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The purpose of this paper is twofold: first, to provide a descriptive analysis of consumers’ ability to comprehend and use common price promotion information when they choose to do so (e.g. to find the least expensive price or to understand the savings amount); second, to identify which consumer groups (in terms of demographic characteristics) find price promotion comprehension particularly challenging.

An online questionnaire with 14 measures (four literacy, ten numeracy) was administered in the study. Data from 607 Australian consumers were analysed using descriptive, cross-tabulation, and multiple regression analysis via IBM SPSS analytics software.

On average, 20 per cent of the consumers surveyed were unable to comprehend the price promotion signage. On average, 13 per cent of the consumers were unable to carry out arithmetic tasks using the information on price promotion signage. Multiple regression models showed that income level was the main driving factor for the consumers’ price promotion literacy and numeracy levels.

The present study is the first exploratory examination of consumers’ levels of comprehension (literacy) and effective use (numeracy) regarding common types of price promotion communication. The use of online samples and data collection method overestimates the results effect.

This is a pilot field study to investigate whether levels of consumers’ price promotion literacy and numeracy are adequate for everyday decision making. The information can be used as evidence and justification for further research.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

The provision of meals has long been regarded as an essential part of treatment of hospitalized patients complementing medical procedures and nursing management. Today, despite changes in the health-care landscape, which focused on improving the quality and efficiency of hospital care, malnutrition among inpatient was still a common worldwide concern.

This was a multi-centre, randomized study conducted in 21 study sites comprising 21 state and specialist government hospitals under the Ministry of Health, Malaysia. The sample size for this study was calculated with purposive sampling method, followed by proportionate sampling to determine the random sample size of each of the study sites. The total sample size required for this study was 2,759 subjects. A validated data collection form was used in the study.

Only 32.2 % and 37.6 % of subjects achieved adequate energy and protein intake, respectively, during their admission to medical ward. The study result showed that the overall mean energy and protein intake was 794.6 ± 487.8 kcal and 35.2 ± 24.3 g, respectively. The estimated energy (p = 0.001) and protein (p = 0.001) intake of all study sites was significantly lower compared to the adequacy value.

The adequacy intake in this study was only carried out in medical wards, thus reproducible result among other wards in different study sites could not be confirmed. Besides, this study assumed that the portion eaten by subjects during lunch and dinner was the same, and therefore, either one was recorded together with breakfast and either lunch or dinner to represent a subject’s daily intake.

This was the first nationwide study to report the adequacy of energy and protein intake of patients receiving therapeutic diets in the government hospital setting in Malaysia.

The purpose of this paper is to analyze manufacturers' production decisions and governments' low-carbon policies in the context of influencer spillover effects.

This paper investigates the impact of the social influencer spillover effect on manufacturers' production decisions when they collaborate with intermediary platforms to sell products through marketplace or reseller modes. Game theory and static numerical comparison are used to analyze our models.

Firstly, under low-carbon policies, the spillover effect does not always benefit manufacturer profits and changes non-monotonically with an increasing spillover effect. Secondly, in cases where there are both a carbon emission constraint and a spillover effect present, if either the manufacturer or intermediary platform holds a strong position, then marketplace mode benefits manufacturer profits. Thirdly, regardless of business mode used when environmental damage coefficient is high for products; government should implement cap-and-trade regulation to optimize social welfare while reducing manufacturers’ carbon emissions.

This study offers theoretical and practical research support to assist manufacturers in optimizing production decisions for compliance with carbon emission limits, enhancing profits through the development of effective influencer marketing strategies, and providing strategies to mitigate carbon emissions and enhance social welfare while sustaining manufacturing activities.

This paper addresses the limitations of prior research by examining how the social influencer spillover effect influences manufacturers' business mode choices under government low-carbon policies and analyzing the social welfare of different carbon emission restrictions when such spillovers occur. Our findings provide valuable insights for manufacturers in selecting optimal marketing strategies and business modes and decision-makers in implementing effective regulations.

To solve the shortcomings of existed search and rescue drones, search and rescue the trapped people trapped in earthquake ruins, underwater and avalanches quickly and accurately, this paper aims to propose a four-axis eight-rotor rescue unmanned aerial vehicle (UAV) which can carry a radar life detector. As the design of propeller is the key to the design of UAV, this paper mainly designs the propeller of the UAV at the present stage.

Based on the actual working conditions of UAVs, this paper preliminarily estimated the load of UAVs and the diameters of propellers and designed the main parameters of propellers according to the leaf element theory and momentum theory. Based on the low Reynolds number airfoil, this paper selected the airfoil with high lift drag ratio from the commonly used low Reynolds number airfoils. The chord length and twist angle of propeller blades were calculated according to the Wilson method and the maximum wind energy utilization coefficient and were optimized by the Asymptotic exponential function. The aerodynamic characteristics of the designed single propeller and coaxial propeller under different installation pitch angles and different installation distances were analyzed.

The results showed that the design of coaxial twin propellers can increase the load capacity by about 1.5 times without increasing the propeller diameter. When the installation distance between the two propellers was 8 cm and the tilt angle was 15° counterclockwise, the aerodynamic characteristics of the coaxial propeller were optimal.

The novelty of this work came from the conceptual design of the new rescue UAV and its numerical optimization using the Wilson method combined with the maximum wind energy utilization factor and the exponential function. The aerodynamic characteristics of the common shaft propeller were analyzed under different mounting angles and different mounting distances.

