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The prevention through design (PtD) concept has been widely recognized as one of the most effective approaches to eliminate or reduce construction site hazards. It encourages engineers and architects to consider occupational safety and health during the planning and design phases. Nevertheless, the implementation of PtD is often inhibited because designers lack adequate knowledge about construction safety and the construction process, and limited design-for-safety tools and procedures are available for designers to use. The purpose of this paper is to provide designers a tool for assessing construction risks during early phases of multistory building projects at an activity level and on a daily basis in a 4D environment. By using the tool, proactive measures could be taken in the design and planning phase to reduce site hazards.

The proposed method consists of four steps including risk quantification at a design element level, 4D model integration with risk values, risk assessment, and design alternative selection and model acceptance. A case study was carried out to test and verify the proposed method.

The proposed tool has the capability to assess the safety risk for an entire multistory project and visualize safety risk in a particular time period, work space and task prior to construction. It benefits designers in conducting risk assessments and selecting design alternatives concerning safety. Contractors could also utilize the visualization and simulation results of the 4D model for site safety planning so that a range of risk mitigation strategies could be implemented during construction.

The study provides an innovative PtD tool targeting designers as primary end-users. The proposed tool helps designers assess construction risks and has potential to incorporate the top levels of the hierarchy of risk controls.

Food safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal pollution and nutrient deficiency have caused regional diseases. Thus, the purpose of this paper is to present a risk early warning method of food safety considering environmental and nutritional factors.

A novel risk early warning modelling method based on the long short-term memory (LSTM) neural network integrating sum product based analytic hierarchy process (AHP-SP) is proposed. The data fuzzification method is adopted to overcome the uncertainty of food safety detection data and the processed data are viewed as the input of the LSTM. The AHP-SP method is used to fuse the risk of detection data and the obtained risk values are viewed as the expected output of the LSTM. Finally, the proposed method is applied on one group of sterilized milk data from a food detection agency in China.

The experimental results show that compared with the back propagation and the radial basis function neural networks, the proposed method has higher accuracy in predicting the development trend of food safety risk. Moreover, the causal factors of the risk can be figured out through the predicted results.

The proposed modelling method can achieve accurate prediction and early warning of food safety risk, and provide decision-making basis for the relevant departments to formulate targeted risk prevention and control measures, thereby avoiding food safety incidents caused by environmental pollution or nutritional deficiency.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

A major theme for studies in international marketing is whether marketing programs and processes can be generalized across countries. This study tests the generalizability of a model that predicts consumers' perception of value based upon extrinsic cues – such as brand name, price, retailer reputation, and country of origin – and their perceptions of quality, sacrifice, and risk. The study extends the perceived value model specified by Agarwal and Teas and tested in the USA. The results of this study, based on an experiment conducted in Sweden, suggest that while the overall structure of the model is supported across countries, the relative importance of the extrinsic cues may vary across countries.

From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

The research activity presented in this paper has the objective of developing models for the evaluation of technological risk and loss of production due to failures, which are among the criterions that enable the choice of optimal scenarios for energy supply. This activity is based on the European Project “Risk of Energy Availability: Common Corridors for Europe Supply Security” (REACCESS), which aims to develop an analytical tool to analyse scenarios for future secure European Union (EU) energy supply.

The paper proposes an innovative approach, since nowadays a generalised analytic model for risk assessment in large‐scale energetic systems does not exist. In particular, the methodology adopted includes models to assess risk for people safety, risk for the environment and availability for corridors and the related infrastructures. As regards technological risk, accidents producing loss of lives in the population and environmental damage are taken into account; while for the loss of production primary attention is paid to technical failures and maintenance.

Since the analytic models developed perform a large‐scale assessment, they must be flexible and simplified to adapt to different situations and to be easily updated when different future scenarios are investigated. Details of the analysis depend on the precision of data collected and inserted in the models. The damage assessment is affected by deficiency and uncertainties related to territorial and statistical data. Nevertheless, the outcomes obtained for each energy commodity are reasonable and often comparable to literature data.

Based on this study output, technological risk can be considered, more systematically than in the past, in the selection of EU strategies for future energy supply. The corridors social cost is included in future strategies selection, in addition to purely economical and environmental evaluations.

The current work studies the cause, process, and effects of financial reform in 10 countries in Eastern Asia for the period of 1993–2002, especially focusing upon comparisons between pre- and post-Asia financial crisis. This study utilizes Mann–Whitney U test and Intervention Analysis to explore the different effects of the changes of GDP, stock index, exchange rate, CPI index, and the changes of the unemployment rate before and after the Asia financial crisis. It shows the consistent relationship between stock index, exchange rate, CPI index, and the changes of unemployment rate.

This paper aims to present the combination of enterprise risk management (ERM) and value-based management as especially suitable methods for companies with a shareholder value imperative. Among its major benefits, these methods make the contribution of risk management for business decisions more effective.

Any possible inconsistencies between ERM, generating value because of imperfect capital markets and the CAPM to calculate cost of capital, which assumes perfect markets, must be avoided. Therefore, it is imperative that valuation methods used are based on risk analysis, and thus do not require perfect capital markets.

Value-based risk management requires the impact of changes in risk on enterprise value to be calculated and the aggregation of opportunities and risks related to planning to calculate total risk (using Monte Carlo simulation) and valuation techniques that reflect the effects changes in risk, on probability of default, cost of capital and enterprise value (and do not assume perfect capital markets). It is recommended that all relevant risks should be quantified and described using adequate probability distributions derived from the best information.

This approach can help to improve the use of risk analysis in decision-making by improving existing risk-management systems.

This extension of ERM is outlined to provide risk-adequate evaluation methods for business decisions, using Monte Carlo simulation and recently developed methods for risk–fair valuation with incomplete replication in combination with the probability of default. It is shown that quantification of all risk using available information should be accepted for the linking of risk analysis and business decisions.

The purpose of this paper is to evaluate the predictive capacity of market risk estimation models in times of financial crises.

For this, value-at-risk (VaR) valuation models applied to the daily returns of portfolios composed of stock indexes of developed and emerging countries were tested. The Historical Simulation VaR model, multivariate ARCH models (BEKK, VECH and constant conditional correlation), artificial neural networks and copula functions were tested. The data sample refers to the periods of two international financial crises, the Asian Crisis of 1997, and the US Sub Prime Crisis of 2008.

The results pointed out that the multivariate ARCH models (VECH and BEKK) and Copula-Clayton had similar performance, with good adjustments in 100 percent of the tests. It was not possible to perceive significant differences between the adjustments for developed and emerging countries and of the crisis and normal periods, which was different to what was expected.

Previous studies focus on the estimation of VaR by a group of models. One of the contributions of this paper is to use several forms of estimation.