bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Chromium on Microstructure and Mechanical Properties of Hot-Dip Galvanized Dual-Phase (DP980) Steel
Authors: Chen Xuefei , Liang Jiawei , Yang Dapeng , Hu Zhiping , Xu Xin , Gu Xingli , Xie Guangming
Published: August 2023

Generally, the addition of Chromium (Cr) into the dual-phase (DP) steels can suppress bainitic transformation. In this work, the continuous hot-dip galvanization of DP980 steel is thermally simulated with various Cr contents of 0/0.3/0.6 wt.%, and the effect of Cr on bainitic transformation and properties of steels is studied. The results indicate that the bainitic transformation is obviously inhibited. The fraction of bainite decreases with the increasing Cr content. The incubation time is prolonged in the 0.6Cr steel with a slower bainitic transformation rate. Compared to that of 0 and 0.3Cr steels, the 0.6Cr steel exhibits a high tensile strength of 1033 MPa and uniform elongation of 9.1% due to the rapid strain-hardening rate. As a result, the mechanical properties of 0.6Cr steel satisfy the requirements of hot-dip galvanized DP980 steel.
Seasonal Diversity and Morphometric Variations of Rotifers in Relation to Selected Environmental Variables from a Tropical High-Altitude Lake in Mexico
Authors: González-Gutiérrez Sergio , Sarma S. S. S. , Nandini S.
Published: August 2023

We studied the species diversity and morphometric variations of rotifers from a high-altitude water body, the Llano reservoir, in central Mexico during September 2021 to August 2022. Samples were collected from four stations in the reservoir every month. During the study period we were able to identify 54 monogonont rotifer species of which Polyarthra vulgaris, Lecane closterocerca, Trichocerca porcellus, Lepadella patella and Keratella cochlearis were numerically the most abundant. Depending on the season, the total rotifer density varied from 250 to 2450 ind L−1. Canonical correspondence analysis (CCA) showed that the species of Trichocerca similis, Keratella cochlearis, Mytilina ventralis and Scaridium longicaudum were directly related to temperature. Using rotifer species richness and abundance data, we derived the Shannon diversity index, saprobic index and rotifer trophic state index. Data on the geometric morphometrics showed that Keratella cochlearis was found in two of the three climatic periods of the year (dry and winter), while Lecane closterocerca and Trichocerca porcellus occurred during the three climatic seasons (dry, winter and rainy) and formed three distinct clusters in relation to body size.
Assessing the “Scale of Effect”: The Impact of Multi-Scale Landscape Characteristics on Urban Bird Species Taxonomic and Functional Diversity
Authors: Mao Qian , Wu Zhaolu , Deng Yuanyuan , Sun Jianxin , Bai Haotian , Gong Lei , Jiang Zihan
Published: August 2023

The influence of landscape structure on species communities is intimately connected to the spatial scales at which measurements are made. If we do not measure landscape structure at its most impactful scale, otherwise known as the “scale of effect”, we might fail to detect crucial community–landscape relationships. In our study, we focused on the “scale of effect” as it pertains to the relationship between urban bird diversity and landscape structure. We investigated eight types of landscape structure variables alongside elevation attributes across 16 spatial scales around 28 sampling sites in Kunming, Yunnan Province, China. Our objective was to identify the most influential scale at which the environmental variables under investigation affect the taxonomic and functional diversity of bird species. We utilized PLSR and VIP scores to overcome challenges posed by multicollinearity among predictors. We found that the influence of landscape characteristics on bird species richness and functional dispersion index (FDis) was scale-dependent. Notably, the influence of the area of open water on FDis is most pronounced at smaller scales, while at larger scales, open water dispersion becomes more essential. Additionally, in a highly fragmented urban matrix, the importance of forest connectivity may exceed that of patch size at medium scales. However, the significance of forest coverage increases with scale, which underscores the importance of preservation efforts at larger scales to prevent forest fragmentation. We also noted a considerable impact from landscape-level subdivision across almost all scales. These findings highlight the importance of shifting urban conservation planning towards a multi-scale approach, which would allow for the identification of priority intervention scales.
The Genus Nama, with the Description of 14 New Species (Curculionidae, Entiminae, Namaini)
Authors: Meregalli Massimo , Borovec Roman
Published: August 2023

