bluuebunny/crossref_metadata_2025 huggingface dataset:
Marginal Fit of Porcelain Laminate Veneer Materials under Thermocycling Condition: An In-Vitro Study
Authors: Hanoon Zanbaq Azeez , Abdullah Huda Abbas , Al-Ibraheemi Zahraa Abdulaali , Alamoush Rasha A. , Sami Suha Mohammad , Haider Julfikar
Published: January 2023

Objective: The purpose of this study was to evaluate the cervical marginal fit of porcelain laminate veneer (PLV) restorations made from two different types of CAD/CAM ceramic laminates: CEREC C PC and E.max (LD). Materials and Methods: This in-vitro experiment used a total of 32 human maxillary first premolars that were clean and free of any cracks or caries, extracted for orthodontic purposes. The samples were divided in a random way into two study groups: A and B (n = 16). Each sample was mounted on a dental surveyor and a silicon impression was made to create a silicone index for each tooth in both groups. Standardized preparation was carried out for all the samples by using preparation bur kit for the ceramic veneer system. Subsequently, digital impressions were made for all the samples by using Trios 3 shape intraoral camera (Sirona Dental Systems). The design of veneer restorations was made using Sirona inLab CAD SW 16.1 with CEREC inLab MC XL (Dentsply, Sirona Dental Systems, Bensheim, Germany). The veneer restorations were cemented using 3M RelyX veneer resin cement (3M ESPE, Seefeld, Germany) and the samples kept in distilled water for two weeks at 37 °C. All the specimens were subjected to thermocycling in a water bath with temperature varying between 5 °C and 55 °C for 500 cycles. The cervical marginal fit of veneers was evaluated by a digital microscope after sectioning the embedded teeth in acrylic resin. Results: The lowest mean of cervical marginal gap was recorded for Group A (91.59431 ± 1.626069) which was restored with CEREC CAD/CAM veneers, while the highest mean value of the gap was recorded for Group B (106.48863 ± 2.506684) which was restored with IPS E.max CAD. The t-test showed that the type of porcelain veneer restoration had a highly significant effect on the cervical marginal fit (p ≤ 0.01). Conclusions: CEREC CAD/CAM veneers showed smaller cervical marginal gaps, indicating a better fit compared to the IPS E.max CAD.
Practical Study of Recurrent Neural Networks for Efficient Real-Time Drone Sound Detection: A Review
Authors: Utebayeva Dana , Ilipbayeva Lyazzat , Matson Eric T.
Published: January 2023

The detection and classification of engine-based moving objects in restricted scenes from acoustic signals allow better Unmanned Aerial System (UAS)-specific intelligent systems and audio-based surveillance systems. Recurrent Neural Networks (RNNs) provide wide coverage in the field of acoustic analysis due to their effectiveness in widespread practical applications. In this work, we propose to study SimpleRNN, LSTM, BiLSTM, and GRU recurrent network models for real-time UAV sound recognition systems based on Mel-spectrogram using Kapre layers. The main goal of the work is to study the types of RNN networks in a practical sense for a reliable drone sound recognition system. According to the results of an experimental study, the GRU (Gated Recurrent Units) network model demonstrated a higher prediction ability than other RNN architectures for detecting differences and the state of objects from acoustic signals. That is, RNNs gave higher recognition than CNNs for loaded and unloaded audio states of various UAV models, while the GRU model showed about 98% accuracy for determining the UAV load states and 99% accuracy for background noise, which consisted of more other data.
Extreme Eigenvalues and the Emerging Outlier in Rank-One Non-Hermitian Deformations of the Gaussian Unitary Ensemble
Authors: Fyodorov Yan V. , Khoruzhenko Boris A. , Poplavskyi Mihail
Published: January 2023

Complex eigenvalues of random matrices J=GUE+iγdiag(1,0,…,0) provide the simplest model for studying resonances in wave scattering from a quantum chaotic system via a single open channel. It is known that in the limit of large matrix dimensions N≫1 the eigenvalue density of J undergoes an abrupt restructuring at γ=1, the critical threshold beyond which a single eigenvalue outlier (“broad resonance”) appears. We provide a detailed description of this restructuring transition, including the scaling with N of the width of the critical region about the outlier threshold γ=1 and the associated scaling for the real parts (“resonance positions”) and imaginary parts (“resonance widths”) of the eigenvalues which are farthest away from the real axis. In the critical regime we determine the density of such extreme eigenvalues, and show how the outlier gradually separates itself from the rest of the extreme eigenvalues. Finally, we describe the fluctuations in the height of the eigenvalue outlier for large but finite N in terms of the associated large deviation function.
FPGA-Based Implementation of Multidimensional Reconciliation Encoding in Quantum Key Distribution
Authors: Lu Qing , Lu Zhenguo , Yang Hongzhao , Yang Shenshen , Li Yongmin
Published: January 2023

