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Deep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students’ learning behavior.

DL technology is used to design a speech evaluation system.

The experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency.

The results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.

How do entrepreneurs use simple rules to build their business models? Based on an inductive study of three Chinese Internet and technology firms, the authors find that business models emerge from simple rules that entrepreneurs learn from their experience. Simple rules also guide entrepreneurs to actualize and exploit opportunities in the marketplace, and they can help business models evolve through market feedback, especially in internationalization. This paper aims to delve into the black box of entrepreneurial decision-making and offer a better depiction of the business model development process in uncertain and fast-changing environments and thus provide guidance for future entrepreneurs.

Following the case method (Eisenhardt, 1989; Yin, 2003), the authors first present a thick description of characteristics of three companies and the dynamics of their business models. They then code these descriptions into first-order measures. Finally, they aggregate these measures into abstract constructs. They constantly compare the theoretical constructs and the emerging theory with the existing literature on business models.

The authors generate three key insights from the findings: business models emerge from simple rules learned from entrepreneurs’ experience, simple rules help entrepreneurs exploit and actualize opportunities in the marketplace and simple rules help businesses expand and evolve business models through market feedback, especially in internationalization.

This paper falls into the intersection of strategy and entrepreneurship – an emerging new field of strategic entrepreneurship – and is concerned with how businesses create and sustain a competitive advantage while simultaneously identifying and exploiting new opportunities. The authors bring people – the individual decision-makers for businesses – back in strategy research and depict a more realistic picture of the behavior of successful entrepreneurs and their business model development process.

Using the remarkable olfaction ability, insects can sense trace amounts of host plant volatiles that are notorious for causing severe damage to fruits and vegetables and in consequence the industry. The purpose of the paper is to investigate the interactions between olfactory proteins, odorant-binding proteins (OBPs) and host plant volatiles through the developed olfactory biosensors. It might be helpful to develop novel pest control strategies.

Using the successfully expressed and purified OBPs of the oriental fruit fly Bactrocera dorsalis, a biosensor was developed by immobilizing the proteins on interdigitated electrodes through nitrocellulose membrane. Based on electrochemical impedance sensing, benzaldehyde emitted by the host plants, such as Beta vulgaris, was detected, which could be used to investigate and analyze the mechanisms of pests’ sense of chemical signals. The relative decreases of charge transfer resistances of the sensor were proportional to the odorant concentrations from 10⁻⁷ M to 10⁻³ M. Meanwhile, the interactions between OBPs and benzaldehyde were studied through the process of molecular docking.

The paper provides a pest OBPs-based biosensor that could sensitively detect the host odorants benzaldehyde. Meanwhile, the most related amino acids of OBPs that bind to host plant volatiles can be distinguished with molecular docking.

An olfactory biosensor was developed to explore interactions and mechanism between the pest OBPs and benzaldehyde, which showed promising potentials for small organic molecule sensing. Simultaneously, it might be helpful for novel pest control strategies.

The literature reports inconsistent findings about the effects of social media usage (SMU). Researchers distinguish between active and passive social media usage (ASMU and PSMU), which can generate different effects on users by social support and social comparison mechanisms, respectively. Drawing on social presence theory (SPT), this study integrates an implicit social presence mechanism with the above two mechanisms to explicate the links between SMU and seniors' loneliness.

Data were collected from a field study by interviewing seniors living in eight aging care communities in China. Loneliness, social media activities and experiences with social media in terms of online social support (OSS), upward social comparison (USC) and social presence (SP) were assessed. Factor-based structural equation modeling was used to analyze the data.

OSS can mediate the relationship between ASMU and seniors' loneliness. Moreover, SP mediates between ASMU, PSMU, and seniors' loneliness, and between OSS, USC and seniors' loneliness. OSS mediates the relationship between ASMU and SP, and USC mediates the relationship between PSMU and SP.

This study shows that social media can alleviate seniors' loneliness, which could help relieve the pressures faced by health and social care systems. Social presence features are suggested to help older users interact with social health technologies in socially meaningful ways.

This study not only demonstrates that SP can play a crucial role in the relationship between both ASMU and PSMU and loneliness, but also unravels the links between SP and OSS, as well as USC.

Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.

This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.

Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.

The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

This paper aims to develop resonant vibratory gyroscopes for high sensitive detection. The dynamic characteristics of resonant vibratory gyroscopes are investigated.

Firstly, the working principle and the dynamic output characteristics of the resonant vibratory gyroscope could be described by the damped Mathieu equation. Moreover, an approximate analytical method based on the small parameter perturbation has been used for the purpose of investigating the approximate solution of the damped Mathieu equation. Finally, to verify the feasibility of the approximate analytical method of the damped Mathieu equation, dynamic output characteristics’ experiments of the resonant vibratory gyroscope are built.

The theoretical analysis and numerical simulations show that the approximate solution of the damped Mathieu equation is close to the dynamic output characteristics of the resonant vibratory gyroscope. On the other hand, it is concluded from the tested result that there exists a correlation between the theoretical curve and the experimental data processing result, meaning the damped dynamics analytical method is effective in building resonant vibratory gyroscopes.

