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To help investors find an investment policy with strong competitiveness, the purpose of this paper is to construct a multi-period investment decision model with practicality and superior performance.

The paper uses a suitable multi-period risk measure to construct a multi-period portfolio selection model, where target returns at intermediate periods and market frictions are taken into account simultaneously. An efficient scenario tree generation approach is proposed in order to transform the complex multi-period portfolio selection problem into a tractable one.

Numerical results show the new scenario tree generation algorithms are stable and can further reduce the tree size. With the scenario tree generated by the new scenario tree generation approach, the optimal investment strategy obtained under the multi-period investment decision model has more superior performance and robustness than the corresponding optimal investment strategy obtained under the single period investment model or the multi-period investment model only paying attention to the terminal cash flow.

The new risk measure and multi-period investment decision models can stimulate readers to find even better models and to efficiently solve realistic multi-period portfolio selection problems.

The empirical results show the superior performance and robustness of optimal investment strategy obtained with the new models. What's more important, the empirical analyses tell readers how different market frictions affect the performance of optimal portfolios, which can guide them to efficiently solve real multi-period investment decision problems in practice.

The paper first derives the concrete structure of the time consistent generalized convex multi-period risk measure, then constructs a multi-period portfolio selection model based on the new multi-period risk measure, and proposes a new extremum scenario tree generation algorithm. The authors construct a realistic multi-period investment decision model. Furthermore, using the proposed scenario tree generation algorithm, the authors transform the established stochastic investment decision model into a deterministic optimization problem, which can provide optimal investment decisions with robustness and superior performance.

The purpose of this paper is to analyze medium‐term risks faced by electrical generation companies in competitive environments. Market risks faced by generation companies are caused by several variables subject to uncertainty. Hydro conditions, fuel (coal and natural gas) prices, system demand, and CO₂ emission price are the risk factors considered in the paper. Taking into account these risk factors, generation companies have to take decisions that would affect their economic results and their risk exposure.

This paper proposes a methodology to support the risk‐analysis decision‐making process. Firstly, different scenarios of risk factors are generated. Then, a market equilibrium model is used in order to assess the impact of the different sources of uncertainty. Finally, decision trees are used in order to analyze the variables subject to interest, such as electricity prices or companies' profits.

The proposed methodology can be enhanced to take into account scenarios of more risk factors, such as equipment failure or agents' behavior. Another future enhancement could be a detailed study of correlation between different risk factors.

A realistic case study is presented, showing the advantages of these techniques for medium‐term risk‐analysis and decision‐making processes. Several decision trees have been generated to assess the impact of the different risk factors in electricity prices and companies' profits. These decision trees provide valuable information for companies when facing their risk‐management process.

The approach presented here constitutes a valuable support to gain useful information for wise decision making and to hedge against risk.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This article proposes a strategy to make the testing step easier, generating user acceptance tests (UATs) in an automatic way from requirements artifacts.

This strategy is based on two modeling frameworks: scenarios and task/method paradigm. Scenarios are a requirement artifact used to describe business processes and requirements, and task/method paradigm is a modeling paradigm coming from the artificial intelligence field. The proposed strategy is composed of four steps. In the first step, scenarios are described through a semantic wiki website. Then scenarios are automatically translated into a task/method model (step two). In the third step, the task/method model obtained in step two is executed in order to produce and store all possible achievements of tasks and thus scenarios. The stored achievements are saved in a data structure called execution tree (ET). Finally, from this ET (step four), the UATs are generated.

The feasibility of this strategy is shown through a case study coming from the agriculture production systems field.

Generally, test design approaches deal with a small number of variables describing one specific situation where a decision table or workflow is used to design tests. The proposed approach can deal with many variables because the authors rely on scenarios that can be composed in order to obtain a tree with all the testing paths that can arise from their description.

The purpose of this paper is to present a discrete compartmental susceptible-asymptomatic-infected-dead (SAID) model to address the expansion of plant pests. The authors examined the case of Xylella fastidiosa in almond trees in the province of Alicante (Spain) to define the best eradication/contention protocol depending on the environmental parameters such as climatic factors, distance between trees, isolation of the plots, etc.

This approach considers the expansion of the disease among the almond trees orchards by means of a grid model. The cells of the grid represent a tree (or even a group of trees) that can be susceptible (healthy), asymptomatic (infected by the bacterium but without symptoms), infected or dead. When time passes, the status of the cells is determined by binary rules that update following both a neighborhood and a delay pattern. The model assumes that the environmental parameters have a crucial impact on the expansion of the disease, so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids.

This proposal shows how the grid architecture, along with an update rule and a neighborhood pattern, is a valuable tool to model the pest expansion. This model has already been analyzed in previous works and has been compared with the corresponding continuous models solved by ordinary differential equations, coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between susceptible and asymptomatic, asymptomatic and infected, infected and recovered/dead) may have a crucial impact on both the peak of infected and the recovery/death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and is also currently under study in the case of expansion of COVID-19.

