bluuebunny/crossref_metadata_2025 huggingface dataset:
NCR, an Inflammation and Nutrition Related Blood-Based Marker in Colon Cancer Patients: A New Promising Biomarker to Predict Outcome
Authors: Langheinrich Melanie , Siebenhüner Alexander Reinhard , Baecker Justus , Miragall Maximilian , Wiesmüller Felix , Schellerer Vera , Merkel Susanne , Brunner Maximilian , Krautz Christian , Weber Klaus , Grützmann Robert , Kersting Stephan
Published: December 2022

Background: Colorectal carcinoma (CRC) is a heterogeneous disease, and differences in outcomes have been reported among patients diagnosed with the same disease stage. Prognostic and predictive biomarkers provide information for patient risk stratification and guide treatment selection. Although numerous studies have analyzed the effects of systemic inflammatory factors on CRC outcomes, clinical significance remains to be elucidated. In particular, the treatment strategy of colon cancer patients is different from that of rectal cancer due to outcome and recurrence differences. The identification of patients with a poor prognosis who might benefit from intensive treatment approaches is clinically necessary. Methods: This study aimed to evaluate the value of different blood-based markers and assess the significance of our newly developed inflammatory-nutrition-related biomarker (NCR = BMI × albumin/CRP) in patients with colon cancer. A two-stage design was used with 212 patients with colon cancer (CC) in the discovery cohort (n = 159) and in an external validation cohort (n = 53). Results: A lower preoperative NCR level was significantly correlated with a worse prognosis, sidedness, undifferentiated histology, nodal involvement, and advanced UICC stage. We compared the NCR with other established prognostic indices and showed that the NCR is a more reliable indicator of a poor prognosis for patients with CC. Patients with low NCR levels experienced a significantly shorter Overall Survival (OS) than patients with high levels. Multivariate analysis confirmed preoperative NCR levels as an independent predictor for overall survival with a hazard ratio of 3.3 (95% confidence interval 1.628–6.709, p < 0.001). Finally, we confirmed the predictive value of the NCR in an independent validation cohort and confirmed NCR as an independent prognostic factor for OS. Conclusion: Taken together, we discovered a new prognostic index (NCR) based on BMI, albumin, and CRP levels as an independent prognostic predictor of OS in patients with colon cancer. In all UICC stages, our newly developed NCR marker is able to distinguish patients with better and worse prognoses. We, therefore, propose that NCR may serve as a supplement to the TNM staging system to optimize the risk stratification in CC patients towards personalized oncology. In particular, NCR can be used in clinical trials to stratify patients with UICC II and III tumors and help better select patients who might benefit from adjuvant treatment.
Cardiac Involvement in Children Affected by COVID-19: Clinical Features and Diagnosis
Authors: Vasichkina Elena , Alekseeva Daria , Karev Vadim , Podyacheva Ekaterina , Kudryavtsev Igor , Glushkova Anzhela , Starshinova Anastasia Y. , Kudlay Dmitry , Starshinova Anna
Published: December 2022

COVID-19 (Coronavirus disease 2019) in children is usually mild. However, multiple organ disorders associated with SARS-CoV-2 (severe acute respiratory syndrome-related coronavirus 2) have been detected with poor respiratory symptoms. Cardiac changes are noted in 17% to 75% of cases, which are associated with diagnostic difficulties in high-risk groups for the development of complications that are associated with myocardial damage by the SARS-CoV-2 virus. The objective of this review is to identify the most significant symptoms of cardiac involvement affected by COVID-19, which require in-depth examination. The authors analyzed publications from December 2019 to the October 2022, which were published in accessible local and international databases. According to the analysis data, the main sign of myocardial involvement was increasing as cardiomarkers in the patient’s blood, in particular troponin I or troponin T. Many authors noted that the increased level of CRP (C-reactive protein) and NT-proBNP, which are accompanied by changes in the ECG (electrocardiogram) and EchoCG (echocardiography), as a rule, were nonspecific. However, the identified cardiac functional dysfunctions affected by SARS-CoV-2, required an cardiac MRI. The lack of timely diagnosis of myocardial involvements, especially in children at high risk for the development of complications associated with SARS-CoV-2 myocardial injury, can lead to death. The direct damage of the structural elements of myocardial blood vessels in patients with severe hypoxic changes resulted from respiratory failure caused by SARS-CoV-2 lung damage, with the development of severe acute diffuse alveolar damage and cell-mediated immune response and myocardial involvement affected by SARS-CoV-2 damage. In this article, the authors introduce a clinical case of a child who dead from inflammatory myocardities with COVID-19 in a background of congenital heart disease and T-cell immunodeficiency.
TLRs Gene Polymorphisms Associated with Pneumonia before and during COVID-19 Pandemic
Authors: Salamaikina Svetlana , Karnaushkina Maria , Korchagin Vitaly , Litvinova Maria , Mironov Konstantin , Akimkin Vasily
Published: December 2022

