Search results

This paper aims to explore question and answer (Q&A) participation and behavioral patterns on academic social networking sites (ASNSs) from the perspective of multiple subjects such as academic, corporate and government institutions.

Focused on the Q&A service of ASNSs, this study chooses ResearchGate (RG) as the target ASNS and collects a large-scale data set from it, involving a sample of users and a Q&A sample about academic, corporate and government institutions. First, it studies the law of Q&A participation and the distribution of the type of user according to the sample of users. Second, it compares question-asking behavior and question-answering behavior stimulated by questions among the three types of institutions based on the Q&A sample. Finally, it discusses the Q&A participation and behavioral patterns of the three types of institutions in academic Q&A exchanges with full consideration of institutional attributes, and provides some suggestions for institutions and ASNSs.

The results show that these three types of institutions generally have a low level of participation in the Q&A service of RG, and the numbers of questions and answers proposed by institutional users conform to the power-law distribution. There are differences in Q&A participation and Q&A behavioral patterns among academic, corporate and government institutions. Government and academic institutions have more users participating in the Q&A service and their users are more willing to ask questions, while corporate institutions have fewer users who participate in the Q&A service and their users are inclined to provide answers. Questions from corporate institutions attract much more attention than those from the other two types of institutions.

This study reveals and compares the Q&A participation and the behavioral patterns of the three types of institutions in academic Q&A, thus deepening the understanding of the attributes of institutions in the academic information exchange context. In practice, the results can help guide different institutions to use the Q&A service of ASNSs more effectively and help ASNSs to better optimize their Q&A service.

Current measurement methods for fabric comfort attributes generally suffer from either complicated testing processes and intricate measuring equipment or partial evaluation objectives and thus are difficult for effectively evaluating multidimensional human perceptions towards the comprehensive comfort of fabrics. The purpose of this paper is to develop a facile test device, namely fabric comfort tester, to achieve a comprehensive evaluation of human sensations in terms of sensorial, thermal and acoustic comfort in clothing.

The prototype of the designed device was introduced, which enables a simultaneous test for multiple physical and mechanical properties of fabrics based on a force sensor and a set of infrared sensors via constructing multi-deformation states of the measured fabrics. Eleven measurement indices extracted from the measurement curves are defined and interpreted based on correlation analysis. A series of regression models are developed by relating the measurement indices with subjective evaluation results and validated by a set of independent samples.

Human perceptions of sensorial, thermal and acoustic comfort in clothing can be predicted by the measured physical indices and the designed test device with the developed regression models provides an alternative method to characterize the fabric comfort attributes effectively.

The work develops a novel device for objective evaluation of fabric comfort properties by a simultaneous test, integrating the mechanical measurement with thermal test and thereby filling the gap between the existing evaluation methods and practical requirements for the digitalization of fabric comfort in present textile and garment trade.

Epoxy resin (EP) is a thermosetting resin commonly characterized by its inherent brittleness, which limits its widespread application. To overcome this limitation, a novel flexible chain-blocking hyperbranched polyester (HBP) was conceptualized and synthesized to enhance the toughness and dielectric properties of EP.

Using P-toluene sulfonic acid (p-TSA) as the catalyst and Bis(hydroxymethyl)propionic acid (DMPA) as the branching unit with pentaerythritol (PER) as the core, an experimental synthesis was conducted. Subsequently, n-hexanoic acid was introduced separately to produce hyperbranched polyester with n-hexanoic acid capped structures. Microstructural, mechanical, insulating and dielectric analyses of the composite were performed to determine the optimal proportion of HBP.

Recent research has demonstrated that the flexible segments within hyperbranched polyester create an interpenetrating network structure with the molecular chains of epoxy resin, thereby effectively augmenting the toughness of the epoxy resin. Additionally, HBP has reduced the ε and tgδ values of the epoxy-anhydride cured product by decreasing the number of polar groups per unit volume of EP through the introduction of free volumes.

Currently, HBP serves as an innovative toughening strategy and modifier for epoxy resin. The toughening mechanism involves the generation of free volume by HBP, providing space for EP molecules to maneuver under load. Additionally, the free volume contributes to a reduction in the dielectric constant of EP by diminishing the polarizable group content. Simultaneously, the incorporation of HBP features flexible chains grafted onto the epoxy resin.

Epoxy resin (EP) is a kind of thermosetting resin, and its application is usually limited by poor toughness. In this case, a type of new flexible chain blocking hyperbranched polyester (HBP) was designed and synthesized. The purpose of this study is to enhance the toughness and dielectric properties of EP.

