bluuebunny/crossref_metadata_2025 huggingface dataset:
Osteoporosis Screening Using Dental Panoramic Radiographs and Age at Menarche
Authors: Triantafyllopoulos George , Mitsea Anastasia , Rontogianni Aliki , Korres Demitrios
Published: February 2023

Since early detection of osteoporosis is essential, the development of an efficient and cost-effective screening model would be incredibly beneficial. The aim of this study was to evaluate the diagnostic accuracy of MCW and MCI indices from dental panoramic radiographs in combination with a new variable, age at menarche, for the detection of osteoporosis. The study enrolled 150 Caucasian women (aged 45 to 86) who met the eligibility criteria, had DXA scans of the left hip and lumbar spine (L2 to L4), and were classified as osteoporotic, osteopenic, or normal based on T-score. Two observers evaluated MCW and MCI indexes on panoramic radiographs. There was a statistically significant correlation between the T-score and MCI and MCW. In addition, age at menarche had a statistically significant correlation with T-score (p = 0.006). In conclusion, in the current study, MCW proved to be more effective in detecting osteoporosis when combined with age at menarche. Individuals with MCW less than 3.0 mm and age at menarche later than 14 years old should be referred for DXA since they present high risk of osteoporosis.
Performance of IOTA Simple Rules Risks, ADNEX Model, Subjective Assessment Compared to CA125 and HE4 with ROMA Algorithm in Discriminating between Benign, Borderline and Stage I Malignant Adnexal Lesions
Authors: Czekierdowski Artur , Stachowicz Norbert , Smolen Agata , Łoziński Tomasz , Guzik Paweł , Kluz Tomasz
Published: February 2023

BACKGROUND: Borderline ovarian tumors (BOTs) and early clinical stage malignant adnexal masses can make sonographic diagnosis challenging, while the clinical utility of tumor markers, e.g., CA125 and HE4, or the ROMA algorithm, remains controversial in such cases. OBJECTIVE: To compare the IOTA group Simple Rules Risk (SRR), the ADNEX model and the subjective assessment (SA) with serum CA125, HE4 and the ROMA algorithm in the preoperative discrimination between benign tumors, BOTs and stage I malignant ovarian lesions (MOLs). METHODS: A multicenter retrospective study was conducted with lesions classified prospectively using subjective assessment and tumor markers with the ROMA. The SRR assessment and ADNEX risk estimation were applied retrospectively. The sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR−) were calculated for all tests. RESULTS: In total, 108 patients (the median age: 48 yrs, 44 postmenopausal) with 62 (79.6%) benign masses, 26 (24.1%) BOTs and 20 (18.5%) stage I MOLs were included. When comparing benign masses with combined BOTs and stage I MOLs, SA correctly identified 76% of benign masses, 69% of BOTs and 80% of stage I MOLs. Significant differences were found for the presence and size of the largest solid component (p = 0.0006), the number of papillary projections (p = 0.01), papillation contour (p = 0.008) and IOTA color score (p = 0.0009). The SRR and ADNEX models were characterized by the highest sensitivity (80% and 70%, respectively), whereas the highest specificity was found for SA (94%). The corresponding likelihood ratios were as follows: LR+ = 3.59 and LR− = 0.43 for the ADNEX; LR+ = 6.40 and LR− = 0.63 for SA and LR+ = 1.85 with LR− = 0.35 for the SRR. The sensitivity and specificity of the ROMA test were 50% and 85%, respectively, with LR+ = 3.44 and LR− = 0.58. Of all the tests, the ADNEX model had the highest diagnostic accuracy of 76%. CONCLUSIONS: This study demonstrates the limited value of diagnostics based on CA125 and HE4 serum tumor markers and the ROMA algorithm as independent modalities for the detection of BOTs and early stage adnexal malignant tumors in women. SA and IOTA methods based on ultrasound examination may present superior value over tumor marker assessment.
Red Blood Cell Alloimmunization and Its Associated Factors among Chronic Liver Disease Patients in a Teaching Hospital in Northeastern Malaysia
Authors: S. Abdullah Siti Zaleha , Hassan Mohd Nazri , Ramli Marini , Abdullah Marne , Mohd Noor Noor Haslina
Published: February 2023

