bluuebunny/crossref_metadata_2025 huggingface dataset:
Fetoscopic Myelomeningocele Repair with Complete Release of the Tethered Spinal Cord Using a Three-Port Technique: Twelve-Month Follow-Up—A Case Report
Authors: Pastuszka Agnieszka , Zamłyński Mateusz , Horzelski Tomasz , Zamłyński Jacek , Horzelska Ewa , Maruniak-Chudek Iwona , Marzec Adrianna , Paprocka Justyna , Gazy Patrycja , Koszutski Tomasz , Olejek Anita
Published: November 2022

Open spina bifida is one of the most common congenital defects of the central nervous system. Open fetal surgery, which is one of the available therapeutic options, remains the gold standard for prenatal repairs. Fetoscopic closure may lower the number of maternal complications associated with open fetal surgery. Regardless of the approach, the outcome may be compromised by the development of tethered spinal cord (TSC) syndrome. At 24.2 weeks of gestation, a primipara was admitted due to fetal myelomeningocele and was deemed eligible for fetoscopic repair. Fetal surgery was performed at 25.0 weeks of gestation. It was the first complete untethering of the spinal cord and anatomic reconstruction (dura mater, spinal erectors, skin) achieved during a fetoscopic repair of spina bifida. Cesarean section due to placental abruption was performed at 31.1 weeks of gestation. VP shunting, with no need for revision, was performed at 5 weeks postdelivery due to progressing ventriculomegaly. No clinical or radiological signs of secondary tethering were observed. Neurological examination at 11 months postdelivery revealed cranial nerves without any signs of damage, axial hypotonia, decreased muscle tone in the lower extremities, and absent pathological reflexes. Motor development was slightly retarded. Complete untethering of the neural structures should always be performed, regardless of the surgical approach, as it is the only course of action that lowers the risk for developing secondary TSC.
Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine
Authors: Prawiningrum Aisyah Fitriannisa , Paramita Rafika Indah , Panigoro Sonar Soni
Published: November 2022

Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein–epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein–epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution.
The Oral Health Status and Treatment Needs of Pediatric Patients Living with Autism Spectrum Disorder: A Retrospective Study
Authors: Hasell Sara , Hussain Ahmed , Da Silva Keith
Published: November 2022

Background: The objective of this retrospective study was to assess the oral health status and treatment needs of children with ASD and to explore the differences in risk factors and oral health care status and the risk factors for treatment under GA. Methods: Dental charts of children between 6 and 14 years of age who were examined at a dental facility associated with the College of Dentistry, University of Saskatchewan between 2016 to 2019 were assessed. Children who were identified as having ASD, as well as an age- and gender-matched control group consisting of otherwise healthy children were included in the study. Results: The sample included 346 dental records, with 173 children having a diagnosis of ASD. Children diagnosed with ASD had significantly higher experience with caries (91.3% vs. 65.9%, p = 0.003) and severity (mean DMFT/dmft = 8.18 ± 1.62 vs. 4.93 ± 0.58 p = 0.007). Children with ASD were also older when visiting the dentist for the first time (age of 5.97 ± 1.18 vs. 2.79 ± 1.09, p = 0.02)). Children with ASD were less likely to brush once a day (66.5% vs. 88.4%, p = 0.02), were more likely to have bruxism (35.8% vs. 10.4%, p = 0.003) and were less likely to have class I occlusion (64.7% vs. 80.9%, p = 0.03). Findings from the logistic regression analysis revealed that children with ASD were also 2.13 times more likely to receive a referral for general anesthesia when all other variables were held constant (p = 0.03). Conclusions: This research demonstrates that children diagnosed with ASD may face more barriers with access to oral health care, leading to poorer outcomes and greater treatment dental needs.
Residual and Past Discrete Tsallis and Renyi Extropy with an Application to Softmax Function
Authors: Jawa Taghreed M. , Fatima Nahid , Sayed-Ahmed Neveen , Aldallal Ramy , Mohamed Mohamed Said
Published: November 2022

