bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Finite Element Model for the Study of Harmful Vibrations on the Aging Spine
Authors: Verma Shivam , Singh Gurpreet , Chanda Arnab
Published: May 2023

The human spine is susceptible to a wide variety of adverse consequences from vibrations, including lower back discomfort. These effects are often seen in the drivers of vehicles, earth-moving equipment, and trucks, and also in those who drive for long hours in general. The human spine is composed of vertebrae, discs, and tissues that work together to provide it with a wide range of movements and significant load-carrying capability needed for daily physical exercise. However, there is a limited understanding of vibration characteristics in different age groups and the effect of vibration transmission in the spinal column, which may be harmful to the different sections. In this work, a novel finite element model (FEM) was developed to study the variation of vibration absorption capacity due to the aging effect of the different sections of the human spine. These variations were observed from the first three natural frequencies of the human spine structure, which were obtained by solving the eigenvalue problem of the novel finite element model for different ages. From the results, aging was observed to lead to an increase in the natural frequencies of all three spinal segments. As the age increased beyond 30 years, the natural frequency significantly increased for the thoracic segment, compared to lumber and cervical segments. A range of such novel findings indicated the harmful frequencies at which resonance may occur, causing spinal pain and possible injuries. This information would be indispensable for spinal surgeons for the prognosis of spinal column injury (SCI) patients affected by harmful vibrations from workplaces, as well as manufacturers of automotive and aerospace equipment for designing effective dampers for better whole-body vibration mitigation.
Info-Autopoiesis and the Limits of Artificial General Intelligence
Authors: Cárdenas-García Jaime F.
Published: May 2023

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might be harmful to society looms in the background, as well as the likelihood that it will never deliver on the purported promise of artificial general intelligence (AGI). Currently, the prospects for the development of AI and AGI are more a matter of opinion than based on a consistent methodological approach. Thus, there is a need to take a step back to develop a general framework from which to evaluate current AI efforts, which also permits the determination of the limits to its future prospects as AGI. To gain insight into the development of a general framework, a key question needs to be resolved: what is the connection between human intelligence and machine intelligence? This is the question that needs a response because humans are at the center of AI creation and realize that, without an understanding of how we become what we become, we have no chance of finding a solution. This work proposes info-autopoiesis, the self-referential, recursive, and interactive process of self-production of information, as the needed general framework. Info-autopoiesis shows how the key ingredient of information is fundamental to an insightful resolution to this crucial question and allows predictions as to the present and future of AGI.
First Pediatric Exercise Oncology Congress (PEOC)
Authors: Culos-Reed S. Nicole , Götte Miriam
Published: May 2023

On behalf of the 1st Pediatric Exercise Oncology Congress, we are pleased to present the abstracts from the 2022 Conference, the inaugural gathering of an international congress. The conference was held virtually on 7 and 8 April 2022. This conference brought together key stakeholders in pediatric exercise oncology, including multidisciplinary professionals from exercise, rehabilitation medicine, psychology, nursing, and medicine. The participants included clinicians, researchers, and community-based organizations. Twenty-four abstracts were selected for presentations (10–15-min oral presentations). In addition, there were five invited speakers with 20 min presentations and two keynotes with 45 min presentations. We congratulate all the presenters on their research work and contribution.
COVID-19 and Breast Cancer: Analysis of Surgical Management of a Large Referral Center during the 2020–2021 Pandemic Period
Authors: Borella Fulvio , Bertero Luca , Di Giovanni Fabrizia , Witel Gianluca , Orlando Giulia , Ricci Alessia Andrea , Pittaro Alessandra , Castellano Isabella , Cassoni Paola
Published: May 2023

