bluuebunny/crossref_metadata_2025 huggingface dataset:
Subjects Conceived through Assisted Reproductive Technologies Display Normal Arterial Stiffness
Authors: Langer Magdalena , Li Pengzhu , Vilsmaier Theresa , Kramer Marie , Sciuk Franziska , Kolbinger Brenda , Jakob André , Rogenhofer Nina , Haas Nikolaus Alexander , Dalla-Pozza Robert , Thaler Christian , Oberhoffer Felix Sebastian
Published: November 2022

Multiple studies reported signs of vascular dysfunction in subjects conceived through assisted reproductive technologies (ART). The assessment of arterial stiffness in this cohort seems beneficial for risk stratification. Regional arterial stiffness of the abdominal aorta (AAO) and the common carotid arteries (CCA) was evaluated sonographically using two-dimensional speckle tracking in subjects conceived through ART and spontaneously conceived peers. Global arterial stiffness was assessed utilizing an oscillometric blood pressure device. The cohorts of 67 ART subjects and 86 spontaneously conceived peers (11.31 (8.10–18.20) years vs. 11.85 (8.72–18.27) years, p = 0.43) did not differ significantly in parameters of regional and global arterial stiffness. In the sub-analysis of study participants ≥10 years of age, markers of arterial stiffness did not display significant differences between both groups. However, a higher tendency of brachial systolic blood pressure was demonstrated in the ART cohort compared to the control group (120.18 ± 9.57 mmHg vs. 116.55 ± 8.05 mmHg, p = 0.050). The present study displayed no significant differences in arterial stiffness between ART subjects and spontaneously conceived peers. Moreover, this study suggests that arterial stiffness does not elevate more profoundly in ART subjects with increasing age. Further studies are required for a more detailed cardiovascular risk stratification of the ART cohort.
IDH Mutations Are Potentially the Intrinsic Genetic Link among the Multiple Neoplastic Lesions in Ollier Disease and Maffucci Syndrome: A Clinicopathologic Analysis from a Single Institute in Shanghai, China
Authors: Chen Chunyan , Li Jian , Jiang Ting , Tang Juan , Zhang Zhichang , Luo Yanli , Wang Xinpei , Sun Keyang , Jiang Zhiming , Zhou Juan , Liu Zhiyan
Published: November 2022

Background: This study aims to investigate isocitrate dehydrogenase gene mutations in patients with the non-hereditary skeletal disorders of Ollier disease and Maffucci syndrome, particularly in the extraosseous tumours. Methods: A total of 16 tumours from three patients with Ollier disease and three patients with Maffucci syndrome were collected. Sanger sequencing was applied to determine the hotspot mutations of IDH1 and IDH2 genes in multiple neoplastic tissues. Results: A majority of the tumours displayed an IDH1 mutation (p.R132C in 11 tumours including the paediatric ovarian tumour from one patient with Ollier disease, 4 cutaneous haemangiomas from three patients with Maffucci syndrome, 5 enchondromas and 1 chondrosarcoma; p.R132H in 2 cartilaginous tumours from one patient). Conclusions: IDH1 mutations were demonstrated in multiple cartilaginous tumours and extraskeletal neoplasms in this case series. Specifically, identical IDH1 mutations were confirmed in the separate lesions of each patient. These results are in concordance with findings that have been reported. However, here, we additionally reported the first case of Ollier disease with an ovarian tumour, which harboured the identical IDH1 mutation with the corresponding cartilaginous tumour. We further provided evidence that IDH mutations are the potential genetic links among the multiple neoplastic lesions of Ollier disease and Maffucci syndrome.
Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey
Authors: Huang Sheng-Yao , Hsu Wen-Lin , Hsu Ren-Jun , Liu Dai-Wei
Published: November 2022

There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for future development, including model development, data augmentation processing, and dataset creation. The strengths and deficiencies of studies on models and data augmentation, as well as their application to medical image segmentation, were analyzed. Fully convolutional network developments have led to the creation of the U-Net and its derivatives. Another noteworthy image segmentation model is DeepLab. Regarding data augmentation, due to the low data volume of medical images, most studies focus on means to increase the wealth of medical image data. Generative adversarial networks (GAN) increase data volume via deep learning. Despite the increasing types of medical image datasets, there is still a deficiency of datasets on specific problems, which should be improved moving forward. Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied.
Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain
Authors: Diwakar Manoj , Singh Prabhishek , Swarup Chetan , Bajal Eshan , Jindal Muskan , Ravi Vinayakumar , Singh Kamred Udham , Singh Teekam
Published: November 2022

