bluuebunny/crossref_metadata_2025 huggingface dataset:
Brazilian Multiethnic Association Study of Genetic Variant Interactions among FOS, CASP8, MMP2 and CRISPLD2 in the Risk of Nonsyndromic Cleft Lip with or without Cleft Palate
Authors: Machado Renato Assis , de Oliveira Lilianny Querino Rocha , Rangel Ana Lúcia Carrinho Ayroza , Reis Silvia Regina de Almeida , Scariot Rafaela , Martelli Daniella Reis Barbosa , Martelli-Júnior Hercílio , Coletta Ricardo D.
Published: December 2022

Associations of CRISPLD2 (cysteine-rich secretory protein LCCL domain containing 2) and genes belonging to its activation pathway, including FOS (Fos proto-oncogene), CASP8 (caspase 8) and MMP2 (matrix metalloproteinase 2), with nonsyndromic orofacial cleft risk, have been reported, but the results are yet unclear. The aim of this study was to evaluate single nucleotide polymorphisms (SNPs) in FOS, CASP8 and MMP2 and to determine their SNP-SNP interactions with CRISPLD2 variants in the risk of nonsyndromic cleft lip with or without cleft palate (NSCL±P) in the Brazilian population. The SNPs rs1046117 (FOS), rs3769825 (CASP8) and rs243836 (MMP2) were genotyped using TaqMan allelic discrimination assays in a case-control sample containing 801 NSCL±P patients (233 nonsyndromic cleft lip only (NSCLO) and 568 nonsyndromic cleft lip and palate (NSCLP)) and 881 healthy controls via logistic regression analysis adjusted for the effects of sex and genomic ancestry proportions with a multiple comparison p value set at ≤0.01. SNP-SNP interactions with rs1546124, rs8061351, rs2326398 and rs4783099 in CRISPLD2 were performed with the model-based multifactor dimensionality reduction test complemented with a 1000 permutation-based strategy. Although the association between FOS rs1046117 and risk of NSCL±P reached only nominal p values, NSCLO risk was significantly higher in carriers of the FOS rs1046117 C allele (OR: 1.28, 95% CI: 1.10–1.64, p = 0.004), TC heterozygous genotype (OR: 1.59, 95% CI: 1.16–2.18, p = 0.003), and in the dominant model (OR: 1.50, 95% CI: 1.10–2.02, p = 0.007). Individually, no significant associations between cleft risk and the SNPs in CASP8 and MMP2 were observed. SNP-SNP interactions involving CRISPLD2 variants and rs1046117 (FOS), rs3769825 (CASP8) and rs243836 (MMP2) yielded several significant p values, mostly driven by FOS rs1046117 and CASP8 rs3769825 in NSCL±P, FOS rs1046117 in NSCLO and CRISPLD2 rs8061351 in NSCLP. Our study is the first in the Brazilian population to reveal the association of FOS rs1046117 with NSCLO risk, and to support that CRISPLD2, CASP8, FOS and MMP2 interactions may be related to the pathogenesis of this common craniofacial malformation.
Integrated Communication and Measurement System with BOC-Assisted OFDM
Authors: Li Xue , Zeng Xiaolin , Xue Linshan
Published: December 2022

For unmanned aerial vehicles (UAVs), high-precision measurement and high-speed communication are necessary to realize flight and operational missions. In this paper, we propose an integrated communication and measurement system in a Doppler frequency offset environment. The system combines orthogonal frequency division multiplexing (OFDM) modulation with binary offset carrier (BOC) modulation to formulate an OFDM+BOC composite signal through power control. High-precision measurement is achieved through BOC modulation, and high data transmission is achieved through OFDM modulation. Furthermore, the high-precision Doppler frequency offset tracked by the BOC signal is adopted to assist in the demodulation of the OFDM signal. This substantially decreases the impact of the Doppler frequency offset on the OFDM signal. Moreover, the ranging error is within 10−2, and the maximum Doppler frequency error is within 2 Hz.
Position/Attitude Cascade Regulation of Nonholonomic Four-Wheeled Mobile Robot via Double-Loop Sliding-Mode Control Mechanism
Authors: Zhang Xin , Qin Dongchen , Wang Shuting , Xie Yuanlong , Li Hu , Li Shiqi
Published: December 2022

Nonholonomic four-wheeled mobile robot (NFMR) is a typical multiple input–multiple output system that formulates its kinematic dynamics concerning position and attitude in a parallel manner. However, due to the lumped disturbances and interconnected states, demand-satisfied performance is difficult to obtain for existing coupled control solutions. To address this problem, a double-loop sliding-mode control (DLSMC) mechanism is proposed for achieving position/attitude cascade regulation. For the outer position tracking loop in the proposed scheme, a sliding mode control method of the bounded time-varying integral nonsingular terminal is designed to guarantee fast tracking in the presence of large initial errors and input saturation. On the other hand, for the inner attitude control loop, a novel adaptive barrier function-based sliding-mode control method is proposed without control gain overestimation. This enables the attitude to follow within a predefined vicinity of the sliding mode surface and holds it subsequently independent of the lumped uncertainties. Theoretical analysis is conducted to demonstrate the asymptotic stability. Comparative experiments implemented on a homemade NFMR show enhanced trajectory tracking performance and system robustness using position/attitude cascade regulation via the proposed DLSMC mechanism.
DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer
Authors: Yu Junxiao , Xu Zhengyuan , He Xu , Wang Jian , Liu Bin , Feng Rui , Zhu Songsheng , Wang Wei , Li Jianqing
Published: December 2022