Background: Coronavirus disease-19 (COVID-19) has spread worldwide since December 2019 and was officially declared a pandemic in March 2020. Due to the rapid transmission and the high fatality rate, drastic emergency restrictions were issued, with a negative impact on routine clinical activities. In particular, in Italy, many authors have reported a reduction in the number of breast cancer diagnoses and critical problems in the management of patients who accessed the breast units during the dramatic first months of the pandemic. Our study aims to analyze the global impact of COVID-19 in the two years of the pandemic (2020–2021) on the surgical management of breast cancer by comparing them with the previous two years. Methods: In our retrospective study, we analyzed all cases of breast cancer diagnosed and surgically treated at the breast unit of “Città della Salute e della Scienza” in Turin, Italy, making a comparison between the 2018–2019 pre-pandemic period and the 2020–2021 pandemic period. Results: We included in our analysis 1331 breast cancer cases surgically treated from January 2018 to December 2021. A total of 726 patients were treated in the pre-pandemic years and 605 in the pandemic period (−121 cases, 9%). No significant differences were observed regarding diagnosis (screening vs. no screening) and timing between radiological diagnosis and surgery for both in situ and invasive tumors. There were no variations in the breast surgical approach (mastectomy vs. conservative surgery), while a reduction in axillary dissection compared to the sentinel lymph node in the pandemic period was observed (p-value < 0.001). Regarding the biological characteristics of breast cancers, we observed a greater number of grades 2–3 (p-value = 0.007), pT stage 3–4 breast cancer surgically treated without previous neoadjuvant chemotherapy (p-value = 0.03), and a reduction in luminal B tumors (p-value = 0.007). Conclusions: Overall, we report a limited reduction in surgical activity for breast cancer treatment considering the entire pandemic period (2020–2021). These results suggest a prompt resumption of surgical activity similar to the pre-pandemic period.
Environmental Factors Affecting Amphibian Communities in River Basins of the Southern Apennines
Authors: Nessi Alessandro , Cioccarelli Sara , Tremolada Paolo , Gariano Pasquale , Grandinetti Maria , Balestrieri Alessandro , Manenti Raoul
Published: May 2023

The study of the environmental features affecting amphibian communities is a priority task for addressing effective conservation initiatives. In the southern edge of the Apennines (Sila Massif, Calabria Region, Italy), we surveyed the distribution of amphibians in lotic freshwater habitats (eight rivers, 17 sampling stations, 87 transects) and recorded nine environmental variables, including the occurrence of potential predators (fish and Eurasian otter Lutra lutra), potentially affecting the distribution and breeding success of amphibian species. A total of seven amphibian species was recorded (75.9% of transects). Fish occurred in all rivers while the otter was found in four rivers (Amato, Lese, Neto, and Savuto). Illuminance and bank heterogeneity were the main factors affecting amphibian reproduction, while neither otter nor fish presence showed significant effects on the amphibian community. Overall, habitat complexity and coevolution history seemed to shape the distribution of amphibians and their breeding sites, while the ongoing recolonization of the study area by the otter is expected to have a negligible impact on the richness of the amphibian community.
Isolating Greater Cane Rat Populations (Thryonomys swinderianus) from Eastern Arc Mountains, Tanzania: Linking Diversity to Morphometric and Molecular Characteristics
Authors: Kilwanila Shadia I. , Lyimo Charles M. , Makundi Rhodes H. , Rija Alfan A.
Published: May 2023