In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in most cases, the proposed method gives better outcomes than existing ones.
Placenta Accreta Spectrum (PAS) Disorder: Ultrasound versus Magnetic Resonance Imaging
Authors: Faralli Ida , Del Negro Valentina , Chinè Alessandra , Aleksa Natalia , Ciminello Enrico , Piccioni Maria Grazia
Published: November 2022

Objective: PAS is one of the most dangerous conditions associated with pregnancy and remains undiagnosed before delivery in from half to two-thirds of cases. Correct prenatal diagnosis is essential to reduce the burden of maternal and fetal morbidity. The purpose of our study is to evaluate the accuracy of US and MRI in the diagnosis of PAS. Study design: In this retrospective study, 104 patients with suspected placenta accreta were enrolled and had been investigated with US and MRI. They were divided into four groups: no PAS, accreta, increta, and percreta. Results: Compared to MRI, US results were higher in the diagnosis and in the identification of PAS severity (85% US vs. 80% MRI). For both methods, in the case of posterior placenta, there is greater difficulty in identifying the presence/absence of the disease (67% in both methods) and the severity level (61% US vs. 55% MRI). Conclusion: US, properly implemented with the application of defined and standardized scores, can be superior to MRI and absolutely sufficient for the diagnosis of PAS, limiting the use of MRI to a few doubtful cases and to cases in which surgical planning is necessary.
D-dimer Testing in Pulmonary Embolism with a Focus on Potential Pitfalls: A Narrative Review
Authors: Wauthier Loris , Favresse Julien , Hardy Michaël , Douxfils Jonathan , Le Gal Grégoire , Roy Pierre-Marie , van Es Nick , Ay Cihan , ten Cate Hugo , Vander Borght Thierry , Dupont Michaël V. , Lecompte Thomas , Lippi Giuseppe , Mullier François
Published: November 2022

D-dimer is a multifaceted biomarker of concomitant activation of coagulation and fibrinolysis, which is routinely used for ruling out pulmonary embolism (PE) and/or deep vein thrombosis (DVT) combined with a clinical pretest probability assessment. The intended use of the tests depends largely on the assay used, and local guidance should be applied. D-dimer testing may suffer from diagnostic errors occurring throughout the pre-analytical, analytical, and post-analytical phases of the testing process. This review aims to provide an overview of D-dimer testing and its value in diagnosing PE and discusses the variables that may impact the quality of its laboratory assessment.
Midpalatal Suture Maturation Method for the Assessment of Maturation before Maxillary Expansion: A Systematic Review
Authors: Shayani Anis , Sandoval Vidal Paulo , Garay Carrasco Ivonne , Merino Gerlach Marco
Published: November 2022

Assessment of midpalatal suture maturation is crucial before deciding which type of maxillary expansion technique will be performed to treat transverse discrepancies. In 2013, Angelieri et al. proposed a new method to evaluate midpalatal maturation using cone-beam computed tomography. The aim of this study was to systematically identify, evaluate, and provide a synthesis of the existing literature about this new method and to rigorously assess the methodological quality of these articles. A bibliographic search was carried out using PubMed, Cochrane Library, SciELO, LILACS, Web of Science, and Scopus using the terms midpalatal suture, cranial sutures, palate, maturation, interdigitation, ossification, maxillary expansion, evaluation, assessment, and assess. Quality assessment was performed using the Observational Cohort and Cross-Sectional Studies tool developed by the National Heart, Lung, and Blood Institute. Hence, 56 articles were obtained, of which only 10 met the selection criteria. We could not include any of the data into an analysis because of the large variation of the data collected and high methodological heterogeneity found among studies. Of all the studies included, 10% had poor quality, 70% fair, and 20% good quality, respectively. Even though age and sex play a role in midpalatal suture obliteration, there is a poor correlation between these variables. Thus, every patient should be assessed individually before choosing the best protocol for maxillary expansion. The midpalatal suture maturation method has the potential to be used for diagnostic purposes, but clinicians should be cautious of routinely using it because an extensive training and calibration program should be performed prior.
Performance of the New 2019 European Alliance of Associations for Rheumatology/American College of Rheumatology Systemic Lupus Erythematosus Classification Criteria in a Large Unicentric Cohort
Authors: Gheorghiu Ana Maria , Vrancianu Cristina , Conea Iuliana , Boca Aida , Bolboceanu Madalina , Draganescu Cristiana , Bojinca Mihai , Ancuta Ioan
Published: November 2022