Text-to-speech (TTS) synthesizers have been widely used as a vital assistive tool in various fields. Traditional sequence-to-sequence (seq2seq) TTS such as Tacotron2 uses a single soft attention mechanism for encoder and decoder alignment tasks, which is the biggest shortcoming that incorrectly or repeatedly generates words when dealing with long sentences. It may also generate sentences with run-on and wrong breaks regardless of punctuation marks, which causes the synthesized waveform to lack emotion and sound unnatural. In this paper, we propose an end-to-end neural generative TTS model that is based on the deep-inherited attention (DIA) mechanism along with an adjustable local-sensitive factor (LSF). The inheritance mechanism allows multiple iterations of the DIA by sharing the same training parameter, which tightens the token–frame correlation, as well as fastens the alignment process. In addition, LSF is adopted to enhance the context connection by expanding the DIA concentration region. In addition, a multi-RNN block is used in the decoder for better acoustic feature extraction and generation. Hidden-state information driven from the multi-RNN layers is utilized for attention alignment. The collaborative work of the DIA and multi-RNN layers contributes to outperformance in the high-quality prediction of the phrase breaks of the synthesized speech. We used WaveGlow as a vocoder for real-time, human-like audio synthesis. Human subjective experiments show that the DIA-TTS achieved a mean opinion score (MOS) of 4.48 in terms of naturalness. Ablation studies further prove the superiority of the DIA mechanism for the enhancement of phrase breaks and attention robustness.
Event-Triggered Tracking Control for Adaptive Anti-Disturbance Problem in Systems with Multiple Constraints and Unknown Disturbances
Authors: Shen Hong , Wang Qin , Yi Yang
Published: December 2022

Aimed at the objective of anti-disturbance and reducing data transmission, this article discusses a novel dynamic neural network (DNN) modeling-based anti-disturbance control for a system under the framework of an event trigger. In order to describe dynamical characteristics of irregular disturbances, exogenous DNN disturbance models with different excitation functions are firstly introduced. A novel disturbance observer-based adaptive regulation (DOBAR) method is then proposed, which can capture the dynamics of unknown disturbance. By integrating the augmented triggering condition and the convex optimization method, an effective anti-disturbance controller is then found to guarantee the system stability and the convergence of the output. Meanwhile, both the augmented state and the system output are constrained within given regions. Moreover, the Zeno phenomenon existing in event-triggered mechanisms is also successfully avoided. Simulation results for the A4D aircraft models are shown to verify the availability of the algorithm.
Predicting Student Performance Using Clickstream Data and Machine Learning
Authors: Liu Yutong , Fan Si , Xu Shuxiang , Sajjanhar Atul , Yeom Soonja , Wei Yuchen
Published: December 2022

Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of 5341 sample students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. The raw clickstream data are transformed, integrating the time and activity dimensions of students’ click actions. Two feature sets are extracted, indicating the number of clicks on 12 learning sites based on weekly and monthly time intervals. For both feature sets, the experiments are performed to compare deep learning algorithms (including LSTM and 1D-CNN) with traditional machine learning approaches. It is found that the LSTM algorithm outperformed other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student performance in the course. The insights from these critical learning sites can inform the design of future courses and teaching interventions to support at-risk students.
Social Media Labs in the Social Education Degree: Exploring Digital Competences of University Students
Authors: Gutiérrez-Pequeño José Miguel , Anguita-Martínez Rocío , Pradena-García Yasna P.
Published: December 2022

Social media labs, as spaces for experimentation, have recently become one of the main mechanisms for innovation. The role of universities, with degrees focused on subjects linked to social innovation, can be a fundamental factor in social development. It is essential to transform traditional centres into spaces for dialogue, into creative ecosystems, simultaneously dedicated to reflection and debate, research and production, training, and socialisation. Within this framework, we carried out ethnographic research on the implementation of a social media laboratory developed with social education students during the last two academic years at the faculty of education in Palencia. The results provide evidence of the development by university students in some skills related to creativity, reflection, and debate, as well as various digital skills. In line with the existing literature, we show how the social media laboratory enables the acquisition of knowledge situated in the social reality of the environment that is of great use to future social educators, as well as some of its limitations in these processes of experimentation and social innovation.
“Following the Breath”: A Trauma-Informed Intervention for Educator Wellness in Rural Montana
Authors: Davis Lauren , Aylward Alexandra , Scott Brandon G. , Jacobs Jonathan
Published: December 2022