Red blood cell (RBC) alloimmunization is an important complication of blood transfusion. Variations in the frequency of alloimmunization have been noted among different patient populations. We aimed to determine the prevalence of RBC alloimmunization and associated factors among chronic liver disease (CLD) patients in our center. This is a case-control study involving 441 patients with CLD who were being treated at Hospital Universiti Sains Malaysia and subjected to pre-transfusion testing from April 2012 until April 2022. Clinical and laboratory data were retrieved and statistically analyzed. A total of 441 CLD patients were included in our study, with the majority being elderly, with the mean age of patients 57.9 (SD ± 12.1) years old, male (65.1%) and Malays (92.1%). The most common causes of CLD in our center are viral hepatitis (62.1%) and metabolic liver disease (25.4%). Twenty-four patients were reported to have RBC alloimmunization, resulting in an overall prevalence of 5.4%. Higher rates of alloimmunization were seen in females (7.1%) and patients with autoimmune hepatitis (11.1%). Most patients developed a single alloantibody (83.3%). The most common alloantibody identified belonged to the Rh blood group, anti-E (35.7%) and anti-c (14.3%), followed by the MNS blood group, anti-Mia (17.9%). There was no significant factor association of RBC alloimmunization among CLD patients identified. Our center has a low prevalence of RBC alloimmunization among CLD patients. However, the majority of them developed clinically significant RBC alloantibodies, mostly from the Rh blood group. Therefore, phenotype matching for Rh blood groups should be provided for CLD patients requiring blood transfusions in our center to prevent RBC alloimmunization.
Update on Lean Body Mass Diagnostic Assessment in Critical Illness
Authors: De Rosa Silvia , Umbrello Michele , Pelosi Paolo , Battaglini Denise
Published: February 2023

Acute critical illnesses can alter vital functions with profound biological, biochemical, metabolic, and functional modifications. Despite etiology, patient’s nutritional status is pivotal to guide metabolic support. The assessment of nutritional status remains complex and not completely elucidated. Loss of lean body mass is a clear marker of malnutrition; however, the question of how to investigate it still remains unanswered. Several tools have been implemented to measure lean body mass, including a computed tomography scan, ultrasound, and bioelectrical impedance analysis, although such methods unfortunately require validation. A lack of uniform bedside measurement tools could impact the nutrition outcome. Metabolic assessment, nutritional status, and nutritional risk have a pivotal role in critical care. Therefore, knowledge about the methods used to assess lean body mass in critical illnesses is increasingly required. The aim of the present review is to update the scientific evidence regarding lean body mass diagnostic assessment in critical illness to provide the diagnostic key points for metabolic and nutritional support.
Development of a Method for Detection of SARS-CoV-2 Nucleocapsid Antibodies on Dried Blood Spot by DELFIA Immunoassay
Authors: Damiani Verena , Pizzinato Erika , Cicalini Ilaria , Demattia Gianmaria , Zucchelli Mirco , Natale Luca , Palmarini Claudia , Di Marzio Claudia , Federici Luca , De Laurenzi Vincenzo , Pieragostino Damiana
Published: February 2023

Antibodies against the SARS-CoV-2 nucleocapsid protein are produced by the immune system in response to SARS-CoV-2 infection, but most available vaccines developed to fight the pandemic spread target the SARS-CoV-2 spike protein. The aim of this study was to improve the detection of antibodies against the SARS-CoV-2 nucleocapsid by providing a simple and robust method applicable to a large population. For this purpose, we developed a DELFIA immunoassay on dried blood spots (DBSs) by converting a commercially available IVD ELISA assay. A total of forty-seven paired plasma and dried blood spots were collected from vaccinated and/or previously SARS-CoV-2-infected subjects. The DBS-DELFIA resulted in a wider dynamic range and higher sensitivity for detecting antibodies against the SARS-CoV-2 nucleocapsid. Moreover, the DBS-DELFIA showed a good total intra-assay coefficient of variability of 14.6%. Finally, a strong correlation was found between SARS-CoV-2 nucleocapsid antibodies detected by the DBS-DELFIA and ELISA immunoassays (r = 0.9). Therefore, the association of dried blood sampling with DELFIA technology may provide an easier, minimally invasive, and accurate measurement of SARS-CoV-2 nucleocapsid antibodies in previously SARS-CoV-2-infected subjects. In conclusion, these results justify further research to develop a certified IVD DBS-DELFIA assay for detecting SARS-CoV-2 nucleocapsid antibodies useful for diagnostics as well as for serosurveillance studies.
Comparison of Lasers and Desensitizing Agents in Dentinal Hypersensitivity Therapy
Authors: Cattoni Francesca , Ferrante Lucrezia , Mandile Sara , Tetè Giulia , Polizzi Elisabetta Maria , Gastaldi Giorgio
Published: February 2023