We propose a multidimensional reconciliation encoding algorithm based on a field-programmable gate array (FPGA) with variable data throughput that enables quantum key distribution (QKD) systems to be adapted to different throughput requirements. Using the circulatory structure, data flow in the most complex pipeline operation in the same time interval, which enables the structural multiplexing of the algorithm. We handle the calculation and storage of eight-dimensional matrices cleverly to conserve resources and increase data processing speed. In order to obtain the syndrome more efficiently, we designed a simplified algorithm according to the characteristics of the FPGA and parity-check matrix, which omits the unnecessary operation of matrix multiplication. The simplified algorithm could adapt to different rates. We validated the feasibility and high speed of the algorithm by implementing the multidimensional reconciliation encoding algorithm on a Xilinx Virtex-7 FPGA. Our simulation results show that the maximum throughput could reach 4.88 M symbols/s.
Improved EAV-Based Algorithm for Decision Rules Construction
Authors: Żabiński Krzysztof , Zielosko Beata
Published: January 2023

In this article, we present a modification of the algorithm based on EAV (entity–attribute–value) model, for induction of decision rules, utilizing novel approach for attribute ranking. The selection of attributes used as premises of decision rules, is an important stage of the process of rules induction. In the presented approach, this task is realized using ranking of attributes based on standard deviation of attributes’ values per decision classes, which is considered as a distinguishability level. The presented approach allows to work not only with numerical values of attributes but also with categorical ones. For this purpose, an additional step of data transformation into a matrix format has been proposed. It allows to transform data table into a binary one with proper equivalents of categorical values of attributes and ensures independence of the influence of the attribute selection function from the data type of variables. The motivation for the proposed method is the development of an algorithm which allows to construct rules close to optimal ones in terms of length, while maintaining enough good classification quality. The experiments presented in the paper have been performed on data sets from UCI ML Repository, comparing results of the proposed approach with three selected greedy heuristics for induction of decision rules, taking into consideration classification accuracy and length and support of constructed rules. The obtained results show that for the most part of datasests, the average length of rules obtained for 80% of best attributes from the ranking is very close to values obtained for the whole set of attributes. In case of classification accuracy, for 50% of considered datasets, results obtained for 80% of best attributes from the ranking are higher or the same as results obtained for the whole set of attributes.
Understanding Influencers of College Major Decision: The UAE Case
Authors: Kuhail Mohammad Amin , Negreiros Joao , Al Katheeri Haseena , Khan Sana , Almutairi Shurooq
Published: January 2023

This study aims to understand and analyze what influences female students to choose a college major in the United Arab Emirates (UAE). To accomplish our target, we conducted a survey with mostly female first-year undergraduate students (N = 496) at Zayed University to understand the personal, social, and financial factors influencing students’ major choices. Further, this study also asked students to specify their actions before deciding on their major and assessed the information that could be helpful for future students to decide on their majors. Last, the study investigated how Science, Technology, Engineering, and Mathematics (STEM) students differ from other students in their major decision. The results show that financial factors such as income and business opportunities related to the major are crucial. Further, gender suitability for the job and passion are influential. Students conduct internet searches, use social media, and read brochures in the process of major decisions. Moreover, students think job alignment with the UAE vision and information related to job availability, income, and skills are critical for future students to decide on their major. Finally, STEM students are more influenced by business opportunities, prestige, and career advancement than others.
Tourism in Geography Textbooks in Secondary Education and High School: The Case of the Balearic Islands
Authors: Coll-Ramis Miquel Àngel , Picó Víctor , Ordinas Antoni
Published: January 2023

Since the mid-20th century, tourism has become a strategic activity for the economy of the Balearic Islands, causing profound social, territorial, and environmental transformations. This fact challenges local society, which must be aware of its environment to better face the future challenges posed by this economic activity. With this goal, the official curriculum has been analyzed, making it possible to ascertain the approach with which this subject is taught by the administration and what objectives are set. Furthermore, a review was carried out of the contents of geography textbooks in the third year of ESO and the second year of the Baccalaureate, which corresponds to the educational stages in which tourism aspects appear. The results obtained represent a fundamental strategic diagnosis to improve the teaching and learning of this key activity for the Balearic Islands, giving it more importance and adapting its approach to the current times.
Teacher Perception of Student Reading Competence and Its Relationship to Teaching Practice: A Comparison between Pre and during Pandemic Teaching in INDONESIA
Authors: Novita Shally , Wijayanti Puspita Adhi Kusuma , Wedyaswari Miryam
Published: January 2023

During the pandemic, learning methods were changed to distance or hybrid learning to ensure both physical distancing as well as educational continuity. This study examines teacher perceptions of student reading competence prior to and during the pandemic in Indonesia. A total of 59 language teachers were asked about their perceptions of reading competence as well as their teaching practices, such as disciplinary climate, teacher support, teacher-directed instruction, teacher feedback, stimulation of reading, and instruction adaptation. Results revealed that Indonesian teachers believed more students would have better learning gains if face-to-face learning were implemented compared to distance or hybrid learning during the pandemic. In addition, teacher perception prior to and during the pandemic was affected by different aspects. Prior to the pandemic, teacher perception was affected by the disciplinary climate and teacher support. Meanwhile, during the pandemic, teacher perception was negatively affected by teacher-directed instruction.
Teacher Professional Development in Changing Circumstances: The Impact of COVID-19 on Schools’ Approaches to Professional Development
Authors: Perry Emily
Published: January 2023