This work develops a new approach for the analysis of expansion of plant pests. This approach provides both behavioral variability at the cell level (by its capability to modify the neighborhood and/or the update rule and/or the delays) and modularity (by easy scaling the number of grids). This provides a wide range of possibilities to deal with realistic scenarios.

Human error data in the form of human error probabilities should ideally form the corner‐stone of human reliability theory and practice. In the history of human reliability assessment, however, the collection and generation of valid and usable data have been remarkably elusive. In part the problem appears to extend from the requirement for a technique to assemble the data into meaningful assessments. There have been attempts to achieve this, THERP being one workable example of a (quasi) database which enables the data to be used meaningfully. However, in recent years more attention has been focused on the PerformanceShaping Factors (PSF) associated with human reliability. A “database for today” should therefore be developed in terms of PSF, as well as task/ behavioural descriptors, and possibly even psychological error mechanisms. However, this presumes that data on incidents and accidents are collected and categorised in terms of the PSF contributing to the incident, and such classification systems in practice are rare. The collection and generation of a small working database, based on incident records are outlined. This has been possible because the incident‐recording system at BNFL Sellafield does give information on PSF. Furthermore, the data have been integrated into the Human Reliability Management System which is a PSF‐based human reliability assessment system. Some of the data generated are presented, as well as the PSF associated with them, and an outline of the incident collection system is given. Lastly, aspects of human common mode failure or human dependent failures, particularly at the lower human error probability range, are discussed, as these are unlikely to be elicited from data collection studies, yet are important in human reliability assessment. One possible approach to the treatment of human dependent failures, the utilisation of human performance‐limiting values, is described.

Mapping the intrinsic value of the art of architecture and the art of nature within the context of Kakunodate and the preservation of its samurai manors and Sakura heritage trees, this paper proposes that from the perception of tourists, the preservation of both items is important. Extending the psychology of pricing that is subject to consumers’ preference and expectation to the context of valuation of cultural heritage assets, the purpose of this paper is to suggest that including the art of nature and the aesthetic of Sakura to the art of architecture (Samurai manors) will add more value to the cultural heritage of Kakunodate than when only the art of architecture is mentioned.

This paper uses Contingency Valuation Method to solicit tourists willingness-to-pay to preserve the architecture and the nature in Kakunodate. The survey uses a double bounded dichotomous choice model to elicit the various levels of tourists preference in various scenarios. Response from more than 1,000 tourists in three scenarios were collected. Through a maximum likelihood method and a subsequent truncated calculation, results are reported.

The results support the conceptual argument that the art of nature adds value to the art of architecture only. Tourists are found to be willing to pay more to support both the architecture (samurai houses) and nature (cherry trees) than the samurai houses alone.

The research findings add to the inventory of research on prior works in heritage trees, conservation of trees and heritage tourism, and cultural heritage assets in general. The empirical findings support prior theoretical works that examined the relationship of nature and art, art and architecture, and architecture as visual consumption.

The findings have managerial implications for policy makers relative to a possible increase of revenue by adding accompanying-nature component to focal architectural assets when soliciting funding support.

The originality and this piece stems from extending trees as an art form in nature and its added value to architecture within the context of cultural heritage assets. The empirical findings add to the much discussed relationships among art, nature, and architecture.

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.

Ubiquitous web applications (UWA) are a new type of web applications which are accessed in various contexts, i.e. through different devices, by users with various interests, at anytime from anyplace around the globe. For such full‐fledged, complex software systems, a methodologically sound engineering approach in terms of model‐driven engineering (MDE) is crucial. Several modeling approaches have already been proposed that capture the ubiquitous nature of web applications, each of them having different origins, pursuing different goals and providing a pantheon of concepts. This paper aims to give an in‐depth comparison of seven modeling approaches supporting the development of UWAs.

This methodology is conducted by applying a detailed set of evaluation criteria and by demonstrating its applicability on basis of an exemplary tourism web application. In particular, five commonly found ubiquitous scenarios are investigated, thus providing initial insight into the modeling concepts of each approach as well as to facilitate their comparability.

The results gained indicate that many modeling approaches lack a proper MDE foundation in terms of meta‐models and tool support. The proposed modeling mechanisms for ubiquity are often limited, since they neither cover all relevant context factors in an explicit, self‐contained, and extensible way, nor allow for a wide spectrum of extensible adaptation operations. The provided modeling concepts frequently do not allow dealing with all different parts of a web application in terms of its content, hypertext, and presentation levels as well as their structural and behavioral features. Finally, current modeling approaches do not reflect the crosscutting nature of ubiquity but rather intermingle context and adaptation issues with the core parts of a web application, thus hampering maintainability and extensibility.

Different from other surveys in the area of modeling web applications, this paper specifically considers modeling concepts for their ubiquitous nature, together with an investigation of available support for MDD in a comprehensive way, using a well‐defined as well as fine‐grained catalogue of more than 30 evaluation criteria.