Given the prevalence of mental health issues for both educators and adolescents in rural Montana, this project is designed to help mitigate the impact of stressors by providing coping strategies linked to improvements in overall mental health outcomes for teachers, which may ultimately lead to improved co-regulation of students and classroom climate. The immediate goal of this pilot study was to measure physical and mental health outcomes of educators resulting from a remotely-delivered trauma-informed yoga intervention. Findings suggest improvements in participants’ depression and anxiety levels, trauma symptoms, sleep quality, and non-significant changes in heart rate variability and cortisol levels.
The Research of Educational Innovation: Perspective and Strategies
Authors: Rubia-Avi Bartolomé
Published: December 2022

Innovation processes in the Spanish and international educational models have evolved, especially over the last 40 years. Technological expansion and development have been integrated, albeit belatedly, into the field of education. There have been changes to the educational model: school practices have been modified so that, while not entailing a completely generalized impact, significant advances have been introduced. This has led to a change in the spaces, times and ways in which these practices are implemented, thus paving the way for a complete shift in the model. Yet this has not led to a systematic and organised reflection on the processes of change, nor on how to approach evaluation and research in education. This paper aims to provide some ideas about the correct targets of these transformations, and it does so by presenting several examples.
Kernel-Based Container File Access Control Architecture to Protect Important Application Information
Authors: Lee Hoo-Ki , Han Sung-Hwa , Lee Daesung
Published: December 2022

Container platforms ease the deployment of applications and respond to failures. The advantages of container platforms have promoted their use in information services. However, the use of container platforms is accompanied by associated security risks. For instance, malware uploaded by users can leak important information, and malicious operators can cause unauthorized modifications to important files to create service errors. These security threats degrade the quality of information services and reduce their reliability. To overcome these issues, important container files should be protected by file-access control functions. However, legacy file-access control techniques, such as umask and SecureOS, do not support container platforms. To address this problem, we propose a novel kernel-based architecture in this study to control access to container files. The proposed container file-access control architecture comprises three components. The functionality and performance of the proposed architecture were assessed by implementing it on a Linux platform. Our analysis confirmed that the proposed architecture adequately controls users’ access to container files and performs on par with legacy file-access control techniques.
ESMD-WSST High-Frequency De-Noising Method for Bridge Dynamic Deflection Using GB-SAR
Authors: Liu Xianglei , Zhao Songxue , Wang Runjie
Published: December 2022

Ground-based synthetic aperture radar (GB-SAR), as a new non-contact measurement technique, has been widely applied to obtain the dynamic deflection of various bridges without corner reflectors. However, it will cause some high-frequency noise in the obtained dynamic deflection with the low signal-to-noise ratio. To solve this problem, this paper proposes an innovative high-frequency de-noising method combining the wavelet synchro-squeezing transform (WSST) method with the extreme point symmetric mode decomposition (ESMD) method. First, the ESMD method is applied to decompose the observed dynamic deflection signal into a series of intrinsic mode functions (IMFs), and the frequency boundary of the original signal autocorrelation is filtered by the mutual information entropy (MIE) for each IMF pair. Second, the high-frequency IMF components are fused into a high-frequency sub-signal. WSST is performed to remove the influence of noise to reconstruct a new sub-signal. Finally, the de-noised bridge dynamic deflection is reconstructed by the new sub-signal, the remaining IMF components, and the residual curve R. For the simulated signal with 5 dB noise, the signal-to-noise ratio (SNR) after noise reduction is increased to 11.13 dB, and the root-mean-square error (RMSE) is reduced to 0.30 mm. For the on-site experiment for the Wanning Bridge, the noise rejection ratio (NRR) is 5.48 dB, and ratio of the variance root (RVR) is 0.05 mm. The results indicate that the proposed ESMD-WSST method can retain more valid information and has a better noise reduction ability than the ESMD, WSST, and EMD-WSST methods.
Self-Calibration for Sparse Uniform Linear Arrays with Unknown Direction-Dependent Sensor Phase by Deploying an Individual Standard Sensor
Authors: Yang Long , Hou Xianghao , Yang Yixin
Published: December 2022

Calibration of the unknown direction-dependent (DD) sensor phase and aliasing-free directions of arrival (DOA) estimation for sparse linear arrays are difficult tasks. In this work, we deploy an individual standard sensor with a known sensor phase response along the axis of the uncalibrated sparse linear array, a self-calibration method is proposed, in which the unknown DD sensor phase and the aliasing-free DOAs are both estimated. The proposed method is realized with a two-step scheme. In the first step, the sensor phase is eliminated by the Kronecker product of the covariance matrices in two different frequency bins, and the frequency difference satisfies the spatial Nyquist sampling theorem. Then, the DOAs can be robustly estimated without the influences of grating lobes and unknown sensor phase parameters. In the second step, the steering matrix is estimated with the known phase parameters of the deployed standard sensor. Then, the DD sensor phase is extracted from the steering matrix using the DOAs obtained in the first step. Hence, the disadvantages of iteration-based strategies in conventional calibration algorithms (e.g., local minima convergence) can be avoided. The performance of the proposed method is evaluated using simulation data and compared with that of Cramer–Rao bounds.
SNPERS: A Physical Exercise Recommendation System Integrating Statistical Principles and Natural Language Processing
Authors: Yang Yuer , Lin Yifeng , Chen Zeguang , Lei Yongjia , Liu Xingxing , Zhang Yan , Sun Ying , Wang Xiangjie
Published: December 2022