Background: The progression of infectious diseases depends on the characteristics of a patient’s innate immunity, and the efficiency of an immune system depends on the patient’s genetic factors, including SNPs in the TLR genes. In this pilot study, we determined the frequency of alleles in these SNPs in a subset of patients with pneumonia. Methods: This study assessed six SNPs from TLR genes: rs5743551 (TLR1), rs5743708, rs3804100 (TLR2), rs4986790 (TLR4), rs5743810 (TLR6), and rs3764880 (TLR8). Three groups of patients participated in this study: patients with pneumonia in 2019 (76 samples), patients with pneumonia caused by SARS-CoV-2 in 2021 (85 samples), and the control group (99 samples). Results: The allele and genotype frequencies obtained for each group were examined using four genetic models. Significant results were obtained when comparing the samples obtained from individuals with pneumonia before the spread of SARS-CoV-2 and from the controls for rs5743551 (TLR1) and rs3764880 (TLR8). Additionally, the comparison of COVID-19-related pneumonia cases and the control group revealed a significant result for rs3804100-G (TLR2). Conclusions: Determining SNP allele frequencies and searching for their associations with the course of pneumonia are important for personalized patient management. However, our results need to be comprehensively assessed in consideration of other clinical parameters.
Modern Endodontics
Authors: Iandolo Alfredo
Published: December 2022

The goal of modern endodontics is the complete removal of damaged tissue and bacteria from the endodontic space [...]
The Design and Implementation of an Innovative Course on the Creation of Cultural Landscape Images: A Case Study of Dalin Township in Taiwan
Authors: Hu Hui-Jiun
Published: December 2022

Innovative design-based education is a student-centered approach that aims to nurture students’ proactivity, creativity, and interdisciplinary integrated skills. The curriculum planning of a course in this study incorporated design-based learning with the 4D design process. Three units were planned: field exploration and concept development, 3D wearable creations, and cultural landscape shaping. Each unit was co-taught by an interdisciplinary teacher. By means of teamwork, the students explored agricultural spaces and cultural stories, then used paper materials and mixed media to create wearable creations. Afterward, they visually recorded their unique views of traditional spaces and created cultural landscape images. To elucidate their learning outcomes and creative expression, this study adopted a mixed-methods approach. The results are as follows: (1) The students experienced positive growth in their five core competencies. Their “field-based knowledge” and “skills and technological value” were significantly improved. (2) The five major perspectives of the students pointed out that creativity stems from multicultural symbols, the learning outside-the-classroom approach reinvigorated motivation, more confidence is gained through learning by doing, teamwork can create more possibilities, and discovering one’s other interests is possible through diverse exploratory approaches. (3) The teacher summarized the feasibility of designing and implementing innovative courses under three themes: conversing with methods, conversing with cultures, and conversing with teams.
Schooling and Occupational Outcomes in Adults with ADHD: Predictors of Success and Support Strategies for Effective Learning
Authors: Varrasi Simone , Boccaccio Francesco Maria , Guerrera Claudia Savia , Platania Giuseppe Alessio , Pirrone Concetta , Castellano Sabrina
Published: December 2022

Attention Deficit Hyperactivity Disorder (ADHD) is a neurobehavioral disorder that is usually diagnosed in childhood. It is characterized by attention deficits, hyperactivity, and impulsivity leading to significant impairment in academic, occupational, familiar, and social functioning. Most of the literature has been focusing on the impact of this condition on infancy and preadolescence, but little is known on its consequences in adulthood. This narrative review addresses this gap by focusing on the studies regarding the schooling outcomes of this population. After identifying the specific clinical and neuropsychological profile of ADHD in adults, this study analyzes their precise needs for effective learning and presents evidence on their academic and occupational achievements. Pharmacological, educational, and rehabilitative factors predicting a positive scholastic and career success are critically reviewed. Finally, this study focuses on the strategies that can improve the learning processes in adults with ADHD by expanding the analysis on executive functions, metacognition, and emotional dysregulation. Schooling outcomes in adults with ADHD, therefore, are conceptualized as a complex measure depending on several variables, like early pharmacological treatment, educational support, neuropsychological intervention, and targeted strategies for life-long learning.
The Volunteer Satisfaction Survey (VSS): Adaptation and Psychometric Properties among Portuguese Volunteers
Authors: Martins Cátia , de Jesus Saúl Neves , da Silva José Tomás , Ribeiro Conceição , Nunes Cristina , Cunha Francisca Ferreira , Marcelo Beatriz
Published: December 2022

