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Profile measurement with boundary information plays a vital role in the detection of quality in the assembly of aviation parts. The purpose of this paper is to improve the evaluation accuracy of the aerodynamic shapes of airplanes, the profiles of large-sized parts need to be measured accurately.

In this paper, an accurate profile measurement method based on boundary reference points is proposed for the industrial stereo-vision system. Based on the boundary-reference points, the authors established a priori constraint for extracting the boundary of the measured part. Combining with the image features of background and the measured part, an image-edge compensation model is established to extract the boundary of the measured part. The critical point of a laser stripe on the edge of the measured part is extracted corresponding to the boundary constraint. Finally, as per the principle of binocular vision, the profile of the measured part is reconstructed.

Laboratory experiments validate the measurement accuracy of the proposed method which is 0.33 mm. In the analysis of results between the measured data and the theoretical model, the measuring accuracy of the proposed method was found to be significantly higher than that of the other traditional methods.

An aviation part was measured in the part-assembly shop by the proposed method, which verified the feasibility and effectiveness of this method. The research can realize the measurement of smooth surface boundary which can solve existing profile reconstruction problems for aviation parts.

According to the two-dimensional contour constraint, critical points of the laser strip sequence at the edge of measured part are extracted and the accurate profile reconstruction with the boundary is realized.

The purpose of this paper is to propose a defect detection method of silicone caps positional deviation on flexible printed circuit board (FPCB) of keyboard based on automatic optical inspection.

First, the center of silicone caps of target keyboard FPCB image was extracted as feature points for generating the feature image which is used for registration rigidly with the reference feature image generated from the CAD drawings. Then, a flexible image registration method based on the surrounding-control-center B-splines (SCCB) strategy was proposed, which could correct the flexible deformation of the image generated by FPCB substrate while keeping the pasting deviation information about silicone caps unchanged. Finally, on this basis, a nearest neighbor strategy was proposed to detect the positional deviation of silicone caps.

Experimental results show that the proposed method can effectively detect the positional deviation defect of silicone caps. The G-mean value of the proposed method is 0.941746, which is 0.3 higher compared to that of similar research.

This paper presents a method to detect positional deviation defect of silicone caps on keyboard FPCB. Different from the classic B-spline image registration method, the proposed SCCB method used the neighborhood information of the pixel to be registered selectively to calculate the displacement vector needed for its registration, which overcame the problem that the silicone cap pasting deviation information disappears with the correction of the flexible deformation of the image.

The paper aims to empirically examine the role of intra-national institutions in business performance. In particular, the article develops hypotheses regarding financial marketization and business venturing with organizational slack and political connections as moderating variables.

The authors choose listed firms from the pharmaceutical industry in China and focus on the period of 2001-2009. Results from the Hausman specification test indicate that the random effects model is appropriate for data. Because the dependent variable is dichotomous, the random effects logistic regression technique in Stata is used. To check the robustness of the estimation, the random-effects Tobit regression technique in Stata is also used. Overall, models are robust and statistically significant.

It was found that the level of regional financial sector marketization is positively associated with the likelihood of engaging in corporate venturing by firms within the region. Moreover, it was found that organizational slack significantly decreases the institutional influence on corporate venturing.

This study is one of the first to theorize and empirically test the impact of intra-national institutions on corporate venturing in China’s pharmaceutical industry. Institutions matter more when organizational slack is low. Firms in the pharmaceutical industry in China do not seem completely dependent on political connections for business venturing and use organizational slack to buffer against (adverse) institutional change.

This study aims to focus on the impact of multi-level knowledge sharing between and within organizations on the risk control of rural inclusive finance. The paper presents a synergistic risk control system integrating external and internal factors for rural inclusive finance by constructing different knowledge-sharing platforms in an environment, which is full of many uncertainties.

This study is based on survey methods. To achieve the research objectives, the authors adopt a single case study approach. For data collection, the authors apply a wide variety of methods such as semi-structured interviews, field visits, second-hand databases and official websites.

The results emphasize that using multi-level knowledge sharing such as the inter- and intra-organizational level, can facilitate the risk control of rural inclusive finance during the post-COVID-19 era. Furthermore, it is also noted that achieving knowledge sharing at different levels by building diverse knowledge-sharing platforms can promote the risk control of rural inclusive finance from the individual-organization level to the chain level of multi-organization collaboration, which contributes to the formation of symbiotic risk control ecology.

The authors have formed the “Chinese wisdom” to deal with inclusive financial risks and to promote in-depth development in relation to the “last mile” practice of inclusive finance, which means the final and the most important phase of a project. The conclusions contribute to enriching the outcomes regarding the risk control of rural inclusive finance, provide experiences to its sustainable development and offer a reference to other countries with their risk control of rural inclusive finance.

Drawing on the knowledge-sharing approach, this study creatively resolves the persistent problems in the risk control of rural inclusive finance, which forms a powerful supplement to the extant literature. Meanwhile, the paper combines the two contextual factors of the post-COVID-19 era and emerging economies, which can be deemed as a novel attempt.