As chronic diseases such as cardiovascular diseases are prevalent and progressively more common in young people, more and more college students are paying attention to exercising, even though they are busy studying. However, some college students are unmindful of their physique and their bodies’ targeted exercise. The exercise they do is either extensive but not refined or too homogeneous. We conducted a statistical analysis of 18,101 college students’ physical examination results. We found that students who exercise regularly but still did not achieve satisfactory results in one or more physical examination items had often exercised in the two unscientific ways mentioned above. This paper presents an intelligent recommendation system that integrates statistical principles and natural language processing, which improves traditional recommendation systems and could provide suitable and targeted exercise suggestions for college students. The R2 increased by about 27.72%.
Radio Interference of Wireless Networks and the Impact of AR/VR Applications in Industrial Environments
Authors: Dionísio Rogério , Ribeiro Fernando , Metrôlho José
Published: December 2022

The use of wireless communications systems on the factory shop floor is becoming an appealing solution with many advantages compared to cable-based solutions, including low cost, easy deployment, and flexibility. This, combined with the continuous growth of low-cost mobile devices, creates opportunities to develop innovative and powerful applications that, in many cases, rely on computing and memory-intensive algorithms and low-latency requirements. However, as the density of connected wireless devices increases, the spectral noise density rises, and, consequently, the radio interference between radio devices increase. In this paper, we discuss how the density of AR/VR mobile applications with high throughput and low latency affect industrial environments where other wireless devices use the same frequency channel. We also discuss how the growing number of these applications may have an impact on the radio interference of wireless networks. We present an agnostic methodology to assess the radio interferences between wireless communication systems on the factory floor by using appropriate radio and system models. Several interference scenarios are simulated between commonly used radio systems: Bluetooth, Wi-Fi, and WirelessHART, using SEAMCAT. For a 1% probability of interference and considering a criterion of C/I = 14 dB, the simulations on an 80 m × 80 m factory shop floor show that low-bandwidth systems, such as Bluetooth and WirelessHART, can coexist with high-bandwidth and low-latency AR/VR applications running on Wi-Fi mobile terminals if the number of 11 Wi-Fi access points and 80 mobile AR/VR devices transmitting simultaneously is not exceeded.
Substrate-Integrated Coaxial Line (SICL) Rotman Lens Beamformer for 5G/B5G Applications
Authors: Ali M. Saad , Naveed Hamna , Babar Abbasi Muhammad Ali , Shoaib Nosherwan , Fusco Vincent F.
Published: December 2022

High-band allocations in the millimeter-wave (mm-Wave) frequency spectrum offer high-capacity wireless information transmission as required by fifth generation (5G) communication standards. Among different beamforming structures, the Rotman lens (RL) is an attractive passive-microwave-lens-based beamforming network due to its low fabrication cost, reliability, design simplicity and wide-angle scanning capabilities. Conventionally, the RL is implemented using microstrip line (MSL) technology for which there are inherent radiation losses that become severe when operating in mm-Wave 5G frequency bands. In this context, a novel substrate-integrated coaxial line (SICL)-based RL is designed, fabricated and tested, for accurate beamforming with extremely low feed line insertion loss. This article presents a complete design, development and performance analysis of an SICL-based RL beamformer. By using an SICL, isolation of up to 15 dB is achieved between the input beam ports of the RL, while the mutual coupling is kept at less than 20 dB. The SICL design shows a −10 dB insertion loss between the array and beam ports when compared to the same RL developed using MSL technology having an insertion loss of −15 dB. Due to the use of low-loss SICL technology, a realized gain of up to 14.2 dBi is achieved with an excellent scanning capability of −30 to 30 degrees, verifying for the first time the beamforming capabilities associated with SICL technology. The operational frequency band is 20–45 GHz, while the center operating frequency is 26 GHz making it appropriate for above 6-GHz 5G New Radio (NR) operating bands n257 (26.5 GHz to 29.5 GHz), n258 (24.25 GHz to 27.5 GHz), n261 (27.5 GHz to 28.35 GHz) and n260 (37 GHz to 40 GHz). Owing to the low-loss and stable beamforming performance, the SICL RL is suitable for mm-Wave 5G and is extendable to B5G applications.
Modeling Cyberattack Propagation and Impacts on Cyber-Physical System Safety: An Experiment
Authors: Serru Théo , Nguyen Nga , Batteux Michel , Rauzy Antoine
Published: December 2022