The COVID-19 pandemic caused fundamental shifts in schools’ and teachers’ practices. At the start of the pandemic, forty schools in England were in their second year of participation in a pilot initiative intended to understand what changes could be put in place by schools in order to implement an entitlement for teachers to high-quality, sustained professional development. This paper explores the impact of COVID-19 on the participating schools’ approaches to professional development. Drawing on findings from the independent evaluation and school leaders’ experiences of the initiative, it describes how, before the pandemic, school leaders constructed professional development plans in alignment with school improvement objectives and address individual teachers’ professional learning needs. During the pandemic, after an initial de-prioritisation of professional development, plans were adapted to new professional development ecologies in schools. Professional learning activities moved to online, hybrid and blended environments and new activities were added in response to emerging teacher professional learning needs. This study demonstrates how schools’ plans for teacher professional development can, even in the most extreme conditions, be reshaped and adapted to changing circumstances and adds to the growing body of knowledge of online and hybrid professional development.
An Analysis of Burnout among Female Nurse Educators in Saudi Arabia Using K-Means Clustering
Authors: Baghdadi Nadiah A. , Alsayed Shatha Khalid , Malki Ghalia Amer , Balaha Hossam Magdy , Farghaly Abdelaliem Sally Mohammed
Published: January 2023

Nurse educators are often burnt out and suffer from depression due to their demanding job settings. Biochemical markers of burnout can provide insights into the physiological changes that lead to burnout and may help us prevent burnout symptoms. Research was conducted using a descriptive cross-sectional survey design and a multi-stage sampling method. The ministry of education website provides a list of Saudi Arabian nursing education programs that offer bachelor of science in nursing programs (BSN). The study consisted of 299 qualified participants. Malsach Burnout Inventory (MBI) was used to measure burnout as the dependent variable. The MBI is a 22-item scale that measures depersonalization, accomplishment, and emotional exhaustion during work. Bootstrapping with 5000 replicas was used to address potential non-normality. During this framework, four deep neural networks are created. They all have the same number of layers but differ in the number of neurons they have in the hidden layers. The number of female nurse educators experiencing burnout is moderate (mean = 1.92 ± 0.63). Burnout is also moderately observed in terms of emotional exhaustion (mean = 2.13 ± 0.63), depersonalization (mean = 2.12 ± 0.50), and personal achievement scores (mean = 12 2.38 ± 1.13). It has been shown that stacking the clusters at the end of a column increases their accuracy, which can be considered an important feature when classifying.
A Novel Approach to Reduce Breaches of Aircraft Communication Data
Authors: Tahir Shahzaib , Shahbaz Muhammad Arslan , Tahir Hasan , Awais Muhammad , Khan Fawad , Tahir Ruhma , Saeed Saqib , Almuhaideb Abdullah M.
Published: January 2023

Aircraft are complex systems that rely heavily on monitoring and real-time communications with the base station. During aviation and flight operations, diverse data are gathered from different sources, including the Cockpit Voice Recorder (CVR), Flight Data Recorder (FDR), logbook, passenger data, passenger manifest etc. Given the high sensitivity of flight data, it is an attractive target for adversaries which could result in operational, financial and safety related incidents. Communications between aircraft pilots and air traffic controllers are all unencrypted. The data, mainly audio communication files, are placed openly within data centers on the ground stations which could lead to a serious compromise in security and privacy. One may rely on the cloud owing to its on-demand features but to thwart possible attacks, the data need to be encrypted first, giving rise to the issue of conducting search over encrypted data. This research presents a novel approach for data security in aviation industry by introducing a semantic-based searchable encryption scheme over the cloud. The designed system has proven to be extraordinarily effective for semantic-based searchable encryption at the word and the text level. The rigorous security and complexity analysis shows that the proposed solution provides a high level of security and efficiency and can be effectively deployed in the aviation sector. The designed scheme is tested through a real-world aviation dataset collected to demonstrate the significance of this research. The proof of concept proves to be secure, privacy-preserving and lightweight while resisting distinguishability attacks.
Application of Fault Overlay Method and CNN in Infrared Image of Detecting Inter-Turn Short-Circuit in Dry-Type Transformer
Authors: Huang Yen-Chih , Wu Wei-Bin , Kuo Cheng-Chien
Published: January 2023