The main objective of this review is to verify the validity of laser therapy in the treatment of dentin hypersensitivity, an extremely common problem in patients, with Nd: YAG lasers or high- and/or low-power diode lasers to obtain a definitive protocol for the treatment of hypersensitivity, given the multiplicity of laser treatments proposed by the numerous authors evaluated. The authors performed an electronic search on PubMed, favouring it as a search engine. Lasers represent a means of treating dentin hypersensitivity, used alone and/or in conjunction with specific products for the treatment of such a pathology. The selected articles that examined diode lasers were divided according to the wattage (w) used: low-level laser therapy protocols, i.e., those using a wattage of less than 1 W, and high-level laser therapy protocols, i.e., those using a wattage of 1 W or more. Regarding the Nd: YAG laser, it was not necessary to subdivide the studies in this way, as they used a wattage of 1 W or more. A total of 21 articles were included in the final selection. Laser therapy was found to be effective in the treatment of dentin hypersensitivity. However, the level of effectiveness depends on the laser used. The results obtained from this review show that both the Nd: YAG laser and the diode laser (high and low power) are effective in the treatment of dentin hypersensitivity. However, the high-power laser appears to be more effective in combination with fluoride varnish and the Nd: YAG laser achieved greater long-term benefits than the diode laser.
Scheduling Drones for Ship Emission Detection from Multiple Stations
Authors: Hu Zhi-Hua , Liu Tian-Ci , Tian Xi-Dan
Published: February 2023

Various port cities and authorities have established emission control areas (ECAs) to constrain ships’ fuel usage in a specified offshore geographical range. However, these ECA policies involve high costs and have low monitoring and regulation enforcement efficiencies. In this study, a meeting model was used to investigate the drone-scheduling problem by considering the simultaneous movements of drones and ships. Set-covering integer linear programs were developed to formulate the assignments of drones to ships, and a model and solution algorithm were devised to determine the moving times and meeting positions for particular drones and ships. The proposed models and algorithms were employed and verified in experiments. The flying times for the datasets with three drone base stations were shorter than those with two. More drones resulted in shorter flying distances. The use of the meeting model enabled the acquirement of shorter flying times and distances than when it was not used. The datasets with more ships had longer flying times and distances, with almost linear relationships. The sensitivity of the effect of varying 5% of the ships’ speeds on the flying time metrics was less than 1%, affecting the flying distance by about 4–5%. Accelerating the drones was more effective towards optimizing the drones’ flying distances than times. Numerical studies showed that the consideration of simultaneous movements in the model allowed for a reduction in the drones’ flying distances and increased efficiency. Based on the modeling and experimental studies, managerial implications and possible extensions are discussed.
Optimization of the Double-Expansion Film-Cooling Hole Using CFD
Authors: Zhang Zhen , Hu Tianyu , Su Xinrong , Yuan Xin
Published: February 2023

Film cooling is a major cooling technique used in modern gas turbines and air engines. The geometry of film-cooling holes is the fundamental aspect affecting the cooling performance. In this paper, a new cooling configuration called the double-expansion film-cooling hole has been put forward, which yields better performance than the widely used shaped holes and is easy to manufacture. The double-expansion holes at inclination angles of α=30∘, 45∘, and 60∘ are optimized using the genetic algorithm and the Kriging surrogate model, which is trained by CFD data randomly sampled using the Latin hypercube method. The numerically optimized double-expansion holes at different inclination angles were experimentally evaluated and compared with the optimized single-expansion laid-back fan-shaped holes, and the optimized double-expansion hole at α=30∘ was manually modified based on experiment results. Compared with the optimal single-expansion holes, the area-averaged cooling effectiveness of the double-expansion holes was increased by 34.5% at α=30∘, by 27.8% at α=45∘, and basically the same at α=60∘, showing the benefit of the double-expansion concept. The loss mechanism of film cooling was also analyzed in the perspective of the entropy generation rate, showing the optimal double-expansion holes have 21% less loss compared to a baseline narrow single-expansion hole. It was also found that CFD sometimes predicts a different trend from the experiment in optimization, and the experimental validation is necessary.
Causal Vector Autoregression Enhanced with Covariance and Order Selection
Authors: Bolla Marianna , Ye Dongze , Wang Haoyu , Ma Renyuan , Frappier Valentin , Thompson William , Donner Catherine , Baranyi Máté , Abdelkhalek Fatma
Published: February 2023

A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e., the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real-life applications are also considered, where for the optimal order p≥1 of the fitted CVAR(p) model, order selection is performed with various information criteria.
Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic
Authors: Haq Inzamam Ul , Ferreira Paulo , Quintino Derick David , Huynh Nhan , Samantreeporn Saowanee
Published: February 2023