The Internet of Things (IoT) is expected to have a huge impact on businesses and, especially, the way we think about supply chain management (SCM). However, there is still a paucity of studies on the impact of IoT adoption on supply chains and on different aspects of the business in general. The purpose of this paper is to examine the perception of the academic community of the impact of the IoT adoption in organizational supply chains with a view to verify potential key benefits and challenges existent in the literature. The research presents the impact on an organization along with the impact across its entire supply chain.

Data were collected through the use of an online survey and 87 participants completed the survey. Participants were mainly from the academic community and were university scholars based in different countries located in six continents. Participants were authors, or co-authors, of academic papers published in the Decision Science Institute 2015 and 2016 annual conference proceedings, the 21st International Symposium of Sustainable Transport and Supply Chain Innovations, the Supply Chain Management: An International Journal 2016 issues, and the Operations and Supply Chain Management: An International Journal 2016 issues.

The authors were able to confirm the significance of some of the examined potential benefits to individual organizations and their entire supply chains. However, the study identified other potential benefits that were not seen as a direct impact of IoT adoption. Most of the examined potential benefits were found to contribute to a number of critical success factors for implementing successful SCM. The authors were also able to confirm that some of the examined potential challenges were still perceived as key hinders to IoT adoption but examined potential challenges were not seen as hurdles to IoT adoption.

To the best of the authors’ knowledge, this is the first study of its kind. Although some literature attempted to provide an overview about the IoT management, no study has specifically explored potential benefits and challenges related to the adoption of IoT in supply chains and ranked them based on their significance. The results can be beneficial to academic scholars interested in the researched topic, business professionals, organizations within different sectors, and any other party interested in understanding more about the impact of adopting IoT on SCM.

The purpose of this paper is to study the evolution of Chinese industrial relations after the market reform of 1978, while basing its arguments and conclusion on analysis of the interactions of key actors in the labour arena in China. The significant phenomena in the evolution of industrial relations are the coming of transnational capital and the emergence of self‐organising protests by migrant workers.

The paper uses a case study approach.

The Labour Contract Law and the local political economy experience strong effects from TNCs and other business players. Meanwhile, globalisation has introduced the civil society movement to China, which has given rise to an increasing number of NGOs working for labour rights. Tight financial and technical connections between grassroots NGOs and international donor organisations make it possible for bottom‐up labour activities to counteract the unilateral influence of the state and market over the Chinese workforce. Since the ACFTU, the official trade union umbrella, has many institutional constraints to undertake a thorough transition towards labour in the near future, workers' representation is diversified.

One implication for further theoretical studies is that tripartism cannot fully disclose the reality of Chinese labour, and that labour representation derives from both unions and self‐organisation of workers, such as NGOs, which opens more room for the entrenchment of the grassroots labour movement to sustain the balance of power among the state, ACFTU, firms, international market forces and individual workers in the long term.

The purpose of this paper is to provide a novel assembly simulation method to reduce the repetitive and tedious assembly simulation work. Currently, assembly simulation is always carried out by human–computer interaction, which is a time-consuming and tedious work. The most important reason for this problem is that the assembly simulation is a mapping between human intent and movements of models; at the same time, assembly information is transferred from semantic level to geometric level. However, some essential assembly information is lost during the transfer, and it must be accomplished through manual definition. To address the issue, a novel assembly simulation method is proposed in this paper based on semantics and geometric constraint.

First, an assembly operation semantic model is put forward to integrate and manage the semantic information of assembly, and some rules for modeling are generalized. Second, method for transferring assembly information from semantic level to geometric level is presented by dividing assembly operation into a set of simulation actions and providing some rules for this division. Then, a geometric constraint-based calculation method is proposed to obtain the essential parameters of each simulation action. Finally, cases are studied to demonstrate the effectiveness of the method.

Results show that laborious work would be reduced, and the redundant human participation would be avoided in assembly simulation.

It has the potential and possibility to change the current pattern of assembly simulation.

A novel assembly simulation method based on semantics and geometric constraint is presented to make assembly simulation more convenient and faster.

To improve the efficiency, accuracy and adaptivity of the parameter inversion analysis method of a rockfill dam, this study aims to establish an adaptive model based on a harmony search algorithm (HS) and a mixed multi-output relevance vector machine (MMRVM).

By introducing the mixed kernel function, the MMRVM can accurately simulate the nonlinear relationship between the material parameters and dam settlement. Therefore, the finite element method with time consumption can be replaced by the MMRVM. Because of its excellent global search capability, the HS is used to optimize the kernel parameters of the MMRVM and the material parameters of a rockfill dam.

Because the parameters of the HS and the variation range of the MMRVM parameters are relatively fixed, the HS-MMRVM can imbue the inversion analysis with adaptivity; the number of observation points required and the robustness of the HS-MMRVM are analyzed. An application example involving a concrete-faced rockfill dam shows that the HS-MMRVM exhibits high accuracy and high speed in the parameter inversion analysis of static and creep constitutive models.

The applicability of the HS-MMRVM in hydraulic engineering is proved in this paper, which should further validate in inversion problems of other fields.

An adaptive inversion analysis model is established to avoid the parameters of traditional methods that need to be set by humans, which strongly affect the inversion analysis results. By introducing the mixed kernel function, the MMRVM can accurately simulate the nonlinear relationship between the material parameters and dam settlement. To reduce the data dimensions and verify the model’s robustness, the number of observation points required for inversion analysis and the acceptable degree of noise are determined. The confidence interval is built to monitor dam settlement and provide the foundation for dam monitoring and reservoir operation management.

Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR).

This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation.

Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others.

Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.