This paper seeks to establish a foundation for optimizing and testing the performance of the resonant vibratory gyroscope. To this end, the approximate analytical method of the damped Mathieu equation was discussed. The result of this research has proved that the dynamic characteristics based on the damped Mathieu equation is an effective approach and is instructional in the practical resonant sensor design.

Traditional simulation research of geological and similar engineering models, such as landslides or other natural disaster scenarios, usually focuses on the change of stress and the state of the model before and after destruction. However, the transition of the inner change is usually invisible. To optimize and make models more intelligent, this paper aims to propose a perceptible design to detect the internal temperature change transformed by other energy versions like stress or torsion.

In this paper, micron diamond particles were embedded in 3D printed geopolymers as a potential thermal sensor material to detect the inner heat change. The authors use synthetic micron diamond powder to reinforced the anti-corrosion properties and thermal conductivity of geopolymer and apply this novel geopolymer slurry in the direct ink writing (DIW) technique.

As a result, the addition of micron diamond powder can greatly influence the rheology of geopolymer slurry and make the geopolymer slurry extrudable and suitable for DIW by reducing the slope of the viscosity of this inorganic colloid. The heat transfer coefficient of the micron diamond (15 Wt.%)/geopolymer was 50% higher than the pure geopolymer, which could be detected by the infrared thermal imager. Besides, the addition of diamond particles also increased the porous rates of geopolymer.

In conclusion, DIW slurry deposition of micron diamond-embedded geopolymer (MDG) composites could be used to manufacture the multi-functional geological model for thermal imaging and defect detection, which need the characteristic of lightweight, isolation, heat transfer and wave absorption.

This study aims to elaborate on the microencapsulation of the plant extract (PE, from Camellia sinensis leaf, clover flower and cocoa flower) and the preparation of a slow-release lining fabric loading the PE microcapsule.

PE was microencapsulated into polyvinyl alcohol (PVA) shells through interfacial polymerization. The morphology, thermal stability, slow-release property and drug loading ratio of the PVA/PE microcapsules were characterized to ensure the availability in coating finishing. To find the optimum parameters, the composite fabrics were prepared from non-woven fabrics coated by calcium alginate hydrogel, which glued mass fractions of microcapsules and dried in different ways. To evaluate the effectiveness, a lipase enzyme activity test was conducted.

Under optimal conditions, the PVA/PE microcapsules with smooth surface have an average particle size of 14.5 um, and they are expected to reach a loading ratio of 38.5 per cent while remaining stable under 220°C. Given a microcapsule of 4 per cent (of the mass), the composite fabric has a good hand feeling, being prepared through calcium chloride coating. It is shown that the inhibition ratios of the microcapsules and composite fabrics on lipase are 31.3 and 21.0 per cent, respectively.

The composite fabric could be prepared through the other finishing methods such as padding and printing. In addition, the release mechanism of the composite could be studied.

This study provided a simple and effective way to prolong the duration of PE. This way was conductive to protect environmental sensitive PEs from being destroyed in compositing processes.

Preparing composite fabrics for transdermal delivery system was novel and other kind of plant extracts could be used in this way.

Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.

Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.

The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.

To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

The purpose of this paper is to discuss the influence of different aspects of top management team (TMT) functional background on short-term performance, long-term performance, innovation performance and oversea performance separately. This research aims to verify whether the social categorization theory and information and decision-making theory are applicable in listed companies of China’s information technology (IT) industry so as to provide key theoretical references for TMT enhancement ad corporate performance improvement.

This paper takes A-share listed companies in Shanghai Stock Exchange and Shenzhen Stock Exchange as its study subjects, and it chooses the data from 2004 to 2010 in all of the 105 companies in IT industry in terms of the classification of Wind Database. The stepwise multiple regressions were run utilizing the regression program in Statistical Product and Service Solutions (SPSS).

The research results show that the social categorization theory can better explain TMT’s influence on corporate performance. TMT functional heterogeneity does not contribute to improving corporate performance and shows significant negative influence on short-term performance and innovation performance in particular. Among the three basic functional backgrounds, TMTs dominated by “throughput” backgrounds show significant positive influence on short-term performance, long-term performance, innovation performance and overseas performance, and the influence turns out to be the largest among these three backgrounds. In terms of the three special professional experiences, top executives with overseas backgrounds have significant positive influence on all of short-term, long-term, innovation and overseas performances. Externally hired executives, however, would impede corporate innovation development, while those with government background would increase corporate overseas performance.

This paper analyzes the relationship between TMT functional background and corporate performance in a comprehensive way for the first time and then takes the lead in considering the dynamics and complexity of corporate performance as well as discussing the influence of TMT functional background on four corporate performances. This study not only supports the effect that the social categorization theory has on TMTs but also offers some inspirations on the development of China’s IT companies.