Background: The recently published 2019 American College of Rheumatology/European Alliance of Associations for Rheumatology (ACR/EULAR) classification criteria for systemic lupus erythematosus (SLE) were developed to increase the reliability and identification of SLE, especially in early disease. With the emergence of several new drugs for SLE, identifying and treating patients early are more important than ever. Methods: Data of 446 SLE patients evaluated in our center between 1996–2019 and 226 controls with other autoimmune diseases evaluated between 2001–2022 were retrospectively analyzed. The sensitivity and specificity of the 2019 ACR/EULAR criteria were compared to the 2012 SLICC and the 1997 ACR criteria. Results: The 2019 ACR/EULAR criteria showed very good sensitivity (86.6%) compared to the 1997 ACR criteria (76.7%), p < 0.001, with a trend toward significance compared to the 2012 SLICC criteria (83.6%), p = 0.072. Their sensitivity remained high (87.6%) in patients with a short disease duration. The specificity of the 2019 ACR/EULAR criteria (91.2%) was statistically lower than the 2012 SLICC (96.0%) and 1997 ACR criteria (95.1%), p = 0.007 and p = 0.012, respectively, but still had a very high value. A total of 22 controls (9.7%) fulfilled at least one set of criteria (15 patients with MCTD, 5 with UCTD, and 2 with SSc). Conclusion: In this large real-life cohort, the 2019 ACR/EULAR criteria had very good performance compared to the 2012 SLICC and 1997 ACR criteria.
A Decision Probability Transformation Method Based on the Neural Network
Authors: Li Junwei , Zhao Aoxiang , Liu Huanyu
Published: November 2022

When the Dempster–Shafer evidence theory is applied to the field of information fusion, how to reasonably transform the basic probability assignment (BPA) into probability to improve decision-making efficiency has been a key challenge. To address this challenge, this paper proposes an efficient probability transformation method based on neural network to achieve the transformation from the BPA to the probabilistic decision. First, a neural network is constructed based on the BPA of propositions in the mass function. Next, the average information content and the interval information content are used to quantify the information contained in each proposition subset and combined to construct the weighting function with parameter r. Then, the BPA of the input layer and the bias units are allocated to the proposition subset in each hidden layer according to the weight factors until the probability of each single-element proposition with the variable is output. Finally, the parameter r and the optimal transform results are obtained under the premise of maximizing the probabilistic information content. The proposed method satisfies the consistency of the upper and lower boundaries of each proposition. Extensive examples and a practical application show that, compared with the other methods, the proposed method not only has higher applicability, but also has lower uncertainty regarding the transformation result information.
Time-Aware Explainable Recommendation via Updating Enabled Online Prediction
Authors: Jiang Tianming , Zeng Jiangfeng
Published: November 2022

There has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and evolving nature of user–item interactions. There are two main issues with these methods. First, their random dataset split setting will result in data leakage that knowledge should not be known at the time of training is utilized. Second, the dynamic characteristics of user preferences are overlooked, resulting in a model aging issue where the model’s performance degrades along with time. In this paper, we propose an updating enabled online prediction framework for the time-aware explainable recommendation. Specifically, we propose an online prediction scheme to eliminate the data leakage issue and two novel updating strategies to relieve the model aging issue. Moreover, we conduct extensive experiments on four real-world datasets to evaluate the effectiveness of our proposed methods. Compared with the state-of-the-art, our time-aware approach achieves higher accuracy results and more convincing explanations for the entire lifetime of recommendation systems, i.e., both the initial period and the long-term usage.
Observing Cryptocurrencies through Robust Anomaly Scores
Authors: Bae Geumil , Kim Jang Ho
Published: November 2022

The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.
A Schmidt Decomposition Approach to Quantum Thermodynamics
Authors: Malavazi André Hernandes Alves , Brito Frederico
Published: November 2022

The development of a self-consistent thermodynamic theory of quantum systems is of fundamental importance for modern physics. Still, despite its essential role in quantum science and technology, there is no unifying formalism for characterizing the thermodynamics within general autonomous quantum systems, and many fundamental open questions remain unanswered. Along these lines, most current efforts and approaches restrict the analysis to particular scenarios of approximative descriptions and semi-classical regimes. Here, we propose a novel approach to describe the thermodynamics of arbitrary bipartite autonomous quantum systems based on the well-known Schmidt decomposition. This formalism provides a simple, exact, and symmetrical framework for expressing the energetics between interacting systems, including scenarios beyond the standard description regimes, such as strong coupling. We show that this procedure allows straightforward identification of local effective operators suitable for characterizing the physical local internal energies. We also demonstrate that these quantities naturally satisfy the usual thermodynamic notion of energy additivity.
An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System
Authors: Yang Lei , Guo Xiaoyu , Song Xiaowei , Lu Deyuan , Cai Wenjing , Xiong Zixiang
Published: November 2022