(1) Background: Volunteering satisfaction is one significant construct that nourishes the sustaining of volunteer work, and it is present in reference models such as the three-stage volunteer process model (VPM). The volunteer satisfaction survey (VSS), created by Vecina, Chacón and Sueiro, evaluates three different domains of volunteer satisfaction: specific motivations, organization management and volunteering tasks. The aim of this study was to adapt the instrument and explore the psychometric properties of the 17 items of the VSS in a sample of Portuguese volunteers. (2) Methods: The sample was composed of 335 Portuguese volunteers (aged between 14 and 81 years), mainly women (76.4%). Measures included volunteer satisfaction, work engagement and organizational commitment. (3) Results: The original three-factor model was tested with a confirmatory factor analysis (CFA) and the model fitted the data. Satisfactory levels of internal consistency, discriminant and convergent validity were found. (4) Conclusions: The VSS reveals good psychometric properties and can be considered a useful tool for professionals and future research for volunteers’ satisfaction assessment.
Modeling and Analysis of the Soil Vapor Extraction Equipment for Soil Remediation
Authors: Shi Yan , Zhao Shijian , Diao Zhuo , Ye Yuan , Wang Qiansuo , Wang Yixuan
Published: December 2022

Soil vapor extraction (SVE) is one of the most commonly used technologies for soil remediation of contaminated sites, and the use of models to accurately predict and evaluate the operational effectiveness of SVE is a necessary part of site contamination treatment projects. A pneumatic model-based equipment model is proposed to comprehensively describe the SVE operation process. Though the numerical simulation, the influence of fan frequency, air valve opening, pressure, and total flow was analyzed, and an optimal extraction strategy was validated. Then, field experiments were carried out to verify the validity of the model. The proposed model and experimental results can provide a theoretical basis for the design and duration evaluation of SVE.
Interrupted-Sampling and Non-Uniform Periodic Repeater Jamming against mDT-STAP System
Authors: Li Jiyang , Duan Xiaohu , Li Jia , Bai Peng
Published: December 2022

The difference between sampling data and detection data can degrade the performance of space-time adaptive processing (STAP). A jamming algorithm with a non-uniform periodic repeater based on interrupted-sampling is proposed against the reduced dimensional space-time adaptive processing (STAP) system for the first time. Firstly, the model of m-bins doppler transform (mDT) STAP training and processing signal samples is described. Then, the method of false targets generated by the non-uniform periodic repeater is analyzed theoretically based on the principle of interrupted-sampling. The simulation shows that numerous false targets with different amplitude and intervals can be generated by changing the retransmitted parameters. The independent identical distribution (IID) of system sample data can be destroyed after these false targets are received by the radar system, and the main lobe will be distorted when the system’s adaptive weight vector is formed. The processing performance of the mDT-STAP system is seriously degraded. The jamming method proposed based on interrupted-sampling and the non-uniform periodic repeater offers great potential for the interference research on STAP in real conditions.
Cluster-Based JRPCA Algorithm for Wi-Fi Fingerprint Localization
Authors: Zhang Li , Zhang Min , Xu Jingao , Xu Yi
Published: December 2022

Indoor localization services are emerging as an important application of the Internet of Things, which drives the development of related technologies in indoor scenarios. In recent years, various localization algorithms for different indoor scenarios have been proposed. The indoor localization algorithm based on fingerprints has attracted much attention due to its good performance without extra hardware devices. However, the occurrence of fingerprint mismatching caused by the complexity and variability of indoor scenarios is unneglectable, which degrades localization accuracy. In this article, by combining weighted kernel norm and L2,1-norm, a joint-norm robust principal component analysis (JRPCA in brief) assisted indoor localization algorithm is proposed, which can improve the localization accuracy through aggregating the reference points (RPs) and conducting robust feature extraction based on clustering. More specifically, a one-way hierarchical clustering termination method is proposed to obtain reasonable RP clusters adaptively according to the preset RPs. A two-phase fingerprint matching algorithm of JRPCA based on clustering is proposed to further increase the difference between similar RPs, thus facilitating rapid identification and reinforcing localization accuracy. To validate the proposed algorithm, extensive experiments are conducted in real indoor scenarios. The experimental results confirm that the proposed cluster-based JRPCA algorithm outperforms other existing algorithms in terms of robustness and accuracy.
Cattle Facial Matching Recognition Algorithm Based on Multi-View Feature Fusion
Authors: Weng Zhi , Liu Shaoqing , Zheng Zhiqiang , Zhang Yong , Gong Caili
Published: December 2022

