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This study aims to propose a visualized model of hot technology evolution to describe its development.

The basic concept is to divide a technological field into a timeline consisting of several patent clusters. Hot technology trajectories are then explored using their continuity, as well as the point in time at which they occur.

Patents in orthopaedics between 1999 and 2014 have been chosen as the research subjects and the field is divided into several hot technology trajectories. A further step is taken by interpreting high-frequency key terms. Three categories – spine-related materials, bone repairing materials and bone plates – have been identified.

The trajectories presented by evolving diagrams allow readers to understand the evolution of hot technology and help analysts to plan layout and strategies to remain competitive.

Patent clusters reflect the knowledge context of technology development. Previous studies have focused on only new technology evolution and have rarely explored the knowledge context of hot patents that have been frequently cited in recent years. Such patents often guide the development of technology.

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of analyses of large data sets. Many manufacturing companies can now seize this opportunity to leverage their data to gain competitive advantages for continuous improvement. Six Sigma has been among the most popular approaches for continuous improvement. The data-driven nature of Six Sigma applied in a big data environment can provide competitive advantages. In the traditional Six Sigma implementation – define, measure, analyze, improve and control (DMAIC) problem-solving strategy where a human team defines a project ahead of data collection. This paper aims to propose a new Six Sigma approach that uses massive data generated to identify opportunities for continuous improvement projects in a manufacturing environment in addition to human input in a measure, define, analyze, improve and control (MDAIC) format.

The proposed Six Sigma strategy called MDAIC starts with data collection and process monitoring in a manufacturing environment using system-wide monitoring that standardizes continuous, attribute and profile data into comparable metrics in terms of “traffic lights.” The classifications into green, yellow and red lights are based on pre-control charts depending on how far a measurement is from its target. The proposed method monitors both process parameters and product quality data throughout a hierarchical production system over time. An attribute control chart is used to monitor system performances. As the proposed method is capable of identifying changed variables with both spatial and temporal spaces, Six Sigma teams can easily pinpoint the areas in need to initiate Six Sigma projects.

Based on a simulation study, the proposed method is capable of identifying variables that exhibit the biggest deviations from the target in the Measure step of a Six Sigma project. This provides suggestions of the candidates for the improvement section of the proposed MDAIC methodology.

This paper proposes a new approach for the identifications of projects for continuous improvement in a manufacturing environment. The proposed framework aims to monitor the entire production system that integrates all types of production variables and the product quality characteristics.

The purpose of this paper is to examine the determinants of continuous brand-customer relationship via company-hosted social networking sites (SNSs). Factors that influence fans to continue using fast-fashion brands’ Facebook fan pages and to maintain the brand-customer relationship are discussed first. Subsequently, the predictors of fans’ engagement and affective commitment to a fast-fashion brand are examined with the aim to explore the key elements that nurture the brand-customer relationship via brands’ SNSs.

A quantitative research was conducted and the structural equation modelling was used to test the hypotheses on a sample of 202 fast-fashion Facebook fan page users in Taiwan.

The results demonstrate that engagement, affective commitment and continued intention to use are predominantly influenced by, in turn, social interaction tie, content value and affective commitment.

The study is limited because it investigated the fast-fashion fan page users in an Asian country, so the findings cannot be generalised to other contexts.

The findings suggest that fan page managers’ initiation and involvement in conversations, frequent responses, listening to fans’ opinions and improving fans’ experiential value may facilitate them to engage in the brand’s activities at a higher level.

The findings of this integrated model suggest managerial guidelines for brand managers in this industry regarding how to maintain the brand-customer relationship through social media strategy and how they contribute to theory building in continuance intention of SNSs.

The purpose of this paper is to describe the major factors affecting health during pregnancy, birth and the postnatal period and outline the evidence for interventions to improve outcomes in women and their children.

Selective review of the literature. A number of electronic bibliographic databases were searched, including the Cochrane Database of Systematic Reviews, PubMed and PsycINFO, for relevant studies published since 1990. Papers were restricted to those published in English which presented data from studies conducted in high-income countries, with priority given to systematic reviews, randomised controlled trials and other quantitative studies which present a higher level of evidence.

Many factors may affect maternal and infant health during and after pregnancy. Potentially modifiable factors with an evidence base to support intervention include improving diet, and the avoidance of smoking, alcohol and illicit drugs. Good clinical management of underlying illness is also important, along with attempts to engage women in improving health prior to conception and postnatally rather than once pregnancy is established.

The evidence base for interventions on some potentially modifiable risk factors is incomplete. There is good evidence of benefit from some health behaviours such as smoking cessation and uptake of breastfeeding and accumulating evidence of the benefit of some models of maternity care.

Good maternal health during and after pregnancy plays a key role in giving the child a better start in life. Improved health behaviours are vital but often these are heavily dependent on social context and hence working to tackle social inequality and provide maternity care tailored to individual need is likely to be just as important as trying to directly alter behaviour.

Pregnancy and the postnatal period present an opportunity to improve maternal health and have a positive effect on future child health. Greater investment is required in this antenatal period of life.

Robots for assembly and machine loading are being developed in Taiwan, as automation becomes a priority.

The purpose of this paper is to demonstrate the construction of a core ontology for food. To construct the core ontology, the authors propose here an approach called, yet another methodology for ontology plus (YAMO+). The goal is to exhibit the construction of a core ontology for a domain, which can be further extended and converted into application ontologies.

To motivate the construction of the core ontology for food, the authors have first articulated a set of application scenarios. The idea is that the constructed core ontology can be used to build application-specific ontologies for those scenarios. As part of the developmental approach to core ontology, the authors have proposed a methodology called YAMO+. It is designed following the theory of analytico-synthetic classification. YAMO+ is generic in nature and can be applied to build core ontologies for any domain.