This paper proposes an improved human-body-segmentation algorithm with attention-based feature fusion and a refined corner-based feature-point design with sub-pixel stereo matching for the anthropometric system. In the human-body-segmentation algorithm, four CBAMs are embedded in the four middle convolution layers of the backbone network (ResNet101) of PSPNet to achieve better feature fusion in space and channels, so as to improve accuracy. The common convolution in the residual blocks of ResNet101 is substituted by group convolution to reduce model parameters and computational cost, thereby optimizing efficiency. For the stereo-matching scheme, a corner-based feature point is designed to obtain the feature-point coordinates at sub-pixel level, so that precision is refined. A regional constraint is applied according to the characteristic of the checkerboard corner points, thereby reducing complexity. Experimental results demonstrated that the anthropometric system with the proposed CBAM-based human-body-segmentation algorithm and corner-based stereo-matching scheme can significantly outperform the state-of-the-art system in accuracy. It can also meet the national standards GB/T 2664-2017, GA 258-2009 and GB/T 2665-2017; and the textile industry standards FZ/T 73029-2019, FZ/T 73017-2014, FZ/T 73059-2017 and FZ/T 73022-2019.
Effects of Relational Benefits in the Model of Customers’ Benefits and Relationship Quality in Vietnam
Authors: Nguyen Phuong T. , Cao Hieu V. , Phuoc Hiep M. , Tran Phong T.
Published: November 2022

With the aim of comparing the influence of economic benefits with social benefits in the model of integrating customer benefits and relationship quality in the context of university–enterprise relationship research in Ho Chi Minh City (HCMC), Vietnam. From the perspective of enterprises, a study combining qualitative and quantitative research was carried out. Data for the main study were collected from 486 enterprises using an online survey. The research model and hypotheses are tested by analyzing the structural equation model. The results of examining the influence of economic benefits and social benefits in the research model indicate that the influence of economic benefits is more significant than the influence of social benefits. This is a new finding of this study in comparison with previous studies on relational benefits. In addition, the study also pointed out that economic benefits and social benefits have a direct impact on perceived service quality.
Online Support for Education in Entrepreneurial and Intrapreneurial Competences: A Proposal for an Assessment Tool and Support for Tailor-Made Training
Authors: Teodoro Jaume , Bernadó Ester , Bratzke Florian , Zehrer Anita , Van Bockhaven Wouter
Published: November 2022

Higher education institutions across Europe are called to offer entrepreneurship education. Despite the rising interest and the increased offerings in the last decades, entrepreneurship education is yet not as mature as other disciplines, and it is still underdeveloped in some faculties and institutions. One way of embedding entrepreneurship education within different disciplines is to take a broader approach beyond teaching how to start up and focusing on developing the entrepreneurial competences of students, which equip them to provide value to society (either economic, social, or cultural). This article describes an online platform—named EICAA—that supports educators and trainers aiming at teaching/guiding/coaching students or employees in developing entre/intrapreneurial competences. The platform is based on a Competence Framework built upon a systematic literature review, which defines 18 key competences grouped in three competence areas. The platform allows the educator to assess the entrepreneurial competences of a group of students/participants, via the so-called Competence Monitor and provides tailor-made training with the Competence Development Kit. The platform is being used in five European universities and is open to be used by any higher education institution or organisation aiming at developing the entrepreneurial competences of their students and/or employees. Future developments of the tool can be enriched from the experiences of the participant institutions and could become an open collaborative online tool.
Enriching Traditional Higher STEM Education with Online Teaching and Learning Practices: Students’ Perspective
Authors: Skliarova Iouliia , Meireles Inês , Martins Natália , Tchemisova Tatiana , Cação Isabel
Published: November 2022

In this paper, we aim to identify online teaching and learning practices that would be beneficial for blended and traditional on-campus education within STEM (Science, Technology, Engineering, and Mathematics) courses. Our university, as well as the majority of higher education institutions worldwide, has had few to no experience in delivering full online courses before 2020. The teaching process was, however, severely affected and modified by the COVID-19 pandemic, forcing an abrupt and unprepared shift towards online education. In this work, we look at the pandemic as causing a very favorable side effect that forced the university to study, test, apply, and evaluate the benefits and drawbacks of online education and assessment methods. The study is a result of joint efforts from different departments at the University of Aveiro, Portugal, connected to STEM undergraduate and graduate programs and is based on a questionnaire targeted towards students. In total, 167 valid STEM students’ answers have been collected and analyzed, both quantitatively and qualitatively. As the result, the best teaching and learning practices are identified and the main difficulties and obstacles experienced by students are detected. Some of the problems are common to many higher education institutions, such as the lack of teacher preparation in delivering quality online synchronous and asynchronous classes, technical limitations (network bandwidth/weak equipment), ineffective communication during synchronous classes, gaps in student skills, and low activity of some students and even teachers. We believe that the presented results would allow for improving future on-campus, distance, and blended learning courses, particularly through avoiding less effective teaching and assessment methods and favoring those techniques that students consider more efficient. This ultimately would lead to a more rewarding teaching/learning experience.
Exploring Learning for On-Campus Students Transitioning to Online Learning during the COVID-19 Pandemic: Perceptions of Students in the Higher Education
Authors: Osobajo Oluyomi Abayomi , Oke Adekunle
Published: November 2022

