Design and analysis of a displacement sensor-integrated compliant microgripper based on parallel structure

This study evaluates the displacement sensitivity of a new compliant microgripper. The microgripper is designed based on a four-bar mechanism and the concept of a compliant mechanism. The effects of the width of the right circular hinge, the thickness of microgripper, and the material properties on the dis-placement sensitivity are considered via using the finite element method. In the beginning, the stress and deformation of the compliant microgripper are evaluated. Subsequently, the displacement of the microgripper is then analyzed. The results showed that the design parameter and the displacement sensitivity have a close relationship. To increase the grasping reliability and measure the displacement or force, a micro-displacement sensor is integrated with the proposed microgripper. Finally, the modeling and analysis of the proposed sensor are conducted

Ownership Concentration and Accounting Conservatism: The Moderating Role of Board Independence

The purpose of this study is to examine the moderating effect of board independence on the relationship between ownership concentration and accounting conservatism. Using fixed-effect regressions for a sample of 165 Vietnamese listed companies from 2007 to 2017, the results revealed that the proportion of outstanding shares owned by the largest shareholder is negatively associated with accounting conservatism and board independence plays a moderating role in this relationship. Our results are robust after applying alternative measures of the largest ownership and correcting for potential endogeneity using fixed-effects regression with instrumental variables. Overall, our evidence shows that firms with concentrated ownership should keep a high non-executive ratio to maintain accounting conservatism. In other words, increasing the number of non-executive directors on boards in firms with a substantial proportion of shares held by the largest shareholder is likely to strengthen the information environment, giving financial reporting more credibility.JEL Classification: G30; G32. Doi: 10.28991/ESJ-2023-07-01-07 Full Text: PD

A robust diagnosis method for speed sensor fault based on stator currents in the RFOC induction motor drive

A valid diagnosis method for the speed sensor failure (SSF) is an essential requirement to ensure the reliability of Fault-Tolerant Control (FTC) models in induction motor drive (IMD) systems. Most recent researches have focused on directly comparing the measured and estimated rotor speed signal to detect the speed sensor fault. However, using that such estimated value in both the fault diagnosis and the controller reconfiguration phases leads to the insufficient performance of FTC modes. In this paper, a novel diagnosis-technique based on the stator current model combined with a confusion prevention condition is proposed to detect the failure states of the speed sensor in the IMD systems. It helps the FTC mode to separate between the diagnosis and reconfiguration phases against a speed sensor fault. This proposed SSF diagnosis method can also effectively apply for IMs’ applications at the low-speed range where the speed sensor signal often suffers from noise. MATLAB/Simulink software has been used to implement the simulations in various speed ranges. The achieved results have demonstrated the capability and effectiveness of the proposed SSF method against speed sensor faults

An Improved Current-Sensorless Method for Induction Motor Drives Applying Hysteresis Current Controller

A novel strategy based on the feed-forward field-oriented control (FOC) method is proposed for the Hysteresis Current technique to control the induction motor (IM) drive without current sensors (CSs). A control scheme is proposed to estimate stator currents from reference rotor flux, rotor flux angle, and state variables as a replacement for the feedback-signal of CSs used in the hysteresis current controller (HCC). Here the rotor flux angle component is extracted from the feed-forward FOC loop. MATLAB/Simulink is applied to implement the simulations under many different operating conditions. The simulation results demonstrated the feasibility of the proposed method to obtain high performance in controlling the IM drives without the current sensors

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

A NOVEL DATASET FOR VIETNAMESE NEW YEAR FOOD CLASSIFICATION

Food classification has always piqued the interest of both domestic and international researchers, but numerous challenges remain. We present the dataset UIT-TASTET21, which contains over 77,000 color images of 18 traditional Vietnamese Lunar New Year dishes. We have experimented with classification using feature vectors from network architectures such as VGG16, Inception-v3, ResNet-50, Xception, and MobileNet-v2 to train support vector machines (SVMs), meeting the dataset’s challenges and laying the groundwork for the development of many optimal methods in the future that promise scientific breakthroughs in the service and commercial industries. At the same time, the authors desire to share a piece of Vietnamese cuisine’s distinctiveness with worldwide friends

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Heart rate variability measured from wearable devices as a marker of disease severity in tetanus