The authors complete the study of the genus Nama Borovec & Meregalli, 2013, one of the seven genera of the tribe Namaini Meregalli & Borovec, 2021, with the description of 14 new species: Nama chaslavskae (type locality: North of Aggeneys, 29°12′ S 18°50′ E); N. startorum (type locality: Danielskuil, 28°11′ S 23°33′ E); N. ginobartalii (type locality: Messelpad Pass, 29°54′ S, 17°44′ E); N. yusrae (type locality: Wildepaarde Hoek, 29°57′ S 17°33′ E); N. goegapensis (type locality: Goegap Park, Kraaifontein, 29°37′ S, 18°02′ E); N. semilunata (type locality: Gemsbok Vlakte frm., 30°30′ S 17°29′ E); N. martiana (type locality: Aggeneys, Gamsberg, 1116 m, 29°15′S, 18°57′ E); Nama smithi (type locality: Tswalu Kalahari Reserve, Pass South, 27°19′ S, 22°31′ E); N. kostali (type locality: near Danielskuil, 28°15′ S, 23°34′ E); Nama seriehispida (type locality: NE Bitterfontein, 30°53′ S, 18°19′ E); N. khalidae (type locality: SW Kliprand, 30°40′ S, 18°41′ E); N. switzerae (type locality: W Gamoep, 29°58′ S, 18°22′ E); N. peternormani (type locality: Anenous Pass, 29°14′ S, 17°35′ E); N. wallacei (type locality: Pofadder, 29°07′ S, 19°25′ E). Short biogeographical remarks are added.
Measuring the Whole Wall Thickness of the Common Femoral Vein as a Distinctive Diagnostic Tool to Distinguish Behçet’s Disease Presenting with Oral Ulcers from Recurrent Aphthous Stomatitis
Authors: Erturk Adem , Sarıkaya Yasin , Coşkun Hüseyin , Turan Çağrı
Published: August 2023

Recurrent oral ulcers, which are the first and most common manifestation of Behçet’s disease (BD), have several etiological causes but are often idiopathic and known as recurrent aphthous stomatitis (RAS). In recent years, publications have drawn attention to the fact that whole-wall thickness (WWT) and intima–media thickness (IMT) measurements of the common femoral vein (CFV) may be useful in the diagnosis of BD, which are independent of organ involvement. In this study, the usefulness of the WWT and IMT of the CFV measured by venous Doppler ultrasound (US) was investigated in differentiating BD presenting with oral ulcers from RAS. Patients with BD (n = 84), patients with RAS (n = 85), and healthy controls (HCs) (n = 70) were the three groups included. Both the right and left WWT of the CFV were significantly higher in the BD compared with the HCs and RAS groups (p < 0.001). The diagnostic cutoff values of ≥0.58 mm for the WWT of the CFV performed well against both the patients with RAS and HCs for the discrimination of BD (sensitivity = 79.8%, specificity = 64.7%). In patients with recurrent oral ulcers, the WWT of the CFV measurement may be a distinctive new diagnostic tool for the differential diagnosis of BD and RAS.
Evaluation of the Abbott Alinity i Thyroid-Stimulating Hormone Receptor Antibody (TRAb) Chemiluminescent Microparticle Immunoassay (CMIA)
Authors: Lee Deborah J. W. , Phua Soon Kieng , Liang Yali , Chen Claire , Aw Tar-Choon
Published: August 2023

Background: We evaluated the performance of the Abbott thyroid-stimulating hormone receptor antibody chemiluminescent microparticle immunoassay (CMIA) on the Alinity i. Methods: Verification studies for precision, linearity, analytical measuring range, diagnostic cut offs for Graves’ disease were performed. We compared the Abbott CMIA to an established TRAb assay (Roche electrochemiluminescence immunoassay). Method comparison analysis was performed between serum and plasma samples on the Abbott CMIA. Results: Repeatability (CV%) for TRAb were 4.07, 1.56, 0.71 and within-laboratory imprecision (CV%) were 4.07, 1.90, 0.71 at 3.0, 10.0, 30.0 IU/L of TRAb, respectively. Linearity and analytical measuring range were verified from 1.07–47.9 IU/L. The limit of the blank was 0 IU/L, limit of detection was 0.15 IU/L, and limit of quantification was 0.5 IU/L. Passing-Bablok analysis showed agreement between the two assays; Y-intercept = 0.787, slope = 1.04. Passing-Bablok analysis also showed agreement between the plasma and serum samples run on the Abbott CMIA; Y-intercept −0.17, slope = 0.97. Conclusions: The Abbott TRAb CMIA on the Alinity i performs within the manufacturer claims for assay precision, linearity, analytical measuring range, limit of blank, limit of detection, limit of quantitation and diagnostic cut offs for Graves’ disease. Thus, the Abbott TRAb CMIA on the Alinity i is fit for clinical use.
Odontogenic Factors Associated with Maxillary Sinus Schneiderian Membrane Thickness and their Relationship to Chronic Sinonasal Symptoms: An Ambispective Cohort Study
Authors: Alghofaily Maha , Alsufyani Noura , Althumairy Riyadh I. , AlSuhaibani Amal , Alfawzan Fatimah , AlSadhan Lama
Published: August 2023

