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As the role of AI on human teams shifts from a tool to a teammate, the implementation of AI teammates into knowledge-intensive crowdsourcing (KI-C) contest teams represents a forward-thinking and feasible solution to improve team performance. Since contest teams are characterized by virtuality, temporality, competitiveness, and skill diversity, the human-AI interaction mechanism underlying conventional teams is no longer applicable. This study empirically analyzes the effects of AI teammate attributes on human team members’ willingness to adopt AI in crowdsourcing contests.

A questionnaire-based online experiment was designed to perform behavioral data collection. We obtained 206 valid anonymized samples from 28 provinces in China. The Ordinary Least Squares (OLS) model was used to test the proposed hypotheses.

We find that the transparency and explainability of AI teammates have mediating effects on human team members’ willingness to adopt AI through trust. Due to the different tendencies exhibited by members with regard to three types of cognitive load, nonlinear U-shaped relationships are observed among explainability, cognitive load, and willingness to adopt AI.

We provide design ideas for human-AI team mechanisms in KI-C scenarios, and rationally explain how the U-shaped relationship between AI explainability and cognitive load emerges.

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.

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.

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.

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.

The aero-engine array electrostatic monitoring technology (AEMT) can provide more and more accurate information about the direct product of the fault, and it is a novel condition monitoring technology that is expected to solve the problem of high false alarm rate of traditional electrostatic monitoring technology. However, aliasing of the array electrostatic signals often occurs, which will greatly affect the accuracy of the information identified by using the electrostatic sensor array. The purpose of this paper is to propose special solutions to the above problems.

In this paper, a method for de-aliasing of array electrostatic signals based on compressive sensing principle is proposed by taking advantage of the sparsity of the distribution of multiple pulse signals that originally constitute aliased signals in the time domain.

The proposed method is verified by finite element simulation experiments. The simulation experiments show that the proposed method can recover the original pulse signal with an accuracy of 96.0%; when the number of pulse signals does not exceed 5, the proposed method can recover the pulse peak with an average absolute error of less than 5.5%; and the recovered aliased signal time-domain waveform is very similar to the original aliased signal time-domain waveform, indicating that the proposed method is accurate.

The proposed method is one of the key technologies of AEMT.

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.

The purpose of this study was to advance the knowledge of pharmaceutical supply chain (PSC) resilience using complex adaptive system theory (CAS).

An exploratory research design, which adopted a qualitative approach was used to achieve the study’s research objective. Qualitative data were gathered through 23 semi-structured interviews with key supply chain actors across the PSC in the UK.

The findings demonstrate that CAS, as a theory, provides a systemic approach to understanding PSC resilience by taking into consideration the various elements (environment, PSC characteristics, vulnerabilities and resilience strategies) that make up the entire system. It also provides explanations for key findings, such as the impact of power, conflict and complexity in the PSC, which are influenced by the interactions between supply chain actors and as such increase its susceptibility to the negative impact of disruption. Furthermore, the antecedents for building resilience strategies were the outcome of the decision-making process referred to as co-evolution from a CAS perspective.

Based on the data collected, the study was able to reflect on the relationships, interactions and interfaces between actors in the PSC using the CAS theory, which supports the proposition that resilience strategies can be adopted by supply chain actors to enhance this service supply chain. This is a novel empirical study of resilience across multiple levels of the PSC and as such adds valuable new knowledge about the phenomenon and the use of CAS theory as a vehicle for exploration and knowledge construction in other supply chains.

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.

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.