Construction of a core ontology needs a thorough understanding of the domain and domain requirements. There are various challenges involved in constructing a core ontology as discussed in this paper. The proposed approach has proven to be sturdy enough to face the challenges that the construction of a core ontology poses. It is observed that core ontology is amenable to conversion to an application ontology.

The constructed core ontology for domain food can be readily used for developing application ontologies related to food. The proposed methodology YAMO+ can be applied to build core ontologies for any domain.

As per the knowledge, the proposed approach is the first attempt based on the study of the state of the art literature, in terms of, a formal approach to the design of a core ontology. Also, the constructed core ontology for food is the first one as there is no such ontology available on the web for domain food.

This paper aims to propose a brand new ontology development methodology, called Yet Another Methodology for Ontology (YAMO) and demonstrate, step by step, the building of a formally defined large-scale faceted ontology for food.

YAMO is motivated by facet analysis and an analytico-synthetic classification approach. The approach ensures quality of the system precisely; it makes the system flexible, hospitable, extensible, sturdy, dense and complete. YAMO consists of two-way approaches: top-down and bottom-up. Based on YAMO, domain food, formally defined as large-scale ontology, is designed. To design the ontology and to define the scope and boundary of the domain, a group of people were interviewed to get a practical overview, which provided more insight to the theoretical understanding of the domain.

The result obtained from evaluating the ontology is a very impressive one. Based on the study, it was found that 94 per cent of the user’s queries were successfully met. This shows the efficiency and effectiveness of the YAMO methodology. An evaluator opined that the ontology is very deep and exhaustive.

The authors envision that the current work will have great implications on ontology developers and practitioners. YAMO will allow ontologists to construct a very deep, high-quality and large-scale ontology.

This paper illustrates a brand new ontology development methodology and demonstrates how the methodology can be applied to build a large-scale high-quality domain ontology.

The purpose of this paper is to detect the degenerate scale of a 2D bending plate analytically and numerically.

To avoid the time-consuming scheme, the influence matrix of the boundary element method (BEM) is reformulated to an eigenproblem of the 4 by 4 matrix by using the scaling transform instead of the direct-searching scheme to find degenerate scales. Analytical degenerate scales are derived from the boundary integral equation (BIE) by using the degenerate kernel only for the circular case. Numerical results of the direct-searching scheme and the eigen system for the arbitrary shape are also considered.

Results using three methods, namely, analytical derivation, the direct-searching scheme and the 4 by 4 eigen system, are also given for the circular case and arbitrary shapes. Finally, addition of a constant for the kernel function makes original eigenvalues (2 real roots and 2 complex roots) of the 4 by 4 matrix to be all real. This indicates that a degenerate scale depends on the kernel function.

The analytical derivation for the degenerate scale of a 2D bending plate in the BIE is first studied by using the degenerate kernel. Through the reformed eigenproblem of a 4 by 4 matrix, the numerical solution for the plate of an arbitrary shape can be used in the plate analysis using the BEM.

A novel integral form time‐integration algorithm for pseudodynamic testing is proposed, based on the Newmark implicit algorithm. The scheme builds on the recently proposed integral form of the Newmark explicit algorithm which exhibits improved abilities to handle rapidly varying loads and stiffness properties during pseudodynamic testing, but displays some numerical damping and conditional stability. The enhancement is based on the inclusion of an additional term in the displacement predictor, which not only renders the algorithm more consistent, but it eliminates numerical damping and makes the algorithm unconditionally stable.

To propose novel predictor‐corrector time‐integration algorithms for pseudo‐dynamic testing.

The novel predictor‐corrector time‐integration algorithms are based on both the implicit and the explicit version of the generalized‐α method. In the non‐linear unforced case second‐order accuracy, stability in energy, energy decay in the high‐frequency range as well as asymptotic annihilation are distinctive properties of the generalized‐α scheme; while in the non‐linear forced case they are the limited error near the resonance in terms of frequency location and intensity of the resonant peak. The implicit generalized‐α algorithm has been implemented in a predictor‐one corrector form giving rise to the implicit IPC‐ρ_∞ method, able to avoid iterative corrections which are expensive from an experimental standpoint and load oscillations of numerical origin. Moreover, the scheme embodies a secant stiffness formula able to approximate closely the actual stiffness of a structure. Also an explicit algorithm has been implemented, the EPC‐ρ_b method, endowed with user‐controlled dissipation properties. The resulting schemes have been tested experimentally both on a two‐ and on a six‐degrees‐of‐freedom system, exploiting substructuring techniques.

The analytical findings and the tests have indicated that the proposed numerical strategies enhance the performance of the pseudo‐dynamic test (PDT) method even in an environment characterized by considerable experimental errors. Moreover, the schemes have been tested numerically on strongly non‐linear multiple‐degrees‐of‐freedom systems reproduced with the Bouc‐Wen hysteretic model, showing that the proposed algorithms reap the benefits of the parent generalized‐α methods.

Further developments envisaged for this study are the application of the IPC‐ρ_∞ method and of EPC‐ρ_b scheme to partitioned procedures for high‐speed pseudo‐dynamic testing with substructuring.

The implicit IPC‐ρ_∞ and the explicit EPC‐ρ_b methods allow a user to have defined dissipation which reduces the effects of experimental error in the PDT without needing onerous iterations.

The paper proposes novel time‐integration algorithms for pseudo‐dynamic testing. Thanks to a predictor‐corrector form of the generalized‐α method, the proposed schemes maintain a high computational efficiency and accuracy.