P-toluene sulfonic acid was used as the catalyst, with dimethy propionic acid as the branch unit and pentaerythritol as the core in the experiment. Then, n-hexanoic acid and n-caprylic acid were, respectively, put to gain HBP with a n-hexanoic acid and n-caprylic acid capped structure. The microstructure, mechanical properties, insulation properties and dielectric properties of the composite were characterized for the purpose of finding the appropriate proportion of HBP.

HBP enhanced the toughness of epoxy-cured products by interpenetrating polymer network structure between the flexible chain of HBP and the EP molecular chain. Meanwhile, HBP reduced the ε and tgδ of the epoxy anhydride-cured product by reducing the number of polar groups per unit volume of the EP through free volumes.

Yet EP is a kind of thermosetting resin, which is widely used in coating, aerospace, electronics, polymer composites and military fields, but it is usually limited by poor toughness. In a word, it is an urgent priority to develop new EP with better toughness and mechanical properties.

At present, HBP has been applied as a new kind of toughening strategy and as a modifier for EP. According to the toughening mechanism of HBP modified EP, the free volume of HBP creates a space for the EP molecule to move around when loaded. Moreover, the free volume could cause the dielectric constant of EP to diminish by reducing the content of polarizable groups. Meanwhile, the addition of HBP with flexible chains grafted to the EP could develop an interpenetrating network structure, thus further enhancing the toughness of EP

The dispersity of graphene oxide (GO) has an important effect on the properties of epoxy resin (EP) composites. Many modification and dispersion methods require the use of inert solvents which do not participate in the modification reaction, although GO can be uniformly dispersed in water and alcohol solvents. Based on this requirement, several inert solvents were selected as dispersion solvents to find out the suitable inert solvent for GO dispersion into EP matrix.

Several inert solvents with different solubility parameters were selected as dispersion solvents to prepare GO/EP composite. The microstructure, mechanical properties, insulation properties, dielectric properties and thermal property of the composite were characterized, which was due to find suitable inert solvent for GO dispersion into EP matrix.

The dispersity of N, N-dimethylformamide (DMF) was the best stable suspension state when it was used as solvent instead of occurring sedimentation and agglomeration. Moreover, DMF was further confirmed as a suitable inert solvent for the dispersion of GO into EP according to the mechanical properties, insulation properties and thermal conductivity characterization.

The dispersion of GO in solvents has already been researched, but the traditional solvents, such as alcohols and water, has shown their limitations with the increase of modification methods, which were not suitable for the modification environment such as cyanate graft modification. Therefore, it was very important to choose a kind of inert solvent for dissolving EP matrix and dispersing GO better.

Several inert solvents were used to disperse GO into EP, and the influence of different dispersing solvents on the adhesive was discussed through the analysis of the mechanical properties, insulation properties and thermal conductivity, which was due to explore the inert solvent suitable for GO dispersion.

The effects of ultrasonication on the epoxy resin and its emulsion were investigated to find out the changes in the M_η and molecular structure of epoxy, as well as its room temperature storage stability, centrifugal stability, particle size and its distribution and particle morphology more importantly with the influence of different ultrasonic irradiation time, power and temperature.

The emulsion was prepared using an emulsifier with epoxy resin and by using phase inversion after subjecting to ultrasound irradiation with a power of 200 W at 50°C for 60 min. The changes in the epoxy resin and its emulsion induced by ultrasound were characterized by Ubbelohde viscometer, FT-IR, ¹³C-NMR, high-speed desktop centrifuge, laser particle size analyzer and transmission electron microscope.

The molecular weight of the epoxy resin was initially decreased and then stabilized by the increasing of ultrasonic irradiation time. The mole rate of the epoxy groups in epoxy molecular were decreased by about 14 per cent, resulting from ultrasonic irradiation. The particle size of the emulsion was decreased, while the particle size distribution became uniform in a certain time. The narrow distribution, stable and uniform of waterborne epoxy resin emulsion with more than 60 days room temperature storage period, 80 per cent of the supernatant volume, about 220 nm average particle size was gained with a power of 200 W at 50°C for 60 min.

To overcome the problems commonly encountered with an epoxy emulsion, for example, short storage period and wider particle size, which limit its practical application, the effects of ultrasonic irradiation on the epoxy resin and its emulsion, were investigated. As the stability of emulsion was improved with the introduction of ultrasonic irradiation, the application of epoxy emulsion was improved.

The room temperature storage stability and centrifugal stability of the emulsion were decreased by the mechanical method, and thus, the benefit of an in-depth understanding of the influence of ultrasonic treatment on epoxy resin and its emulsion could further promote the development of water-based coatings.