In this paper, based on the discrete lifetime distribution, the residual and past of the Tsallis and Renyi extropy are introduced as new measures of information. Moreover, some of their properties and their relation to other measures are discussed. Furthermore, an example of a uniform distribution of the obtained models is given. Moreover, the softmax function can be used as a discrete probability distribution function with a unity sum. Thus, applying those measures to the softmax function for simulated and real data is demonstrated. Besides, for real data, the softmax data are fit to a convenient ARIMA model.
A Dual-Stage Attention Model for Tool Wear Prediction in Dry Milling Operation
Authors: Qin Yongrui , Li Jiangfeng , Zhang Chenxi , Zhao Qinpei , Ma Xiaofeng
Published: November 2022

The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results.
Student Teachers’ Knowledge of Multiperspectivity and Its Implementation in Geography Lesson Plans: Results from an Exploratory Qualitative Study with German and Dutch Student Teachers
Authors: Vasiljuk Dina , Budke Alexandra , Maier Veit , Krause Uwe
Published: November 2022

This exploratory qualitative study reports student teachers’ knowledge of multiperspectivity as well as how student teachers consider multiperspectivity in lesson planning. The study was embedded in a project in which German and Dutch student teachers dealt with multiperspectivity for one semester. Based on the theoretical literature and the empirical results, we identified a set of criteria for multiperspectivity in geography lessons. These criteria were then applied to analyse the student teachers’ lesson plans and teaching materials as well as the student teachers’ answers in the qualitative questionnaires, which the student teachers answered at the beginning and at the end of the semester. The results of this study showed that the professional knowledge of student teachers in terms of multiperspectivity was not extensively represented or apparent from the answers to either the pre- or the postquestionnaire. The analysis of the lesson plans and the teaching materials showed that the student teacher groups were able to form a multiperspective topic didactically. However, not all groups had considered promoting evaluation competence in lesson planning, and the reflection competence was hardly considered. Therefore, our developed criteria for multiperspectivity in geography lessons could help student teachers to better understand and consider multiperspectivity when planning lessons.
Counselling Services and Mental Health for International Chinese College Students in Post-Pandemic Thailand
Authors: Cao Huan , Lin Weixin , Chen Pengfei
Published: November 2022

Research has proven that counselling services are essential to solving the troubles in the mental health of international Chinese students in the post-epidemic stage. Online questionnaires were implemented for about 1000 international Chinese college students from three universities in Thailand. Results showed that female junior and senior students who stayed in post-pandemic Thailand longer than others were likely to suffer from mental health disorders. In addition, in Thailand, counselling has a significant positive association with the mental health status of the students. Therefore, it is recommended in this study that Thai universities should provide more counselling services to support students in focusing on education and adjusting or adapting to the environment abroad.
Numeracy for Adults with Learning Disabilities: A Focus on Concepts of Time
Authors: Gaunt Lorraine
Published: November 2022

Being numerate is a vital skill for participating in the community and helps individuals to become active and informed citizens. Understanding concepts of time supports adults to organise and participate in crucial tasks, both at home and at work. This research explored how two adults with learning disabilities used concepts of time in their workplaces. Data were collected using observations and interviews to establish participants’ current understandings and applications of concepts of time in their workplaces, and the numeracy demands of their work tasks. Results demonstrated that participants required deep understandings of duration and succession concepts of time to be more actively involved in their workplace tasks. The findings demonstrate the need for individuals with learning disabilities to develop abstract concepts of time throughout their learning and highlight the scant attention paid to these concepts in the school curriculum. Recommendations for how to support an understanding of these important concepts within the school curriculum are made.
Association between Education and Intra-Regional Variation on Child Marriage in Sub-Saharan Africa
Authors: Chukwudeh Okechukwu Stephen , Oduaran Akpovire
Published: November 2022