Evolutionary information on the greater cane rat (Thryonomys swinderianus) in the Eastern and Southern African regions is scarce, making population management and conservation of the species challenging. We studied T. swinderianus populations from two spatially isolated Eastern Arc Mountains in Tanzania to link molecular and geometric–morphological evidence to characterize these populations’ diversity. Fecal samples (n = 50) and skulls (n = 99) of T. swinderianus were collected from Udzungwa (north and south) and Uluguru mountains (urban and rural sites) and analyzed using molecular and geomorphometry techniques. Molecular analysis grouped the population into three distinct clades based on the location where the samples were collected, while the morphometric method was not able to distinctively separate the populations. Both methods revealed that the population obeyed the isolation by distance model with higher genetic distance between the Udzungwa and Uluguru populations and lower distance between Uluguru urban and rural populations. Both Mahalanobis and Procrustes distances in skull landmarks between the Udzungwa and Uluguru populations were significantly higher across the dorsal, ventral, and lateral views of the skulls, suggesting strongly that molecular and morphometric methods applied together can be useful in characterizing the population traits of the least known species. Our study suggests genetic and morphometric methods could complement each other in understanding the evolutionary biology and within-species diversity of vertebrate species that do not exhibit strong intra-species differentiation.
Ecological Basis of Ecosystem Services and Management of Wetlands Dominated by Common Reed (Phragmites australis): European Perspective
Authors: Čížková Hana , Kučera Tomáš , Poulin Brigitte , Květ Jan
Published: May 2023

The common reed (Phragmites australis) is a frequent dominant species in European wetlands. Yet, its performance can vary in response to different combinations of environmental factors. This accounts for P. australis decline on deep-water sites, its stable performance in constructed wetlands with subsurface horizontal flow and its expansion in wet meadows. Reed stands provide habitats for nesting, feeding or roosting of vulnerable bird species. Conservation measures aim at preventing or stopping the decline of P. australis stands, increasing their micro-habitat heterogeneity and reducing the reed penetration into wet meadows. Service-oriented measures aim at providing suitable conditions for direct use of reed stalks for roof thatching or as a renewable energy crop or the use of the reed-dominated habitats for waterfowl hunting, cattle grazing or fishing. The compatibility between nature conservation and different socioeconomic uses can be promoted by collective agreements, agri-environmental contracts or payments for ecosystem services of the reedbeds. In situations with multiple uses, a modelling approach considering the participation of all the stakeholders concerned can be a useful tool for resolving conflicts and developing a shared vision of the respective socio-ecosystem.
Comparative Study of the Myocardium of Patients from Four COVID-19 Waves
Authors: Mitrofanova Lubov Borisovna , Makarov Igor Aleksandrovich , Gorshkov Andrey Nikolaevich , Runov Andrey Leonidovich , Vonsky Maxim Sergeevich , Pisareva Maria Mikhailovna , Komissarov Andrey Borisovich , Makarova Taiana Alekseevna , Li Qingli , Karonova Tatiana Leonidovna , Konradi Alexandra Olegovna , Shlaykhto Evgeny Vladimirovich
Published: May 2023

Background: Few studies have compared COVID-19 patients from different waves. This study aims to conduct a clinical and morphological analysis of patients who died from COVID-19 during four waves. Methods: The study involved 276 patients who died from COVID-19 during four waves, including 77 patients in the first wave, 119 patients in the second wave, and 78 patients in the third wave. We performed a histological examination of myocardium samples from autopsies and additionally analyzed the samples by PCR. We conducted immunohistochemistry of the myocardium for 21 samples using antibodies against CD3, CD45, CD8, CD68, CD34, Ang1, VWF, VEGF, HLA-DR, MHC1, C1q, enteroviral VP1, and SARS-CoV-2 spike protein. We also did immunofluorescent staining of three myocardial specimens using VP1/SARS-CoV-2 antibody cocktails. Further, we ran RT-ddPCR analysis for 14 RNA samples extracted from paraffin-embedded myocardium. Electron microscopic studies of the myocardium were also performed for two samples from the fourth wave. Results: Among the 276 cases, active myocarditis was diagnosed in 5% (15/276). Of these cases, 86% of samples expressed VP1, and individual cells contained SARS-CoV-2 spike protein in 22%. Immunofluorescence confirmed the co-localization of VP1 and SARS-CoV-2 spike proteins. ddPCR did not confidently detect SARS-CoV-2 RNA in the myocardium in any myocarditis cases. However, the myocardium sample from wave IV detected a sub-threshold signal of SARS-CoV-2 by qPCR, but myocarditis in this patient was not confirmed. Electron microscopy showed several single particles similar to SARS-CoV-2 virions on the surface of the endothelium of myocardial vessels. A comparison of the cardiovascular complication incidence between three waves revealed that the incidence of hemorrhage (48 vs. 24 vs. 17%), myocardial necrosis (18 vs. 11 vs. 4%), blood clots in the intramural arteries (12 vs. 7 vs. 0%), and myocarditis (19 vs. 1 vs. 6%) decreased over time, and CD8-T-killers appeared. Immunohistochemistry confirmed the presence of endotheliitis in all 21 studied cases. Conclusions: This study compared myocardial damage in patients who died during three COVID-19 waves and showed a decrease in the incidence of endotheliitis complications (thrombosis, hemorrhage, necrosis) and myocarditis over time. However, the connection between myocarditis and SARS-CoV-2 infection remains unproven.
HERMES: A Data and Specimens Transporter from the Stratosphere to the Ground—The First Experimental Flight
Authors: Romeo Giovanni , Adobbato Pasquale , Bacci Simone , Di Stefano Giuseppe , Iarocci Alessandro , Lepore Amedeo , Mari Massimo , Masi Silvia , Pongetti Francesco , Spinelli Giuseppe , Vallocchia Massimiliano
Published: May 2023