In this article, we present an experiment we conducted with discrete event simulations to analyze the effects of multi-step cyberattacks on the safety of cyber-physical systems. We show how to represent systems, their components (either software and/or hardware), communication links, security measures, and attacks from a malicious intruder. The latter are typically taken from the MITRE ATT&CK knowledge base. The discrete event simulation method makes it possible to represent any event affecting the system. We illustrate our approach by means of an illustrative example involving cyberattacks against the navigation system of an autonomous ship. We show how the formal modeling language AltaRica, primarily dedicated to safety analyses, can assess this illustrative example by representing the system and automatically extracting sequences of attacks, leading to a safety-critical situation, namely the deviation of the ship by the attacker. This article aims to discuss this approach and to outline the lessons learned from our experience.
Gamma Ray Effects on Multi-Colored Commercial Light-Emitting Diodes at MGy Level
Authors: Weninger Luca , Clerc Raphaël , Ferrari Matteo , Morana Adriana , Allanche Timothé , Pecorella Roberto , Boukenter Aziz , Ouerdane Youcef , Marin Emmanuel , Duhamel Olivier , Gaillardin Marc , Paillet Philippe , Girard Sylvain
Published: December 2022

Light-emitting diodes (LEDs) are of interest for implementation in radiation environments, such as part of illumination systems of radiation-tolerant cameras able to provide images at high doses (>MGy). It is then mandatory to characterize the radiation effects on all of the LED key properties exploited for such applications. To this aim, the evolution of the optical properties of commercial LEDs after they have been exposed to γ-rays, up to total ionizing dose (TID) levels of 2 MGy(air) at room temperature, is discussed. The devices under test include four LEDs of different colors (red, green, blue and white) in the same package. This allows a direct comparison between the responses of the different structures and technologies, as the proximity between the diodes ensures the uniformity of their irradiation conditions. The radiation effect on the electron–photon conversion mechanisms inside these LEDs is investigated through the evolution of their external quantum efficiency (EQE) vs. current characteristics. The spectral emission pattern of LEDs after irradiation at different dose levels is then characterized to estimate the TID effects on the lens which surrounds the LED package. The presented results show a monotone radiation-induced EQE decrease as a function of the TID, especially in the red LEDs. For the tested red LEDs, the EQE decreased up to 78% after a TID of 1 MGy when they were OFF during irradiation, and up to 8% when they were ON during irradiation. A visual inspection of the devices after irradiation shows a mechanical degradation of the lens shared among the four diodes. However, the emission pattern analysis does not show any significant radiation-induced changes in the optical properties of the lens. Based on these results, it appears possible to design LED-based illumination systems able to survive to MGy dose levels that can be integrated as subsystems of radiation-hardened cameras.
Intelligent Distributed Swarm Control for Large-Scale Multi-UAV Systems: A Hierarchical Learning Approach
Authors: Dey Shawon , Xu Hao
Published: December 2022

In this paper, a distributed swarm control problem is studied for large-scale multi-agent systems (LS-MASs). Different than classical multi-agent systems, an LS-MAS brings new challenges to control design due to its large number of agents. It might be more difficult for developing the appropriate control to achieve complicated missions such as collective swarming. To address these challenges, a novel mixed game theory is developed with a hierarchical learning algorithm. In the mixed game, the LS-MAS is represented as a multi-group, large-scale leader–follower system. Then, a cooperative game is used to formulate the distributed swarm control for multi-group leaders, and a Stackelberg game is utilized to couple the leaders and their large-scale followers effectively. Using the interaction between leaders and followers, the mean field game is used to continue the collective swarm behavior from leaders to followers smoothly without raising the computational complexity or communication traffic. Moreover, a hierarchical learning algorithm is designed to learn the intelligent optimal distributed swarm control for multi-group leader–follower systems. Specifically, a multi-agent actor–critic algorithm is developed for obtaining the distributed optimal swarm control for multi-group leaders first. Furthermore, an actor–critic–mass method is designed to find the decentralized swarm control for large-scale followers. Eventually, a series of numerical simulations and a Lyapunov stability proof of the closed-loop system are conducted to demonstrate the performance of the developed scheme.
Multi-Scale Residual Aggregation Feature Pyramid Network for Object Detection
Authors: Wang Hongyang , Wang Tiejun
Published: December 2022

The effective use of multi-scale features remains an open problem for object detection tasks. Recently, proposed object detectors have usually used Feature Pyramid Networks (FPN) to fuse multi-scale features. Since Feature Pyramid Networks use a relatively simple feature map fusion approach, it can lead to the loss or misalignment of semantic information in the fusion process. Several works have demonstrated that using a bottom-up structure in a Feature Pyramid Network can shorten the information path between lower layers and the topmost feature, allowing an adequate exchange of semantic information from different layers. We further enhance the bottom-up path by proposing a multi-scale residual aggregation Feature Pyramid Network (MSRA-FPN), which uses a unidirectional cross-layer residual module to aggregate features from multiple layers bottom-up in a triangular structure to the topmost layer. In addition, we introduce a Residual Squeeze and Excitation Module to mitigate the aliasing effects that occur when features from different layers are aggregated. MSRA-FPN enhances the semantic information of the high-level feature maps, mitigates the information decay during feature fusion, and enhances the detection capability of the model for large objects. It is experimentally demonstrated that our proposed MSRA-FPN improves the performance of the three baseline models by 0.5–1.9% on the PASCAL VOC dataset and is also quite competitive with other state-of-the-art FPN methods. On the MS COCO dataset, our proposed method can also improve the performance of the baseline model by 0.8% and the baseline model’s performance for large object detection by 1.8%. To further validate the effectiveness of MSRA-FPN for large object detection, we constructed the Thangka Figure Dataset and conducted comparative experiments. It is experimentally demonstrated that our proposed method improves the performance of the baseline model by 2.9–4.7% on this dataset and can reach up to 71.2%.
A Brief Analysis of Multimodal Medical Image Fusion Techniques
Authors: Saleh Mohammed Ali , Ali AbdElmgeid A. , Ahmed Kareem , Sarhan Abeer M.
Published: December 2022