The purpose of the research is to explore the dynamic multiscale linkage between economic policy uncertainty, equity market volatility, energy and sustainable cryptocurrencies during the COVID-19 period. We use a multiscale TVP-VAR model considering level (EPUs and IDEMV) and returns series (cryptocurrencies) from 1 December 2019 to 30 September 2022. The data are then decomposed into six wavelet components, based on the wavelet MODWT method. The TVP-VAR connectedness approach is used to uncover the dynamic connectedness among EPUs, energy and sustainable cryptocurrency returns. Our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter. IDEMV is the most consistent volatility NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Energy cryptocurrencies, i.e., GRID, POW and SNC, are more likely to receive volatility spillovers than sustainable cryptocurrencies during a turbulent period (COVID-19). XLM (XNO) is least (most) affected by volatility spillover in system-wide connectedness, and XLM (ADA and MIOTA) showed a consistent (heterogeneous) non-recipient behavior across the six wavelet (D1~D6) scales and original return series. This study uncovers the dynamic connectedness across multiscale, which will support investors considering different investment horizons (D1~D6).
Comparison of Four Universities on Both Sides of the Taiwan Strait Regarding the Cognitive Differences in the Transition from STEM to STEAM in Design Education
Authors: Sun Yikang , Ni Chien-Chih , Kang Yen-Yu
Published: February 2023

There have been many studies on the effectiveness of the STEAM model since its integration into design education, but further investigation is needed to determine whether teachers and students truly understand the meaning of STEAM. The aim of this study is to evaluate people’s perceptions of the STEAM model in design education. Respondents from four universities on both sides of the Taiwan Strait participated in the study. Following expert evaluation and a number of tests, the revised questionnaire was used to survey the attitudes of respondents. The results indicate the following: (1) Respondents were more familiar with universities in their area and therefore rated them relatively highly. While this is reasonable, it suggests that respondents may lack a global perspective. (2) The proportion of arts courses is generally high, but further analysis is required to determine whether they in fact play a role in connecting to STEM. This study concluded that educators and researchers need to have a deep understanding of the essence and connotations of STEAM. Students must also consider how to acquire the knowledge and skills needed for 21st-century design through STEAM courses. Furthermore, the use of STEAM in design education needs to be continuously evaluated and improved.
WhatsApp Use in a Higher Education Learning Environment: Perspective of Students of a Malaysian Private University on Academic Performance and Team Effectiveness
Authors: Lee Cheng Ean (Catherine) , Chern Huei Huei , Azmir Dzafran Adris
Published: February 2023

The mobile instant messaging application, WhatsApp Messenger (WhatsApp), has become a popular form of communication among adolescents, especially university students, and it has increasingly been used as a tool in collaborative learning in higher education. The use of WhatsApp for education to facilitate ubiquitous learning has been practised worldwide due to its popularity and potential to support teaching and learning processes derived from the diffusion of mobile technology and empowered by the use of smartphones. This study investigates the impact of the use of WhatsApp in a higher education learning environment on students’ perceived academic performance and team effectiveness. A convergent parallel mixed-methods research design was adopted with data collected through a self-administered online survey and two focus group interviews with students of a private university in the Sunway City, Malaysia. The findings of this study present insights into the popularity of WhatsApp among university students and that students use it for social and educational purposes due to its perceived ease of use and usefulness in enhancing academic performance and team effectiveness. Although WhatsApp is recognised as a rich and powerful collaborative tool for students with a positive impact on academic performance, it has a limited impact on the cohesion and openness of team effectiveness.
University Coaching Experience and Academic Performance
Authors: Rodríguez Fuentes Antonio , Navarro Rincón Antonia , Carrillo López María José , Isla Navarro Laura
Published: February 2023

The university education process has changed, involving new roles for the student body and the teaching staff. While teachers train for these new functions, it is not the case for students who are neither prepared for group work nor to construct their learning strategy. Coaching can be an essential success factor here. Having been applied in other educational areas, such as educational coaching, and in other non-educational ones, such as sports or business coaching, this original work proposes to evaluate the effectiveness of a university coaching programme in academic performance. Therefore, the aim is to measure the impact of an independent variable, a hybrid coaching programme, on a dependent variable, university academic performance. Trainee teachers in the Melilla Campus of the University of Granada in Spain participated in this coaching programme. The research followed a quasi-experimental design with experimental and control groups (n = 70 students, and n = 69 students, respectively) with repeated initial and final evaluation measurements (pre-treatment and post-treatment). The professor and the students conducted academic performance evaluations. The performance of the experimental group was increased compared to that of the control one, considering significant differences concerning the starting point both in the average marks provided by the professor for the practices and in those of the students. Other works about coaching have highlighted its advantages, although they are not exclusively focused on the academic area like this study. This study has limitations and needs further research. However, it concludes that the coaching applied to trainee teachers enhances their performance in practical group tasks.
Teaching the Effectiveness of Integrated Studies and Social Engagement: A Case Study on SDG Education in Depopulated Areas in Japan
Authors: Osanai Shiori , Yu Jeongsoo
Published: February 2023