Tetanus is a disease associated with significant morbidity and mortality. Heart rate variability (HRV) is an objective clinical marker with potential value in tetanus. This study aimed to investigate the use of wearable devices to collect HRV data and the relationship between HRV and tetanus severity. Data were collected from 110 patients admitted to the intensive care unit in a tertiary hospital in Vietnam. HRV indices were calculated from 5-minute segments of 24-hour electrocardiogram recordings collected using wearable devices. HRV was found to be inversely related to disease severity. The standard deviation of NN intervals and interquartile range of RR intervals (IRRR) were significantly associated with the presence of muscle spasms; low frequency (LF) and high frequency (HF) indices were significantly associated with severe respiratory compromise; and the standard deviation of differences between adjacent NN intervals, root mean square of successive differences between normal heartbeats, LF to HF ratio, total frequency power, and IRRR, were significantly associated with autonomic nervous system dysfunction. The findings support the potential value of HRV as a marker for tetanus severity, identifying specific indices associated with clinical severity thresholds. Data were recorded using wearable devices, demonstrating this approach in resource-limited settings where most tetanus occurs

Genetic Interaction Between Two VNTRs in the SLC6A4 Gene Regulates Nicotine Dependence in Vietnamese Men

Nicotine dependence is an addiction to tobacco products and a global public health concern. Association between the SLC6A4 polymorphisms and nicotine dependence is controversial. Two variable number tandem repeat (VNTR) domains, termed HTTLPR and STin2, in the SLC6A4 gene are well characterized transcriptional regulatory elements. Their polymorphism in the copy number of the repeat correlates with the particular personality and psychiatric traits. We analyzed nicotine dependence in 1,804 participants from Central Vietnam. The Fagerström Test (FTND) was used to evaluate the nicotine dependence and PCR was used to determine the SLC6A4 HTTLPR and STin2 VNTRs. The HTTLPR VNTR was associated with difficulties to refrain from smoking in a prohibiting environment. The STIn2 10/10 genotype was associated with (1) years of smoking, (2) difficulties to quit the first cigarette, and (3) higher number of cigarettes smoked per day (CPD). Stratification analysis was used to find the genetic interaction between these two VNTRs in nicotine dependence as they may synergistically regulate the SLC6A4 expression. Smokers with the S/S HTTLPR genotypes showed a much stronger association between STin2 10/10 variant and CPD. This finding is consistent with the molecular evidence for the functional interaction between HTTLPR and STin2 in cell line models, where STin2 has described as a stronger expressional regulator. Similarly, we found that STin2 is a much stronger modifier of smoking with 10/10 genotype related to higher nicotine dependence. The present study supports genetic interaction between HTTLPR and STin2 VNTRs in the regulation of nicotine dependence with the dominance of the STin2 effects. This finding could be explained by their differential effect on the SLC6A4 expression

Genetic Interaction Between Two VNTRs in the SLC6A4 Gene Regulates Nicotine Dependence in Vietnamese Men

Nicotine dependence is an addiction to tobacco products and a global public health concern. Association between the SLC6A4 polymorphisms and nicotine dependence is controversial. Two variable number tandem repeat (VNTR) domains, termed HTTLPR and STin2, in the SLC6A4 gene are well characterized transcriptional regulatory elements. Their polymorphism in the copy number of the repeat correlates with the particular personality and psychiatric traits. We analyzed nicotine dependence in 1,804 participants from Central Vietnam. The Fagerström Test (FTND) was used to evaluate the nicotine dependence and PCR was used to determine the SLC6A4 HTTLPR and STin2 VNTRs. The HTTLPR VNTR was associated with difficulties to refrain from smoking in a prohibiting environment. The STIn2 10/10 genotype was associated with (1) years of smoking, (2) difficulties to quit the first cigarette, and (3) higher number of cigarettes smoked per day (CPD). Stratification analysis was used to find the genetic interaction between these two VNTRs in nicotine dependence as they may synergistically regulate the SLC6A4 expression. Smokers with the S/S HTTLPR genotypes showed a much stronger association between STin2 10/10 variant and CPD. This finding is consistent with the molecular evidence for the functional interaction between HTTLPR and STin2 in cell line models, where STin2 has described as a stronger expressional regulator. Similarly, we found that STin2 is a much stronger modifier of smoking with 10/10 genotype related to higher nicotine dependence. The present study supports genetic interaction between HTTLPR and STin2 VNTRs in the regulation of nicotine dependence with the dominance of the STin2 effects. This finding could be explained by their differential effect on the SLC6A4 expression