Child marriage is a bane to sustainable development, as it has been associated with the prevalence of sexually transmitted diseases, intimate partner violence, and maternal mortality. This study examines the association between education and child marriage is sub-Saharan Africa. A subsample of one thousand, four hundred and sixty-one females, aged 12 years to 18 years was extracted from the 2018 Nigeria Demographic and Health Survey dataset. The independent variables were intra-regional groups and education. The dependent variable was child marriage (<18 years). The associations between intra-regional groups and education on child marriage were examined from the ever-given birth subsample to estimate odds ratios (ORs) and 95% CIs, using binary logistic regression models. There was a positive significant relationship between maternal education (χ2 = 65.5, p < 0.05) and intra-regional ethnic groups (χ2 = 106.58, p < 0.05) on child marriage. The odds were higher among the Hausa/Fulani groups that do not have a formal education (OR = 1.34; p < 0.05), have a poor financial status (OR = 1.67; p < 0.05), and those who had previously terminated a pregnancy (OR = 1.24; p < 0.05). The findings indicate that the intra-regional ethnic group affiliation and the lack of a formal education influences the incidence of child marriage, especially among socio-economically disadvantage females. Interventions aimed at curtailing child marriage should utilize community-based education to engage relevant stakeholders within the grassroots.
Breakout Rooms Serve as a Suitable Tool for Interprofessional Pre-Service Online Training among Students within Health, Social, and Education Study Programs
Authors: Almendingen Kari , Skotheim Torhild , Magnus Ellen Merethe
Published: November 2022

Higher education institutions must prepare students from health, social, and teacher education programs for interprofessional collaboration (IPC) among children and young people with challenging childhood experiences. We wanted to explore if digital small group rooms, breakout rooms, are feasible for students to learn about, from, and with each other in an interprofessional learning (IPL) initiative, in order to practice IPC. This study is a repeated cross-sectional study from the academic years 2020/21 and 2021/22 with 5412 students. The students worked in student-led IPL groups with a case-based learning approach and limited interaction with supervisors. The youngest students agreed to a larger extent that they learned more about, from, and with other students. The teacher students were more positive towards discussing sensitive issues in breakout rooms than the health students. The health students agreed to a larger extent than the teacher students that breakout rooms were suited for pre-service IPC training. The results from this study indicate that breakout rooms provide a potential arena to practice generic skills. If properly organized and structured, breakout rooms can be an excellent learning resource. Breakout rooms provide a safe online environment for learning and practicing IPC, and for training on talking about sensitive issues.
Education in Cultural Heritage: A Case Study of Redesigning Atayal Weaving Loom
Authors: Lin Rungtai , Chiang I-Ying , Taru Yuma , Gao Yajuan , Kreifeldt John G. , Sun Yikang , Wu Jun
Published: November 2022

Employing cultural ergonomics concepts to product innovation for cultural education and sustainable development is the target of this study. Atayal culture is famous for its weaving craft and art. The Atayal tribe of Taiwanese aborigines weave on the Atayal loom. Traditional culture is disappearing rapidly due to colonial history and modernization. The Atayal people consider the weaving loom a cultural object when used as a weaving device. This study attempts to transform the ancient Atayal loom into a teaching device and extend Atayal weaving from execution in daily living to cultural experience and cultural heritage development. This study first explores and identifies the meaning of cultural objects and extracts their cultural features. Then, employing cultural ergonomics, a weaving box is redesigned from an ancient part of the loom into a modern weaving box. Finally, the new weaving device is evaluated and discussed. This case study examines a paradigm of how designers interact with cultural heritage and transform cultural objects into new instructional aids. Through cultural product design, it provides an intertwined experience in theoretical and practical approaches to accomplishing the development of cultural sustainability.
A Novel Knowledge Base Question Answering Method Based on Graph Convolutional Network and Optimized Search Space
Authors: Hou Xia , Luo Jintao , Li Junzhe , Wang Liangguo , Yang Hongbo
Published: November 2022