Odontogenic sinusitis is a common maxillary sinus disease. It develops due to the violation of the Schneiderian membrane due to pathological, iatrogenic, or traumatic causes from dental and dentoalveolar structures. The aim of this cohort study was to investigate local and systemic factors associated with Schneiderian mucosal thickening (MT) in patients referred for evaluation of apical periodontitis (AP) and examine their relationship with chronic sinonasal symptoms. Cone-beam computed tomography (CBCT) scans of 197 patients referred for evaluation of endodontic diseases were reviewed. Mucosal thickening in relation to the affected tooth was measured in the coronal section in millimeters at the maximum area perpendicular to the bone. Based on this measurement, the sinus floor was categorized for MT as present (>1 mm) or absent (<1 mm). The sociodemographic and clinical characteristics of the study participants were assessed and compared according to the presence or absence of MT. Furthermore, the relationship between odontogenic sinusitis and chronic sinonasal symptoms was assessed using a chronic sinusitis survey. Male patients had a higher MT than female patients. The presence of periapical lesions and inadequate endodontic treatment were significantly associated with MT. The multivariate logistic regression model showed that the presence of AP increased the odds of MT by approximately 2.5 times (p = 0.028), and nasal obstruction was significantly higher in patients diagnosed with mucosal thickness at different times of their follow-up period (p = 0.018). MT was 2.5 times more likely in the presence of apical periodontitis, and nasal obstruction was the most significant factor associated with the presence of MT.
Endodontic Outcome of Root Canal Treatment Using Different Obturation Techniques: A Clinical Study
Authors: Winkler Alexander , Adler Philipp , Ludwig Julia , Hofmann Norbert , Soliman Sebastian , Krastl Gabriel , Krug Ralf
Published: August 2023

Objective: To evaluate the clinical outcome of root canal treatment by obturation technique, root canal filling quality, and tooth- and patient-related variables. Methods: This clinical study investigated the endodontic outcome of 114 teeth treated between the years 2009 and 2012. Three different obturation methods were used: (1) adhesive obturation using the continuous wave of condensation technique with Resilon® (CWR), (2) matching-taper single-cone technique with gutta-percha and AH Plus® (SCGP), and (3) matching-taper single-cone technique with gutta-percha and GuttaFlow® (SCGF). Pre- and postoperative periapical radiographs were performed to detect the presence of endodontic lesions (PAI classification) and to assess the quality of both the obturation and the restoration. Tooth- and patient-related data were collected. Results: The overall endodontic success rate was 75.4% after a mean observation period of 6.3 years. There were no significant correlations between the type or overall quality of obturation and the treatment outcome. Teeth with preoperative lesions had the highest odds ratio (factor of 4.98) for endodontic failure. Tooth- and patient-related variables had no significant effect on endodontic outcome. Conclusions: The preoperative periapical status of teeth requiring endodontic treatment was a substantial prognostic factor for endodontic outcome, whereas the type of obturation material or technique did not affect it.
Range–Visual–Inertial Odometry with Coarse-to-Fine Image Registration Fusion for UAV Localization
Authors: Hao Yun , He Mengfan , Liu Yuzhen , Liu Jiacheng , Meng Ziyang
Published: August 2023

In Global Navigation Satellite System (GNSS)-denied environments, image registration has emerged as a prominent approach to utilize visual information for estimating the position of Unmanned Aerial Vehicles (UAVs). However, traditional image-registration-based localization methods encounter limitations, such as strong dependence on the prior initial position information. In this paper, we propose a systematic method for UAV geo-localization. In particular, an efficient range–visual–inertial odometry (RVIO) is proposed to provide local tracking, which utilizes measurements from a 1D Laser Range Finder (LRF) to suppress scale drift in the odometry. To overcome the differences in seasons, lighting conditions, and other factors between satellite and UAV images, we propose an image-registration-based geo-localization method in a coarse-to-fine manner that utilizes the powerful representation ability of Convolutional Neural Networks (CNNs). Furthermore, to ensure the accuracy of global optimization, we propose an adaptive weight assignment method based on the evaluation of the quality of image-registration-based localization. The proposed method is extensively evaluated in both synthetic and real-world environments. The results demonstrate that the proposed method achieves global drift-free estimation, enabling UAVs to accurately localize themselves in GNSS-denied environments.
A Wealth Distribution Agent Model Based on a Few Universal Assumptions
Authors: Calvelli Matheus , Curado Evaldo M. F.
Published: August 2023

We propose a new agent-based model for studying wealth distribution. We show that a model that links wealth to information (interaction and trade among agents) and to trade advantage is able to qualitatively reproduce real wealth distributions, as well as their evolution over time and equilibrium distributions. These distributions are shown in four scenarios, with two different taxation schemes where, in each scenario, only one of the taxation schemes is applied. In general, the evolving end state is one of extreme wealth concentration, which can be counteracted with an appropriate wealth-based tax. Taxation on annual income alone cannot prevent the evolution towards extreme wealth concentration.
Spreading Dynamics of Capital Flow Transfer in Complex Financial Networks
Authors: Peng Wenyan , Chen Tingting , Zheng Bo , Jiang Xiongfei
Published: August 2023