Large stratospheric balloons are the easiest access to near space. Large long duration balloons (LDBs) can float in the stratosphere for weeks collecting measurements (e.g., astrophysical or geophysical data) or samples (e.g., contaminants, volcanic ash, micrometeorites). The recovery of data media and samples is a common problem in this type of experiment because direct radio communication becomes useless when the balloon crosses the horizon, and satellite links are too slow and expensive. For this reason, physical recovery of the payload is mandatory to obtain experimental results, which is a difficult task, especially in polar regions. The goal of HERMES (HEmera Returning MESsenger) is to allow researchers to obtain experimental data prior to payload recovery. HERMES is a system equipped with an autonomous glider capable of physically transporting data and samples from the stratosphere to a recovery point on the ground. The glider is installed on the balloon payload via a remotely controlled release system and is connected to the main computer to store a copy of the scientific data and to receive the geographic coordinates of the recovery point. This allows scientists to obtain experimental results before recovering the payload. The article describes HERMES and the first experimental flight of the entire system, which was conducted at Esrange Space Center (Kiruna, Sweden) in July 2022.
On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)
Authors: Saeed Zubair , Yousaf Muhammad Haroon , Ahmed Rehan , Velastin Sergio A. , Viriri Serestina
Published: May 2023

Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of many object detectors. The accuracy of the detection and the efficiency of the inference are always trade-offs. We modified the architecture of CenterNet and used different CNN-based backbones of ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Res2Net50, Res2Net101, DLA-34, and hourglass14. A comparison of the modified CenterNet with nine CNN-based backbones is conducted and validated using three challenging datasets, i.e., VisDrone, Stanford Drone dataset (SSD), and AU-AIR. We also implemented well-known off-the-shelf object detectors, i.e., YoloV1 to YoloV7, SSD-MobileNet-V2, and Faster RCNN. The proposed approach and state-of-the-art object detectors are optimized and then implemented on cross-edge platforms, i.e., NVIDIA Jetson Xavier, NVIDIA Jetson Nano, and Neuro Compute Stick 2 (NCS2). A detailed comparison of performance between edge platforms is provided. Our modified CenterNet combination with hourglass as a backbone achieved 91.62%, 75.61%, and 34.82% mAP using the validation sets of AU-AIR, SSD, and VisDrone datasets, respectively. An FPS of 40.02 was achieved using the ResNet18 backbone. We also compared our approach with the latest cutting-edge research and found promising results for both discrete GPU and edge platforms.
Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory
Authors: Tang Yongchuan , Zhou Yonghao , Zhou Ying , Huang Yubo , Zhou Deyun
Published: May 2023