Recently, image fusion has become one of the most promising fields in image processing since it plays an essential role in different applications, such as medical diagnosis and clarification of medical images. Multimodal Medical Image Fusion (MMIF) enhances the quality of medical images by combining two or more medical images from different modalities to obtain an improved fused image that is clearer than the original ones. Choosing the best MMIF technique which produces the best quality is one of the important problems in the assessment of image fusion techniques. In this paper, a complete survey on MMIF techniques is presented, along with medical imaging modalities, medical image fusion steps and levels, and the assessment methodology of MMIF. There are several image modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT). Medical image fusion techniques are categorized into six main categories: spatial domain, transform fusion, fuzzy logic, morphological methods, and sparse representation methods. The MMIF levels are pixel-level, feature-level, and decision-level. The fusion quality evaluation metrics can be categorized as subjective/qualitative and objective/quantitative assessment methods. Furthermore, a detailed comparison between obtained results for significant MMIF techniques is also presented to highlight the pros and cons of each fusion technique.
Towards Channel-Wise Bidirectional Representation Learning with Fixed-Point Positional Encoding for SoH Estimation of Lithium-Ion Battery
Authors: Pham Thien , Truong Loi , Bui Hung , Tran Thang , Garg Akhil , Gao Liang , Quan Tho
Published: December 2022

5G is the fifth generation of cellular networks and has been used in a lot of different areas. 5G often requires sudden rises in power consumption. To stabilize the power supply, a 5G system requires a lithium-ion battery (LIB) or a mechanism called AC main modernization to provide energy support during the power peak periods. The LIB approach is the best option in terms of simplicity and maintainability. Moreover, a 5G system requires not only high-performance energy but also the ability of tracking and prediction. Therefore, the requirement for a smart power supply for lithium-ion batteries with temporal monitoring and estimation is highly desirable. In this paper, we focus on artificial intelligence (AI) improvements to increase the accuracy of LIB state-of-health prediction. By observing the SeqInSeq nature of the battery data, our approach uses self-attention and fixed-point positional encoding. We also take advantage of autoregression to archive the trainable dependency from a non-linear branch and a linear branch in creating the final output. Compared with the current state-of-the-art (SOTA) method, our experimental results show that we provide better accuracy, compared with the baseline output using the NASA and CALCE datasets. From the same setting, we archive a reduction of 20.08% root mean square error (RMSE) and 29.01% mean absolute percentage error (MAPE) on NASA loss, compared to the SOTA approaches. On CALCE, the numbers are a 5.99% RMSE and 12.59% MAPE decrement, which is significant.
Demagnetization Analysis of Modular SPM Machine Based on Coupled Electromagnetic-Thermal Modelling
Authors: Zhang Wei , Li Guang-Jin , Zhu Zi-Qiang , Ren Bo , Chong Yew , Michon Melanie
Published: December 2022

This paper investigates magnet demagnetization characteristics of the modular permanent magnet machine. The influence of flux gaps on magnet flux density, losses distribution, torque and demagnetization are analyzed for different operating conditions. The magnet demagnetizations caused by three sources, such as the PM field, the armature field, and the magnet temperature rise, are individually investigated using the frozen permeability method. Furthermore, coupled electromagnetic (EM)-thermal modelling is also adopted in this paper to fully reveal the advantages of the modular machine in improving machine EM performances. This is essential due to the temperature-dependent properties of the machines, such as the magnet remanence, coercivity, and copper resistivity. For comparison propose, the EM performances with a particular focus on the demagnetization withstand capability for both the modular and non-modular machines are investigated based on the EM-thermal coupling. It is found that, compared to the non-modular machine, the modular machine can achieve higher torque, higher efficiency, and better demagnetization withstand capability.
The Quest for Renewable Energy—Effects of Different Asphalt Mixes and Laboratory Loading on Piezoelectric Energy Harvesters
Authors: Walubita Lubinda F. , Faruk Abu N. M. , Helffrich Jerome , Dessouky Samer , Kamisa Luckson , Roshani Hossein , Montoya Arturo
Published: December 2022