In the process of collecting facial images of cattle in the field, some features of the collected images end up going missing due to the changeable posture of the cattle, which makes the recognition accuracy decrease or impossible to recognize. This paper verifies the practical effects of the classical matching algorithms ORB, SURF, and SIFT in bull face matching recognition. The experimental results show that the traditional matching algorithms perform poorly in terms of matching accuracy and matching time. In this paper, a new matching recognition model is constructed. The model inputs the target cattle facial data from different angles into the feature extraction channel, combined with GMS (grid-based motion statistics) algorithm and random sampling consistent algorithm, to achieve accurate recognition of individual cattle, and the recognition process is simple and fast. The recognition accuracy of the model was 85.56% for the Holstein cow face dataset, 82.58% for the Simmental beef cattle, and 80.73% for the mixed Holstein and Simmental beef cattle dataset. The recognition model constructed in the study can achieve individual recognition of cattle in complex environments, has good robustness to matching data, and can effectively reduce the effects of data angle changes and partial features missing in cattle facial recognition.
Federated Learning for Condition Monitoring of Industrial Processes: A Review on Fault Diagnosis Methods, Challenges, and Prospects
Authors: Berghout Tarek , Benbouzid Mohamed , Bentrcia Toufik , Lim Wei Hong , Amirat Yassine
Published: December 2022

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.
TextDC: Exploring Multidimensional Text Detection via a New Benchmark and Solution
Authors: Zhou Fenfen , Cai Yuanqiang , Tian Yingjie
Published: December 2022

Text detection has been significantly boosted by the development of deep neural networks but most existing methods focus on a single kind of text instance (i.e., overlaid text, layered text, scene text). In this paper, we expand the text detection task from a single dimension to multiple dimensions, thus providing multi-type text descriptions for the scene and content analysis of videos. Specifically, we establish a new task to detect and classify text instances simultaneously, termed TextDC. As far as we know, existing benchmarks cannot meet the requirements of the proposed task. To this end, we collect a large-scale text detection and classification dataset, named Text3C, which is annotated using multilingual labels, location information, and text categories. Together with the collected dataset, we introduce a multi-stage and strict evaluation metric, which penalizes detection approaches for missing text instances, false positive detection, inaccurate location boxes, and error text categories, developing a new benchmark for the proposed TextDC task. In addition, we extend several state-of-the-art detectors by modifying the prediction head to solve the new task. Then, a generalized text detection and classification framework is designed and formulated. Extensive experiments using the updated methods are conducted on the established benchmark to verify the solvability of the proposed task, the challenges of the dataset, and the effectiveness of the solution.
n-Dimensional Chaotic Time Series Prediction Method
Authors: Liu Fang , Yin Baohui , Cheng Mowen , Feng Yongxin
Published: December 2022

Chaotic time series have been involved in many fields of production and life, so their prediction has a very important practical value. However, due to the characteristics of chaotic time series, such as internal randomness, nonlinearity, and long-term unpredictability, most prediction methods cannot achieve high-precision intermediate or long-term predictions. Thus, an intermediate and long-term prediction (ILTP) method for n-dimensional chaotic time series is proposed to solve this problem. Initially, the order of the model is determined by optimizing the preprocessing and constructing the joint calculation strategy, so that the observation sequence can be decomposed and reorganized accurately. Furthermore, the RBF neural network is introduced to construct a multi-step prediction model of future sequences, with a feedback recursion mechanism. Compared with the existing prediction methods, the error of the ILTP method can be reduced by 1–6 orders of magnitude, and the prediction step can be increased by 10–20 steps. The ILTP method can provide reference technology for the application of time series prediction with chaotic characteristics.
Human–Computer Interaction for Intelligent Systems
Authors: Pleva Matúš , Liao Yuan-Fu , Bours Patrick
Published: December 2022

The further development of human–computer interaction applications is still in great demand as users expect more natural interactions [...]
A Sparse Recovery Algorithm Based on Arithmetic Optimization
Authors: Zhang Qingfeng , Hu Dong , Tang Chao , Xie Jufang
Published: December 2022

At present, the sparse recovery problem is mainly solved by convx optimization algorithm and greedy tracking method. However, the former has defects in recovery efficiency and the latter in recovery ability, and neither of them can obtain effective recovery under large sparsity or small observation degree. In this paper, we propose a new sparse recovery algorithm based on arithmetic optimization algorithm and combine the ideas of greedy tracking method. The proposed algorithm uses arithmetic optimization algorithm to solve the sparse coefficient of the signal in the transform domain, so as to reconstruct the original signal. At the same time, the greedy tracking technique is combined to design the initial position of the operator before solving, so that it can be searched better. Experiments show that compared with other methods, the proposed algorithm can not only obtain more effective recovery, but also run faster under general conditions of observation number. At the same time, It can also recover the signal better in the presence of noise.
Programmable Beam-Steering Capabilities Based on Graphene Plasmonic THz MIMO Antenna via Reconfigurable Intelligent Surfaces (RIS) for IoT Applications
Authors: Khaleel Sherif A. , Hamad Ehab K. I. , Parchin Naser Ojaroudi , Saleh Mohamed B.
Published: December 2022