From the 2020 academic year, new courses of study were introduced in Japan, and the necessity of SDG education was newly specified in them. Therefore, in this study, Miyagi Prefectural Shiroishi High School was selected as a case study as it is conducting community-based SDG education in the Tohoku region, which has problems with sustainability, and the educational effects and changes in awareness before and after SDG education were examined. The purpose of this study is to analyze examples of SDG education practice at Miyagi Prefectural Shiroishi High School based on a case analysis of educational practices related to SDGs that clarifies what changes have been introduced to teachers and students via SDG education, and we suggest activities that are beneficial to both teachers and students. The survey was conducted in a questionnaire format before and after SDG education, and a five-point scale was used to confirm the significance of the change. As a result of SDG education, students became interested in social issues in collaboration with local governments, companies, and schools, and teachers were able to cover specialized content that was difficult without any stakeholders.
Swin-UperNet: A Semantic Segmentation Model for Mangroves and Spartina alterniflora Loisel Based on UperNet
Authors: Wang Zhenhua , Li Jing , Tan Zhilian , Liu Xiangfeng , Li Mingjie
Published: February 2023

As an ecosystem in transition from land to sea, mangroves play a vital role in wind and wave protection and biodiversity maintenance. However, the invasion of Spartina alterniflora Loisel seriously damages the mangrove wetland ecosystem. To protect mangroves scientifically and dynamically, a semantic segmentation model for mangroves and Spartina alterniflora Loise was proposed based on UperNet (Swin-UperNet). In the proposed Swin-UperNet model, a data concatenation module was proposed to make full use of the multispectral information of remote sensing images, the backbone network was replaced with a Swin transformer to improve the feature extraction capability, and a boundary optimization module was designed to optimize the rough segmentation results. Additionally, a linear combination of cross-entropy loss and Lovasz-Softmax loss was taken as the loss function of Swin-UperNet, which could address the problem of unbalanced sample distribution. Taking GF-1 and GF-6 images as the experiment data, the performance of the Swin-UperNet model was compared against that of other segmentation models in terms of pixel accuracy (PA), mean intersection over union (mIoU), and frames per second (FPS), including PSPNet, PSANet, DeepLabv3, DANet, FCN, OCRNet, and DeepLabv3+. The results showed that the Swin-UperNet model achieved the best PA of 98.87% and mIoU of 90.0%, and the efficiency of the Swin-UperNet model was higher than that of most models. In conclusion, Swin-UperNet is an efficient and accurate model for mangrove and Spartina alterniflora Loise segmentation synchronously, which will provide a scientific basis for Spartina alterniflora Loise monitoring and mangrove resource conservation and management.
Maximizing Regenerative Braking Energy Harnessing in Electric Vehicles Using Machine Learning Techniques
Authors: Prasanth Bathala , Paul Rinika , Kaliyaperumal Deepa , Kannan Ramani , Venkata Pavan Kumar Yellapragada , Kalyan Chakravarthi Maddikera , Venkatesan Nithya
Published: February 2023

Innovations in electric vehicle technology have led to a need for maximum energy storage in the energy source to provide some extra kilometers. The size of electric vehicles limits the size of the batteries, thus limiting the amount of energy that can be stored. Range anxiety amongst the crowd prevents the entire population from shifting to a completely electric mode of transport. The extra energy harnessed from the kinetic energy produced due to braking during deceleration is sent back to the batteries to charge them, a process known as regenerative braking, providing a longer range to the vehicle. The work proposes efficient machine learning-based methods used to harness maximum braking energy from an electric vehicle to provide longer mileage. The methods are compared to the energy harnessed using fuzzy logic and artificial neural network techniques. These techniques take into consideration the state of charge (SOC) estimation of the battery, or the supercapacitor and the brake demand, to calculate the energy harnessed from the braking power. With the proposed machine learning techniques, there has been a 59% increase in energy extraction compared to fuzzy logic and artificial neural network methods used for regenerative energy extraction.
Estimating the Value of Airport Access Time in Developing Countries with a Case Study of Nanjing, China
Authors: Zhu Renwei , Yang Zhao , Chen Jun
Published: February 2023

The valuation of time is one of the most important public policy issues in project cost-benefit analysis. This paper estimates the value of airport access time and time variability in developing countries with a case study of Nanjing, China. An international meta-analysis is being used to identify the factors that may affect heterogeneity in the value of travel time. Regression models are then established for the prediction of the value of travel time. The results provide some new insights into the impacts of survey region, traffic mode, and trip purpose on the value of travel time. Considering the significant influencing factors that were obtained, stated preference surveys are designed and used to collect data on preferred arrival time and decision choice under various hypothetical situations. A multivariate regression model is used with the data to explore the significant factors that influence the travelers’ preferred arrival time. Mixed logit models are developed to estimate the value of airport access time, value of schedule delay early, and value of schedule delay late by incorporating the effects of travel delay variability on users’ scheduling costs. The tax system is being used to illustrate the contribution of different income groups to social funds, which also calculates the social value of airport access time, social value of schedule delay early, and social value of schedule delay late. The results identify the significant factors that may affect the valuation of airport access time and provide reasonable estimates for these values. The findings also bring new enlightenment on the effects of the variation of airport access time.
Glueless Multiple Input Multiple Output Dielectric Resonator Antenna with Improved Isolation
Authors: Singhwal Sumer Singh , Matekovits Ladislau , Kanaujia Binod Kumar
Published: February 2023