Knowledge base question answering (KBQA) aims to provide answers to natural language questions from information in the knowledge base. Although many methods perform well when dealing with simple questions, there are still two challenges for complex questions: huge search space and information missing from the query graphs’ structure. To solve these problems, we propose a novel KBQA method based on a graph convolutional network and optimized search space. When generating the query graph, we rank the query graphs by both their semantic and structural similarities with the question. Then, we just use the top k for the next step. In this process, we specifically extract the structure information of the query graphs by a graph convolutional network while extracting semantic information by a pre-trained model. Thus, we can enhance the method’s ability to understand complex questions. We also introduce a constraint function to optimize the search space. Furthermore, we use the beam search algorithm to reduce the search space further. Experiments on the WebQuestionsSP dataset demonstrate that our method outperforms some baseline methods, showing that the structural information of the query graph has a significant impact on the KBQA task.
A Fast and Accurate Approximation of IEEE 802.11 Physical Layer Models for Network Simulators
Authors: Reinoso-Chisaguano Diego Javier , Flores Cabezas Xavier Alejandro , Astudillo León Juan Pablo , Paredes Paredes Martha Cecilia , Lupera Morillo Pablo Anibal , Urquiza-Aguiar Luis F.
Published: November 2022

Network simulators are used for the research and development of several types of networks. However, one of the limitations of these simulators is the usage of simplified theoretical models of the Packet Error Rate (PER) at the Physical Layer (PHY) of the IEEE 802.11 family of wireless standards. Although the simplified PHY model can significantly reduce the simulation time, the resulting PER can differ considerably from other more realistic results. In this work, we first study several PER theoretical models. Then, we propose a curve fitting algorithm, which is able to obtain a fast and accurate approximation of other PER models. The curve fitting algorithm uses simulated data as input and outputs the coefficients of a simple model that offers a very accurate approximation of the original PER. Finally, we implemented this approximation in the ns-3 network simulator, thus obtaining high realism since now we can select several theoretical PER models or even a more realistic scenario with the effect of the high Peak-to-Average Power Ratio (PAPR) in the signal. The ns-3 results show how the selection of the PER model at the PHY can significantly impact the Packet Loss Rate (PLR) of a scenario composed of a linear chain of several nodes, one of the simplest multi-hop scenarios.
Certificateless Data Integrity Auditing in Cloud Storage with a Designated Verifier and User Privacy Preservation
Authors: Bian Genqing , Guo Xusen , Li Rong , Qu Wenjing , Zhao Yu
Published: November 2022

With the rapid development of science and technology, enterprises will provide their customers with cloud data storage services. These massive amounts of data bring huge management costs to enterprises. Therefore, enterprises choose to store their data in professional cloud service providers and have third-party auditors check the integrity of cloud data to ensure security. Although the appearance of auditors reduces the enormous calculation pressure on enterprises, if the number of auditors is not limited, it will also bring an expensive management burden to enterprises. At the same time, in the process of performing data integrity auditing on behalf of the enterprise, auditors may be interested in some sensitive information of the enterprise’s customers (such as customer’s identity and specific content of customer data). Therefore, this paper proposes a remote data integrity auditing scheme based on designated verifiers. An essential feature of the scheme is that the auditor cannot obtain any customer’s identity information and data in the process of auditing; data integrity, the anonymity of the user’s identity, and data privacy are maintained in the process of auditing. Both theoretical analysis and experimental results show that our scheme is efficient and feasible.
Development of an Enhanced Selective Harmonic Elimination for a Single-Phase Multilevel Inverter with Staircase Modulation
Authors: S. Govind , Chappa Anilkumar , Rao K. Dhananjay , Dawn Subhojit , Ustun Taha Selim
Published: November 2022

A low device switching frequency is recommended for the operation of multilevel inverters (MLIs) to achieve reduced switching losses. Selective harmonic elimination (SHE) and total harmonic distortion (THD) minimization are the two primary switching angle estimation methodologies for low-frequency modulation control. In this regard, a new generalized condition has been developed in this paper for the SHE technique. This original condition will give an output voltage with improved THD in comparison to the conventional SHE technique, while achieving its primary objective of eliminating the specific harmonic content from the output voltage. The proposed condition has been formulated by estimating the error associated with the staircase waveform and the desired sinusoidal output at the fundamental frequency. An infinite harmonic count has been considered for the evaluation of the quality of output, to obtain an accurate THD value without any underestimation. The proposed technique is analyzed, and its critical features are studied in Simulink. The effectiveness of the present work has been also validated by the experimental results.
Research on Topology Recognition Technology Based on Intelligent Measurement Switches
Authors: Xiong Dezhi , Du Jingjuan
Published: November 2022

