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The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

For decades, conflict resolution (CR) educators working cross-culturally have struggled with a fundamental dilemma – whether to offer western, evidence-based approaches through a top-down (prescriptive) training process or to use a bottom-up (elicitive) strategy that builds on local cultural knowledge of effective in situ conflict management. This study aims to explore which conditions that prompted experienced CR instructors to use more prescriptive or elicitive approaches to such training in a foreign culture and the implications for training outcomes.

There are two parts to this study. First, the authors conducted a literature review to identify basic conditions that might be conducive to conducting prescriptive or elicitive cross-cultural CR training. The authors then tested the identified conditions in a survey with experienced CR instructors to identify different conditions that afforded prescriptive or elicitive approaches. Exploratory factor analysis and regression were used to assess which conditions determined whether a prescriptive or elicitive approach produced better outcomes.

In general, although prescriptive methods were found to be more efficient, elicitive methods produced more effective, culturally appropriate, sustainable and culturally sensitive training. Results revealed a variety of instructor, participant and contextual factors that influenced whether a prescriptive or elicitive approach was applied and found to be more suitable.

This study used empirical survey data with practicing experts to provide insight and guidance into when to use different approaches to CC-CR training effectively.

Several new network types have emerged over the last couple of years. Many of these networks are being connected together to provide mobile users with the capability of staying always connected to the Internet which requires seamless transitions from one network to another without causing interruption to on‐going connections. To maintain connectivity during handoff, all the networks that are accessible to the mobile station need to be known. The paper aims to propose a novel energy‐efficient network discovery approach that enables fast discovery and selection of available access networks.

The performance of the proposed approach over an actual network testbed consisting of heterogeneous networks (such as wireless local area, cellular) is evaluated.

It was found that the approach yields between 12.4 and 19.1 per cent improvement in battery power consumption over other network discovery approaches.

The paper proposes and implements a novel scheme to discover wireless networks in a multi‐access environment consisting of heterogeneous networking technologies.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The purpose of this paper is the development of an efficient approach for extraction of the microwave FET noise wave temperatures.

The proposed approach is based on an artificial neural network (ANN) trained to determine the noise wave temperatures from the given measured transistor noise parameters.

The presented approach enables not only efficient, but also an accurate direct extraction of the noise wave temperatures. This is confirmed by the validation of the proposed approach that is done by comparison of the transistor noise parameters obtained using the extracted noise wave temperatures with the measured noise parameters.

Application of ANN is a novel approach to extract the noise wave temperatures, which provides more efficient microwave FET noise wave modeling.

In e-government domain, an efficient semantic interoperability of services is a big challenge. In this context, semantic technologies play a fundamental role in numerous e-government programs. Indeed, these technologies enable enriching the public service description provided by different public administrations with additional semantic information, which allows automatic services processing and information exchange between involved members in comprehensive and interpretable manner, thus facilitating service integration and cooperation. The most frequently used technologies in this area are semantic web services (SWS) technology and ontology. Thus, for modelling efficiently public services, the authors have exploring, studying and analyzing some of e-government researches and projects that applied semantic technologies.

In this paper, the authors address how semantic technologies transform the e-government domain through exploring and studying the use of SWS and ontology in numerous researches and projects applying these technologies in this area. Then, the authors have compared them through a set of criteria defined according to e-government requirements such as the SWS modelling approach for describing semantically public services, ontologies for representing governmental specific features (e.g. regulation and organizational structures) and developed portal for supporting user ' s perspectives. Based on this study, the authors have identified the appropriate standards, frameworks, and models for developing the framework.

The authors have developed a framework for public services which enhances existing approaches with additional aspects. Thus, the approach comparing to other works improves the service ontology with a cognitive semantic approach to support the multiple usage contexts and situations of services. This semantic is developed in form of a Sit/Ctx ontology developed based on CSs model represented through conceptual graphs theory. The authors have used this model besides OWL-S, improving thus the level of automation of discovery and composition process of public services, which become more efficient with taken into consideration their interrelations and situations.

The approach comparing to other works improves the service ontology with a cognitive semantic approach to support the multiple usage contexts and situations of public services, providing thus the end-users (people and enterprises) a better orientation in discovery process.

The purpose of this paper is to present an alternative modeling approach in terms of the determination of a physically equivalent circuit model for one-dimensional (1D) planar metamaterials in the high-frequency regime, without a postprocessing optimization procedure. Thereby, an efficient implementation of physical relationships is aimed.