In this dissemination, a glueless compact dual port dielectric resonator antenna (DRA) is proposed for X-band applications. A prototype has been fabricated with RT Duroid substrate and Eccostock (ϵr = 10)-made DRA. The ring shaped DRA is excited by aperture coupled feeds maintaining symmetry between both the ports. Four cylindrical copper rods with four strips have been used to fix the DRA on the substrate and provide additional mechanical stability. Eight copper strips are used to provide impedance matching and impedance bandwidth (IBW) widening. The measured IBW of dual port DRA is 10.5% (8.05–8.95 GHz) and maximum gain of radiator is 6.2 dBi. The proposed antenna becomes compact when the net volume of DRA is approximately 3.5 cm3 and the volume of the substrate is 2.88 cm3, with a surface area of 36 cm2 and operating in X-band, which finds applications in satellite communication, weather radar, synthetic aperture radar, and telemetry tracking and control.
Global Regulation by Integral Feedback for Lower-Triangular Nonlinear Systems with Actuator Failures and Limited Delays
Authors: Chen Xiandong , Wei Dajun , Zhang Guoteng , Li Jinbao
Published: February 2023

This paper studies the global asymptotic regulation problem for a class of lower-triangular nonlinear systems with actuator failures and limited delays. New integral controllers consisting of an integral dynamic are constructed to make all system states bounded and asymptotically convergent to zero. First, an integral dynamic is constructed and a novel state transformation is introduced, which ensures that the involved systems with actuator failures are converted into a class of auxiliary nonlinear systems without actuator failures. Second, by introducing the static high-gain technique, the problem of designing integral controllers for auxiliary nonlinear systems is converted into that of designing the gain parameter and determining the limit of the actuator delay. At last, with the help of the Lyapunov stability theorem, the gain parameter and the limit of the actuator delay are determined, and the stabilization of the auxiliary nonlinear systems yields the global asymptotic regulation of the involved systems. A physical system example is given to demonstrate the effectiveness of the proposed integral controllers.
Open-Circuit Fault-Tolerant Control of a Six-Phase Asymmetric Permanent Magnet Synchronous Motor Drive System
Authors: Liu Linyin , Zhang Qinghui
Published: February 2023

One innovative composite fault-tolerant control tactic is presented for the reliable operation of a power transmission system, which consists of both an asymmetric six-phase permanent magnet synchronous motor (PMSM) and a T-type mid-point clamp type (T-NPC) three-level inverter. First, in order to inherit the better harmonic property of simplified space vector modulation (SVM) and the rapid dynamic capability of direct torque control (DTC), the SVM-DTC control scheme was determined, and the harmonic electric current suppression unit was added to the basic control scheme to obtain good harmonic electric current suppression. In addition, a strategy for open-circuit fault-tolerant control under the SVM-DTC scheme was designed by analyzing the mutual influence between the stator flux linkage and the stator voltage of each phase under an open-circuit fault. Finally, the PMSM drive system principle prototype was tested. By comparing the waveforms of output torque and current of each phase before and after fault tolerance, it shows that the large torque fluctuation (±5%) before fault tolerance was suppressed to ±2% and smoothed out, verifying the effectiveness of fault tolerance control.
Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques
Authors: Arshad Jehangir , Ashraf Muhammad Adil , Asim Hafiza Mahnoor , Rasool Nouman , Jaffery Mujtaba Hussain , Bhatti Shahid Iqbal
Published: February 2023

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.
Improving the Spatial Characteristics of Three-Level LUT-Based Mealy FSM Circuits
Authors: Barkalov Alexander , Titarenko Larysa , Mazurkiewicz Małgorzata , Krzywicki Kazimierz
Published: February 2023