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies.
Multilingual Education Policy and LOTE Teacher Development: Focusing on China’s LOTE Programs in Higher Education
Authors: Yu Wenjun , Xu Hao
Published: August 2023

This article, drawing on recent research on Language Other than English (LOTE) programs in China’s universities, examines the impact of multilingual education policy on the professional development of LOTE teachers. We argue for the mediating roles of LOTE programs as they carry the influence of education policy on teacher development. In other words, LOTE programs may influence the way in which education policy impacts teacher development. Specifically, LOTE programs seem to play three mediating roles: (1) How education policy is perceived and understood by the program designer or/and coordinator impacts how LOTE teachers who engage in the program are influenced, (2) how educational resources are allocated to teachers teaching in the LOTE program impacts teachers’ professional development, (3) how teachers actually position themselves in the LOTE program as opposed to their identities positioned by education policy and the university administration impacts their professional development. In a word, the influence of multilingual education policy on the professional development of LOTE teachers may be mediated by such meso- and micro-level factors as administration, resource, and identity work.
Developing a Next-Generation Model for Massive Digital Learning
Authors: Dede Chris , Lidwell William
Published: August 2023

A decade ago, massively open online courses (MOOCs) were heralded as the solution to universal, global access to higher education. While they failed to reach this vision, primarily because of teaching-by-telling and learning-by-listening (a PDF of the residential classroom), MOOCs provided the foundational models and infrastructure for emergency remote learning in the pandemic. Reports of remote learning’s death post-pandemic are greatly exaggerated, since the world is now irreversibly hybrid—and will stay that way because many people and organizations value the new opportunities this presents. From now on, when students leave the shelter of classrooms to interact with the world beyond schooling, they must have skills for adept performance both face-to-face and across distance. Colleges, universities, and regions that force all teaching and learning to be face-to-face are dooming their graduates to reduced agency in every other aspect of life. As discussed in recent reports from Harvard, MIT, and Stanford, innovative approaches to digital learning were developed during the pandemic that are now improving campus-based learning. Insights from these approaches offer the opportunity for student engagement at scale, taking advantage of strengths of online instruction such as collapsing time, bridging space, personalizing via rich datastreams, using AI-based instructional assistants and learning partners, delivering content and experience across universities, and sustaining online learning communities after formal instruction ends. Combined, these advances can enable next-generation massive digital hybrid learning, a means to achieve the aspirational vision of universal global access to higher education. A coalition of higher education institutions could begin to realize this vision, an essential step in enabling all learners to survive and thrive in our increasingly turbulent, disruptive global economy and civilization.
Elementary School Teachers’ Self-Assessment of Use of Positive Behavior Support Strategies and Goal Setting Related to Equity-Focused Features
Authors: Owens Julie Sarno , Exner-Cortens Deinera , DeShazer Madeline , Seipp John , Cappella Elise , May Natalie , Zieg Nick
Published: August 2023

The goal of the Maximize Program is to collaborate with educators to develop resources and procedures to facilitate teachers’ use of equity-focused behavioral supports. In this study, we describe teachers’ responses to the first iteration of the interactive Maximize Technology Platform. Ninety elementary school teachers from three schools were encouraged to use the platform to learn about the foundational concept of equity literacy, complete a self-assessment of practices, and set a goal for improvement. We observed teachers’ platform use, self-reported use of 10 behavior support strategies, goals set for improving equity-focused features of these strategies, and reported progress during the first quarter of the academic year. Over 70% of teachers reported frequent use of four strategies: Classroom Expectations, Praise, Greetings, and Community Circles. Fewer teachers reported using Student Choice, Effective Questioning, and Corrective Feedback. Variations in use between general education and other teachers were observed. Over 60% of teachers set an equity-focused goal. Variability in the types of goals set and rates of reported improvement highlight the complexity of this work. Results offer promise about the use of interactive technology to facilitate professional learning and goal-setting about equity initiatives and offer insights for leveraging interactive technology to facilitate teachers’ implementation of equity-focused practices.
German and French Students’ Strategies While Performing Geographical Comparisons in a Group Task Setting
Authors: Simon Marine , Budke Alexandra
Published: August 2023

Today’s challenges, such as climate change, require developing geographical literacy, which includes discussion and argumentation around scientific results. One important geographical method and competency is comparison. However, learning geographical methods, such as comparison, can be a challenge for students if they rarely solve open tasks that do not require simple answers. In this study, we analysed group discussions that took place during an intervention, aiming to develop comparison competency with 44 German and French students from the experimental group. Through the use of the documentary method, students’ main orientations and strategies to solve the open comparison tasks were reconstructed. We related the implementation of the comparison method during group discussions to students’ individual progress during the intervention and explored differences between French and German students. Results show that students’ main task completion orientation was challenged by their uncertainty towards the comparison task. Groups developed strategies to solve the task, showing, in a few cases, competency acquisition processes. Only a few differences were found between German and French students. Overall, implementing scientific literacy means to operate a shift in task culture at schools towards more open tasks aiming to enhance geographical competencies and argumentation.
Digital Redesign of Problem-Based Learning (PBL) from Face-to-Face to Synchronous Online in Biomedical Sciences MSc Courses and the Student Perspective
Authors: Nicolaou Stella A. , Petrou Ioanna
Published: August 2023

