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This paper presents concepts important for understanding the potential of sustainable low-income housing in Vietnam, with a focus on key environmental, socio-economic, and cultural dimensions that bear on its housing sector. It examines challenges for sustainable urban development in Hanoi and HCMC, Vietnam's two main cities. Recognising the current challenges in balancing affordability and sustainability, the study explores Vietnam's lack of adequate and affordable housing and the problem of its urban slums. Synergistic strategies suitable for the Vietnamese context are then suggested for sustainable low-income housing in these two cities.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

Waste production and related environmental problems have caused urban services management many problems in collecting, transporting and disposal of waste. Since collecting and transporting waste are important parts of waste management budget, it is necessary to apply an appropriate method to reduce costs of collection. The aim of this study is to design a new model for urban waste collection vehicle routing problem with time windows and energy generating from waste.

A multiobjective model is presented with the objectives of minimizing cost of waste collection, reducing emissions from environmental pollutants and increasing income of recycles trade and energy recovery. Concerning the complexity of the model and its inability to solve large-scale problems, hyperactive genetic algorithms and multiple objective particle swarm optimization are applied.

The proposed model not only affects costs and income but also reduces the emissions of environmental pollutants. To solve the formulated model, multitarget approaches are applied on the processing site of Kermanshah city as a case study. The solutions of these algorithms and the exact method of partial constraints are compared and the outcomes are verified by numerical analysis by solving various examples in small, medium and large scales. The proposed model helps to collect urban wastes in the shortest possible time in addition to reducing the total cost, revenues from the sale of recycled materials and energy generation.

Waste collection is related to VRP issue. Considering the environmental requirements in waste management, the concept of green supply chain is approached. This study provides a better understanding of urban waste management by examining various articles and combining economic and environmental dimensions. Waste management with a green approach and energy production from disposable waste involves many common stakeholders.

This study suggests that proper waste management can generate revenue from municipal waste. Moreover, it reduces cost of collecting and emission of environmental pollutants. These findings could motivate companies to waste management with a sustainable approach.

The proposed model boosts the current understanding of the waste management and energy generation of waste. The paper adds additional values by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

Research organisations and individual researchers increasingly choose to share their research findings by providing lists of their published works on the World Wide Web. To facilitate the exchange of ideas, the lists often include links to published papers in portable document format (PDF) or Postscript (PS) format. Generally, these publication Web sites are updated regularly to include new works. While manual monitoring of relevant Web sites is tedious, commercial search engines and information monitoring systems are ineffective in finding and tracking scholarly publications. Analyses the characteristics of publication index pages and describes effective automatic extraction techniques that the authors have developed. The authors’ techniques combine lexical and syntactic analyses with heuristics. The proposed techniques have been implemented and tested for more than 14,000 Web pages and achieved consistently high success rates of around 90 percent.

The purpose of this study is to investigate the effects of congruent and incongruent anonymous comments posted to an online health news article on personal risk perception. This association is examined through testing the moderating roles of perceived similarity and approval ratings, and the mediating roles of source credibility and content credibility.

Hypotheses regarding the impact of comments on personal risk perception were tested experimentally using a news article, comments and approval ratings about the ear picking behavior. Data were collected from 391 young Vietnamese respondents.

The influence of online comments (congruent vs incongruent) on personal risk perception was mediated by source credibility and content credibility. Further, data showed a direct effect of online comments on personal risk perception. Interestingly, the direct and indirect effects of online comments on personal risk perception were observed among participants who perceived that anonymous commenters were similar to them. Approval ratings had neither a main nor interaction effect with comments on the dependent variables.

The results indicate that marketers should consider online comments as a powerful form of social influence, which may alter consumers’ personal risk perception.

The present study adds to social marketing literature by showing how consumers’ online comments influence personal risk perception in the context of changing media ecosystem.