The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system’s performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation.

In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand.

The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods.

This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement.

The purpose of this paper is to identify corrosion types and corrosion transitions by a novel electrochemical noise analysis method based on Adaboost.

The corrosion behavior of Q235 steel was investigated in typical passivation, uniform corrosion and pitting solution by electrochemical noise. Nine feature parameters were extracted from the electrochemical noise data based on statistical analysis and shot noise theory. The feature parameters were analysis by Adaboost to train model and identify corrosion types. The trained Adaboost model was used to identify corrosion type transitions.

Adaboost algorithm can accurately identify the corrosion type, and the accuracy rate is 99.25%. The identification results of Adaboost for the corrosion type are consistent with corroded morphology analysis. Compared with other machine learning, Adaboost can identify corrosion types more accurately. For corrosion type transition, Adaboost can effectively identify the transition from passivation to uniform corrosion and from passivation to pitting corrosion consistent with corroded morphology analysis.

Adaboost is a suitable method for prediction of corrosion type and transitions. Adaboost can establish the classification model of metal corrosion, which can more conveniently and accurately explore the corrosion types. Adaboost provides important reference for corrosion prediction and protection.

The purpose of this paper is to research the induced flashover laws of different insulation materials under electrostatic electromagnetic pulse, and the induced flashover characteristics of different electrode structures are further explored.

According to standard IEC 61000–4-2, an experimental system of electrostatic electromagnetic pulse flashover for insulation materials is established. The induction flashover laws of polytetrafluoroethylene, epoxy resin and polymethyl methacrylate surface-mounted finger electrodes under the different intensity of electrostatic electromagnetic pulse are researched. The influence of the finger electrode, needle–needle electrode and needle–plate electrode on insulation flashover was compared. Secondary electron emission avalanche (SEEA) and field superposition theory are used to analyze the experimental results of electrostatic electromagnetic pulse induced flashover.

The larger the dielectric strength of insulation materials, the more difficult flashover occurs on the surface. The field superposition enhances collision ionization between electrons and gas molecules, which leads to the insulation materials surface induced flashover easily by electrostatic electromagnetic pulse. The sharper the electrode shapes on the insulation materials surface, the stronger the electric field intensity at the cathode triple junction, more initial electrons are excited to form the discharge channel, which easily leads to flashover on the surface of the insulating material.

The proposed field superposition combined with the SEEA method provides a new study perspective and enables a more rational, comprehensive analysis of electrostatic electromagnetic pulse induced flashover of insulation materials. The work of this paper can provide a reference for the safety protection of spacecraft in orbit under a strong electromagnetic field environment, increase the service life of spacecraft and improve the reliability of spacecraft’s safe operation in orbit. It provides a basis for the selection of insulation materials for equipment under the different intensities of the external electromagnetic environment.

The maturity of artificial intelligence technology and the emergence of AI-generated content (AIGC) tools have endowed college students with a human-AIGC tools collaboration learning mode. However, there is still a great controversy about its impact on learning effect. This paper is aimed at investigating the impact of the human-AIGC tools collaboration on the learning effect of college students.

In this paper, a hypothesized model was constructed to investigate the effects of dependence, usage purpose, trust level, frequency, and proficiency of using AIGC tools on the learning effect, respectively. This paper distributed questionnaires through random sampling. Then, the improved Analytic Hierarchy Process (AHP) was used to assign weights and normalize data. Lastly, one-way ANOVA and multiple linear regression analyses were used to measure and analyze variables, revealing the mechanism of influence.

The usage purpose, frequency, and proficiency of using AIGC tools have a significant positive effect on learning. Being clear about the usage purpose of AIGC tools and matching the specific study tasks will enhance the learning effect. College students should organically integrate AIGC tools into each learning process, which is conducive to building a learning flow applicable to oneself, improving efficiency, and then enhancing learning effects. The trust level in AIGC tools is significant, but positively and weakly correlated, indicating that college students need to screen the generated content based on their knowledge system framework and view it dialectically. The dependence on AIGC tools has a negative and significant effect on learning effect. College students are supposed to systematically combine self-reflection and the use of AIGC tools to avoid overdependence on them.

Based on the findings, the learning suggestions for college students in human-machine collaboration in the AIGC era are proposed to provide ideas for the future information-based education system. For further research, scholars can expand on different groups, professions, and fields of study.

Previous studies have focused more on the impact of AIGC on the education system. This paper analyzed the impact of the various factors of using AIGC tools in the learning process on the learning effect from the perspective of college students.