Inter-turn short-circuit (ITSC) faults do not necessarily produce high temperatures but have special heat distribution and characteristics. Therefore, a new recognition solution for diagnosing faults is proposed, based on the fault overlay method, and the convolutional neural network (CNN) is trained to achieve the automatic identification of infrared images. In this method, through the coverage of layers, the proposed image augmentation method is carried out and simulates the fault data of increasing training. We produce 43 fault traces through the fault overlay method on the three-phase winding of a transformer and use 90 infrared images of transformers in normal operation combined with them to enhance the amount of image data. The fault recognition ability is realized based on CNN model training, including analysis of experimental results of grayscale and color images, and Gaussian noise. In the test of the practical case, a short-circuit test of the 11.4 kV dry-type transformer is carried out, and the ITSC fault is identified when the load is about 15%. The fault characteristic block on this thermal image is 36.3 degrees, which verifies the identification available by this method, and has a certain reference value for the development of infrared image diagnosis technology for power equipment.
Influence Analysis of SiC MOSFET’s Parasitic Capacitance on DAB Converter Output
Authors: Choi Cheol-Woong , So Jae-Hyeon , Ko Jae-Sub , Kim Dae-Kyong
Published: January 2023

This paper proposes the influence analysis of silicon carbide (SiC) MOSFET’s parasitic output capacitance on a dual active bridge (DAB) converter. Power converters are required for DC grids and energy storage. Because SiC metal-oxide-semiconductor FETs (MOSFETs) have lower on-state resistance and faster reverse recovery time than Si MOSFETs, they can be controlled with lower losses and higher frequencies. MOSFETs have a parasitic capacitance. Because of the output parasitic capacitance, the switch voltage does not rise instantaneously during switching but has a delay. The output parasitic capacitance of the switch depends on its drain-to-source voltage, and this parasitic capacitance affects the output of the DAB converter by delaying the switch voltage. In this paper, in order to analyze the effect of the parasitic capacitance on the DAB converter output, the delay time was calculated through a formula, and this value was compared with a simulated value. In addition, the effect of the parasitic capacitance of the SiC MOSFET on the output of the DAB converter was presented by comparing the actual output voltage with the ideal output voltage and analyzing the effect of the output voltage according to the delay.
Small Defect Detection Based on Local Structure Similarity for Magnetic Tile Surface
Authors: Zhong Zhiyan , Wang Hongxin , Xiang Dan
Published: January 2023

Surface defect detection is critical in manufacturing magnetic tiles to improve production yield. However, existing detection methods are difficult to use to accurately locate and segment small defects on magnetic tile images, because these defects always occupy extremely low proportions of images, and their visual features are difficult to identify, which means their feature representation for defect detection is quite weak. To address this issue, we propose an effective and feasible detection algorithm for small defects on magnetic tile surfaces. Firstly, based on local structure similarity of magnetic tile surfaces, the image is decomposed into low-rank and sparse matrices for estimating possible defect regions. To accurately locate defect areas while filtering out stains, textures, and noises, the sparse matrix is binarized and used for connected components analysis. Then, pixel values in the defect area are normalized, and the Retinex theory is applied to enhance the contrast between defects and background. Finally, an optimal threshold is determined by an automatic threshold segmentation method to segment the defect areas and edges precisely. Experimental results on a number of magnetic tile samples containing different types of defects demonstrated that the proposed algorithm outperforms the existing methods in terms of all evaluation metrics, showing broad industrial application prospects.
Coordination Control of a Hybrid AC/DC Smart Microgrid with Online Fault Detection, Diagnostics, and Localization Using Artificial Neural Networks
Authors: Jasim Ali M. , Jasim Basil H. , Neagu Bogdan-Constantin , Alhasnawi Bilal Naji
Published: January 2023

In this paper, a solar and wind renewable energies-based hybrid AC/DC microgrid (MG) is proposed for minimizing the number of DC/AC/DC power conversion processes. High penetration rates of renewable energy increase MG instability. This instability can be mitigated by maintaining a balance between consumption demand and production levels. Coordination control is proposed in this study to address coordinated electricity flowing through both AC and DC links and to achieve system stability under variability of generation, load, and fault conditions. The MG adopts a bidirectional main converter that is controlled using a digital proportional resonant (PR) current controller in a synchronous reference frame. The PR controller plays a role as a digital filter with infinite impulse response (IIR) characteristics by virtue of its high gain at the resonant frequency, thereby reducing harmonics. Moreover, the applied PR controller quickly follows the reference signal, can adapt to changes in grid frequency, is easy to set up, and has no steady-state error. Moreover, the solar photovoltaic (PV)-based distribution generation (DG) is supported by a maximum power point tracker (MPPT)-setup boost converter to extract maximum power. Due to the usage of converter-connected DG units in MGs, power electronic converters may experience excessive current during short circuit faults. Fault detection is critical for MG control and operation since it empowers the system to quickly isolate and recover from faults. This paper proposed an intelligent online fault detection, diagnostic, and localization information system for hybrid low voltage AC/DC MGs using an artificial neural network (ANN) due to its accuracy, robustness, and quickness. The proposed scheme enables rapid detection of faults on the AC bus, resulting in a more reliable MG. To ensure the neural network’s validity, it was trained on various short circuit faults. The performance of the MG was evaluated using MATLAB software. The simulation findings indicate that the suggested control strategy maintains the dynamic stability of the MG, meets the load demand, and achieves energy balance as well as properly predicts faults.
Characterization of a Novel Packaged Hydrogel Wound Dressing by 2.35 T Magnetic Resonance Imaging
Authors: Corradini Valentina , Pajewski Leonardo , Di Censo Davide , Alecci Marcello , Galante Angelo
Published: January 2023

