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Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

The purpose of this paper is to arrange zinc oxide (ZnO) nanowires into an appropriate position on electrodes and to research the properties of ZnO nanowires.

In this paper, dielectrophoresis (DEP) was used to fabricate ZnO nanowire devices, and the responses to temperature, ultraviolet (UV) light and breath of the device were studied.

The number of the bridged nanowires is increased with alternating current voltage. ZnO nanowires demonstrate a good photoconductivity illuminated by 365-nm UV light, and show a stable performance in monitoring unnatural breath of high frequency and low strength.

In this paper, DEP is a promising method for controllable assembly of ZnO nanowires. ZnO nanowires demonstrate a good response to 365-nm UV light and exhaled breath, which show great potential application in UV detector and medical monitor.

This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment.

According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state.

These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use.

This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.