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.
Optimized Traffic Light System with AIC and Application to the 2021 M6.7 Yangbi Earthquake Sequence
Authors: Wang Rui , Chang Ying , Han Peng , Miao Miao , Zeng Zhiyi , Shi Haixia , Li Danning , Liu Lifang , Su Youjin
Published: May 2023

One important question in earthquake prediction is whether a moderate or large earthquake will be followed by an even bigger one. Through temporal b-value evolution analysis, the traffic light system can be used to estimate if an earthquake is a foreshock. However, the traffic light system does not take into account the uncertainty of b-values when they constitute a criterion. In this study, we propose an optimization of the traffic light system with the Akaike Information Criterion (AIC) and bootstrap. The traffic light signals are controlled by the significance level of the difference in b-value between the sample and the background rather than an arbitrary constant. We applied the optimized traffic light system to the 2021 Yangbi earthquake sequence, which could be explicitly recognized as foreshock–mainshock–aftershock using the temporal and spatial variations in b-values. In addition, we used a new statistical parameter related to the distance between earthquakes to track earthquake nucleation features. We also confirmed that the optimized traffic light system works on a high-resolution catalog that includes small-magnitude earthquakes. The comprehensive consideration of b-value, significance probability, and seismic clustering might improve the reliability of earthquake risk judgment.
An SSL-PUF Based Access Authentication and Key Distribution Scheme for the Space–Air–Ground Integrated Network
Authors: Xu Liwei , Wu Han , Xie Jianguo , Yuan Qiong , Sun Ying , Shi Guozhen , Luo Shoushan
Published: May 2023

The Space–Air–Ground Integrated Network (SAGIN) expands cyberspace greatly. Dynamic network architecture, complex communication links, limited resources, and diverse environments make SAGIN’s authentication and key distribution much more difficult. Public key cryptography is a better choice for terminals to access SAGIN dynamically, but it is time-consuming. The semiconductor superlattice (SSL) is a strong Physical Unclonable Function (PUF) to be the hardware root of security, and the matched SSL pairs can achieve full entropy key distribution through an insecure public channel. Thus, an access authentication and key distribution scheme is proposed. The inherent security of SSL makes the authentication and key distribution spontaneously achieved without a key management burden and solves the assumption that excellent performance is based on pre-shared symmetric keys. The proposed scheme achieves the intended authentication, confidentiality, integrity, and forward security, which can defend against masquerade attacks, replay attacks, and man-in-the-middle attacks. The formal security analysis substantiates the security goal. The performance evaluation results confirm that the proposed protocols have an obvious advantage over the elliptic curve or bilinear pairings-based protocols. Compared with the protocols based on the pre-distributed symmetric key, our scheme shows unconditional security and dynamic key management with the same level performance.
Semi-Poisson Statistics in Relativistic Quantum Billiards with Shapes of Rectangles
Authors: Dietz Barbara
Published: May 2023

Rectangular billiards have two mirror symmetries with respect to perpendicular axes and a twofold (fourfold) rotational symmetry for differing (equal) side lengths. The eigenstates of rectangular neutrino billiards (NBs), which consist of a spin-1/2 particle confined through boundary conditions to a planar domain, can be classified according to their transformation properties under rotation by π (π/2) but not under reflection at mirror-symmetry axes. We analyze the properties of these symmetry-projected eigenstates and of the corresponding symmetry-reduced NBs which are obtained by cutting them along their diagonal, yielding right-triangle NBs. Independently of the ratio of their side lengths, the spectral properties of the symmetry-projected eigenstates of the rectangular NBs follow semi-Poisson statistics, whereas those of the complete eigenvalue sequence exhibit Poissonian statistics. Thus, in distinction to their nonrelativistic counterpart, they behave like typical quantum systems with an integrable classical limit whose eigenstates are non-degenerate and have alternating symmetry properties with increasing state number. In addition, we found out that for right triangles which exhibit semi-Poisson statistics in the nonrelativistic limit, the spectral properties of the corresponding ultrarelativistic NB follow quarter-Poisson statistics. Furthermore, we analyzed wave-function properties and discovered for the right-triangle NBs the same scarred wave functions as for the nonrelativistic ones.
Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm
Authors: Zhao Kai , Du Chunjie , Tan Guangxin
Published: May 2023