Hydrogel wound dressing makes easier the treatment of patients suffering from difficult wounds. A new process for the manufacturing of a sterile, packaged hydrogel wound dressing, based on an interpenetrating structure of calcium alginate, agar, and polyvinylpyrrolidone, was recently developed. The new formulation overtakes some previous technologies’ drawbacks expressing a better resistance to mechanical deformations compared to products on the market. In this work, the 2.35 T proton density, spin-lattice relaxation time, spin-spin relaxation time, phase-coherence relaxation, and water apparent diffusion coefficient analysis in the new hydrogel and several alternative formulations, including a commercial one (Neoheal®), are reported. Specifically, the combination of agar, acting as a thermolabile forming agent, with calcium alginate and γ irradiated polyvinylpyrrolidone, acting, respectively, as physical, and chemical crosslinking agents with an irreversible (temperature independent) effect, have been investigated. The new hydrogel formulation brings a qualitative improvement in its handling due to its increased mechanical stiffness when compared to the commercial hydrogel reference. This comes together with a reduced water content (100 vs. 112 for proton density in arbitrary units) and swelling capacity (88% vs. 124%) but with improved water mobility (1.42 vs. 1.34 × 10−3 mm2 s−1 for the apparent diffusion coefficient).
Research on Human Gait Phase Recognition Algorithm Based on Multi-Source Information Fusion
Authors: Wang Yu , Song Quanjun , Ma Tingting , Yao Ningguang , Liu Rongkai , Wang Buyun
Published: January 2023

Gait phase detection is of great significance in the field of motion analysis and exoskeleton-assisted walking, and can realize the accurate control of exoskeleton robots. Therefore, in order to obtain accurate gait information and ensure good gait phase detection accuracy, a gait recognition framework based on the New Hidden Markov Model (NHMM) is proposed to improve the accuracy of gait phase detection. A multi-sensor gait data acquisition system was developed and used to collect the training data of eight healthy subjects to measure the acceleration and plantar pressure of the human body. Accuracy of the recognition framework, filtering algorithm and window selection, and the missing validation of the generalization performance of the method were evaluated. The experimental results show that the overall accuracy of NHMM is 94.7%, which is better than all other algorithms. The generalization of the performance is 84.3%. The results of this study provide a theoretical basis for the design and control of the exoskeleton.
Deep Learning-Based Approaches for Classifying Foraminal Stenosis Using Cervical Spine Radiographs
Authors: Park Jiho , Yang Jaejun , Park Sehan , Kim Jihie
Published: January 2023

Various disease detection models, based on deep learning algorithms using medical radiograph images (MRI, CT, and X-ray), have been actively explored in relation to medicine and computer vision. For diseases related to the spine, primarily MRI-based or CT-based studies have been conducted, but most studies were associated with the lumbar spine, not the cervical spine. Foraminal stenosis offers important clues in diagnosing cervical radiculopathy, which is usually detected based on MRI data because it is difficult even for experts to diagnose using only an X-ray examination. However, MRI examinations are expensive, placing a potential burden on patients. Therefore, this paper proposes a novel model for diagnosing foraminal stenosis using only X-ray images. In addition, we propose methods suitable for cervical spine X-ray images to improve the performance of the proposed classification model. First, the proposed model adopts data preprocessing and augmentation methods, including Histogram Equalization, Flip, and Spatial Transformer Networks. Second, we apply fine-tuned transfer learning using a pre-trained ResNet50 with cervical spine X-ray images. Compared to the basic ResNet50 model, the proposed method improves the performance of foraminal stenosis diagnosis by approximately 5.3–6.9%, 5.2–6.5%, 5.4–9.2%, and 0.8–4.3% in Accuracy, F1 score, specificity, and sensitivity, respectively. We expect that the proposed model can contribute towards reducing the cost of expensive examinations by detecting foraminal stenosis using X-ray images only.
Which Factors Contribute to the Global Expansion of M-Commerce?
Authors: Ciupac-Ulici Maria , Beju Daniela-Georgeta , Bresfelean Vasile Paul , Zanellato Gianluca
Published: January 2023

