Search results

A dust cloud is formed by a high-pressure air blast in dust explosion experiments in the spherical 20 L chamber. The state of the dust cloud has a significant impact on the dust explosion. However, it is difficult to observe the dust distribution in the chamber during the dust dispersion. Numerical simulation was used to examine the dust distribution in the chamber with the rebound nozzle in this work. The paper aims to discuss these issues.

Through a series numerical simulations, the influences of the dust particle size and the pressure for dust dispersion on the have been analyzed, and the results are discussed.

Dust in the spherical 20 L chamber is in the state of very intensifying motion within 30 ms from dispersion starting. Dust in the chamber reaches a uniform state beyond 200 ms. The pressure for dust dispersion should be higher than 0.5 MPa for the aluminum dusts of larger than 50. The higher blast pressure is not always applicable to achieve a uniform dispersion. There is a best blast pressure value for a given dust to achieve a uniform dispersion in the spherical 20 L chamber.

Dust cloud generation is essential for understanding dust explosions. Dust cloud deflagration parameters depend on the uniformity and concentration of dusts dispersed by a high-pressure air blast. Numerical simulation was used to examine the multiphase flow of the dust air mixture in this work. Through a series numerical simulations, the influences of the dust particle size and the pressure for dust dispersion on the have been analyzed, and the results are discussed. The data are useful for understanding the basics of dust cloud formation.

The data are useful for evaluating dust explosion experimental parameters.

Dispersible uniformity has a strong impact on measured parameters of dust explosion in a chamber. However, it is difficult to observe the dust particles distribution during the dispersion. Numerical simulation was used to examine the dust particles distribution and its influencing factors during the dispersion in this work. New finding is: the approach to examine the distribution of dust particles dispersed by a high-pressure blast in a chamber; the variation of dispersible uniformity and its influencing factors when dust is injected into the spherical 20 L chamber by high-pressure air blast.

The purpose of this paper is to study propagation characteristics of methane explosion in the pipe network and analyze the propagation laws of methane explosion wave along the elbow pipe and pipe network.

Numerical simulation using software package AutoReaGas, a finite-volume computational code for fluid dynamics suitable for gas explosion and blast problems, is adopted to simulate the propagation characteristics of methane explosion and the property of flow field in complex structures.

Due to reflection effects of corners of elbow pipe, the peak overpressures at corner locations in the elbow pipe go about two times higher than that in the straight pipe. In the parallel pipe network, the peak overpressure increases significantly at the intersection point, while the flame speed decreases at the junction. All these indicate that pipe corners and bifurcations could substantially enhance explosion partly which can bring more severe damage at the corner area. The explosion violence is strengthened after flames and blast waves are superimposed, such that equipments and people in these areas need special strengthening protection.

The numerical results presented in this paper may provide some useful guidance for the design of the underground laneway structures and to take protective measures at corners and bifurcations in coal mines.

This paper aims to propose a novel system design and control algorithm of lower limb exoskeleton, which provides walking assistance and load sharing for the wearer.

In this paper, the valve-controlled asymmetrical hydraulic cylinder is selected for driving the hip and knee joint of exoskeleton. Pressure shoe is developed that purpose on detecting changes in plantar force, and a fuzzy recognition algorithm using plantar pressure is proposed. Dynamic model of the exoskeleton is established, and the sliding mode control is developed to implement the position tracking of exoskeleton. A series of prototype experiments including benchtop test, full assistance, partial assistance and loaded walking experiments are set up to verify the tracking performance and power-assisted effect of the proposed exoskeleton.

The control performance of PID control and sliding mode control are compared. The experimental data shows the tracking trajectories and tracking errors of sliding mode control and demonstrate its good robustness to nonlinearities. sEMG of the gastrocnemius muscle tends to be significantly weakened during assisted walking.

In this paper, a structure that the knee joint and hip joint driven by the valve-controlled asymmetrical cylinder is used to provide walking assistance for the wearer. The sliding mode control is proposed to deal with the nonlinearities during joint rotation and fluids. It shows great robustness and frequency adaptability through experiments under different motion frequencies and assistance modes. The design and control method of exoskeleton is a good attempt, which takes positive impacts on the productivity or quality of the life of wearers.