The main purpose of the method proposed in this article is to reduce the number of look-up-table (LUT) elements in logic circuits of sequential devices. The devices are represented by models of Mealy finite state machines (FSMs). Thesee are so-called MPY FSMs based on two methods of structural decomposition (the replacement of inputs and encoding of output collections). The main idea is to use two types of state codes for implementing systems of partial Boolean functions. Some functions are based on maximum binary codes; other functions depend on extended state codes. The reduction in LUT counts is based on using the method of twofold state assignment. The proposed method makes it possible to obtain FPGA-based FSM circuits with four logic levels. Only one LUT is required to implement the circuit corresponding to any partial function. An example of FSM synthesis using the proposed method is shown. The results of the conducted experiments show that the proposed approach produces LUT-based FSM circuits with better area-temporal characteristics than for circuits produced using such methods as Auto and One-hot of Vivado, JEDI, and MPY FSMs. Compared to MPY FSMs, the values of LUT counts are improved. On average, this improvement is 8.98%, but the gain reaches 13.65% for fairly complex FSMs. The maximum operating frequency is slightly improved as compared with the circuits of MPY FSMs (up to 0.64%). For both LUT counts and frequency, the gain increases together with the growth for the numbers of FSM inputs, outputs and states.
Kinematically Constrained Jerk–Continuous S-Curve Trajectory Planning in Joint Space for Industrial Robots
Authors: Wu Guanglei , Zhang Ning
Published: February 2023

This work deals with jerk–continuous trajectory planning for robotic manipulators by means of the fourth-order S-curve to ensure motion smoothness. The algorithm presented in this work can cause the acceleration and jerk to stay in a saturated state in order to improve the efficiency of a robot’s programming and operation. Moreover, a multi-axis synchronization planning algorithm is proposed and integrated for enhanced motion stability in terms of generated synchronized and continuous motion trajectories, for which the effectiveness of the proposed trajectory planning algorithm is verified in both the joint and Cartesian spaces. The proposed algorithm does not involve any optimization procedures or iterative processes, as the kinematically constrained trajectory is generated by polynomial equations to realize the real-time motion control of robots. Moreover, the presented algorithm can generate the jerk continuity trajectory, rather than only the acceleration continuity, as in most reported works.
Identifying Adversary Impact Using End User Verifiable Key with Permutation Framework
Authors: Anjum Mohd , Shahab Sana , Yu Yang , Guye Habib Figa
Published: February 2023

In the Internet of Things (IoT), security is a crucial aspect that ensures secure communication, transactions, and authentication for different applications. In IoT security, maintaining the user interface and platform security is a critical issue that needs to be addressed due to leaky security distribution. During communication, synchronisation and security are important problems. The security problems are caused by the adversary impact and vulnerable attacks, leading to service failure. Therefore, the Permutated Security Framework (PSF) is designed to manage security in the IoT by providing secure communication, transactions, and authentication for different applications. The PSF uses time intervals to manage transaction security. These intervals are secured using end-verifiable keys generated using the conventional Rivest–Shamir–Adleman (RSA) technique in IoT-based communication-related applications. In this approach, the key validity is first provided for the interval, and in the latter, the access permitted time modifies its validity. The security of transactions is managed by dividing time into smaller intervals and providing different levels of security for each interval. By using time intervals, the framework is adaptable and adjustable to changes in the system, such as user density and service allocation rate, adapting parallel transactions per support vector classifications’ recommendations. The proposed framework aims to synchronise interval security, service allocation, and user flexibility to mitigate adversary impact, service failures, and service delays while improving the access rate and transactions. This allows for more flexibility and better management of transaction security. The proposed framework reduces adversary impact (10.98%), service failure (11.82%), and service delay (10.19%) and improves the access rate by 7.73% for different transactions.
Appraising Early Reliability of a Software Component Using Fuzzy Inference
Authors: Goswami Puneet , Noorwali Abdulfattah , Kumar Arvind , Khan Mohammad Zubair , Srivastava Prakash , Batra Shivani
Published: February 2023

(1) Objectives: Reliability is one of the major aspects for enhancing the operability, reusability, maintainability, and quality of a system. A software component is an independent entity that deploys to form a functional system (CBSS). The component becomes unreliable mainly because of errors introduced during its design and development; it is essential to estimate the reliability of a software component in advance. This research work proposes a novel Mamdani Fuzzy-Inference (M-FIS) model to estimate the components’ reliability and provides an intuitive solution for industry personnel; (2) Scope: The technology moves forward from traditional monolithic software development to scalable, integrated, business-driving software applications. Henceforth, the proposed paradigm can give a preliminary estimate of the reliability of software components, and it helps developers and vendors to produce it at high-quality; (3) Methods: In the component development and realization phase, failure data is unavailable; hence, designing metrics, inspections, statistical methods, soft-computing techniques are used to predict early reliability. The present work applies soft computing techniques to validate metrics. Moreover, estimating premature reliability reduces follow-up effort and component-development cost and time; (4) Finding: The proposed model aids the project manager in better estimating and predicting a components’ reliability. Adopting both an expert-based fuzzy inference system and an unsupervised, or self-learning, algorithm provides the basis for cross checking, and concludes with a better decision in an ambivalence state.
F-LSTM: FPGA-Based Heterogeneous Computing Framework for Deploying LSTM-Based Algorithms
Authors: Liang Bushun , Wang Siye , Huang Yeqin , Liu Yiling , Ma Linpeng
Published: February 2023