The purpose of this study is to analyze the factors that are contributing to the remarkable growth of the m-commerce sector. The article examines eight variables, including socioeconomic (Internet access, mobile users, mobile Internet penetration rates, card payment transactions, consumer confidence, Internet use: selling goods or services) and macroeconomic (GDP and wage), that are considered to influence global m-commerce growth. The Generalized Method of Moments (DPD/GMM) was used to analyze a panel of data that covers the years 2011 through 2020, on a sample of 42 developed and developing countries. The empirical findings show that wages, GDP, consumer confidence index, card payment transactions, mobile users, Internet access and Internet use (selling goods and services) have a positive impact on m-commerce, whereas the mobile Internet penetration rate has a negative impact. Using a sizable and representative panel data set on socioeconomic and macroeconomic indicators, this research advances the state of the art in customer comprehension of these topics. The study’s novel contribution is the incorporation of under-researched m-commerce drivers into empirical analysis. Detailed policy recommendations are provided, with an emphasis on practical, implementable measures to enhance the m-commerce industry.
Use of UAVs and Deep Learning for Beach Litter Monitoring
Authors: Pfeiffer Roland , Valentino Gianluca , D’Amico Sebastiano , Piroddi Luca , Galone Luciano , Calleja Stefano , Farrugia Reuben A. , Colica Emanuele
Published: January 2023

Stranded beach litter is a ubiquitous issue. Manual monitoring and retrieval can be cost and labour intensive. Therefore, automatic litter monitoring and retrieval is an essential mitigation strategy. In this paper, we present important foundational blocks that can be expanded into an autonomous monitoring-and-retrieval pipeline based on drone surveys and object detection using deep learning. Drone footage collected on the islands of Malta and Gozo in Sicily (Italy) and the Red Sea coast was combined with publicly available litter datasets and used to train an object detection algorithm (YOLOv5) to detect litter objects in footage recorded during drone surveys. Across all classes of litter objects, the 50%–95% mean average precision (mAP50-95) was 0.252, with the performance on single well-represented classes reaching up to 0.674. We also present an approach to geolocate objects detected by the algorithm, assigning latitude and longitude coordinates to each detection. In combination with beach morphology information derived from digital elevation models (DEMs) for path finding and identifying inaccessible areas for an autonomous litter retrieval robot, this research provides important building blocks for an automated monitoring-and-retrieval pipeline.
End-to-End Light License Plate Detection and Recognition Method Based on Deep Learning
Authors: Ma Zongfang , Wu Zheping , Cao Yonggen
Published: January 2023

In the field of intelligent robot and automatic drive, the task of license plate detection and recognition (LPDR) are undertaken by mobile edge computing (MEC) chips instead of large graphics processing unit (GPU) servers. For this kind of small computing capacity MEC chip, a light LPDR network with good performance in accuracy and speed is urgently needed. Contemporary deep learning (DL) LP recognition methods use two-step (i.e., detection network and recognition network) or three-step (i.e., detection network, character segmentation method, and recognition network) strategies, which will result in loading two networks on the MEC chip and inserting many complex steps. To overcome this problem, this study presents an end-to-end light LPDR network. Firstly, this network adopts the light VGG16 structure to reduce the number of feature maps and adds channel attention at the third, fifth, and eighth layers. It can reduce the number of model parameters without losing the accuracy of prediction. Secondly, the prediction of the LP rotated angle is added, which can improve the matching between the bounding box and the LP. Thirdly, the LP part of the feature map is cropped by the relative position of detection module, and the region-of-interest (ROI) pooling and fusion are performed. Seven classifiers are then used to identify the LP characters through the third step’s fusion feature. At last, experiments show that the accuracy of the proposed network reaches 91.5 and that the speed reaches 63 fps. In the HiSilicon 3516DV300 and the Rockchip Rv1126 Mobile edge computing chips, the speed of the network has been tested for 15 fps.
Sparse Reconstruction of SFCWSAR Based on an Approximate Observation Operator
Authors: Hou Xiaoze , Ma Yanheng
Published: January 2023

The traditional compressed sensing SFCWSAR (Stepped Frequency Continuous Wave Synthetic Aperture Radar) sparse reconstruction algorithm consumes a lot of computer memory and cannot compensate the range migration in the same pulse group. Based on this, this paper proposes a SFCWSAR sparse reconstruction algorithm based on an approximate observation operator. First, the algorithm replaces the accurate observation operator with the approximate observation operator, which greatly reduces the computer memory consumption while the algorithm is running and realizes the compensation of the range migration in the SFCWSAR pulse group. Furthermore, the SFCWSAR sub-band echo data under full sampling conditions are used to modify the important parameter of the Doppler center frequency of the approximate observation operator, which significantly improves the reconstruction accuracy of the scene. The SFCWSAR data show that, compared with the conventional sparse autofocus algorithm, the proposed algorithm takes less memory and can reconstruct scenes with high accuracy.
Human-Computer Interaction System: A Survey of Talking-Head Generation
Authors: Zhen Rui , Song Wenchao , He Qiang , Cao Juan , Shi Lei , Luo Jia
Published: January 2023

