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The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller.

Two Kinect® cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator's face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for “coarse positioning” of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject's face.

The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon. Tasks’ completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection, and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects.

Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.

Detecting O₂ gas in a confined space at room temperature is particularly important to monitor the work process of precision equipment. This study aims to propose a miniaturized, low-cost, mass-scale produced O₂ sensor operating around 30°C.

The O₂ sensor based on lanthanum fluoride (LaF₃) solid electrolyte thin film was developed using MEMS technology. The principle of the sensor was a galvanic cell H₂O, O₂, Pt | LaF₃ | Sn, SnF₂ |, in which the Sn film was prepared by magnetron sputtering, and the LaF₃ film was prepared by thermal resistance evaporation.

Through pretreatments, the sensor’s response signal to 40% oxygen concentration was enhanced from 1.9 mV to 46.0 mV at 30°C and 97.0% RH. Tests at temperatures from 30°C to 50°C and humidity from 32.4% RH to 97.0% RH indicated that the output electromotive force (EMF) has a linear relationship with the logarithm of the oxygen concentration. The sensitivity of the sensor increases with an increase in both humidity and temperature in the couple mode, and the EMF of the sensor follows well with the Nernst equation at different temperatures and humidity.

This research could be applied to monitor the oxygen concentration below 25% in confined spaces at room temperature safely without a power supply.

The relationship between temperature and humidity coupling and the response of the sensor was obtained. The nano-film material was integrated with the MEMS process. It is expected to be practically applied in the future.

The purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model.

The joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market.

Empirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model.

The proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks.

A novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market.

To realize the operation optimizing of today’s distribution power system (DPS), like economic dispatch, contingency analysis, and reliability and security assessment etc., it is beneficial and indispensable that a faster linear load flow method is adopted with a reasonable accuracy. Considering the high R/X branch ratios and unbalanced features of DPS, the purpose of this paper is to propose a faster and non-iterative linear load flow solution for DPS.

Based on complex function theory, the derivations of the injection current linear approximation have been proposed for the balanced and the single-, double- and three-phase unbalanced loads of DPS on complex plane. Then, a simple and direct linear load flow has been developed with loop-analysis theory and node-branch incidence matrix.

The methodology is appropriate for balanced and single-, double- and three-phase hybrid distribution system with different load models. It provides a fast and robust load flow method with a satisfactory accuracy to handle the problems of DPS whenever the load flow solutions are required.

The distributed generators (DGs) with unity or fixed power factors can be easily included. But the power and voltage nodes cannot be dealt with directly and need to be further studied.

By combining the current linear approximation with the loop theory-based method, a new linear load flow method for DPS has been proposed. The method is valid and acute enough for balanced and unbalanced systems and has no convergent problems.

This research identifies the critical factors of online service delivery of electronic commerce (e-commerce) websites, including website attractiveness, website functionality, website security and consumer fulfillment during an unprecedented Coronavirus (COVID-19) pandemic.

A structured questionnaire was used to gather data from 430 online respondents who were members of popular social media groups. The survey instrument relied on valid and reliable measures relating to electronic service quality (e-SERVQUAL) to better understand the participants’ satisfaction with shopping websites, as well as their loyal behaviors and word-of-mouth activities.

The findings reported that consumers valued the e-commerce websites’ features and their consumer order fulfillment capabilities. These factors increase the consumers’ satisfaction with online shopping experiences, generate repeat business, as well as positive reviews on social media.

This study addresses a knowledge gap in academia. To date, little research has focused on the consumer order fulfillment aspect of e-commerce transactions and on after-sales of online businesses.

This contribution posits that e-commerce websites ought to be appealing, functional and offer secure transactions. More importantly, it suggests that merchants should consistently deliver personalized service in all stages of an online purchase, including after the delivery of the ordered products.