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The purpose of this paper is to prepare a water-resistant adhesive (SA) from soy flour (SF) with less water-soluble components.

Defatted SF was suspended and stirred in water. Then, the pH of dispersion was adjusted to a predetermined value (i.e. 8, 9 or 10) by the addition of 2M sodium hydroxide (NaOH) solution. After stirring at a predetermined temperature (25°C, 35°C, 45°C) for different time (1 h, 2 h, 3 h), the 2M hydrochloric acid (HCl) solution was added in a dropwise manner into the dispersion until the pH value was adjusted to 4.5. Then, the dispersion was centrifuged at 6,000 rpm for 2 min. The obtained precipitate with less water-soluble components was used as an adhesive (SA) directly.

SA had a wet strength of 1.02 MPa when used for the fabrication of poplar plywood. Polyvinyl alcohol (PVA) solution was applied to improve the tack of SAs to wood surface and the viscosities of SAs were decreased from 10,200 cP to 4,100 cP at room temperature after the PVA addition. The soy proteins in SAs were not denatured to a large extent according to the differential scanning calorimetry and light microscopy. The remained multilevel structures of soy protein played a positive contribution to the water resistance of SAs, and the bond lines of cured SAs were much more stable than those of the cured SF and soy protein concentrate (SPC).

The fluidity and solid content of soy adhesives is much lower than formaldehyde adhesives. Further investigations are needed to improve the fluidity of soy adhesives with high solid contents.

Novel water-resistant soy adhesives were provided.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

There is a growing trend among online merchants to conduct help-request marketing campaigns (HMCs), which refers to a kind of marketing campaign that leverages participants' help-request to encourage the subsequent engagement of participants' online friends. The paper aims to investigate how individuals respond to online HMCs in social networking groups (SNGs). Integrating the norm activation model and regulatory focus theory, this paper examines the mediation effects of the two facets of responsibility perception, i.e. perceived causality and perceived answerability.

A field experiment was conducted by organizing a real HMC on WeChat. To manipulate request individuation, experimental confederates were engaged to serve as requesters in the HMC. The actual responses provided by the recipients (subjects) were captured via the HMC pages. The multiple-group analysis was used for data analysis.

Empirical results reveal that request individuation strengthens the effect of relationship closeness on perceived causality but reverses the effect of relationship closeness on perceived answerability from being positive to negative. Except for the negligible impact of perceived answerability on inaction, both perceived causality and perceived answerability affect recipients' reactions to HMCs as expected.

First, social media platforms should promote other-oriented prosocial values when designing features or launching campaigns. Second, the designers of HMCs should introduce a “tagging” feature in HMCs and provide additional bonuses for requesters who perform tagging. Third, HMC requesters should prudently select tagging targets when making a request.

First, this paper contributes to the literature on social media engagement by identifying responsibility as an other-oriented motivation for individuals' social media engagement. Second, this paper also extends our understanding of responsibility by dividing it into perceived causality and answerability as well as measuring them with self-developed instruments. Third, this study contributes to the research on WOM by demonstrating that individuals' response behaviors toward help-requests embedded in HMCs can take the form of proactive helping, reactive helping or inaction.