Distribution network topology identification provides information on low-voltage station areas in a power system. However, it requires either heavy computation or additional measuring equipment. This paper proposes topology identification technology based on intelligent measurement switches. The topology is identified by using the characteristic current measured by the designed intelligent measurement switch. The modulation/demodulation method and information encoding method for the topology identification are presented. The communication protocol stack structure and message encapsulation format of the topology identification unit are designed. The experimental verification and analysis show that the topology identification technology proposed in this paper has a short identification time and an identification accuracy of 100%, and it can be widely promoted and applied in low-voltage distribution networks.
SKG-Lock+: A Provably Secure Logic Locking SchemeCreating Significant Output Corruption
Authors: Nguyen Quang-Linh , Dupuis Sophie , Flottes Marie-Lise , Rouzeyre Bruno
Published: November 2022

The current trend to globalize the supply chain in the Integrated Circuits (ICs) industry has raised several security concerns including, among others, IC overproduction. Over the past years, logic locking has grown into a prominent countermeasure to tackle this threat in particular. Logic locking consists of “locking” an IC with an added primary input, the so-called key, which, unless fed with the correct secret value, renders the ICs unusable. One of the first criteria ensuring the quality of a logic locking technique was the output corruption, i.e., the corruption at the outputs of a locked circuit, for any wrong key value. However, since the introduction of SAT-based attacks, resulting countermeasures have compromised this criterion in favor of a better resilience against such attacks. In this work, we propose SKG-Lock+, a Provably Secure Logic Locking scheme that can thwart SAT-based attacks while maintaining significant output corruption. We perform a comprehensive security analysis of SKG-Lock+ and show its resilience against SAT-based attacks, as well as various other state-of-the-art attacks. Compared with related works, SKG-Lock+ provides higher output corruption and incurs acceptable overhead.
Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
Authors: Xu Bo , Min Tianxin , Yue Chik Patrick
Published: November 2022

Wireless communication technology adopting electromagnetic waves in an unlicensed spectrum, such as visible light, for communication has attracted wide research efforts. Visible light communication (VLC) utilizes visible light as a communication medium to transmit signals but faces a limited communication bandwidth and low data rate, which is caused by the intrinsic characteristics of LEDs. This paper first studies a mathematical model of limited bandwidth and its effect on transmitted signals and then analyzes the free space and underwater channel loss. With the theoretical analysis, a VLC transceiver system is presented for solving bandwidth limitation by utilizing a pulse-amplitude modulation-8 (PAM-8) scheme and a hybrid equalization method. The proposed hybrid equalization combined a passive equalizer, a neural network (NN)-based feed-forward equalization (FFE), and a radial basis function neural network (RBF-NN). The feasibility of this VLC system was verified through a co-simulation platform with both free-space and underwater channels. Compared with a VLC system adopting a deep neural network (DNN)-based post-equalization method, the proposed VLC system could achieve a data rate of 3.6 Gbps with a bit error rate (BER) of 3.8 × 10−3 over a 3 m free-space channel. The RBF-NN achieved a reduced training time of 10 min, which was 86.7% lower than the conventional DNN-based post-equalization method.
UFace: An Unsupervised Deep Learning Face Verification System
Authors: Solomon Enoch , Woubie Abraham , Cios Krzysztof J.
Published: November 2022

Deep convolutional neural networks are often used for image verification but require large amounts of labeled training data, which are not always available. To address this problem, an unsupervised deep learning face verification system, called UFace, is proposed here. It starts by selecting from large unlabeled data the k most similar and k most dissimilar images to a given face image and uses them for training. UFace is implemented using methods of the autoencoder and Siamese network; the latter is used in all comparisons as its performance is better. Unlike in typical deep neural network training, UFace computes the loss function k times for similar images and k times for dissimilar images for each input image. UFace’s performance is evaluated using four benchmark face verification datasets: Labeled Faces in the Wild (LFW), YouTube Faces (YTF), Cross-age LFW (CALFW) and Celebrities in Frontal Profile in the Wild (CFP-FP). UFace with the Siamese network achieved accuracies of 99.40%, 96.04%, 95.12% and 97.89%, respectively, on the four datasets. These results are comparable with the state-of-the-art methods, such as ArcFace, GroupFace and MegaFace. The biggest advantage of UFace is that it uses much less training data and does not require labeled data.
Application of Knowledge Graph in Water Conservancy Education Resource Organization under the Background of Big Data
Authors: Yang Yangrui , Zhu Yaping , Jian Pengpeng
Published: November 2022

