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Background: Internet hospitals, which refer to service platforms that integrate consultation, prescription, payment, and drug delivery based on hospital entities, have been developing at a rapid pace in China since 2014. However, assessments regarding their service quality and patient satisfaction have not been well developed. There is an urgent need to comprehensively evaluate and improve the service quality of internet hospitals. Objective: This study aims to investigate the current status of patients' use of internet hospitals, as well as familiarity and willingness to use internet hospitals, to evaluate patients' expected and perceived service qualities of internet hospitals using the Chinese version of the Service Quality Questionnaire (SERVQUAL-C) with a national representative sample, and to explore the association between service quality of internet hospitals and patients' overall satisfaction toward associated medical platforms. Methods: This cross-sectional survey was conducted through face-to-face or digital interviews from June to September 2022. A total of 1481 outpatient participants (635 men and 846 women; mean age 33.22, SD 13.22). Participants reported their use of internet hospitals, and then rated their expectations and perceptions of service quality toward internet hospitals via the SERVQUAL-C, along with their demographic information. Results: Among the surveyed participants, 51.2% (n=758) of participants had used internet hospital service or services. Use varied across age, education level, and annual income. Although the majority of them (n=826, 55.8%) did not know internet hospital services well, 68.1% (n=1009) of participants expressed the willingness to adopt this service. Service quality evaluation revealed that the perceived service quality did not match with the expectation, especially the responsiveness dimension. Important-performance analysis results further alerted that reliable diagnosis, prompt response, clear feedback pathway, and active feedback handling were typically the services awaiting substantial improvement. More importantly, multiple linear regressions revealed that familiarity and willingness to use internet hospital services were significant predictors of satisfaction, above and over tangibles, reliability, and empathy service perspectives, and demographic characteristics such as gender, age, education level, and annual income. Conclusions: In the future, internet hospitals should focus more on how to narrow the gaps between the expected and perceived service quality. Promotion of internet hospitals should also be facilitated to increase patients' familiarity with and willingness to use these services. [ABSTRACT FROM AUTHOR]

Sensor-based home Activities of Daily Living (ADLs) monitoring systems have emerged to monitor elderly people’s self-care ability remotely. However, the unobtrusive, privacy-friendly object motion sensor-based systems face challenges such as scarce labeled data and ADL performer confusion in a multi-resident setting. This study adopts the design science paradigm to develop an innovative deep transfer learning framework for human identification (DTL-HID) to address both challenges. A novel convolutional neural network (CNN) is proposed to automatically extract comprehensive temporal and cross-axial motion patterns for the DTL-HID framework. We rigorously evaluate the DTL-HID framework against state-of-the-art benchmarks (e.g., k Nearest Neighbors, Support Vector Machines, and alternative CNN designs). Results demonstrate our proposed DTL-HID framework can identify the ADL performer accurately even on a small amount of labeled data. We demonstrate a case study and discuss how stakeholders can further apply this approach to unobtrusive smart home monitoring for senior citizens. Beyond demonstrating the framework’s practical utility, we discuss two implications of our design principles to mobile analytics and design science research: (1) extracting temporal and axial local dependencies can capture richer information from multi-axial time-series data and (2) transferring knowledge learned on a relevant source domain with sufficient data can improve the performance of the desired task on the target domain with scarce data. [ABSTRACT FROM AUTHOR]

Ga2O3-based photovoltaic-type (i.e., self-driven) deep ultraviolet (DUV) photodetectors have attracted extensive attention due to their broad application prospects in civilian and military fields. However, their common drawback of poor light sensitivity (i.e., responsivity and detectivity) has greatly hindered their practical applications. In this work, we propose a strategy for constructing a Ga2O3/BiFe0.95Mn0.05O3 (BFMO) ferroelectric/semiconductor heterojunction-based photodetector with higher responsivity and detectivity than single BFMO and single Ga2O3 devices by exploiting the built-in electric field at the Ga2O3/BFMO heterojunction interface (EGa2O3/BFMO) and the ferroelectric depolarization electric field across the BFMO (Edp) to promote the separation of photogenerated carriers. The fabricated Ga2O3/BFMO heterojunction photodetector exhibits tunable performance under zero bias. Higher responsivity (19.01 mA W−1 @ 260 nm) and detectivity (4.52 × 1011 Jones @ 260 nm) are observed for the photodetector in the upward poling state than that in the unpoled state, which can be attributed to the coupling effect of Edp and EGa2O3/BFMO. Moreover, the photodetector shows even higher responsivity (23.54 mA W−1) and detectivity (5.58 × 1011 Jones) under weak light illumination (P260nm = 0.002 mW cm−2), with values much higher than those reported for Ga2O3-based heterojunction photodetectors. This work offers an effective approach for boosting the performance of self-driven UV photodetectors. [ABSTRACT FROM AUTHOR]

The paper introduced types and causes of common electrical and mechanical faults of motor, and described application of signal processing methods and intelligent diagnosis methods in fault diagnosis of motor. The signal processing methods include short-time Fourier transform, wavelet transform, wavelet packet transform and empirical mode decomposition. The intelligent diagnosis methods include expert system, fuzzy theory, support vector machine and neural network. It also pointed out development trend of fault diagnosis technologies of motor is combination of multiple diagnosis methods and information fusion method.