Virtual human is widely employed in various industries, including personal assistance, intelligent customer service, and online education, thanks to the rapid development of artificial intelligence. An anthropomorphic digital human can quickly contact people and enhance user experience in human–computer interaction. Hence, we design the human–computer interaction system framework, which includes speech recognition, text-to-speech, dialogue systems, and virtual human generation. Next, we classify the model of talking-head video generation by the virtual human deep generation framework. Meanwhile, we systematically review the past five years’ worth of technological advancements and trends in talking-head video generation, highlight the critical works and summarize the dataset.
Photoplethysmography Data Reduction Using Truncated Singular Value Decomposition and Internet of Things Computing
Authors: Abdelaziz Abdulrahman B. , Rahimi Mohammad A. , Alrabeiah Muhammad R. , Ibrahim Ahmed B. , Almaiman Ahmed S. , Ragheb Amr M. , Alshebeili Saleh A.
Published: January 2023

Biometric-based identity authentication is integral to modern-day technologies. From smart phones, personal computers, and tablets to security checkpoints, they all utilize a form of identity check based on methods such as face recognition and fingerprint-verification. Photoplethysmography (PPG) is another form of biometric-based authentication that has recently been gaining momentum, because it is effective and easy to implement. This paper considers a cloud-based system model for PPG-authentication, where the PPG signals of various individuals are collected with distributed sensors and communicated to the cloud for authentication. Such a model incursarge signal traffic, especially in crowded places such as airport security checkpoints. This motivates the need for a compression–decompression scheme (or a Codec for short). The Codec is required to reduce the data traffic by compressing each PPG signal before it is communicated, i.e., encoding the signal right after it comes off the sensor and before it is sent to the cloud to be reconstructed (i.e., decoded). Therefore, the Codec has two system requirements to meet: (i) produce high-fidelity signal reconstruction; and (ii) have a computationallyightweight encoder. Both requirements are met by the Codec proposed in this paper, which is designed using truncated singular value decomposition (T-SVD). The proposed Codec is developed and tested using a publicly available dataset of PPG signals collected from multiple individuals, namely the CapnoBase dataset. It is shown to achieve a 95% compression ratio and a 99% coefficient of determination. This means that the Codec is capable of delivering on the first requirement, high-fidelity reconstruction, while producing highly compressed signals. Those compressed signals do not require heavy computations to be produced as well. An implementation on a single-board computer is attempted for the encoder, showing that the encoder can average 300 milliseconds per signal on a Raspberry Pi 3. This is enough time to encode a PPG signal prior to transmission to the cloud.
A Single-Stage Bimodal Transformerless Inverter with Common-Ground and Buck-Boost Features
Authors: Vo Dai-Van , Nguyen Khai M. , Lim Young-Cheol , Choi Joon-Ho
Published: January 2023

This paper proposes a single-phase, single-stage common-ground inverter with a non-electrolytic capacitor and buck-boost ability. The proposed single-stage inverter is employed by a boost stage DC-DC converter and bimodal circuit, which makes it satisfactory for PV systems with a wide input voltage range and lower switch voltage stress. The leakage current of the proposed single-stage inverter can effectively suppress because the parasitic capacitor between the PV panel and the ground is shortened. In addition, the proposed single-stage inverter does not include electrolytic capacitors, which reduces the equivalent series resistance of electrolytic capacitors and also the size of the inverter system. The topology, operating principle, and PWM control method of the proposed single-stage inverter are given. The design guidelines of components and comparative studies of the proposed single-stage inverter are provided. A 500 W laboratory prototype of the proposed single-stage inverter is built to verify the correctness of the simulation and theoretical analysis.
Integrating Chatbot and Augmented Reality Technology into Biology Learning during COVID-19
Authors: Chuang Chi-Hung , Lo Jung-Hua , Wu Yan-Kai
Published: January 2023

The novel coronavirus (COVID-19) pandemic is rampant around the world, and teachers and students are unable to attend physical classes in the midst of a serious outbreak. This study aims to design a user-friendly, educational chatbot application interface that can be used as an after-school self-learning tool for students to enhance their interest and comprehension and increase the effectiveness of their learning at home. The system adopts the Chatfuel platform as the core interface and incorporates augmented reality technology to build a chatbot that allows users to interact with it after they have logged in to Facebook. The content is based on the biology subject of the first year of junior high school and is integrated into the online teaching with augmented reality teaching materials. A user survey is conducted to understand students’ attitudes towards learning biology with the aid of the ARCS motivation model, with 102 valid questionnaires received. The results show that the AR-based chatbot system developed in this study significant influenced the indicators in the ARCS motivation model; therefore, the intention to use the system is presumed to result in a noticeable increase in student learning outcomes when using the system. Accordingly, this study proposes new online learning tools for students to use at home during the pandemic, and the system also provides references for the future development and modification of educational chatbots.
A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
Authors: Zhang Xinyu , Du Qifei , Liu Cheng , Zhang Hao , Ma Yue , Li Yefei , Li Jinhuan
Published: January 2023