In this paper, a method based on quasi-stationary simulations and mathematical conversions to derive the values for a physically equivalent circuit model is proposed. Because the electromagnetic coupling mechanisms are investigated in detail, a simplification for the considered multiconductor transmission line structure is achieved.

The results show that the proposed modeling approach is an efficient and physically meaningful alternative to classical full-wave simulation techniques for the investigated inhomogeneous transmission line structure in both the time domain as well as in the frequency domain. In the course of this, the effort is reduced while a comparable accuracy is maintained, whereby specific coupling mechanisms are considered in circuit simulations.

The process to obtain information about physically interpretable lumped element values for a given structure or to determine a layout for known ones is simplified with the aid of the proposed approach. An advantageous adaption of the presented procedure to other areas of application is well conceivable.

This paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses this model to evaluate several initial bike inventory policies inspired by the operation of the bike-sharing system in Mexico City, which is one of the largest around the world. The model captures the heterogeneous demand (in time and space) and this paper analyzes the trade-offs between the performance to take and return bikes. This study also includes a simulation-optimization algorithm to determine the initial inventory and present a method to deal with the bias caused by dynamic rebalancing on observed demand.

This paper is based on the analysis of an alternate and efficient discrete-event simulation modeling framework. This framework captures the heterogeneity of demand and allows one to experiment with large-scale models. This study uses this model to test several initial bike inventory policies and also combined them with an optimization engine. The results, provide valuable insights not only for the particular system that motivated the study but also for the administrators of any bike-sharing system.

The findings of this paper include: most of the best policies use a ratio of bikes: docks near to 1:2; however, it is important the way they are initially allocated; a policy that contradicts the demand profile of the stations can lead to poor performance, regardless the quick and dynamic changes of bike locations during the morning period; the proposed simulation-optimization algorithm achieves the best results.

The findings are limited to the initial inventory of the system under study. The model assumes a homogeneous probability distribution function for the travel time. This assumption seems reasonable for the system under study. This paper limits the tested inventory policies to simple practical rules. There might be other sophisticated methods to obtain better solutions, but they might be system-specific.

The insights of this paper are valuable for operators of bike-sharing systems because this study focuses on the analysis of the impact of the initial inventory assuming that dynamic rebalancing may not be existing during the morning peak-time. This paper finds that initial inventory has a great impact on the performance, regardless of how quickly the bikes are dispersed across the system. This study also provides insights into the effect of dynamic rebalancing on observed demand.

Increasing knowledge about the operation of the bike-sharing system has a positive effect on society because more cities around the world could consider implementing these systems as a public transportation mode. Furthermore, delivering suggestions on how to increase the user service level could incentivize people to adopt bikes as a mobility option, which would contribute to improve their health and also reduce air pollution caused by motorized vehicles.

This paper considers that the contributions of this work to existing literature are the following: this study proposes a novel efficient and scalable simulation framework to evaluate initial bike inventory policies; the analysis presented in the paper includes an approach to deal with the bias in the observed demand caused by dynamic rebalancing and the analysis includes the value of demand information to determine an effective initial bike inventory policy.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

This paper aims to examine impediments to the adoption of sustainable water-efficient technological innovation in agriculture. Farming is the largest water consumer and food production expansion in response to global population growth, combined with increasing droughts from climate change, threatens water and food insecurity for many countries. Yet, climate smart agriculture (CSA) innovation adoption has been slow, and in this regard, governments and the agricultural sector are not fulfilling their social responsibility and sustainability obligations.

Barriers to water-efficient drip irrigation (DI) adoption in Australia were investigated via 46 depth interviews with agricultural stakeholders and a survey of 148 farmers.

While DI water efficiency is recognised, this is not the key determinant of farmers’ irrigation method selection. Complex interrelationships between internal and external barriers impede DI adoption are identified. These include costs, satisfaction with alternative irrigation methods, farmer characteristics that determine the suitability of the innovation and the extent it is incremental or radical, plus various multidimensional risks. Government support of alternative, less water-efficient irrigation methods is also a critical barrier.

A conceptual framework for understanding barriers to sustainability oriented innovation adoption is presented. Its insights should be applicable to researchers and practitioners concerned with understanding and improving the adoption of socially responsible and sustainable innovation in a wide range of contexts. Recommendations for overcoming such adoption barriers are discussed in relation to the research focus of water-efficient agriculture and encouraging uptake of DI.