Due to the emergence of COVID-19, the education sector has embraced online learning as the main delivery method to engage and impact knowledge and skills acquisition of their students. However, learning is not just about knowledge and skills acquisition but is an activity contributing to change and enrichment of the learner. This paper draws upon the results of a qualitative interview conducted amongst postgraduate students enrolled in an AMBA-accredited and top business school in the UK. The results revealed that students’ experience of the sudden transition to online learning is shaped by four distinct but interrelated areas: benefits of online learning, challenges of online learning, success factors in online learning and support in online learning. These findings will help higher education institutions and online learning tutors concentrate more on areas important to student learning when migrating from face-to-face to online modes of teaching.
Dynamic Fractional-Order Nonsingular Terminal Super-Twisting Sliding Mode Control for a Low-Cost Humanoid Manipulator
Authors: Hu Rong , Xu Xiaolei , Zhang Yi , Deng Hua
Published: November 2022

Prosthetic humanoid manipulators manufacturing requires light overall weight, small size, compact structure, and low cost to realize wearing purpose. These requirements constrain hardware configuration conditions and aggravate the nonlinearity and coupling effects of manipulators. A dynamic fractional-order nonsingular terminal super-twisting sliding mode (DFONTSM-STA) control is proposed to realize multi-joints coordination for a low-cost humanoid manipulator. This method combines a dynamic fractional-order nonsingular terminal sliding mode (DFONTSM) manifold with the super-twisting reaching law, which can enhance the entire control performance by dynamically changing the position of the sliding mode manifold. By hiding the sign function in a higher-order term, chattering can be effectively suppressed. The stability of the low-cost humanoid manipulator system has been proven based on the Lyapunov stability theory. Experimental results show that the terminal trajectory tracking accuracy of DFONTSM-STA control was promoted by 53.3% and 23.7% respectively compared with FONTSM control and FONTSM-STA control. Thus, the DFONTSM-STA controller is superior in error convergence speed, chattering suppression, and accurate position tracking performance.
Electrical Response Analysis of a Piezoelectric Energy Harvester Power Source Based on Electromechanical Parameters
Authors: Perez-Alfaro Irene , Gil-Hernandez Daniel , Hernando Eduardo , Quero Fernando , Bernal Carlos
Published: November 2022

A piezoelectric energy harvester generator is a device capable of transforming environmental mechanical energy into electrical energy. The piezoelectric electromechanical parameters determine the maximum electrical power which is able to be transferred to an electric load. In this research work, an exhaustive study of the electromechanical parameters related to the piezoelectric material is carried out, modeling them as components of an electrical circuit, in order to analyze their influence on the transmitted power. On the other hand, some electrical loads are simulated to determine different matrix scenarios for a model developed by state-space equations in the Laplace transform domain. The results obtained have allowed to know how the piezoelectric material properties and mechanical characteristics influence the electrical power output of the energy harvester generator and the energy transmission behavior for different electric loads. The conclusions show how the different electromechanical parameters are related to each other, and how their combination transforms the mechanical environmental energy into the required electrical energy. The novelty of this research is the presentation of a model capable of obtaining the optimized working point of the harvester, taking into account not only the electric loads and current demands but also the piezoelectric material parameters.
Switching Mechanism on the Order of Affine Projection Algorithm
Authors: Niu Zhiwei , Gao Ying , Xu Jindong , Ou Shifeng
Published: November 2022

Conventional affine projection (AP) algorithm with a fixed order is subject to a tradeoff between convergence speed and steady-state misalignment. In order to address such problem, a switching mechanism on the order of AP algorithm is proposed by comparing the performance of two AP algorithms with different orders. Firstly, the mean square deviations (MSD) behavior of the AP algorithm is analyzed, and a calculation formula for computing MSD at each iteration is derived. Secondly, we design a switching mechanism to select the better order of the two AP algorithms by comparing the MSDs of them; the MSD of the chosen order is smaller than that of the other. We also give the theoretical analysis, including steady-state mean square error (MSE) and computational complexity. Finally, the experiments in system identification and echo-cancellation scenarios demonstrate that the proposed algorithm has good performance not only in a stationary environment but also in a non-stationary environment.
Data Mining in the Vibration Signal of the Trip Mechanism in Circuit Breakers Based on VMD-PSR
Authors: Wang Xuezong , Ruan Jiangjun , Zhou Taotao , Peng Xuelin , Deng Yongqing , Yang Qiuyu
Published: November 2022