In furtherance of the quest for green renewable and sustainable energy, an effort was made in this laboratory study to generate and harvest electric power from hot-mix asphalt (HMA); a viscoelastic material that is widely used for road construction. The underlying hypothesis is that the mechanical vibrations and strain energy induced by vehicle loading on the road (pavement) can be harnessed and converted into usable electric power by embedding piezoelectric sensors within the HMA layers of the pavement structure. To investigate the effects of HMA mix type on the generated energy, four commonly used Texas mix types, namely Type B (coarse-graded), Type C (dense-graded), Type D (dense-to-fine graded), and Type F (fine-graded), with up to seven different HMA mix-design volumetric characteristics were comparatively evaluated in the laboratory. In the study, the effects of loading, namely load magnitude and loading frequency, were investigated by simulating the traffic loading in the laboratory through comparative testing with the Hamburg wheel-tracking tester (HWTT) and the universal testing machine (UTM), respectively, at different temperature conditions. A prototype highway sensing and energy conversion (HiSEC) module with piezoelectric sensors was utilized for converting the applied stress on the HMA into harvestable electric energy during HWTT and UTM laboratory testing, respectively. The generated electric current, voltage, and power were measured and quantified using a multipurpose power meter. Overall, the study demonstrated that there is promising potential to harvest energy from HMA when subjected to transient loading under different temperature conditions. However, further refinement of the HiSEC module and piezoelectric sensors is still warranted to optimize the power generation and harvesting capacity, both in terms of efficiency and power output.
Design and Analysis of a Novel Opposite Trapezoidal Flow Channel for Solid Oxide Electrolysis Cell Stack
Authors: Zhang Zhen , Guan Chengzhi , Xie Leidong , Wang Jian-Qiang
Published: December 2022

High efficiency, raw material availability, and compatibility with downstream systems will enable the Solid Oxide Electrolysis Cell (SOEC) to play an important role in the future energy transition. However, the SOEC stack’s performance should be improved further by utilizing a novel flow-field design, and the channel shape is a key factor for enhancing gas transportation. To investigate the main effects of the novel channel design with fewer calculations, we assumed ideal gas laminar flows in the cathode channel. Furthermore, the cathode support layer thickness and electrical contact resistance are ignored. The conventional channel flow is validated first with mesh independence, and then the performance difference between the conventional and novel designs is analyzed using COMSOL Multiphysics. The process parameters such as velocity, pressure, current density, and mole concentration are compared between the conventional and novel designs, demonstrating that the novel design significantly improves electrolysis efficiency. Furthermore, it directly increased the concentration of product hydrogen in the novel channel. In addition to enhancing convection and diffusion of reaction gases in neighboring channels, the simple structure makes it easy to manufacture, which is advantageous for accelerating commercial use of the novel design.
Comparative Analysis of XLPE and Thermoplastic Insulation-Based HVDC Power Cables
Authors: Lee Jae-In , Jeong Woo-Hee , Dinh Minh-Chau , Yu In-Keun , Park Minwon
Published: December 2022

The application of cross-linked polyethylene (XLPE) cables to voltage sourced converter (VSC)-based high voltage direct current (HVDC) systems has already been technically verified and has become common, and thermoplastic (TP) is attracting attention as an insulation material for next-generation cables due to the recent development of material-related technologies. However, studies related to TP cables are mainly focused on improving material properties, and studies related to cable systems are insufficient. In this paper, XLPE and TP cables were designed for application to VSC-based HVDC systems, and major characteristics such as electric field distribution and thermal stability were compared and analyzed through overvoltage simulation. The insulation design method of HVDC cable was presented, and the design was performed using XLPE and TP insulation materials. The temperature and electric field profiles of the cables were also analyzed through a finite element method simulation. To analyze the performance of the designed cable, it was simulated with the PSCAD/EMTDC program. Based on the simulation results, the major characteristics of XLPE and TP cables were compared and analyzed. Results showed that in the case of TP cables, insulation properties were excellent, but thermal conductivity was relatively low; therefore, countermeasures are needed.
The Economic Dimension of Using the Integration of Highway Sound Screens with Solar Panels in the Process of Generating Green Energy
Authors: Coban Hasan , Lewicki Wojciech , Miśkiewicz Radosław , Drożdż Wojciech
Published: December 2022