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

Current reinforcement learning (RL) algorithms are facing issues such as low learning efficiency and poor generalization performance, which significantly limit their practical application in real robots. This paper aims to adopt a hybrid model-based and model-free policy search method with multi-timescale value function tuning, aiming to allow robots to learn complex motion planning skills in multi-goal and multi-constraint environments with a few interactions.

A goal-conditioned model-based and model-free search method with multi-timescale value function tuning is proposed in this paper. First, the authors construct a multi-goal, multi-constrained policy optimization approach that fuses model-based policy optimization with goal-conditioned, model-free learning. Soft constraints on states and controls are applied to ensure fast and stable policy iteration. Second, an uncertainty-aware multi-timescale value function learning method is proposed, which constructs a multi-timescale value function network and adaptively chooses the value function planning timescales according to the value prediction uncertainty. It implicitly reduces the value representation complexity and improves the generalization performance of the policy.

The algorithm enables physical robots to learn generalized skills in real-world environments through a handful of trials. The simulation and experimental results show that the algorithm outperforms other relevant model-based and model-free RL algorithms.

This paper combines goal-conditioned RL and the model predictive path integral method into a unified model-based policy search framework, which improves the learning efficiency and policy optimality of motor skill learning in multi-goal and multi-constrained environments. An uncertainty-aware multi-timescale value function learning and selection method is proposed to overcome long horizon problems, improve optimal policy resolution and therefore enhance the generalization ability of goal-conditioned RL.

Medicine shortages have a detrimental impact on stakeholders in the pharmaceutical supply chain (PSC). Existing studies suggest that building resilience strategies can mitigate the effects of these shortages. As such, this research aims to examine whether resilience strategies can reduce the impact of medicine shortages in the United Kingdom's (UK) PSC.

A sequential mixed-methods approach that involved qualitative and quantitative research enquiry was employed in this study. The data were collected using semi-structured interviews with 23 key UK PSC actors at the qualitative stage. During the quantitative phase, 106 respondents completed the survey questionnaires. The data were analysed using partial least square-structural equation modelling (PLS-SEM).

The results revealed that reactive and proactive elements of resilience strategies helped tackle medicine shortages. Reactive strategies increased relational issues such as behavioural uncertainty, whilst proactive strategies mitigated them.

The findings suggest that PSC managers and decision-makers can benefit from adopting structural flexibility and proactive strategies, which are cost-effective measures to tackle medicine shortages. Also engaging in strategic alliances as a proactive strategy mitigates relational issues that may arise in a complex supply chain (SC).

This study is the first to provide empirical evidence of the impact of resilience strategies in mitigating medicine shortages in the UK's PSC.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

This study aims to explore the effect of power-based behaviours on pharmaceutical supply chain (PSC) resilience.

This study used a mixed-method approach to explore the role of power-based behaviours in PSC resilience. Qualitative interviews from 23 key PSC stakeholders, followed by thematic analysis, revealed the underlying perceptions regarding PSC resilience. Quantitative propositions were then developed based on the themes adopted from PSC resilience literature and the qualitative findings. These were tested via a survey questionnaire administered to 106 key stakeholders across the various levels in the PSC. Structural equation modelling with partial least squares was used to analyse the data.

The data analysed identified proactive and reactive strategies as resilience strategies in the PSC. However, power-based behaviours represented by quota systems, information and price control influenced these resilience strategies. From a complex adaptive system (CAS) perspective, the authors found that when power-based behaviours were exhibited, the interactions between PSC actors were mixed. There was a negative influence on reactive strategies and a positive influence on proactive strategies. The analysis also showed that PSC complexities measured by stringent regulations, long lead times and complex production moderated the effect of power-based behaviour on reactive strategies. Thus, the negative impact of power-based behaviours on reactive strategies stemmed from PSC complexities.