To address the difficulty in characterizing early mechanical faults in the trip mechanism of circuit breakers, a data mining method based on variational mode decomposition (VMD) and phase space reconstruction (PSR) method was proposed. First, the vibration signal in the trip stage was separated from the whole according to the current features. Then, it was decomposed using the VMD algorithm to obtain the intrinsic mode functions (IMFs) and these sub signals were mapped to high-dimensional phase space based on the PSR algorithm. Then, the features of the attractor trail shape and the recurrence plot matrixes were extracted. In order to judge the fault in the trip mechanism, a fault simulation test was carried out and the characteristic under different faults was analyzed. Based on these samples, a fault identification model is established by support vector machine (SVM) and the effectiveness is verified by other test samples. The accuracy of the SVM model is 98%, which is higher than that of the BPNN and KNN clustering models. This research supplements the existing method for condition evaluation of the trip mechanism and can provide a reference for circuit breaker fault diagnosis.
Vision-Based Quadruped Pose Estimation and Gait Parameter Extraction Method
Authors: Gong Zewu , Zhang Yunwei , Lu Dongfeng , Wu Tiannan
Published: November 2022

In the study of animal behavior, the prevention of sickness, and the gait planning of legged robots, pose estimation, and gait parameter extraction of quadrupeds are of tremendous importance. However, there are several varieties of quadrupeds, and distinct species frequently have radically diverse body types, limb configurations, and gaits. Currently, it is challenging to forecast animal pose estimation with any degree of accuracy. This research developed a quadruped animal pose estimation and gait parameter extraction method to address this problem. A computational framework including three components of target screening, animal pose estimation model, and animal gaits parameter extraction, which can totally and efficiently solve the problem of quadruped animal pose estimation and gait parameter extraction, makes up its core. On the basis of the HRNet network, an improved quadruped animal keypoint extraction network, RFB-HRNet, was proposed to enhance the extraction effect of quadruped pose estimation. The basic concept was to use a DyConv (dynamic convolution) module and an RFB (receptive field block) module to propose a special receptive field module DyC-RFB to optimize the feature extraction capability of the HRNet network at stage 1 and to enhance the feature extraction capability of the entire network model. The public dataset AP10K was then used to validate the model’s performance, and it was discovered that the proposed method was superior to alternative methods. Second, a two-stage cascade network was created by adding an object detection network to the front end of the pose estimation network to filter the animal object in input images, which enhanced the pose estimation effect of small targets and multitargets. The acquired keypoints data of animals were then utilized to extract the gait parameters of the experimental objects. Experiment findings showed that the gait parameter extraction model proposed in this research could effectively extract the gait frequency, gait sequence, gait duty cycle, and gait trajectory parameters of quadruped animals, and obtain real-time and accurate gait trajectory.
Improved Linear Quadratic Regulator Lateral Path Tracking Approach Based on a Real-Time Updated Algorithm with Fuzzy Control and Cosine Similarity for Autonomous Vehicles
Authors: Wang Zhaoqiang , Sun Keyang , Ma Siqun , Sun Lingtao , Gao Wei , Dong Zhuangzhuang
Published: November 2022

Path tracking plays a crucial role in autonomous driving. In order to ensure the real-time performance of the controller and at the same time improve the stability and adaptability of the path tracking controller, a lateral path control strategy based on the improved LQR algorithm is proposed in this paper. To begin with, a discrete LQR controller with feedforward and feedback components is designed based on the error model of vehicle lateral dynamics constructed by the natural coordinate system. Then, a fuzzy control method is applied to adjust the weight coefficients of the LQR in real time according to the state of the vehicle. Furthermore, an update mechanism based on cosine similarity is designed to reduce the computational effort of the controller. The improved LQR controller is tested on a joint Simulink–Carsim simulation platform for a two-lane shift maneuver. The results show that the control algorithm improves tracking accuracy, steering stability and computational efficiency.
Weed Detection in Potato Fields Based on Improved YOLOv4: Optimal Speed and Accuracy of Weed Detection in Potato Fields
Authors: Zhao Jiawei , Tian Guangzhao , Qiu Chang , Gu Baoxing , Zheng Kui , Liu Qin
Published: November 2022