The approaching sixth-generation (6G) communication network will modernize applications and satisfy user demands through implementing a smart and reconfigurable system with a higher data rate and wider bandwidth. The controllable THz waves are highly recommended for the instantaneous development the new technology in wireless communication systems. Recently, reconfigurable intelligent surfaces (RIS), also called codded/tunable programmable metasurfaces, have enabled a conspicuous functionality for THz devices and components for influencing electromagnetic waves (EM) such as beam steering, multi-beam-scanning applications, polarization variation, and beam focusing applications. In this article, we proposed a graphene plasmonic two-port MIMO microstrip patch antenna structure that operates at a 1.9 THz resonance frequency. An E-shape MTM unit cell is introduced to enhance the isolation of the antenna from −35 dB to −54 dB. An implementation of controllable and reconfigurable surfaces based on graphene meta-atoms (G-RIS) placed above the radiating patches with a suitable separated distance to control the radiated beam to steer in different directions (±60°). The reconfigurable process is carried out via changing the (ON/OFF) meta-atoms states to get a specific code with a certain beam direction. The gain enhancement of the antenna can be implemented through an artificial magnetic conductor (AMC) based on graphene material. The G-AMC layer is located underneath the (MIMO antenna, G-RIS layer) to improve the gain from 4.5 dBi to 10 dBi. The suggested antenna structure results are validated with different techniques CST microwave studio and ADS equivalent circuit model. The results have asymptotic values. So, the proposed design of the MIMO antenna that is sandwiched between G-RIS and G-AMC is suitable for IoT applications.
An Enhanced LBPH Approach to Ambient-Light-Affected Face Recognition Data in Sensor Network
Authors: Chen Yeong-Chin , Liao Yi-Sheng , Shen Hui-Yu , Syamsudin Mariana , Shen Yueh-Chun
Published: December 2022

Although combining a high-resolution camera with a wireless sensing network is effective for interpreting different signals for image presentation on the identification of face recognition, its accuracy is still severely restricted. Removing the unfavorable impact of ambient light remains one of the most difficult challenges in facial recognition. Therefore, it is important to find an algorithm that can capture the major features of the object when there are ambient light changes. In this study, face recognition is used as an example of image recognition to analyze the differences between Local Binary Patterns Histograms (LBPH) and OpenFace deep learning neural network algorithms and compare the accuracy and error rates of face recognition in different environmental lighting. According to the prediction results of 13 images based on grouping statistics, the accuracy rate of face recognition of LBPH is higher than that of OpenFace in scenes with changes in ambient lighting. When the azimuth angle of the light source is more than +/−25° and the elevation angle is +000°, the accuracy rate of face recognition is low. When the azimuth angle is between +25° and −25° and the elevation angle is +000°, the accuracy rate of face recognition is higher. Through the experimental design, the results show that, concerning the uncertainty of illumination angles of lighting source, the LBPH algorithm has a higher accuracy in face recognition.
Recommendation of Music Based on DASS-21 (Depression, Anxiety, Stress Scales) Using Fuzzy Clustering
Authors: Wang Eunyoung , Lee Hyeokmin , Do Kyunghee , Lee Moonhwan , Chung Sungtaek
Published: December 2022

The present study proposes a music recommendation service in a mobile environment using the DASS-21 questionnaire to distinguish and measure certain psychological state instability symptoms—viz. anxiety, depression, and stress—that anyone can experience regardless of job or age. In general, the outcome of the DASS-21 from almost every participant did not reveal any single psychological state out of the abovementioned three states. Therefore, the weighted scores were calculated for each scale and fuzzy clustering was used to cluster users into groups with similar states. For the initial dataset’s generation, we used the DASS inventory collected from the Open-Source Psychometrics Project conducted from 2017 to 2019 on approximately 39,000 respondents, and the results of the survey showed that the average scores for each scale were 23.6 points for depression, 17.4 for anxiety, and 23.3 for stress. Based on the datasets collected from fuzzy clustering, the individuals were classified into three groups: Group 1 was recommended with music for “high” depression, “high” anxiety, and “low” stress; Group 2 was recommended with music for “normal” depression, “low” anxiety, and “normal” stress; and Group 3 was recommended with music for “high” depression, “high” anxiety, and “high” stress. Especially, the largest numbers of recommended music in the three groups were for Group 1 with “High” depressive (4.64), Group 2 for “Low” anxiety (4.54), and Group 3 for “High” anxiety (4.76). In addition, to compare the results of fuzzy clustering with other data, the silhouette coefficient of the samples extracted with the same severity ratio and those generated by simple random sampling were 0.641 and 0.586, respectively, which were greater than 0. The proposed service can recommend not only the music of users with similar trends at all psychological states, but also the music of users with similar psychological states in part.
Exploring the Impact of Soft Errors on the Reliability of Real-Time Embedded Operating Systems
Authors: Azimi Sarah , De Sio Corrado , Portaluri Andrea , Rizzieri Daniele , Vacca Eleonora , Sterpone Luca , Merodio Codinachs David
Published: December 2022