The smart image sensor (SIS) which integrated with both sensor and smart processor has been widely applied in vision-based intelligent perception. In these applications, the linearity of the image sensor is crucial for better processing performance. However, the simple source-follower based readout circuit in the conventional SIS introduces significant nonlinearity. This paper aims to design a low-power in-pixel buffer circuit applied in the high-linearity SIS for the smart perception applications.

The linearity of the SIS is improved by eliminating the non-ideal effects of transistors and cancelling dynamic threshold voltage that changes with the process variation, voltage and temperature. A low parasitic capacitance low leakage switch is proposed to further improve the linearity of the buffer. Moreover, an area-efficient SIS architecture with a sharing mechanism is presented to further reduce the number of in-pixel transistors.

A low parasitic capacitance low leakage switch and a gate-source voltage pre-storage method are proposed to further improve the linearity of the buffer. Nonlinear effects introduced by parasitic capacitance switching leakage, etc., have been investigated and solved by proposing low-parasitic and low-leakage switches. The linearity is improved without a power-hungry operational amplifier-based calibration circuit and a noticeable power consumption increment.

The proposed design is implemented using a standard 0.18-µm CMOS process with the active area of 102 µm². At the power consumption of 5.6 µW, the measured linearity is −63 dB, which is nearly 27 dB better than conventional active pixel sensor (APS) implementation. The proposed low-power buffer circuit increase not only the performance of the SIS but also the lifetime of the smart perception system.

The purpose of this paper is to describe and discuss the use of Chinese medicine in the prevention and treatment of coronavirus disease 2019 (COVID-19) in China and Asia.

This paper provides a brief overview of the COVID-19. Based on the syndrome differentiation (辨證論治), the concept of clearing heat and detoxifying lung in traditional Chinese medicine is used to prevent and treat COVID-19 through restoring the vital qi (正氣) in human body and regulating the lung as well as spleen to strengthen the immune system. Traditional Chinese medicine has been used as a complementary therapy for the possible intervention of COVID-19 including traditional Chinese herbal decoctions, Chinese traditional patent medicines, acupuncture and moxibustion as well as the traditional health exercises in China and parts of Asia.

Traditional Chinese medicine plays a significant role in the prevention and treatment of COVID-19 pandemic. The infection cases of China are around 80,000 and a steady decline compared with the USA which has 5,000,000 infection cases and continuous increases. It is shown that more than 90% of patients recovered after the treatment of traditional Chinese herbal decoctions and Chinese traditional patent medicines without any side-effect compared to the use of Remdesivir (GS-5734). Acupuncture (針灸) and moxibustion (艾灸) stimulate the immune and nervous systems for preventing infectious diseases. Taichi (太極) and Baduanjin (八段錦) as the auxiliary aerobic exercise under the theory of Chinese medicine can enhance the immune system and improve the lung function. Thus, an integration of traditional Chinese Medicine and Western medicine is the best strategy for the prevention, treatment and control of COVID-19 pandemic in the future.

This paper describes traditional Chinese medicine as an effective way for the prevention and treatment of COVID-19.

This research investigates the complex relationship between economic policy uncertainty (EPU), energy consumption and institutional factors in the Gulf region. The purpose of this study is to examine how institutional factors moderate the impact of EPU on energy consumption in Gulf countries.

This paper uses the dynamic panel autoregressive distributed lag (PARDL) method, over a period stretching from 1996 to 2021 in the Gulf countries.

The results show that, only in the long term, EPU has a positive and significant impact on energy consumption, suggesting that increased EPU leads to increased energy use. Furthermore, this study found that, only in the long term, government effectiveness and regulatory quality have positive and significant effect on energy consumption. Accordingly, the two institutional factors play a moderating role in the EPU−energy consumption nexus.

This study highlights the importance of considering the time dimension when formulating energy and economic policies in Gulf countries. Policymakers should take into consideration the nature of these relationships to make informed decisions that promote energy efficiency and economic stability in the region.

To the best of the authors’ knowledge, this is the first study examining the relationship between EPU and energy consumption in the Gulf countries while incorporating the role of institutional factors as potential mediators.

Visual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information from multiple modalities to address these limitations has emerged as a key research focus. This study aims to provide a comprehensive review of the development of vision-based SLAM (including visual SLAM) for navigation and pose estimation, with a specific focus on techniques for integrating multiple modalities.

This paper initially introduces the mathematical models and framework development of visual SLAM. Subsequently, this paper presents various methods for improving accuracy in visual SLAM by fusing different spatial and semantic features. This paper also examines the research advancements in vision-based SLAM with respect to multi-sensor fusion in both loosely coupled and tightly coupled approaches. Finally, this paper analyzes the limitations of current vision-based SLAM and provides predictions for future advancements.

The combination of vision-based SLAM and deep learning has significant potential for development. There are advantages and disadvantages to both loosely coupled and tightly coupled approaches in multi-sensor fusion, and the most suitable algorithm should be chosen based on the specific application scenario. In the future, vision-based SLAM is evolving toward better addressing challenges such as resource-limited platforms and long-term mapping.

This review introduces the development of vision-based SLAM and focuses on the advancements in multimodal fusion. It allows readers to quickly understand the progress and current status of research in this field.