The key to precise weeding in the field lies in the efficient detection of weeds. There are no studies on weed detection in potato fields. In view of the difficulties brought by the cross-growth of potatoes and weeds to the detection of weeds, the existing detection methods cannot meet the requirements of detection speed and detection accuracy at the same time. This study proposes an improved YOLOv4 model for weed detection in potato fields. The proposed algorithm replaces the backbone network CSPDarknet53 in the YOLOv4 network structure with the lightweight MobileNetV3 network and introduces Depthwise separable convolutions instead of partial traditional convolutions in the Path Aggregation Network (PANet), which reduces the computational cost of the model and speeds up its detection. In order to improve the detection accuracy, the convolutional block attention module (CBAM) is fused into the PANet structure, and the CBAM will process the input feature map with a channel attention mechanism (CAM) and spatial attention mechanism (SAM), respectively, which can enhance the extraction of useful feature information. The K-means++ clustering algorithm is used instead of the K-means clustering algorithm to update the anchor box information of the model so that the anchor boxes are more suitable for the datasets in this study. Various image processing methods such as CLAHE, MSR, SSR, and gamma are used to increase the robustness of the model, which eliminates the problem of overfitting. CIoU is used as the loss function, and the cosine annealing decay method is used to adjust the learning rate to make the model converge faster. Based on the above-improved methods, we propose the MC-YOLOv4 model. The mAP value of the MC-YOLOv4 model in weed detection in the potato field was 98.52%, which was 3.2%, 4.48%, 2.32%, 0.06%, and 19.86% higher than YOLOv4, YOLOv4-tiny, Faster R-CNN, YOLOv5 l, and SSD(MobilenetV2), respectively, and the average detection time of a single image was 12.49ms. The results show that the optimized method proposed in this paper outperforms other commonly used target detection models in terms of model footprint, detection time consumption, and detection accuracy. This paper can provide a feasible real-time weed identification method for the system of precise weeding in potato fields with limited hardware resources. This model also provides a reference for the efficient detection of weeds in other crop fields and provides theoretical and technical support for the automatic control of weeds.
Fast L2 Calibration for Inexact Highway Traffic Flow Systems
Authors: Huang Jingru , Wang Yan , Han Mei
Published: November 2022

Transportation systems need more accurate predictions to further optimize traffic network design with the development and application of autonomous driving technology. In this article, we focus on highway traffic flow systems that are often simulated by the modified Greenshields model. However, this model can not perfectly match the true traffic flow due to its underlying simplifications and assumptions, implying that it is inexact. Specifically, some parameters affect the simulation accuracy of the modified Greenshields model, while tuning these parameters to improve the model’s accuracy is called model calibration. The parameters obtained using the L2 calibration have the advantages of high accuracy and small variance for an inexact model. However, the method is calculation intensive, requiring optimization of the integral loss function. Since traffic flow data are often massive, this paper proposes a fast L2 calibration framework to calibrate the modified Greenshields model. Specifically, the suggested method selects a sub-design containing more information on the calibration parameters, and then the empirical loss function obtained from the optimal sub-design is utilized to approximate the integral loss function. A case study highlights that the proposed method preserves the advantages of L2 calibration and significantly reduces the running time.
Fault Diagnosis Method for an Underwater Thruster, Based on Load Feature Extraction
Authors: Gan Wenyang , Dong Qishan , Chu Zhenzhong
Published: November 2022

Targeting the problem of fault diagnosis in magnetic coupling underwater thrusters, a fault pattern classification method based on load feature extraction is proposed in this paper. By analyzing the output load characteristics of thrusters under typical fault patterns, the load torque model of the thrusters is established, and two characteristic parameters are constructed to describe the different fault patterns of thrusters. Then, a thruster load torque reconstruction method, based on the sliding mode observer (SMO), and the fault characteristic parameter identification method, based on the least square method (LSM), are proposed. According to the identified fault characteristic parameters, a thruster fault pattern classification method based on a support vector machine (SVM) is proposed. Finally, the feasibility and superiority of the proposed aspects are verified, through comparative simulation experiments. The results show that the diagnostic accuracy of this method is higher than 95% within 5 seconds of the thruster fault. The lowest diagnostic accuracy of thrusters with a single failure state is 96.75%, and the average diagnostic accuracy of thrusters with five fault states is 98.65%.
Cybersecurity in Smart Cities: Detection of Opposing Decisions on Anomalies in the Computer Network Behavior
Authors: Protic Danijela , Gaur Loveleen , Stankovic Miomir , Rahman Md Anisur
Published: November 2022