The continuous scaling of electronic components has led to the development of high-performance microprocessors that are suitable even for safety-critical applications where radiation-induced errors such as Single Event Effects (SEEs) can have a significant impact on the performance and reliability of the system. This work is dedicated to investigating the reliability of systems based on programmable hardware and Real-time operating Systems (RTOS) in the presence of architectural faults induced by soft errors in the configuration memory of the programmable hardware. We performed a proton radiation test campaigned at PSI radiation facility to identify the fault model affecting the configuration memory of Xilinx Zynq-7020 reconfigurable AP-Soc Device. The identified fault model in terms of SEU and MBU clusters has been used to evaluate the impact of proton-induced faults on applications running within FreeRTOS on a Microblaze soft processor. A Single Event Multiple Upset fault model resulting from a proton test is presented, focusing on characteristics such as shape, size, and frequency of observed cluster of errors. We conduct two fault injection campaigns and analyze the results to assess the effect of cluster size on system reliability. Moreover, we discuss software exceptions caused by faults that can affect the hardware structure of the soft processor.
A New Fast Logarithm Algorithm Using Advanced Exponent Bit Extraction for Software-Based Ultrasound Imaging Systems
Authors: Park Seongjun , Yoo Yangmo
Published: December 2022

Ultrasound B-mode imaging provides anatomical images of the body with a high resolution and frame rate. Recently, to improve its flexibility, most ultrasound signal and image processing modules in modern ultrasound B-mode imaging systems have been implemented in software. In a software-based B-mode imaging system, an efficient processing technique for calculating a logarithm instruction is required to support its high computational burden. In this paper, we present a new method to efficiently implement a logarithm operation based on exponent bit extraction. In the proposed method, the exponent bit field is first extracted and then some algebraic operations are applied to improve its precision. To evaluate the performance of the proposed method, the peak signal-to-noise ratio (PSNR) and the execution time were measured. The proposed efficient logarithm operation method substantially reduced the execution time, i.e., eight times, compared to direct computation while providing a PSNR of over 50 dB. These results indicate that the proposed efficient logarithm computation method can be used for lowering the computational burden in software-based ultrasound B-mode ultrasound imaging systems while improving or maintaining the image quality.
Modelling the Effect of Thermal Aging on Transformer Oil Electrical Characteristics Using a Regression Approach
Authors: Abdi Sifeddine , Haddad Abderrahmane Manu , Harid Noureddine , Boubakeur Ahmed
Published: December 2022

The effect of thermal aging on the electrical properties of the insulating oil used for transformers has been explored in this experimental work. In particular the dielectric dissipation factor, the resistivity and the breakdown voltage have been measured and correlated. The numerical results predicted by mathematical model and those measured in the laboratory have been compared by using a regression analysis. Experiments on thermal aging were performed on insulating oil (Borak 22, Nynas, Austria) during a period of time of 5000-h at three different temperatures. First, the transformer oil’s dielectric dissipation factor, the resistivity and the breakdown voltage are measured after every 500 h of aging. Then, polynomial and exponential regression expressions are proposed for modelling the oil’s electrical parameters variations with thermal ageing at different aging temperatures and periods. The results show that after thermal aging, the resistivity and the breakdown voltage decrease with thermal aging, however, the dielectric dissipation factor which increases. This trend is similar for all different aging temperatures. The numerical results show close agreement with the measured results for all the samples and all studied properties. The regression model presents strong correlation with high coefficients (>94%).
Design for a Four-Stage DC/DC High-Voltage Converter with High Precision and a Small Ripple
Authors: Zheng Wei , Hu Cong , Zhao Bin , Su Xiaobao , Wang Gang , Hou Xiaowan
Published: December 2022

This paper presents a four-stage DC/DC converter with high precision and a small ripple utilized in an electronic power conditioner (EPC). The galvanically isolated four-stage topology contains four cascade connections: a buck circuit, a push–pull circuit, a power converter, and a voltage regulator. The push–pull switches, as well as the diodes in the output-side rectifier, operate in zero-voltage switching (ZVS) and zero-current switching (ZCS) modes at both switch off and switch on, which helps increase the efficiency. The maximum efficiency of the converter can reach 94.5%. The buck circuit and voltage regulator operate in a two-stage closed-loop condition and, thus, the precision is greater than 0.02%. Due to the voltage regulator, the ripple is less than 1 V when the output voltage reaches 7000 V.
Systematic Development of a Multi-Objective Design Optimization Process Based on a Surrogate-Assisted Evolutionary Algorithm for Electric Machine Applications
Authors: Choi Mingyu , Choi Gilsu , Bramerdorfer Gerd , Marth Edmund
Published: December 2022