PBL is a widely used teaching approach that is increasingly incorporating digital components. Although, by its nature, a face-to-face approach is the preferred mode of delivery, its digital counterpart is gaining ground. The current paper discusses the digital redesign of PBL in an MSc in Biomedical Sciences. Face-to-face and online PBL followed the seven steps of the PBL process, and each case was completed in three sessions. For the delivery of online PBL, collaborative tools were utilized, including CiscoWebex, the online platform for synchronous meetings, and OneDrive, shareable PPT, and Moodle for synchronous and asynchronous self-directed learning. Three cohorts were followed, and students had both face-to-face and online PBL experiences. Student feedback was obtained using focus groups, and data analysis utilized a deductive and inductive approach. Our data indicate that CiscoWebex is a suitable and user-friendly platform for synchronous online PBL. The students enjoyed both formats and stated that online PBL is an effective teaching approach for promoting student learning. In regards to student interaction, the face-to-face mode was preferred, while online PBL was perceived as more organized. The redesign allowed for effective student learning and could pave the way forward for a fully online MSc program in Biomedical Sciences.
Design of a Four-Wheel Steering Mobile Robot Platform and Adaptive Steering Control for Manual Operation
Authors: Bae Beomsu , Lee Dong-Hyun
Published: August 2023

The recent advancementsin autonomous driving technology have led to an increased utilization of mobile robots across various industries. Notably, four-wheel steering robots have gained significant attention due to their robustness and agile maneuvering capabilities. This paper presents a novel four-wheel steering robot platform for research purposes and an adaptive four-wheel steering control algorithm for efficient manual operation. The proposed robot platform is specifically designed as a simple and compact research-oriented platform for developing navigation and manual operation of four-wheel steering robots. The compact design of the robot platform allows for additional space utilization, while the horizontal independent steering system provides precise control and enhanced maneuverability. The adaptive four-wheel steering control algorithm aims to offer efficient and intuitive manual operation of the four-wheel steering robot, aligning with the intentions of the human operator. It enables the platform to utilize front-wheel steering under normal circumstances and efficiently reduce the turning radius by employing rear wheel steering when additional steering input is required. Experimental results demonstrated the accurate steering performance of the robot platform and effectiveness of the adaptive steering algorithm. The developed four-wheel steering robot platform and the adaptive steering control algorithm serve as valuable tools for further research and development in the fields of autonomous driving and steering algorithms.
A Period Energy Method for Demagnetization Detection in Surface Permanent Magnet Motors with Search Coils
Authors: Huang Wen , Chen Junquan , Su Wu , Liu Haitao , Lv Ke , Hu Jinghua
Published: August 2023

Irreversible demagnetization of permanent magnets (PMs) in PM synchronous motors (PMSMs) degrades the performance and efficiency of a machine and its drive system. There are numerous fault diagnosis methods for detecting demagnetization under steady-state conditions. However, only a few works could be found on fault diagnosis under dynamic conditions, whereas the dynamic operation of a motor is a very common scenario, e.g., electric vehicles. The voltage and current signal-based traditional fault detection method is not only affected by the structure of the motor, but it also becomes complicated to extract signals involving fault characteristics. Hence, this paper proposes a search coil-based online method for detecting demagnetization faults in PMSMs under dynamic conditions, which are not affected by the motor structure. To gather the flux of the stator tooth, flexible Printed circuit board (FPCB) search coils are positioned at the stator slot. The search coil is made up of two branches that are one pole apart and arranged in reverse sequence. In this installation option, the output signal in the fault state cannot be eliminated, and the output signal in the health state is zero. This paper defines only that characteristic value related to the position angle of the rotor. Further, the aim was to simultaneously eliminate the influence of elements like the search coil installation error and the inherent dispersion of the permanent magnet on the detection results. To characterize the fault degree, the measurement differential between the health state and the fault state is further integrated according to a predetermined angle range. Last but not least, speed-independent detection of individual permanent magnet demagnetization faults is possible using rotor position and stator tooth flux. A six-phase PMSM was used in experiments to show the efficiency of the suggested approach. The findings of the experiment demonstrate that the suggested strategy may precisely ascertain when a defect will occur.
Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer
Authors: Xie Fengyun , Wang Gan , Zhu Haiyan , Sun Enguang , Fan Qiuyang , Wang Yang
Published: August 2023

Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault diagnosis method based on SVD-GST combined with the Vision Transformer. Firstly, the one-dimensional vibration signal is preprocessed to reduce noise using singular value decomposition (SVD) to obtain a more accurate and useful signal. Then, the generalized S-transform (GST) is used to convert the processed one-dimensional vibration signal into a two-dimensional time–frequency image and make full use of the advantages of deep learning in image classification with higher recognition accuracy. In order to avoid the problem of limited sensory fields in CNN and the need for an RNN to compute step by step over time when processing sequence data, the use of a Vision Transformer model for pattern recognition classification is proposed. Finally, an experimental platform for the fault diagnosis of rolling bearings is built. The model is experimentally validated, achieving an average accuracy of 98.52% over multiple tests. Additionally, compared with the SVD-GST-2DCNN, STFT-CNN-LSTM, SVD-GST-LSTM, and GST-ViT fault diagnosis models, the proposed method has higher diagnostic accuracy and stability, providing a new method for rolling bearing fault diagnosis.
Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study
Authors: Park Jihyun , Shin Jaeyoung , Choi Byoungju
Published: August 2023

In software development, early defect detection using static analysis can be performed without executing the source code. However, defects are detected on a non-execution basis, thus resulting in a higher ratio of false positives. Recently, studies have been conducted to effectively perform static analyses using machine learning (ML) and deep learning (DL) technologies. This study examines the techniques for detecting runtime errors used in existing static analysis tools and the causes and rates of false positives. It analyzes the latest static analysis technologies that apply machine learning/deep learning to decrease false positives and compares them with existing technologies in terms of effectiveness and performance. In addition, machine-learning/deep-learning-based defect detection techniques were implemented in experimental environments and real-world software to determine their effectiveness in real-world software.
A Visually Enhanced Neural Encoder for Synset Induction
Authors: Chen Guang , Feng Fangxiang , Zhang Guangwei , Li Xiaoxu , Li Ruifan
Published: August 2023

The synset induction task is to automatically cluster semantically identical instances, which are often represented by texts and images. Previous works mainly consider textual parts, while ignoring the visual counterparts. However, how to effectively employ the visual information to enhance the semantic representation for the synset induction is challenging. In this paper, we propose a Visually Enhanced NeUral Encoder (i.e., VENUE) to learn a multimodal representation for the synset induction task. The key insight lies in how to construct multimodal representations through intra-modal and inter-modal interactions among images and text. Specifically, we first design the visual interaction module through the attention mechanism to capture the correlation among images. To obtain the multi-granularity textual representations, we fuse the pre-trained tags and word embeddings. Second, we design a masking module to filter out weakly relevant visual information. Third, we present a gating module to adaptively regulate the modalities’ contributions to semantics. A triplet loss is adopted to train the VENUE encoder for learning discriminative multimodal representations. Then, we perform clustering algorithms on the obtained representations to induce synsets. To verify our approach, we collect a multimodal dataset, i.e., MMAI-Synset, and conduct extensive experiments. The experimental results demonstrate that our method outperforms strong baselines on three groups of evaluation metrics.
Memristors in Cellular-Automata-Based Computing:A Review
Authors: Karamani Rafailia-Eleni , Fyrigos Iosif-Angelos , Ntinas Vasileios , Vourkas Ioannis , Adamatzky Andrew , Sirakoulis Georgios Ch.
Published: August 2023

The development of novel hardware computing systems and methods has been a topic of increased interest for researchers worldwide. New materials, devices, and architectures are being explored as a means to deliver more efficient solutions to contemporary issues. Along with the advancement of technology, there is a continuous increase in methods available to address significant challenges. However, the increased needs to be fulfilled have also led to problems of increasing complexity that require better and faster computing and processing capabilities. Moreover, there is a wide range of problems in several applications that cannot be addressed using the currently available methods and tools. As a consequence, the need for emerging and more efficient computing methods is of utmost importance and constitutes a topic of active research. Among several proposed solutions, we distinguish the development of a novel nanoelectronic device, called a “memristor”, that can be utilized both for storing and processing, and thus it has emerged as a promising circuit element for the design of compact and energy-efficient circuits and systems. The memristor has been proposed for a wide range of applications. However, in this work, we focus on its use in computing architectures based on the concept of Cellular Automata. The combination of the memristor’s performance characteristics with Cellular Automata has boosted further the concept of processing and storing information on the same physical units of a system, which has been extensively studied in the literature as it provides a very good candidate for the implementation of Cellular Automata computing with increased potential and improved characteristics, compared to traditional hardware implementations. In this context, this paper reviews the most recent advancements toward the development of Cellular-Automata-based computing coupled with memristor devices. Several approaches for the design of such novel architectures, called “Memristive Cellular Automata”, exist in the literature. This extensive review provides a thorough insight into the most important developments so far, helping the reader to grasp all the necessary information, which is here presented in an organized and structured manner. Thus, this article aims to pave the way for further development in the field and to bring attention to technological aspects that require further investigation.
Power Transformer Fault Diagnosis Based on Improved BP Neural Network
Authors: Jin Yongshuang , Wu Hang , Zheng Jianfeng , Zhang Ji , Liu Zhi
Published: August 2023

Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to significant economic losses and social impacts. The traditional detection methods rely on the technical level of power system operation and maintenance personnel, and are based on Dissolved Gas Analysis (DGA) technology, which analyzes the components of dissolved gases in transformer oil for preliminary fault diagnosis. However, with the increasing accuracy and intelligence requirements for transformer fault diagnosis in power grids, the DGA analysis method is no longer able to meet the requirements. Therefore, this article proposes an improved transformer fault diagnosis method based on a residual BP neural network. This method deepens the BP neural network by stacking multiple residual network modules, and fuses and expands gas feature information through an improved BP neural network. In the improved residual BP neural network, SVM is introduced to judge the extracted feature vectors at each layer, screen out feature vectors with high accuracy, and increase their weights. The feature vector with the highest cumulative weight is selected as an input for transformer fault diagnosis. This method utilizes multi-layer neural network mapping to extract gas feature information with more significant feature differences after fusion expansion, thereby effectively improving diagnostic accuracy. The experimental results show that, compared with traditional BP neural network methods, the proposed algorithm has higher accuracy in transformer fault diagnosis, with an accuracy rate of 92%, which can ensure the sustainable, normal, and safe operation of power grids.
Simple Learning-Based Robust Nonlinear Control of an Electric Pump for Liquid-Propellant Rocket Engines
Authors: Jafari Mohammad , Reyhanoglu Mahmut , Kozhabek Zhandos
Published: August 2023

This paper presents a robust nonlinear control strategy for an electric pump for liquid-propellant rocket engines. In order to compensate for model uncertainties and disturbances, a gradient-descent-based simple learning control strategy is employed that minimizes the cost function defined on the error dynamics of the nonlinear system. Detailed stability analysis for the nonlinear system is provided. Computer simulation results are included to demonstrate the effectiveness of the nonlinear control method using an electric pump model consisting of a brushless permanent-magnet direct current (DC) motor and a centrifugal pump. In particular, it is shown that by employing the developed nonlinear controller, the mass flow rate can be successfully kept at a certain level, can be changed instantly from one level to another (immediate decrease or increase), or can be changed linearly/nonlinearly, gradually, and continually for a certain period.
Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction
Authors: Oh Seungmin , Oh Sangwon , Shin Hyeju , Um Tai-won , Kim Jinsul
Published: August 2023

Electricity consumption has been increasing steadily owing to technological developments since the Industrial Revolution. Technologies that can predict power usage and management for improved efficiency are thus emerging. Detailed energy management requires precise power consumption forecasting. Deep learning technologies have been widely used recently to achieve high performance. Many deep learning technologies are focused on accuracy, but they do not involve detailed time-based usage prediction research. In addition, detailed power prediction models should consider computing power, such as that of end Internet of Things devices and end home AMIs. In this work, we conducted experiments to predict hourly demands for the temporal neural network (TCN) and transformer models, as well as artificial neural network, long short-term memory (LSTM), and gated recurrent unit models. The study covered detailed time intervals from 1 to 24 h with 1 h increments. The experimental results were analyzed, and the optimal models for different time intervals and datasets were derived. The LSTM model showed superior performance for datasets with characteristics similar to those of schools, while the TCN model performed better for average or industrial power consumption datasets.
A Novel JSF-Based Fast Implementation Method for Multiple-Point Multiplication
Authors: Chen Xinze , Fu Yong
Published: August 2023

ECC is a popular public-key cryptographic algorithm, but it lacks an effective solution to multiple-point multiplication. This paper proposes a novel JSF-based fast implementation method for multiple-point multiplication. The proposed method requires a small storage space and has high performance, making it suitable for resource-constrained IoT application scenarios. This method stores and encodes the required coordinates in the pre-computation phase and uses table lookup operations to eliminate the conditional judgment operations in JSF-5, which improves the efficiency by about 70% compared to the conventional JSF-5 in generating the sparse form. This paper utilizes Co-Z combined with safegcd to achieve low computational complexity for curve coordinate pre-computation, which further reduces the complexity of multiple-point multiplication in the execution phase of the algorithm. The experiments were performed with two short Weierstrass elliptic curves, nistp256r1 and SM2. In comparison to the various CPU architectures used in the experiments, our proposed method showed an improvement of about 3% over 5-NAF.
Investigation of the Proton Exchange Membrane Fuel Cell System Cathode Exhaust Gas Composition Based on Test Bed Measurements
Authors: Reithuber Peter , Frühwirth Christian , Buchberger Simon , Eichlseder Helmut
Published: August 2023