The increased use of urban technologies in smart cities brings new challenges and issues. Cyber security has become increasingly important as many critical components of information and communication systems depend on it, including various applications and civic infrastructures that use data-driven technologies and computer networks. Intrusion detection systems monitor computer networks for malicious activity. Signature-based intrusion detection systems compare the network traffic pattern to a set of known attack signatures and cannot identify unknown attacks. Anomaly-based intrusion detection systems monitor network traffic to detect changes in network behavior and identify unknown attacks. The biggest obstacle to anomaly detection is building a statistical normality model, which is difficult because a large amount of data is required to estimate the model. Supervised machine learning-based binary classifiers are excellent tools for classifying data as normal or abnormal. Feature selection and feature scaling are performed to eliminate redundant and irrelevant data. Of the 24 features of the Kyoto 2006+ dataset, nine numerical features are considered essential for model training. Min-Max normalization in the range [0,1] and [−1,1], Z-score standardization, and new hyperbolic tangent normalization are used for scaling. A hyperbolic tangent normalization is based on the Levenberg-Marquardt damping strategy and linearization of the hyperbolic tangent function with a narrow slope gradient around zero. Due to proven classification ability, in this study we used a feedforward neural network, decision tree, support vector machine, k-nearest neighbor, and weighted k-nearest neighbor models Overall accuracy decreased by less than 0.1 per cent, while processing time was reduced by more than a two-fold reduction. The results show a clear benefit of the TH scaling regarding processing time. Regardless of how accurate the classifiers are, their decisions can sometimes differ. Our study describes a conflicting decision detector based on an XOR operation performed on the outputs of two classifiers, the fastest feedforward neural network, and the more accurate but slower weighted k-nearest neighbor model. The results show that up to 6% of different decisions are detected.
Evolution Characteristics of Energy Change Field in a Centrifugal Pump during Rapid Starting Period
Authors: Chen Xiaoping , Zhang Xiaoming , Li Xiaojun
Published: November 2022

Although the energy change field in the centrifugal pump has been investigated under quasi-steady conditions (QSC), equivalent information is not yet known during the rapid starting period (RSP). A centrifugal pump loop system is constructed to investigate the energy change field in the centrifugal pump during RSP. The RSP is selected as a linear rotational speed from 0 rev/min to 2900 rev/min (design rotational speed) and a constant valve opening of 0.569. Results show that the flow rate lags behind the pump head value with the linear increase in rotational speed. The large values of partial derivations of mechanical energy in normal and tangential components are mainly concentrated at the impeller outlet, whose position is insensitive to rotational speed. The region of dominant energy loss is negatively correlated with rotational speed, and an opposite phenomenon is observed in the region of dominant energy increase. With the rotational speed increasing, the mean energy gradient function in the pump impeller and pump volute gradually increases, and the slope of the former is less than that of the latter. After reaching the design rotational speed, the energy change field gradually approaches that under quasi-steady conditions.
Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data
Authors: Balcilar Mehmet , Gupta Rangan , Pierdzioch Christian
Published: November 2022

We investigate whether oil-price uncertainty helps forecast the international stock returns of ten advanced and emerging countries. We consider an out-of-sample period of August 1925 to September 2021, with an in-sample period between August 1920 and July 1925, and employ a quantile-predictive-regression approach, which is more informative relative to a linear model, as it investigates the ability of oil-price uncertainty to forecast the entire conditional distribution of stock returns Based on a recursive estimation scheme, we draw the following main conclusions: the quantile-predictive-regression approach using oil-price uncertainty as a predictor statistically outperforms the corresponding quantile-based constant-mean model for all ten countries at certain quantiles (capturing normal, bear, and bull markets), and over specific forecast horizons, compared to forecastability being detected for eight countries under the linear predictive model. Importantly, we detect forecasting gains in many more horizons (at particular quantiles) compared to the linear case. In addition, an oil-price uncertainty-based state-contingent spillover analysis reveals that the ten equity markets are connected more tightly at the upper regime, suggesting that heightened oil-market volatility erodes the benefits from diversification across equity markets.
Characterization of Fluid Flow and Heat Transfer of Expanded Metal Meshes for Catalytic Processes
Authors: Iwaniszyn Marzena , Sindera Katarzyna , Gancarczyk Anna , Leszczyński Bartosz , Korpyś Mateusz , Suwak Mikołaj , Kołodziej Andrzej , Jodłowski Przemysław J.
Published: November 2022

In this work, three raised expanded metal meshes (EMMs) differing in mesh size were tested experimentally with regard to their flow and transport properties. Empirical equations for the Nusselt number and Fanning friction factor were developed. Alongside the experiments, simple computational fluid dynamics (CFD) models were used to simulate the pressure drop and heat transfer coefficients within EMMs. Finally, the Performance Efficiency Criterion (PEC) was applied to compare EMMs with other reactor packings.