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method.
Modern Bitumen Oil Mixture Models in Ashalchinsky Field with Low-Viscosity Solvent at Various Temperatures and Solvent Concentrations
Authors: Zakirova Gulnur , Pshenin Vladimir , Tashbulatov Radmir , Rozanova Lyubov
Published: December 2022

The article analyzes the modern theory and practice of pipeline transport of bituminous oil together with low-viscosity solvent. In addition, a detailed analysis of the rheological models of non-Newtonian fluids is carried out, which establishes a number of assumptions on the rheology model selection algorithm currently in use (limited number of rheological models, variability in model coefficient assignment, etc.). Ways of their elimination are proposed. Dependencies for determination of the dynamic viscosity coefficient of binary oil mixtures are investigated. Calculation of the parameters of the bituminous oil mixture with solvent is considered. Complex experimental studies on rheology mixture models of bituminous oil and solvent on the example of the Ashalchinsky field (Russia, Tatarstan) in a wide range of temperatures and concentrations of the solvent are conducted. A two-dimensional field of rheological models of the oil mixture is constructed, which makes it possible to determine the rheological model of the pumped oil mixture depending on the solvent concentration and the temperature of the mixture. Formulas for forecasting the rheological properties of the oil mixture on the basis of statistical processing of the results of experimental studies are theoretically substantiated. It is proven that the viscosity of binary oil mixtures in the Newtonian fluid field should be determined by a modified Arrhenius equation. The proposed models with a high degree of accuracy describe the rheological properties of the oil mixture. It is shown that in the case of complex mixtures, not one rheological model should be applied, but their hierarchy should be established depending on the solvent concentration and temperature.
Voltage Stability Assessment of AC/DC Hybrid Microgrid
Authors: Chang Fangyuan , O’Donnell John , Su Wencong
Published: December 2022

AC/DC hybrid microgrids are becoming potentially more attractive due to the proliferation of renewable energy sources, such as photovoltaic generation, battery energy storage systems, and wind turbines. The collaboration of AC sub-microgrids and DC sub-microgrids improves operational efficiency when multiple types of power generators and loads coexist at the power distribution level. However, the voltage stability analysis and software validation of AC/DC hybrid microgrids is a critical concern, especially with the increasing adoption of power electronic devices and various types of power generation. In this manuscript, we investigate the modeling of AC/DC hybrid microgrids with grid-forming and grid-following power converters. We propose a rapid simulation technique to reduce the simulation runtime with acceptable errors. Moreover, we discuss the stability of hybrid microgrids with different types of faults and power mismatches. In particular, we examine the voltage nadir to evaluate the transient stability of the hybrid microgrid. We also design a droop controller to regulate the power flow and alleviate voltage instability. During our study, we establish a Simulink-based simulation platform for operational analysis of the microgrid.
Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems
Authors: Le Xuan Chau , Duong Minh Quan , Le Kim Hung
Published: December 2022

Wind energy conversion systems (WECSs) are considered green generators, environmentally friendly, and fully suitable energy sources to replace fossil energy sources. WECS’s output power is hugely dependent on the random nature of the wind. There are many solutions to improve the output power for WECSs, such as adjusting the profile of turbine blades, locating installation places, improving generators, etc. Nevertheless, maximum power point tracking (MPPT) algorithms for WECSs are optimal and the most effective because they are flexible in controlling different variable wind speeds and match all types of WECS. The parameters on the generator side control or the grid side control will be adjusted when MPPT algorithms are used, allowing the output power of WECSs to be maximized while maintaining stability in variable-speed wind. There are various MPPT algorithms, but the current problem is their efficiency and whether it requires deep knowledge to select the best MPPT solutions because each method has different advantages and disadvantages. This study has implemented an overview of modern maximum power tracking algorithms applied to permanent magnet synchronous generators in WECS with MPP methods based on speed convergence, efficiency, self-training, complexity, and measurement of wind parameters.
Economic Feasibility of Power/Heat Cogeneration by Biogas–Solid Oxide Fuel Cell (SOFC) Integrated Systems
Authors: Athanasiou Costas , Drosakis Christos , Booto Gaylord Kabongo , Elmasides Costas
Published: December 2022