The key to improving the readability and usage of educational resources is their orderly arrangement and integration. Knowledge graphs, which are a large-scale form of knowledge engineering, are an effective tool for managing and organizing educational resources. The water conservancy’s educational big data is separated into three tiers of objectives–courses–knowledge units based on the connotation level of self-directed learning. Combined with the idea of Outcome-based Education(OBE), the goal-oriented knowledge graph structure of water conservancy disciplines and graph creation method is proposed. The focus is the error accumulation problem brought about by the traditional relational extraction method of Named Entity Recognition based on rules or sequence labeling. We first complete this objective, and then the relationship classification is performed according to the water conservancy disciplines entity and relations joint extraction (WDERJE) model, on which the prompt mechanism design is based. Think of the entity-relationship extraction task as a sequence-to-sequence generation task, and take the structured extraction language to unify the coding entity extraction and relationship extraction structures. The evaluation results of the WDERJE model show that the F_0.5 value of each entity extraction is above 0.76, and the cumulative extraction relationship triple is nearly 180,000. The graph fully optimizes the organization and management of water conservancy education resources and effectively improves the readability and utilization rate of water conservancy teaching resources.
Joint Analysis of Front-Door and Back-Door Couplings of PIN Limiter Based on Improved Equivalent Circuit Model
Authors: Liu Tao , Xu Le , Li Qiwei , Yao Bin , Shi Xiaowei
Published: November 2022

Based on the previous research on electromagnetic pulse coupling, which pays more attention to the front-door coupling or the back-door coupling alone, this paper analyzes the influence of an electromagnetic pulse on electronic devices and systems through the joint analysis of front-door and back-door couplings using the finite-difference time-domain method (FDTD). This specific measure is used to simplify the front-door coupling to the voltage source injection, which occurs simultaneously with plane wave irradiation. This coupling scheme of the front door and back door with the voltage source and plane wave acting simultaneously is rarely seen in previous analyses, which also gives consideration to the working state of the circuit. Although the equivalent circuit model is widely used, it cannot effectively reflect the working state of the diode circuit under the conditions of large injection and high frequency. In view of the limited application scenarios of the traditional equivalent circuit model, which cannot accurately describe the internal response characteristics of the diode under different electromagnetic pulse coupling, this paper introduces an improved equivalent circuit model based on the physical model. Taking the Positive Intrinsic-Negative (PIN) limiter as the target, this paper analyzes the influence of the front-door and back-door joint coupling on its performance under different electromagnetic pulses and then gives protection suggestions.
Efficient Clustering Based Routing for Energy Management in Wireless Sensor Network-Assisted Internet of Things
Authors: Firdous Sadia , Bibi Nargis , Wahid Madiha , Alhazmi Samah
Published: November 2022

Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR’s performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life.
Investigating the Effect of Perceived Security, Perceived Trust, and Information Quality on Mobile Payment Usage through Near-Field Communication (NFC) in Saudi Arabia
Authors: Almaiah Mohammed Amin , Al-Rahmi Ali , Alturise Fahad , Hassan Lamia , Lutfi Abdalwali , Alrawad Mahmaod , Alkhalaf Salem , Al-Rahmi Waleed Mugahed , Al-sharaieh Saleh , Aldhyani Theyazn H. H.
Published: November 2022

