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System dynamics has several applications in the built environment, and few studies indicate that it has potential in evaluating sustainability. Sustainability in the built environment involves numerous entities and multiple trade-offs. Hence, a Multi-Criteria Decision-Making (MCDM) method is ideal for promoting sustainability-based decision-making in the built environment. Therefore, this study integrates system dynamics with an MCDM method to enable the sustainability assessment by capturing the time-induced dynamic changes affecting long time sustainability performance of buildings.

Conventional sustainability assessment tools in the built environment lack a comprehensive evaluation that balances the needs of the society, economy and environment. This study develops a system dynamics-based framework to enable sustainability-conscious decision-making and policy analysis in the built environment.

Various material, technology and water-related policies specific to the buildings are investigated for a case study building. It is found that the effect of penetration of renewable energy technology to the tune of 80% and above in the energy mix is a much superior policy in sustainability improvement in comparison to material and water-related policies. The study also demonstrates the effect of weights assigned for the different indicators on sustainability-based decisions.

The study provides a methodological framework for a sustainability-based decision support system for the built environment that enables dynamic performance evaluation by coupling system dynamics with the MCDM. This coupling further strengthens system dynamics as a decision-making and policy analysis for sustainability evaluation in the built environment.

The implementation of new technology in the workplace has produced some negative responses from employers. Two field surveys investigating the prevalence of repetitive VDU work in the UK are discussed and responses from four categories of workers – data entry operators, administrators, programmers and supervisors – are analysed. Critical psychological variables show that in all cases data entry operators gave more extreme (worse) responses than any other group. It is concluded that there appears to be widespread evidence of more routine menial tasks having been created as a result of the introduction of new technology. Guidelines regarding the use of VDUs, especially over protracted periods, are advocated, with the introduction of timely rest breaks.

The number of English learners (ELs) has increased dramatically in the public schools across the nation. In 2000, 3.8 m students, or approximately 8% of the US public school student population, were identified as ELs. In 2016, the number reached 4.9 m or 9.6% of the general student population. In some states, the rise in the EL population has been even more pronounced. Their increased presence in content classes poses opportunities as well as great challenges for teachers. As these learners are learning fundamental language skills, they are also responsible for learning content and skills associated with various disciplines. In social studies, it includes developing a foundational understanding of US history, geography and government, while also developing the ability to comprehend, analyze and evaluate complex informational texts, which include contextualized language, low-incidence concepts and discrete facts.

One way schools are adapting practice for ELs is through collaboration between general education and EL resource teachers. This article reports findings from interviews with a high school social studies teacher and an English as a second language (ESL) colleague.

The collaboration between these two teachers illustrated how specific strategies thoughtfully designed, yet sensibly implemented, can help teachers feel effective and empowered in their social studies classes. Their story also highlighted ways they have worked together in spite of the system confines.

Together, the authors hope their experiences inform teachers, teacher educators and school leaders about the importance of adapting practices for ELs in social studies and the power of collaboration to make those efforts successful.

The purpose of this paper is to develop an artificial neural network (ANN) based prediction model via integration with data envelopment analysis (DEA) to provide the means of predicting incremental performance goals. The findings confirm the usefulness of the herein developed prediction approach, based on the results of analyses of time series data from the smartphone industry.

A two-stage hybrid model was developed, incorporating sequential measurement and prediction capability. In the first stage, a Chames, Cooper, and Rhodes DEA model is the preprocessor, generating efficiency scores (ES) of decision-making units (DMUs). In the second or follow-on stage, the ANN prediction module utilizes knowledge variables and ES to predict the change in performance needed for a desired level of improvement.

This combined approach effectively captured the information contained in the industry’s turbulent characteristics, and subsequently demonstrated an adaptive prediction capability. The back propagating neural network successfully predicted the incremental performance targets of DMUs, which translated the desired improvement levels into actionable performance goals, e.g., revenue and operating income.

This paper presents an incremental prediction approach that supports better practice benchmarking. This study differentiates itself from previous research by introducing an adaptive prediction method which generates relevant quantity outputs based upon desired improvement levels. The proposed modeling approach integrates performance measurement with a prediction framework and advances benchmarking practices to enable better performance prediction.

The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement.

Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms.

The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation.

Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis.

This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

In order to represent, analyze, optimize, and manufacture a component made of multi‐heterogeneous materials for high‐tech applications, a computer model of the heterogeneous component needs to be built first. Heterogeneous materials include composite, functionally graded materials, and heterogeneous materials with a periodic microstructure. Current modeling techniques focus only on capturing the geometric information and cannot satisfy the requirements from modeling the components made of multi‐heterogeneous materials. This paper develops a modeling method, which can be implemented by employing the functions of current CAD graphic software and can obtain the model including both the material information (about its microstructures and constituent composition) and the geometry information without the problems arising from too many data.

The purpose of this paper is to investigate the way Hong Kong drinkers have internalised the meanings associated with alcoholic beverages and how these meanings influence the motivation to drink. Also of interest was how symbolic meanings and motivations are similar or different to those in Western nations and the implications for the marketing of alcohol products.

An ethnographic approach comprising participant observations and interviews is used to generate data relating to alcohol consumption. Observations are conducted at 11 venues including pubs, clubs, restaurants, and a convention centre. More than 40 h of observations yield data pertaining to public drinking while the interview data also provides insight into the nature of private drinking in Hong Kong.

Alcohol consumption in Hong Kong may be primarily a function of the need to convey desired images to specific and generalised others. The finding that product symbolism dominates taste considerations supports previous research relating to beer consumption but varies somewhat from identified motivations for wine consumption in developed markets.

Alcohol marketers may benefit from adapting their products to suit the specific taste preferences of Chinese consumers, although care would need to be taken to ensure the symbolic value of the beverage is not diminished in the process. A focus on the situational context and moderate consumption in promotional messages may increase perceived salience.

Little previous research on alcohol consumption motivations has been conducted in Hong Kong. The findings provide insight into likely characteristics of the future alcohol market in mainland China.