The current work employs the HMC830 phase-locked loop chip to design a frequency synthesizer operating in the L-band. The frequency synthesizer can provide a local oscillation signal for the RF receiver front end. This article employs the phase-locked synthesis technique to describe the design scheme. Due to the advantages of the passive loop filters, such as simplicity, low cost, and low phase noise, a passive fourth-order RLC loop filter is proposed to improve the output signal quality and reduce phase noise. The performance of this loop filter is compared with the passive fourth-order RC loop filter. The effects of these two loop filters on phase noise, loop capture time, and spur suppression are analyzed. Subsequently, the design scheme, simulation analysis, and test results of the frequency synthesizer are presented under these two loop filters. The test results indicate that the passive fourth-order RLC loop filter outperforms the passive fourth-order RC loop filter; its output signal phase noise is higher than −100 dBc/Hz@1 kHz, loop capture time is less than 100 us, and spur suppression is better than 60 dBc. This frequency synthesizer can provide high-performance local oscillation signals for wireless communication equipment such as transmitters and receivers. It meets the application requirements of many radio communication circuit structures and has good application prospects.
Review of the Impact of Biofuels on U.S. Retail Gasoline Prices
Authors: Janda Karel , Michalikova Eva , Rocha Luiz Célio Souza , Rotella Junior Paulo , Schererova Barbora , Zilberman David
Published: January 2023

This study aims to provide a review of the state-of-the-art literature regarding the impacts and contributions of corn ethanol on retail gasoline prices in the US. For this, a systematic literature review following PRISMA statement was carried out, seeking to answer four research questions: (1) What are the main characteristics of the literature regarding the impact and contributions of ethanol on US retail gasoline prices? (2) What are the main article clusters identified in the evaluated literature? (3) What was the numerical impact of the Volumetric Ethanol Excise Tax Credit/Renewable Fuel Standard (VEETC/RFS) mandate on the price of gasoline and what are the main methods used for calculation in the literature? (4) What are the main trends and possibly new research directions for this literature? As a result of the characterization of the sample, driving themes, such as energy policy, costs, price dynamics, trade and energy market, were identified. Furthermore, three main clusters were identified in the sample: (i) impacts of biofuels on commodity prices and general price dynamics; (ii) impacts of public policies on the implementation of ethanol and flexibility in formulating fuel blends; and (iii) impact of biofuels on environmental aspects. As a practical implication, the prevailing result in the analyzed literature is that the addition of ethanol reduces the price of gasoline at the pump, and estimates range from no effect to nearly 10% off the price of gasoline. Finally, the topic on the impacts of biofuels on commodity prices and on the general dynamics of prices is the most relevant research line and the trend suggested by the proposed research agenda.
First Law Comparison of a Forced-Circulation Solar Water Heating System with an Identical Thermosyphon
Authors: Sakellariou Evangelos I. , Axaopoulos Petros J. , Bot Bill Vaneck , Kavadias Kosmas A.
Published: January 2023

The main categories of solar water heating systems (SWHSs) are the thermosyphon and the forced circulation (FC). This paper presents an experiment carried out with the aim to compare the energy performance of the FC with a thermosyphon SHWS. Identical SWHSs were installed side by side at the University of West Attica in Athens, Greece. Domestic hot water load was applied to both systems via a microcontroller-based dispensing unit which mimics the demand profile. The trial period comprised the last two months of spring (April and May). For the first law assessment, two energy indicators were utilized: the solar fraction (SF) and the thermal efficiency of the system (ηth). On days with distinctive weather conditions, both systems obtained approximately equal SF and ηth values, without a specific preference between the ambient conditions and the type of SWHS. Regarding a four-day nonstop operation, the FC overperformed the thermosyphon system at both energy indicators. Namely, for the FC and the thermosyphon SWHS, the SF was calculated to be 0.62 and 0.48, and the ηth was 68.2% and 53.3%, respectively.
SR-GNN Based Fault Classification and Location in Power Distribution Network
Authors: Mo Haojie , Peng Yonggang , Wei Wei , Xi Wei , Cai Tiantian
Published: January 2023

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.
Perspectives on Hydrogen
Authors: Abánades Alberto
Published: January 2023

Humankind has an urgent need to reduce carbon dioxide emissions. Such a challenge requires deep transformation of the current energy system in our society. Achieving this goal has given an unprecedented role to decarbonized energy vectors. Electricity is the most consolidated of such vectors, and a molecular vector is in the agenda to contribute in the future to those end uses that are difficult to electrify. Additionally, energy storage is a critical issue for both energy vectors. In this communication, discussion on the status, hopes and perspectives of the hydrogen contribution to decarbonization are presented, emphasizing bottlenecks in key aspects, such as education, reskilling and storage capacity, and some concerns about the development of a flexible portfolio of technologies that could affect the contribution and impact of the whole hydrogen value chain in society. This communication would serve to the debate and boost discussion about the topic.