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The purpose of this paper is to propose an availability modeling method of complex multiple units system (CMUS) based on the multi-agent technique.

Based on the multi-agent technique, this paper describes the availability model structure for CMUS and develops agent-based models of components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff, as well as the communication method among the different agents. On the basis of the agent-based availability modeling theory, the availability simulation scheme of CMUS is given using MATLAB. Thus, the availability modeling theory of CMUS and its simulation method are developed. To demonstrate the applicability of the proposed availability modeling method, a numerical example is given.

The proposed agent-based modeling method is applicable to availability modeling of CMUS, including the modeling of component failure, maintenance tools/fields/staff, maintenance policy, and structural/economic dependence among components.

As a bottom-top, modular, expandable, and reusable modeling theory, the agent-based modeling method might be useful for availability modeling of different CMUSs in reality.

The multi-agent technique is introduced into availability modeling of multi-component systems in this paper. Thus, it is possible to model failure of many components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff together for availability analysis of complex systems of equipment.

The aim of the research is to achieve a robot skin which is easy to use, and can detect both position and force interacted between robot and environments.

The new type of robot skin proposed in this paper includes two functional modules – contact position sensor and contact force sensor. The contact position sensor module is based on the resistor divider principle, which consists of two perpendicular conductive fiber layers and insulated dot spacer between them. The contact force sensor module is based on capacitance change theory, which consists of two soft conductive plates and a viscoelastic layer between them. By combining the two modules, the soft robot skin was designed.

Simulation and experiment results demonstrate that the proposed robot skin design is feasible and effective enough to sense contact position and contact force simultaneously.

This robot skin is low-cost and easy to make and use, which provides safety solutions for most of the robot.

For the first time, an integrated robot skin which can get contact position and force information simultaneously is designed. Unlike general tactile sensor matrices, this robot skin has only six leads. Furthermore, the number of leads does not increase with the enlarging of sensor area. Soft and simple structure of the robot skin makes it possible to cover any region of the robot body.

As the complexity and uncertainty of infrastructural megaprojects challenge traditional management models, there is an increasing focus on value co-creation as an organizational strategy to streamline management. However, the role of value co-creation behavior in facilitating the value realization process remains underexplored. This study examines how justice perception (distributive, procedural and interactional justice) improves contractor value co-creation behavior, focusing on the mediating role of psychological ownership.

Ten hypotheses in the proposed research model were tested through partial least squares structural equation modeling using 199 valid questionnaires from China.

The results show that contractor value co-creation behavior is directly and positively influenced by procedural, distributive and interactional justice and indirectly influenced by them through the underlying psychological mechanism of psychological ownership.

The findings fill a knowledge gap by examining the effect of justice perception on contractor value co-creation behavior based on social exchange theory. Discovering justice perception will contribute to contractor value co-creation behavior, and psychological ownership mediates this relationship.

This study aims to explore the effect of urban green space (UGS) on residents' subjective well-being (SWB) among different social groups.

Using national SWB and UGS data obtained from the China Urban Construction Statistical Yearbook and the Chinese General Social Survey, a multiple regression model was developed to estimate the effect of UGS on residents' SWB. Grouping regression for samples from distinct socioeconomic groups was performed to further discuss group-wise differences in SWB.

The green coverage rate of built-up areas and the number of parks accessed by every 10,000 individuals are significantly positively correlated with residents' SWB, whereas the green space area per capita and greening investment ratio are significantly negatively correlated with residents' SWB; the effect of UGS on residents' SWB varies among individuals with respect to gender, age, and income, with the most significant difference observed among groups with different incomes.

The empirical results of this study are expected to support the planning and construction of UGS by providing a reference for optimizing their service capabilities and highlighting their positive role in improving residents' SWB.

Decision-making problems in emergency plan selection for epidemic prevention and control (EPAC) are generally characterized by risky and uncertainty due to multiple possible emergency states and vagueness of decision information. In the process of emergency plan selection for EPAC, it is necessary to consider several obvious features, i.e. different states of epidemics, dynamic evolvement process of epidemics and decision-makers' (DMs') psychological factors such as risk preference and loss aversion.

In this paper, a novel decision-making method based on cumulative prospect theory (CPT) is proposed to solve emergency plan selection of an epidemic problem, which is generally regarded as hybrid-information multi-attribute decision-making (HI-MADM) problems in major epidemics. Initially, considering the psychological factors of DMs, the expectations of DMs are chosen as reference points to normalize the expectation vectors and decision information with three different formats. Subsequently, the matrix of gains and losses is established according to the reference points. Furthermore, the prospect value of each alternative is obtained and the comprehensive prospect values of alternatives under different states are calculated. Accordingly, the ranking of alternatives can be obtained.