This study aims to investigate the perceptions of near-field communication (NFC) usage for mobile payments in Saudi Arabia. In order to develop a mathematical framework for the acceptance of NFC quality of information for mobile payments, researchers have combined the technological acceptance model (TAM) and the idea of perceived risk. An online and physical study of 1217 NFC portable credit card holders in Saudi Arabia was conducted. Exploratory and confirmatory analyses were utilized to analyze the factor structure of the measurement items, and Smart PLS 2.0 from structural equation modeling (SEM) was used to assess the theories and hypotheses that had been put forth. The results show that (1) social influence, perceived element of risk, and subjective norms each have a negative influence on preconceptions of trust in online payment methods using NFC; (2) social influence, perceived element of risk, and social norms all have a positive effect on satisfaction with the security of electronic payment using NFC; (3) perceived ease of use has a negative effect on perceived confidence in digital payment using NFC; and (4) perceived ease of use has a negative effect on perceived trust in online payment using NFC. As a consequence of these findings, users’ attitudes regarding the use of NFC and behavioral intentions to utilize NFC mobile payment can be revealed. This study created a unique approach for assessing perceptions, perceived trust, and NFC information quality in mobile payment uptake in Saudi Arabia. As a consequence, banks may find this research useful as they implement new strategies to attract more customers, such as perceived security, brand trust, and NFC information quality in mobile payment adaption.
Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures
Authors: Karim Faten Khalid , Elmannai Hela , Seleem Abdelrahman , Hamad Safwat , Mostafa Samih M.
Published: November 2022

Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R2 score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods.
A Method of Transparent Graceful Failover in Low Latency Stateful Microservices
Authors: Pakrijauskas Kęstutis , Mažeika Dalius
Published: November 2022

Microservice architecture is a preferred way to build applications. Being flexible and loosely coupled, it allows to deploy code at a high pace. State, or, in other words, data is not only a commodity but crucial to any business. The high availability and accessibility of data enables companies to remain competitive. However, maintaining low latency stateful microservices, for example, performing updates, is difficult compared to stateless microservices. Making changes to a stateful microservice requires a graceful failover, which has an impact on the availability budget. The method of graceful failover is proposed to improve availability of a low latency stateful microservice when performing maintenance. By observing database connection activity and forcefully terminating idle client connections, the method allows to redirect database requests from one node to another with negligible impact on the client. Thus, the proposed method allows to keep the precious availability budget untouched while performing maintenance operations on low latency stateful microservices. A set of experiments was performed to evaluate stateful microservice availability during failover and to validate the method. The results have shown that near-zero downtime was achieved during a graceful failover.
Characteristics Evaluation of a Segmental Rotor Type Switched Reluctance Motor with Concentrated Winding for Torque Density and Efficiency Improvement
Authors: Xu Zhenyao , Li Tao , Zhang Fengge , Wang Huijun , Lee Dong-Hee , Ahn Jin-Woo
Published: November 2022

With the rapid development of power electronic techniques and the increasing cost of permanent magnets (PMs) materials, switched reluctance motors (SRMs) have recently gained more attention. However, traditional SRMs have a relatively low torque density. For the sake of increasing the motor torque density, this paper proposes a novel segmental rotor type SRM. The proposed motor adopts hybrid stator poles and concentrated windings in the stator side and a segmental rotor structure in the rotor side, which is completely different from the structures of the traditional SRM. The special structure of the motor shortens the magnetic flux paths of the motor, separates the parts of the magnetic flux paths from one another, and eradicates the magnetic flux reversal in the motor stator in order to improve the electric utilization and output torque density of the motor. Meanwhile, the requirement of the magneto-motive force and the core loss of the motor is also decreased, thereby improving the efficiency of the motor. For the purpose of proving the proposed structure, the characteristics of the motor are analyzed using the finite element method and are compared to those of the traditional 12/8 SRM, which is designed for the same application. Moreover, the prototypes of the traditional 12/8 and proposed SRMs are manufactured, and experiments based on the prototypes are performed. Finally, the effectiveness of the structure of the proposed motor is further proven by the experimental results.
The Development of the Toxic and Flammable Gases Concentration Monitoring System for Coalmines
Authors: Martirosyan Alexander Vitalevich , Ilyushin Yury Valerievich
Published: November 2022