One of the research areas related to renewable energy sources is the search for new applications for currently used technologies. An important postulate is to achieve the synergy effect by including the existing infrastructure in this process. The innovation described in this article is the integration of solar cells into a modular sound barrier on the highway. The contribution of this article is mainly based on the introduction of the potential feasibility of the first Turkish solar highway, describing the installation of PV systems based on a multi-criteria evaluation (azimuth angle, loss of shade, and dirt). The aim of the study is to determine the economic dimension by examining the costs of implementing such an investment and answering the question concerning the efficiency of solar cells on sound barriers. The research took into account various scenarios regarding the shadow effect and inclination of solar panels and their impact on solar energy production in the example of a given case study, an identified urban area. The research is limited to the model for assessing the feasibility of solar cells on sound barriers based on NPV and the essence of the relationship regarding the LCOE averaged electricity costs. In this study, the function and technical parameters of solar cells are not specified, but the article is limited to the basic information and characteristics required to investigate the payback period and profitability of an investment. Research has shown that if the investment is carried out in accordance with the proposed model, the expected amount of energy produced is 62,257 kWh per year. This value corresponds to the average consumption of electricity in a household by 24 apartments for a given destination, or to providing energy for a selected electric vehicle for one year of its operation, at the same time leading to a significant reduction of CO2. The presented research can find practical application in the area of developing a strategy for the development and popularization of renewable energy sources while increasing the use of the existing road infrastructure, presenting an important postulate in promoting this type of solution for other geographic destinations.
Building Energy Simulations Based on Weather Forecast Meteorological Model: The Case of an Institutional Building in Greece
Authors: Giama Effrosyni , Chantzis Georgios , Kontos Serafim , Keppas Stavros , Poupkou Anastasia , Liora Natalia , Melas Dimitrios
Published: December 2022

The vision of decarbonization creates the need to design and construct even more energy-efficient buildings. This current target is even more compelling and challenging. The main issue when designing energy-efficient buildings is to identify present and future building energy requirements. A trending method for solving this problem is dynamic building energy simulation. One of the main inputs during energy simulation is weather data. However, the real problem lies in the fact that standard weather data are good at defining the present situation, and they help in designing buildings that behave efficiently under current climate conditions. To achieve the goal of constructing climate proof buildings, the Weather Research and Forecast meteorological model (WRF) was used to predict future climate scenarios. At first, data from previous years (2006–2010) were used to represent the current climate. The model was used to generate future climate data. Thus, results were produced for 5 year periods 2046–2050 and 2096–2100. These data were used for the energy simulation of an office building in Thessaloniki, Greece. The simulation results showed a reduction in heating loads by approximately 20% in the long term and a simultaneous impressive increase in cooling loads by 60%, highlighting the inadequacy of the existing building shell, as well as the heating, ventilation, and air-conditioning (HVAC) system design.
Coal or Biomass? Case Study of Consumption Behaviour of Households in the Czech Republic
Authors: Zimmermannova Jarmila , Smilnak Richard , Perunova Michaela , Ameir Omar
Published: December 2022

Renewable sources play a crucial role in the decarbonisation process of the current linear economy, aimed at reaching the 2030 climate objectives and fulfilling the EU’s long-term strategy of achieving carbon neutrality by 2050. All economic subjects, including households, can contribute to the Green Deal goals. The main goal of this paper is to evaluate households’ consumption of energy sources for heating purposes in the Czech Republic in the period 2003–2020 and depict possible drivers of switching to biomass. For this, various data were collected, such as data published by the Czech Statistical Office, ministries, and other national authorities, as well as data from Eurostat. Concerning methods, data analysis, correlation analysis, and regression analysis were used. Different models focus on the substitution effect, rebound effect, and behaviour of different kinds of households. The results show a substitution effect connected with the consumption of coal, electricity, and biomass for heating purposes. Many households substituted coal for biomass in the observed period. On the other hand, the environmental impact of such substitution is not significant, as carbon emissions and emissions of other pollutants are stable. The substitution of fuels should be accompanied by technological change, e.g., improvement of combustion boilers. Moreover, households of pensioners are the key economic subjects with the highest impact on biomass consumption for heating purposes. Against this background we recommend policies to support households in replacing their boilers for more environmentally friendly ones.
Analysis of the Influence of Structure–Soil–Structure Interaction on the Seismic Response for Nuclear Power Plant ASTS
Authors: Li Liang , Chen Qun , Pan Rong
Published: December 2022

After the Fukushima nuclear accident in Japan, whether the structural safety of the nuclear power plant (NPP) can be guaranteed under an earthquake has been of wide concern. Automatic seismic trip systems (ASTS) have been deployed in NPPs. There are generally two units or more double units on the one NPP site, and the vibration energy of the structure can certainly affect its adjacent structures through the soil, and there is energy transfer and conversion between adjacent structures. At present, the distance between two reactors of NPPs of different reactor types is generally 100–200 m (this distance is referring to the distance between the centers of two reactors, which is slightly different for different reactor types). In the past, seismic instruments were used in the ASTS and the setting of shutdown threshold, so the impact of only one unit was considered in the structural analysis of NPPs, and the interaction of two reactors through site conditions was not considered. In addition, as the site conditions of NPPs become more and more complex, it is necessary to consider the impact of one reactor structure on another reactor structure through the soil on the same site under an earthquake. In order to analyze the influence of structure–soil–structure interaction (SSSI), a three-dimensional refined finite element model of NPPs is established in this paper. The soil–structure interaction (SSI) is considered by using viscoelastic boundary. The seismic responses of different positions of the raft foundation and NPP structure, under the conditions of considering SSI effect (one reactor) and SSSI effect (two reactors), are compared. The influence of SSSI effect on the seismic responses of the raft foundation and NPP structure is revealed. It is proposed that SSSI effect should be reasonably considered according to the site conditions for the structural analysis of NPPs for the NPP ASTS.