is designed to illustrate the impact of CBMs in the manufacturing and service sectors through a combination of real-life examples and direct references to existing literature.

The underlying research study follows a systematic literature review approach where the relevant CBMs are explored in the context of the manufacturing and service sector (FI.) The journals most cited in the context of CBMs and their implication in the manufacturing and service sector were chosen for this study. There was no fixed timeframe applied to complete the research. A total of 54 articles were selected which referenced: discussion in the context of the concept of CBMs; different types of CBM; definition of the manufacturing sector; definition of the service sector (FI); application of different CBMs in these two sectors; and included a comparison of the application of CBMs. In total, 40 of the 54 articles were shortlisted for best relevance and used in the research study.

The underlying research study was limited to 40 articles and the data contained within them. The article search was limited to the keywords of the CBM; the implication of CBM; the CBM in manufacturing (textiles); circular business in service (FI); and comparison and application of the CBM.

The final analysis demonstrates that circular business promotes sustainability by allowing companies to generate maximum return from given resources and reach zero waste targets. Greater customer satisfaction is also achieved through service innovation that cuts down negative impact on the environment. These findings are relevant and applicable to the FI.

A review of existing literature showed that whilst there is significant research on the implications of the CBM in general, there has been little focus on the use of different CBMs specifically in the manufacturing and service industry (FI). This research study is designed to tease out the specific benefits of CBMs in this context, highlighting the contribution they can make to efficiency in both the manufacturing and service sectors (FI) through useful comparison.

This paper aims to consolidate the results of various researchers focusing the different applications, so that this paper could become the torch bearer for the futuristic researchers working in the domain of cold gas dynamics spray coating.

A study on the cold spray coating is presented by summarizing the data present in literature. Important factors such as coating temperature, pressure, coating thickness, particle size, which affect the erosion-corrosion (E-C) resistance, physical and mechanical properties of boiler steel are stated. This paper also addresses the use of cold spray coating and compares it with other different thermal spray processes.

From the literature review, it was noticed that cold spray technology is best as compare to other thermal spray processes to reduce porosity, increase hardness, adhesion strength and retention in properties of feedstock powders.

Cold spray coating technology has a great potential in almost every field especially in restoration of surfaces, generation of complex surface, biomedical application, resist hot corrosion, wear, oxidation and erosion corrosion.

This study compared per selectee utility estimates for the job of medical claims examiner based on applications of the Brogden‐Cronbach‐Gleser (BCG) and Raju‐Burke‐Normand (RBN) utility analysis models. The RBN model's per selectee utility estimate, based on a transformed observed performance rating standard deviation (σR), was closest to the per selectee utility estimate computed with an empirically‐derived σY value. The implications of these results for estimating human resource program utility are discussed.

The BEKK GARCH class of models presents a popular set of tools for applied analysis of dynamic conditional covariances. Within this class the analyst faces a range of model choices that trade off flexibility with parameter parsimony. In the most flexible unrestricted BEKK the parameter dimensionality increases quickly with the number of variables. Covariance targeting decreases model dimensionality but induces a set of nonlinear constraints on the underlying parameter space that are difficult to implement. Recently, the rotated BEKK (RBEKK) has been proposed whereby a targeted BEKK model is applied after the spectral decomposition of the conditional covariance matrix. An easily estimable RBEKK implies a full albeit constrained BEKK for the unrotated returns. However, the degree of the implied restrictiveness is currently unknown. In this paper, we suggest a Bayesian approach to estimation of the BEKK model with targeting based on Constrained Hamiltonian Monte Carlo (CHMC). We take advantage of suitable parallelization of the problem within CHMC utilizing the newly available computing power of multi-core CPUs and Graphical Processing Units (GPUs) that enables us to deal effectively with the inherent nonlinear constraints posed by covariance targeting in relatively high dimensions. Using parallel CHMC we perform a model comparison in terms of predictive ability of the targeted BEKK with the RBEKK in the context of an application concerning a multivariate dynamic volatility analysis of a Dow Jones Industrial returns portfolio. Although the RBEKK does improve over a diagonal BEKK restriction, it is clearly dominated by the full targeted BEKK model.

This paper aims to suggest an empirically based typology of hotels according to their marketing information systems (MrkIS) configurations. The study seeks to examine major antecedents of the effectiveness of MrkIS and their influence on the adoption of specific marketing applications. Finally, this paper seeks to expand the general understanding of the relationship between the effectiveness of a hotel's MrkIS and that hotel's overall effectiveness, compared with that of others from the suggested typology.

The paper collected empirical data from a sample of 254 luxury and five‐star Greek hotels. They conducted a cluster analysis in order to define various clusters of hotels based on their use of specific marketing applications. They considered the role of basic antecedents – namely the hotel's degree of market orientation adoption, system quality, the quality of the information that the MrkIS produce, and support service quality – examining their influence on the MrkIS configuration in use. In addition, the paper considers the relationship between MrkIS effectiveness and overall hotel effectiveness as evidenced throughout the different clusters.

The study offers insights that can help hoteliers to analyse realistically the potential benefits of MrkIS for their businesses. Toward this end, the paper identifies three specific types of hotels: the “transactional‐oriented”, the “sales‐oriented”, and the “market‐oriented”.

The results of the study may not be broadly applicable due to differences in national context, hotel category, and other characteristics – a fact that suggests future research opportunities.

Based on the current results, a hotel might better evaluate its existing MrkIS and be prepared to maximise the opportunities offered by the full utilisation of these systems.

The scarcity of empirical evidence with regard to MrkIS utilisation in the lodging industry makes this kind of study essential. The results of the paper expand the general knowledge about hotels' MrkIS adoption, its antecedents, its effectiveness and hotels' overall effectiveness. The results provide an integrated picture of the utilisation of these systems.

Modeling of the methods for providing improvements in business processes by value adding is researched with an integrated approach. The main purpose in this approach is improving the processes by determining and analyzing the weak points and reducing the weakness degrees.

The designed model determines weak points that need to be improved, analyzes them to find privileged processes in improvement by considering improvement costs and obtains the improvement degrees for defining the improvement strategy by four‐phase business process improvement framework: start‐up, self analysis, defining improvement strategy for making changes, feedback, and continuous improvement.

The key factor in keeping up with changes in market conditions is systematic application of improvement efforts and providing planned and controlled value addition with these improvements. Decision problems in process improvement can be structured to provide input data suitable for multi‐criteria decision making techniques and results meet the solution expectations.

The reason for using analytical hierarchy process, goal programming, and linear programming model is to select the process and determine its improvement degree. Solving the decision problems by these techniques is a time consuming process, so forming suitable programs with decision support systems will be useful.

The theoretical structure of the modeled techniques in this study was examined with an industrial application. The application process and the results suitability were determined.

The proposed model shows improvement amounts according to the amount of defined importance degrees. It presents an advantage to decision makers by giving concrete improvement values from model results determining their improvement strategies.