Basketball is a popular sport worldwide, and many researchers have utilized various machine learning models to predict the outcome of basketball games. However, prior research has primarily focused on traditional machine learning models. Furthermore, models that rely on vector inputs tend to ignore the intricate interactions between teams and the spatial structure of the league. Therefore, this study aimed to apply graph neural networks to basketball game outcome prediction, by transforming structured data into unstructured graphs, to represent the interactions between teams in the 2012–2018 NBA season dataset. Initially, the study used a homogeneous network and undirected graph to build a team representation graph. The constructed graph was fed into a graph convolutional network, which yielded an average success rate of 66.90% in predicting the outcome of games. To improve the prediction success rate, feature extraction based on the random forest algorithm was combined with the model. The fused model yielded the best results, and the prediction accuracy was improved to 71.54%. Additionally, the study compared the results of the developed model with previous studies and the baseline model. Our proposed method considers the spatial structure of teams and the interaction between teams, resulting in superior performance in basketball game outcome prediction. The results of this study provide valuable insights for basketball performance prediction research.
Universality of Form: The Case of Retinal Cone Photoreceptor Mosaics
Authors: Beygi Alireza
Published: May 2023

Cone photoreceptor cells are wavelength-sensitive neurons in the retinas of vertebrate eyes and are responsible for color vision. The spatial distribution of these nerve cells is commonly referred to as the cone photoreceptor mosaic. By applying the principle of maximum entropy, we demonstrate the universality of retinal cone mosaics in vertebrate eyes by examining various species, namely, rodent, dog, monkey, human, fish, and bird. We introduce a parameter called retinal temperature, which is conserved across the retinas of vertebrates. The virial equation of state for two-dimensional cellular networks, known as Lemaître’s law, is also obtained as a special case of our formalism. We investigate the behavior of several artificially generated networks and the natural one of the retina concerning this universal, topological law.
From the Big Bang to Life beyond Earth: German Preservice Physics Teachers’ Conceptions of Astronomy and the Nature of Science
Authors: Hennig Fabian , Lipps Maximilian , Ubben Malte S. , Bitzenbauer Philipp
Published: May 2023

This article reports the findings of a qualitative study that aimed to explore the ideas of 22 preservice physics teachers regarding astronomy concepts both within and beyond the solar system, as well as their understanding of the Nature of Science in the context of astronomy. The study employed a combination of open-ended vignette tasks and ranking tasks, which were adapted from previous research. The results reveal that while the majority of the preservice teachers held appropriate conceptions of astronomy concepts, some of their ideas lacked depth and were superficial. Moreover, their understanding of the Nature of Science in the context of astronomy was found to be limited. This study highlights the importance of providing further education and training in this area, as well as the need to develop and test effective teaching strategies to enhance preservice teachers’ and physics education students’ understanding of astronomy concepts and the Nature of Science.
Insulated Gate Bipolar Transistor Reliability Study Based on Electro-Thermal Coupling Simulation
Authors: Yang Xiaoqing , Zhai Xiaoyu , Long Shanshan , Li Meng , Dong Hexin , He Yi
Published: May 2023