Long Short-Term Memory (LSTM) networks have been widely used to solve sequence modeling problems. For researchers, using LSTM networks as the core and combining it with pre-processing and post-processing to build complete algorithms is a general solution for solving sequence problems. As an ideal hardware platform for LSTM network inference, Field Programmable Gate Array (FPGA) with low power consumption and low latency characteristics can accelerate the execution of algorithms. However, implementing LSTM networks on FPGA requires specialized hardware and software knowledge and optimization skills, which is a challenge for researchers. To reduce the difficulty of deploying LSTM networks on FPGAs, we propose F-LSTM, an FPGA-based framework for heterogeneous computing. With the help of F-LSTM, researchers can quickly deploy LSTM-based algorithms to heterogeneous computing platforms. FPGA in the platform will automatically take up the computation of the LSTM network in the algorithm. At the same time, the CPU will perform the pre-processing and post-processing in the algorithm. To better design the algorithm, compress the model, and deploy the algorithm, we also propose a framework based on F-LSTM. The framework also integrates Pytorch to increase usability. Experimental results on sentiment analysis tasks show that deploying algorithms to the F-LSTM hardware platform can achieve a 1.8× performance improvement and a 5.4× energy efficiency improvement compared to GPU. Experimental results also validate the need to build heterogeneous computing systems. In conclusion, our work reduces the difficulty of deploying LSTM on FPGAs while guaranteeing algorithm performance compared to traditional work.
A Survey on Zero-Knowledge Authentication for Internet of Things
Authors: Chen Zhigang , Jiang Yuting , Song Xinxia , Chen Liqun
Published: February 2023

The Internet of Things (IoT) is ubiquitous in our lives. However, the inherent vulnerability of IoT smart devices can lead to the destruction of networks in untrustworthy environments. Therefore, authentication is a necessary tool to ensure the legitimacy of nodes and protect data security. Naturally, the authentication factors always include various sensitive users’ information, such as passwords, ID cards, even biological information, etc. How to prevent privacy leakage has always been a problem faced by the IoT. Zero-knowledge authentication is a crucial cryptographic technology that uses authenticates nodes on the networks without revealing identity or any other data entered by users. However, zero-knowledge proof (ZKP) requires more complex data exchange protocols and more data transmission compared to traditional cryptography technologies. To understand how zero-knowledge authentication works in IoT, we produce a survey on zero-knowledge authentication in privacy-preserving IoT in the paper. First, we overview the IoT architecture and privacy, including security challenges and open question in different IoT layers. Next, we overview zero-knowledge authentication and provide a comprehensive analysis of designing zero-knowledge authentication protocols in various IoT networks. We summarize the advantages of ZKP-based authentication in IoT. Finally, it summarizes the potential problems and future directions of ZKP in IoT.
Autonomous Vehicles Technological Trends
Authors: Iclodean Calin , Varga Bogdan Ovidiu , Pfister Felix
Published: February 2023

Twenty years ago, only the most adventurous scientist might have been in the position of dreaming up such a dramatic change for the automotive industry, where fossil fuels are in a position of being banned and vehicles are driverless [...]
A Battery Capacity Configuration Method of a Photovoltaic and Battery System Applied in a Building Complex for Increased Self-Sufficiency and Self-Consumption
Authors: Li Shaojie , Zhang Tao , Liu Xiaochen , Liu Xiaohua
Published: February 2023

Photovoltaic (PV) systems have been growing in popularity as an energy conservation and carbon reduction approach. Generally, battery storage is integrated with a PV system to solve the intermittent and fluctuant problems of solar resources, enhancing the relative independence of the PV–battery (PVB) system. In consideration of the economic benefits and system efficiency, it is necessary to investigate battery capacity allocation methods. A battery capacity configuration method was established in this study to increase the self-sufficiency rate (SSR) and self-consumption rate (SCR) of the system for a building complex by exploiting the battery resources. The PVB system designed for the building complex is divided into two categories: distributed and centralized storage. The SSR and SCR significantly increase with the increasing battery capacity for both schemes. The SCR of centralized storage is always higher than that of distributed storage, considering different battery and PV capacities. However, the SSR of distributed storage scheme was found to be slightly higher than that of the centralized storage scheme when the energy generated by PV is half of the energy consumed by the building load. For instance, when the battery capacity is four, SSR values for optimal distributed and centralized storage schemes are 47.62% and 47.19%, respectively. For the distributed storage scheme, there is a slight difference between the optimal allocation ratios achieved by SSR and SCR, considering that they have the same total battery capacity. In addition, the effects of converter loss, complementarity in load curves, and centralized batteries were analyzed to achieve greater SSR and SCR. The comparison results of this study can be used as a guide for battery capacity design in the PVB systems of building complexes.