This research particularly reveals the role of power-based behaviours in building PSC resilience. By evaluating the nexus from a CAS perspective, the analysis considered power-based behaviours and the moderating role of PSC complexities in developing resilience strategies. This study considers the interactions of PSC actors. This study shows that power asymmetry is a relational concept that inhibits the efficacy of reactive strategies. This study thus advocates the importance of power in achieving a more resilient PSC from a holistic perspective by highlighting the importance of the decision-making process among supply chain (SC) partners. The findings are particularly relevant if PSC resilience is viewed as a CAS. All the interactions and decision-making processes affect outcomes because of their inherent complexities. Although this study focused on the PSC, its implications could be extended to other SCs.

The authors identified that power-based behaviours influenced resilience strategies. It was detrimental to reactive strategies because of the complexities of the PSC but beneficial to proactive strategies through resource-sharing. PSC actors are therefore encouraged to pursue proactive strategies as this may aid in mitigating the impact of disruptions. However, power-based behaviours bred partner dissatisfaction. This dissatisfaction may occur even within strategic alliances indicating that power could be detrimental to proactive strategies. Therefore, it is pertinent to identify conditions that lead to dissatisfaction when pursuing strategic partnerships. This study provides insight into actual behaviours influencing resilience and quantifies their effects on the PSC. These insights will be valuable for all SC partners wanting to improve their resilience strategies.

Previous PSC management and resilience studies have not examined the role of power in building resilience in the PSC. This paper thus provides a unique contribution by identifying the role of power in PSC resilience, offers empirical evidence and a novel theoretical perspective for future practice and research in building PSC resilience strategies.

The outdoor event market was devastated during the COVID-19 pandemic because of social distancing measures. Therefore, this study aimed to explore stereotyped tendencies and behavioral intentions associated with the prosocial and sustainable practices of outdoor event participants to assess shifts in industry paradigms.

This study adopted structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to relatively examine sequential and combined effects of cognitive (knowledge of COVID-19, awareness of consequences, ascribed responsibility and perceived threat of COVID-19), affective (positive and negative anticipated emotions) and normative drivers (social and moral norms) on intention to practice social distancing requirements. The impact of cultural differences was further explored by comparing attendees from China and USA.

The SEM results showed that most cognitive drivers significantly affected affective drivers and normative drivers, leading to the intention to practice social distancing requirements. In addition, China and the USA showed significant differences on six paths including the path from moral norm to intention to practice social distancing requirements. Further, fsQCA results revealed the important combination of the factors that affects social distancing intention.

This study provides meaningful theoretical and practical implications for outdoor events scholars and managers. The research suggests a changing direction in event studies and shares ideas on how to manage and make outdoor events a new success after the pandemic.

This is the first study to adopt a mixed method of SEM and fsQCA attempt to explore the driving forces of outdoor participants’ pro-social behavior from cognitive, affective and normative perspectives.

To clarify divergent conclusions on the impact of alliances on green innovation (GI), this study aims to examine the non-linear relationships between dual alliance and GI, as well as the mediation of green knowledge reconstruction (GKR) and the moderation of alliance tie strength.

Based on the theory of knowledge-based view, a moderated intermediary model is constructed by introducing GKR and alliance tie strength. The hypotheses are validated by using hierarchical regression analysis and bootstrapping method, with questionnaire survey data collected from 316 manufacturing firms in China.

Empirical results show that both exploratory alliance and exploitative alliance have an inverted U-shaped effect on GI, in which GKR plays a mediating role in the above relationship. Moreover, alliance tie strength weakens the intermediary role of GKR in the relationship between exploratory alliance and GI, whereas it enhances the intermediary role of GKR in the relationship between exploitative alliance and GI.

Findings reveal the non-linear effects of dual alliance on GI and clarify the inconsistent conclusions by proposing the moderated intermediary effect model. Moreover, this research reveals the mechanism of dual alliance on GI through the mediation of GKR and enriches the boundary conditions by integrating the moderating role of alliance tie strength.