Based upon the thermodynamic simulation of a biogas-SOFC integrated process and the costing of its elements, the present work examines the economic feasibility of biogas-SOFCs for combined heat and power (CHP) generation, by the comparison of their economic performance against the conventional biogas-CHP with internal combustion engines (ICEs), under the same assumptions. As well as the issues of process scale and an SOFC’s cost, examined in the literature, the study brings up the determinative effects of: (i) the employed SOFC size, with respect to its operational point, as well as (ii) the feasibility criterion, on the feasibility assessment. Two plant capacities were examined (250 m3·h−1 and 750 m3·h−1 biogas production), and their feasibilities were assessed by the Internal Rate of Return (IRR), the Net Present Value (NPV) and the Pay Back Time (PBT) criteria. For SOFC costs at 1100 and 2000 EUR·kWel−1, foreseen in 2035 and 2030, respectively, SOFCs were found to increase investment (by 2.5–4.5 times, depending upon a plant’s capacity and the SOFC’s size) and power generation (by 13–57%, depending upon the SOFC’s size), the latter increasing revenues. SOFC-CHP exhibits considerably lower IRRs (5.3–13.4% for the small and 16.8–25.3% for the larger plant), compared to ICE-CHP (34.4%). Nonetheless, according to NPV that does not evaluate profitability as a return on investment, small scale biogas-SOFCs (NPVmax: EUR 3.07 M) can compete with biogas-ICE (NPV: EUR 3.42 M), for SOFCs sized to operate at 70% of the maximum power density (MPD) and with a SOFC cost of 1100 EUR·kWel−1, whereas for larger plants, SOFC-CHP can lead to considerably higher NPVs (EUR 12.5–21.0 M) compared to biogas-ICE (EUR 9.3 M). Nonetheless, PBTs are higher for SOFC-CHP (7.7–11.1 yr and 4.2–5.7 yr for the small and the large plant, respectively, compared to 2.3 yr and 3.1 yr for biogas-ICE) because the criterion suppresses the effect of SOFC-CHP-increased revenues to a time period shorter than the plant’s lifetime. Finally, the economics of SOFC-CHP are optimized for SOFCs sized to operate at 70–82.5% of their MPD, depending upon the SOFC cost and the feasibility criterion. Overall, the choice of the feasibility criterion and the size of the employed SOFC can drastically affect the economic evaluation of SOFC-CHP, whereas the feasibility criterion also determines the economically optimum size of the employed SOFC.
End-of-Charge Temperature Rise and State-of-Health Evaluation of Aged Lithium-Ion Battery
Authors: Han Binghong , Harding Jonathon R. , Goodman Johanna K. S. , Cai Zhuhua , Horn Quinn C.
Published: December 2022

An increasing demand to repurpose used lithium-ion batteries in secondary applications is driving the need to develop methods of evaluating the state-of-health of used batteries. In this paper, we discover a self-terminated end-of-charge temperature rise (ECTR) phenomenon in 18650 lithium-ion cells, both recycled from the field and aged under controlled conditions in the lab. ECTR is characterized by an additional temperature rise near the end of the charging process and is accompanied by low coulombic efficiency. A higher charge rate and longer inactive time at low state-of-charge appear to increase the occurrence of ECTR. The intensity of ECTR is found to closely correlate with the excess charge capacity but is less affected by the charge current or cell impedance. ECTR is weakly dependent on the remaining cell capacity in recycled cells, and the controlled aging study shows that aging condition, not remaining capacity or internal resistance, determines the presence and intensity of ECTR behavior, which indicates that usable capacity or internal resistance should not be the single criterion to effectively evaluate the state-of-health of used cells intended for repurposing. We hypothesize that the origin of the ECTR is due to the formation of an internal lithium metal short that forms near the end of the charge process and self-terminates over time. The investigation of ECTR in this work provides a new criterion and approach to evaluate the state-of-health of cells required to safely handle aged/recycled cells.
Partial Discharge Detection and Defect Location Method in GIS Cable Terminal
Authors: Li Songyuan , Song Pengxian , Wei Zhanpeng , Li Xu , Tang Qinghua , Meng Zhengzheng , Li Ji , Liu Songtao , Wang Yuhuai , Li Jin
Published: December 2022

The complex structure of gas-insulated switchgear (GIS) cable terminals leads to serious electric field concentration, which is a frequent fault position of a high-voltage cable system. At present, due to the differences in the frequency bands of sensors, various partial discharge detection technologies have certain differences in their scope of application and anti-interference performance, resulting in a low defect detection rate in GIS cable terminals. In this paper, a comprehensive diagnosis scheme is proposed, which integrates transient earth voltage (TEV), ultra-high frequency (UHF), high frequency (HF), and ultrasonic methods. Two abnormal discharge defects of GIS terminals in two 220 kV substations in Tianjin were tracked and monitored, and the joint diagnosis was carried out using the proposed scheme; the type of discharge defect and the phase sequence of the defect were determined, and the UHV was employed to precisely locate and analyze the defect source. Finally, through the disassembly analysis and electric field simulation of the GIS cable terminal, the accuracy and effectiveness of the discharge detection and location method were verified, providing a typical detection demonstration for the defect diagnosis of a GIS cable terminal.