Coal mining is still an important part of the global energy complex. Despite the active development of technologies and modern equipment, coal mining remains one of the most dangerous jobs in the world. The main danger is associated with a large number of the hard-predictable factors, such as the opening of a toxic and flammable gas blister, the collapse of a mine due to a violation of the mines’ structural integrity, etc. There are software and hardware systems capable of monitoring the necessary parameters, but the problem lies in the complexity and costliness of the implementation and maintenance, so even for the largest enterprises, the widespread implementation of such systems is unprofitable. Previously conducted studies have established that most of the developed coal mining monitoring systems are very expensive to implement and labor-consuming to use and support. This article proposes to consider the developed prototype of a system for toxic and flammable gases concentration real-time monitoring, capable of reading the information with sufficient speed and transferring it to a system which triggers the response mechanism in cases where the specified concentration limits would be exceeded.
Strength Failure of CO2 Injection Tubular Strings Considering CO2 Phase Transition
Authors: Qin Yanbin , Cao Yinping , Dou Yihua , Lin Wenwen , Cao Jiahao , Wang Luyao
Published: November 2022

Compared with traditional injection tubular strings, the stresses on CO2 injection tubular strings are more complex. The results from field applications show that the phase transition of CO2 fluid in CO2 injection strings is an important factor in the calculation of temperature distribution and analysis of string mechanics. Therefore, we propose a strength analysis method for CO2 injection tubular strings that considers the CO2 phase transition. We selected four CO2 injection strings in an oil field in China as examples to evaluate their strength and safety. First, we established coupled differential equations for the temperature, pressure, and physical parameters of CO2 injection strings according to the theory of fluid flow and heat transfer. Then, we used an adaptive fuzzy neural network to construct the model for calculating the CO2 convection heat transfer coefficient and used this to obtain the high-precision convection heat transfer coefficients of tubular strings under conditions of CO2 flooding. We analyzed the injection-string deformations that resulted from the piston, spiral bending, expansion, friction and temperature effects according to the stress characteristics of the CO2 injection strings with packers under different working conditions. Finally, we performed mechanical analyses on the collapse resistance, internal pressure resistance, and tensile and triaxial stresses of the CO2 injection strings, and the results of these analyses provide a theoretical basis for the strength analysis of CO2 injection strings.
Hydrogen Internal Combustion Engine Vehicles: A Review
Authors: Wróbel Kamil , Wróbel Justyna , Tokarz Wojciech , Lach Jakub , Podsadni Katarzyna , Czerwiński Andrzej
Published: November 2022

Motor vehicles are the backbone of global transport. In recent years, due to the rising costs of fossil fuels and increasing concerns about their negative impact on the natural environment, the development of low-emission power supply systems for vehicles has been observed. In order to create a stable and safe global transport system, an important issue seems to be the diversification of propulsion systems for vehicles, which can be achieved through the simultaneous development of conventional internal combustion vehicles, electric vehicles (both battery and fuel cell powered) as well as combustion hydrogen-powered vehicles. This publication presents an overview of commercial vehicles (available on the market) powered by internal combustion hydrogen engines. The work focuses on presenting the development of technology from the point of view of introducing ready-made hydrogen-powered vehicles to the market or technical solutions enabling the use of hydrogen mixtures in internal combustion engines. The study covers the history of the technology, dedicated hydrogen and bi-fuel vehicles, and vehicles with an engine powered by a mixture of conventional fuels and hydrogen. It presents basic technology parameters and solutions introduced by leading vehicle manufacturers in the vehicle market.
A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors
Authors: Kumar Rahul R. , Andriollo Mauro , Cirrincione Giansalvo , Cirrincione Maurizio , Tortella Andrea
Published: November 2022

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.
A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction
Authors: Fahim Samuel R. , Hasanien Hany M. , Turky Rania A. , Aleem Shady H. E. Abdel , Ćalasan Martin
Published: November 2022

Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functions that are employed in extracting the parameters of PV models are comprehensively expressed. Finally, this work aims to develop a comprehensive layout for the previous, current, and possible future areas of PV module modeling.