Web services play vital role in the development of emerging technologies such as Cloud computing and Internet of Things. Although, there is a close relationship among the discovery, selection and composition tasks of Web services, research community has treated these challenges at individual level rather to focus on them collectively for developing efficient solution, which is the purpose of this work. This paper aims to propose an approach to integrate the service discovery, selection and composition of Semantic Web services on runtime basis.

The proposed approach defined as a quality of service (QoS)-aware approach is based on QoS model to perform discovery, selection and composition tasks at runtime to enhance the user satisfaction and quality guarantee by incorporating non-functional parameters such as response time and throughput with the Web services and user request. In this paper, the proposed approach is based on ontology for semantic description of Web services, which provides interoperability and automation in the Web services tasks.

This work proposed an integrated framework of Web service discovery, selection and composition which supports end user to search, select and compose the Web services at runtime using semantic description and non-functional requirements. The proposed approach is evaluated by various data sets from the Web Service Challenge 2009 (WSC-2009) to show the efficiency of this work. A use case scenario of Healthcare Information System is implemented using proposed work to demonstrate the usability and requirement the proposed approach.

The main contribution of this paper is to develop an integrated approach of Semantic Web services discovery, selection and composition by using the non-functional requirements.

The need to flexibly react to changing demands and to cost-efficiently manage customized production even for lot size of one requires a dynamic and holistic integration of service-based processes within and across enterprises of the value chain. In this context, this paper aims at presenting ODERU, the authors’ novel pragmatic approach for automatically implementing service-based manufacturing processes at design and runtime within a cloud-based elastic manufacturing platform.

ODERU relies on a set of semantic annotations of business process models encoded into an extension of the business process model and notation (BPMN) 2.0 standard. Leveraging the paradigms of semantic SOA and XaaS, ODERU integrates pattern-based semantic composition of process service plans with QoS-based optimization based on multi-objective constraint optimization problem solving.

The successful validation of ODERU in two industrial use cases for maintenance process optimization and automotive production in the European project CREMA revealed its usefulness for service-based process optimization in general and for significant cost reductions in maintenance in particular.

ODERU provides a pragmatic and flexible solution to optimal service composition with the following three main advantages: full integration of semantic service selection and composition with QoS-based optimization; executability of the generated optimal process service plans by an execution environment as they include service assignments, data flow (variable bindings) and optimal variable assignments; and support of fast replanning in a single model and plan.

The purpose of this paper is to estimate technical efficiency obtained from both data envelopment analysis (DEA) and stochastic frontier approaches using household survey data for rice farming households in Vietnam.

A bootstrap method is used to provide statistical precision of the DEA estimator. Technical efficiency is modeled as a function of household and production factors.

The results from the deterministic, semi‐parametric and parametric approaches indicate that among other things, technical efficiency is significantly influenced by primary education and regional factors. In addition, scale efficiency analysis shows that many farms in Vietnam are operating with less than optimal scale of operation.

The study is among the first that employ a bootstrap method and compare estimates from both Data Envelopment Analysis (DEA) and stochastic frontier approaches.

This paper aims to provide empirical insight into the trends and structural changes in meat consumption in Vietnam.

This study applies the Quadratic Almost Ideal Demand System model on multiple cross-sectional data sets derived from the Vietnam Household Living Standards Survey (VHLSS) of 2004–2016 and follows a consistent two-step procedure to deal with the zero consumption issue. The estimated demand elasticities are then compared and analyzed over time.

The empirical results show that in the last decade, meat consumption patterns in Vietnam have undergone a remarkable structural change, with poultry and beef increasingly taking important roles in the meat consumption structure of households. In addition, demographic characteristics, including settlement type, household size and the age of the household head, have significantly influenced meat expenditure patterns in Vietnam.

The paper considers the demand for meat consumed at home but not meat consumed away from home.

In many developing countries, increased disposable income, together with rapid urbanization and international integration, has significantly changed consumers' food consumption behaviors. This is one of the first studies using household survey data, which permits the exploration of heterogeneous preferences between consumers, to explore structural changes in food consumption patterns in Vietnam.

Cloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web.

In this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction.

This paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks.

As a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.