Proton exchange membrane fuel cells are gaining increasing importance in vehicle applications. The exhaust gas composition regarding the water and oxygen content and the mass flow are important parameters in fuel cell research (e.g., for designing the test bed, quantifying the hydrogen loss in the exhaust, performing experiments with air pollutants, and monitoring degradation). The exhaust gas composition is also important for vehicle applications (e.g., ensuring safe hydrogen levels in the exhaust). Performing direct measurements of the exhaust mass flow and the relative humidity is challenging due to the high-humidity environment. This article presents a mathematical thermodynamic model used to calculate the exhaust gas mass flow and relative humidity, validated by balancing the gas species composition between cathode inlet and exhaust and by using data measured at the fuel cell system test bed. Four calculation model variations and their analyses are discussed. Furthermore, the exhaust gas composition throughout the fuel cell system operating range is presented. The results of air pollutant experiments provide comprehensive examples for the application of the calculation model. These results demonstrate the suitability of the model for its application in fuel cell system research.
Toward Reducing Undesired Rotation Torque in Maglev Permanent Magnet Synchronous Linear Motor
Authors: Xing Feng , Song Xiaoyu , Gao Yuge , Zhang Chaoning
Published: August 2023

The dq current transformation method can be used to achieve the drive control of the maglev permanent magnet synchronous linear motor (MPMSLM). Specifically, the control of the suspension force Fz can be achieved by controlling the d-axis current, and the control of the thrust force Fx can be achieved by controlling the q-axis current. However, the direct use of the dq current transformation method on traditional MPMSLM structures produces an additional rotation torque around the y-axis. In order to reduce this additional torque, a new MPMSLM structure is proposed in this work. First, the characteristics of additional torque through finite element analysis are analyzed. Second, the Halbach permanent magnet array and coil size are optimized, and the topology of the MPMSLM coil is designed to reduce the additional torque. The decoupling performance and current of the proposed MPMSLM are experimentally verified through open-loop experiments using finite element simulation software. Finally, the decoupling algorithm of the generalized inverse matrix is used to achieve the decoupling between forces Fx and Fz and torque Ty and the d-axis and q-axis currents in each drive unit. Based on this, a three-degree-of-freedom closed-loop control system of the MPMSLM is designed. The LabVIEW 2018 software is used for the simulation analysis of the three-degree-of-freedom MPMSLM motion control system, and the results show that the proposed motor structure has superior closed-loop control performance.
The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets
Authors: Thomson Lesley , Jenkins David
Published: August 2023

The availability of empirical energy data from Advanced Metering Infrastructure (AMI)—which includes household smart meters—has enabled residential energy demand to be characterised in different forms. This paper first presents a literature review of applications of measured electricity, gas, and heat consumption data at a range of temporal resolutions, which have been used to characterise and develop an understanding of residential energy demand. User groups, sectors, and policy areas that can benefit from the research are identified. Multiple residential energy demand datasets have been collected in the UK that enable this characterisation. This paper has identified twenty-three UK datasets that are accessible for use by researchers, either through open access or defined processes, and presents them in an inventory containing details about the energy data type, temporal and spatial resolution, and presence of contextual physical and socio-demographic information. Thirteen applications of data relating to characterising residential energy demand have been outlined in the literature review, and the suitability of each of the twenty-three datasets was mapped to the thirteen applications. It is found that many datasets contain complementary contextual data that broaden their usefulness and that multiple datasets are suitable for several applications beyond their original project objectives, adding value to the original data collection.
A Reliability-Optimized Maximum Power Point Tracking Algorithm Utilizing Neural Networks for Long-Term Lifetime Prediction for Photovoltaic Power Converters
Authors: Shahbazi Mahmoud , Smith Niall Andrew , Marzband Mousa , Habib Habib Ur Rahman
Published: August 2023

The reliability of power converters in photovoltaic systems is critical to the overall system reliability. This paper proposes a novel active thermal-controlled algorithm that aims to reduce the rate of junction temperature increase, therefore, increasing the reliability of the device. The algorithm works alongside a normal perturb and observe maximum power point tracking algorithm, taking control when certain temperature criteria are met. In conjunction with a neural network, the algorithm is applied to long-term real mission profile data. This would grant a better understanding of the real-world trade-offs between energy generated and lifetime improvement when using the proposed algorithm, as well as shortening study cycle times. The neural network, when applied to 365 days of data, was 28 times faster than using standard electrothermal modeling, and the lifetime consumption was predicted with greater than 96.5% accuracy. Energy generated was predicted with greater than 99.5% accuracy. The proposed algorithm resulted in a 3.3% reduction in lifetime consumption with a 1.0% reduction in the total energy generated. There is a demonstrated trade-off between lifetime consumption reduction and energy-generated reduction. The results are also split by environmental conditions. Under very variable conditions, the algorithm resulted in a 4.4% reduction in lifetime consumption with a 1.4% reduction in the total energy generated.