The validity and robustness of the proposed method are demonstrated by a case calculation of emergency plan selection. Meanwhile, sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and TODIM (an acronym in Portuguese for interactive and MADM) method illustrate the effectiveness and superiority of the proposed method.

An emergency plan selection method is proposed for EPAC based on CPT, taking into account the psychological factors of DMs.

Highlights

1. This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

2. The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

3. This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

4. The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

This research aims to examine which cooperative contract (wholesale-price contract or cost-sharing contract) can more effectively upgrade the green degree of product and promote demand when considering consumer reference price effect under different power structures.

This research investigates a dyadic green supply chain composed of one manufacturer and one retailer. Four Stackelberg game models with a cost-sharing contract or a wholesale-price contract are built in retailer-led and manufacturer-led scenarios, respectively. Using backward induction, the optimal green decision under each model is obtained. In addition, the optimal cooperative contract is proposed by comparing these four models.

It is found that under consumer reference price effect, a cost-sharing contract outperforms a wholesale-price contract in upgrading product greenness and promoting demand. Under any single contract, the retailer-led situation is more conducive to improving product greenness than the manufacturer-led situation. Moreover, consumer reference price effect would reduce the sharing ratio of a cost-sharing contract when the manufacturer dominates, but it could mitigate the problem of double marginalization by reducing wholesale and retail prices under both types of contracts, which would enhance consumer surplus.

It is a new attempt to incorporate consumer reference price effect and power structure into a green supply chain framework and proposes a novel demand function that simultaneously emphasizes consumer reference price effect, consumer environmental awareness and product green attribute. In addition, it provides managerial insights for business managers to choose green cooperative contracts with consumer reference price effect under different power structures.

Advertisements facilitate certain emotions, subsequently influencing purchase intentions. Humor, as an influential way of information expression, is frequently used in ads to elicit emotions. Drawing upon literature on advertisement humor and new product purchase intention and the theory of planned behavior, the study proposes that humor stimulation in advertisements can affect consumers' new product purchase intentions, in which two process mechanisms, namely, emotional arousal and cognitive flexibility, play a mediating effect.

To test the assertions, the authors conduct three experimental studies. The authors' first study assesses the main effect between advertisement humor and purchase intentions. In the second study, the authors show the mediating effects of emotional pleasure, emotional arousal, and cognitive flexibility on the relationship between advertisement humor and purchase intentions. In the first two experiments, the authors study incremental new products. In the third study, the authors study the same mediating relationships for radically new products.

This study's results show consumers that watch humorous ads are more likely to choose new products than those who watch non-humorous ads (Study 1); compared with non-humorous ads, humorous ads can enhance emotional arousal, thus promoting cognitive flexibility and making consumers more inclined to choose new products (Study 2 and Study 3). That said, the authors find that these mediation effects are only partial.

This study's results have important implications for firms vying to enhance consumers' new product purchase intentions by deploying humorous ads.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.

South Africa is the highest consumer of commercial energy per capita in Africa, ranking 16th in the world for primary energy consumption. It is also ranked among the bottom 50 of the 150 countries regarding energy efficiency. The cold chain is a large contributor through refrigerated transport vehicles. To comply with the changing climate regulations, cryogenic and eutectic systems are systems with great potential for small distance refrigerated transport. The purpose of this paper is to introduce eutectic system to medium distance refrigerated transport.

This study presents the potential use of Eutectic plates inside a medium refrigerated transport vehicle, by numerically investigating the characteristics of phase change material eutectic plates applied at low-temperature ranges. A physical model and a mathematical model for three-dimensional transient natural flow were developed as proposed by Xiaofeng and Zhang. Using the governing equation of mass, momentum and energy conservation, three Eutectic plate configurations were modeled and simulated in ANSYS Fluent for 5 h.

A uniform heat transfer and airflow condition inside a refrigerated compartment were predicted using the Reynolds stress model. The configuration with eutectic plates placed at the top and side showed great potential for the system functioning in the South African climate.

Medium refrigerated transport vehicle.

This configuration had a high-temperature distribution across the compartment and promoted high air circulations, showing that it could be ideal for medium refrigerated transport vehicles delivering perishable foodstuffs or non-food goods.

This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models.

An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between crypto-price and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision.

The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models.

The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies’ prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.