This paper presents a new electro-thermal coupling simulation method for evaluating the reliability of IGBT modules, which combines a numerical power loss model and a finite element model. To illustrate the method, a specific case of the Infineon FF50R12RT4 module operated with an SPWM signal is considered. Temperature and stress data are obtained and analyzed via electro-thermal simulation, and the service life of modules is calculated accordingly. The influence of ambient temperature and gate signal characteristics on power loss and service life of IGBT modules is discussed, which can be used for the optimization of work points. In this method, the effect of junction temperature on power loss is considered, and a more accurate electro-thermal simulation is achieved. The proposed method can be used to analyze the thermal and mechanical stresses of IGBT modules, predict the failure location, and assess the service life.
A New Control for Improving the Power Quality Generated by a Three-Level T-Type Inverter
Authors: Deffaf Brahim , Debdouche Naamane , Benbouhenni Habib , Hamoudi Farid , Bizon Nicu
Published: May 2023

A new controller based on a fractional-order synergetic controller (FOSC) is proposed for a three-level T-type inverter using a shunt active power filter (SAPF). The SAPF is designed to compensate for the reactive power and eliminate the current harmonics caused by non-linear loads, in cases of distorted or unbalanced source voltages. The proposed FOSC technique with the designed parameters and defined macro-variable is a robust control technique that operates well in both transient and steady-state scenarios, ensuring fast convergence and closed-loop system stability. The FOSC technique utilizes a phase-locked loop (PLL) technique on a self-tuning filter (STF) to enhance the SAPF’s ability to compensate current harmonics and reactive power in all situations involving non-linear loads and source voltage variations according to IEEE Std. 519. The proposed control was implemented and verified using Matlab software, where the obtained results were compared with the results of the conventional control based on proportional-integral (PI) controllers in different operating conditions. The results indicate that the proposed FOSC technique outperformed the traditional control in terms of DC voltage tracking and the minimization of the total harmonic distortion of the current.
The ZVS Class E/F3 Inverter Using Piezoelectric Transformers for Energy Extraction
Authors: Ashique Ratil H. , Arif M. Saad Bin , Bhatti Abdul Rauf , Al Mansur Ahmed , Maruf Md. Hasan , Shihavuddin ASM
Published: May 2023

Enhanced class E inverters (EFn or E/Fn) reduce the high peak switch voltage that is prevalent in class E inverters. Additionally, their stability and load regulation capabilities are improved as compared to class E inverters. This paper proposes an enhanced class E inverter (E/F3) with a piezoelectric transformer (PT) replacing the auxiliary resonant networks. This class E/F3 inverter is designed by adding a tuned auxiliary LC network at the third harmonic of the switching frequency to the class E inverter. Both the primary and auxiliary resonant networks are realized using a piezoelectric transformer (PT). The converter is simulated in LTspice and an experimental prototype is built and tested. It is found that the experimental results concur with the simulation results, with a measured efficiency of 90%. Thus, the theoretical design is verified and the concept of energy extraction is achieved.
Design of Plasmon Absorbing Structure Suitable for Super High Frequency
Authors: Bai Jin , Yang Qingzhen
Published: May 2023

This paper proposed a plasmonic absorbing structure suitable for super high frequency. The plasmon absorbing structure is a periodic square cavity structure; the bottom of the cavity is a metal plate, the wall of the cavity is a polyester plate, and metal frames loaded with lumped resistors are printed on both sides of the polyester plate. The transmission characteristics of the absorbing structure are studied using the finite difference time domain method. The results show that the plasmon resonance characteristics can be effectively improved by loading the lumped resistance reasonably. The absorption rate of the absorbing structure is over 80% in the 4.3–21.5 GHz frequency band, and in the 4.3–7.7 GHz and 14.2–21.5 GHz frequency bands, the absorption rate is around 90%. Under different polarization modes, it is less sensitive to the incident angle. The transmission response of the absorbing structure is measured in a microwave anechoic chamber, and the measurement results agree well with the simulation results. After replacing the metal bottom plate of the absorber with a metal cavity, the radar cross-section of the cavity is reduced by 99.45% at 10 GHz. It proves that the designed and fabricated absorbing structure has the broadband absorbing ability, high angular stability, and broad application prospects.
HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network
Authors: Zhong Hongwei , Wang Mingyang , Zhang Xinyue
Published: May 2023

Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively.
Instance Segmentation of Irregular Deformable Objects for Power Operation Monitoring Based on Multi-Instance Relation Weighting Module
Authors: Chen Weihao , Su Lumei , Lin Zhiwei , Chen Xinqiang , Li Tianyou
Published: May 2023

Electric power operation is necessary for the development of power grid companies, where the safety monitoring of electric power operation is difficult. Irregular deformable objects commonly used in electrical construction, such as safety belts and seines, have a dynamic geometric appearance which leads to the poor performance of traditional detection methods. This paper proposes an end-to-end instance segmentation method using the multi-instance relation weighting module for irregular deformable objects. To solve the problem of introducing redundant background information when using the horizontal rectangular box detector, the Mask Scoring R-CNN is used to perform pixel-level instance segmentation so that the bounding box can accurately surround the irregular objects. Considering that deformable objects in power operation workplaces often appear with construction personnel and the objects have an apparent correlation, a multi-instance relation weighting module is proposed to fuse the appearance features and geometric features of objects so that the relation features between objects are learned end-to-end to improve the segmentation effect of irregular objects. The segmentation mAP on the self-built dataset of irregular deformable objects for electric power operation workplaces reached up to 44.8%. With the same 100,000 training rounds, the bounding box mAP and segmentation mAP improved by 1.2% and 0.2%, respectively, compared with the MS R-CNN. Finally, in order to further verify the generalization performance and practicability of the proposed method, an intelligent monitoring system for the power operation scenes is designed to realize the actual deployment and application of the proposed method. Various tests show that the proposed method can segment irregular deformable objects well.
Assessing Impedance Analyzer Data Quality by Fractional Order Calculus: A QCM Sensor Case Study
Authors: Burda Ioan
Published: May 2023

The paper presents the theoretical, simulation, and experimental results on the QCM sensor based on the Butterworth van Dyke (BVD) model with lumped reactive motional circuit elements of fractional order. The equation of the fractional order BVD model of the QCM sensor has been derived based on Caputo definitions and its behavior around the resonant frequencies has been simulated. The simulations confirm the ability of fractional order calculus to cover a wide range of behaviors beyond those found in experimental practice. The fractional order BVD model of the QCM sensor is considered from the perspective of impedance spectroscopy to give an idea of the advantages that fractional order calculus brings to its modeling. For the true values of the electrical parameters of the QCM sensor based on the standard BVD model, the experimental investigations confirm the equivalence of the measurements after the standard compensation of the virtual impedance analyzer (VIA) and the measurements without compensation by fitting with the fractional order BVD model. From an experimental point of view, using fractional order calculus brings a new dimension to impedance analyzer compensation procedures, as well as a new method for validating the compensation.
Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems
Authors: Bae Sanghyeon , Joo Sunghyeon , Choi Junhyeon , Pyo Jungwon , Park Hyunjin , Kuc Taeyong
Published: May 2023

Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments.
An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction
Authors: Wang Yibing , Zhou Liang , Yang Zhutian , Wu Longwen , Yin Zhendong , Zhao Yaqin , Wu Zhilu
Published: May 2023

Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this paper, aiming at addressing the issue of poor recognition performance at low signal-to-noise ratios (SNRs) and the inability of deep features to effectively distinguish among all modulation types, we propose an optimization scheme for modulation recognition based on fine-tuning and feature re-extraction. In the proposed scheme, the network is firstly trained with the signals at high SNRs; then, the trained network is fine-tuned to the untrained network at low SNRs. Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. The simulation results demonstrate that the proposed optimization scheme can maximize the performance of the neural network in the recognition of signals that are easily confused and at low SNRs. Notably, the average recognition accuracy of the proposed scheme was 91.28% within an SNR range of −8 dB to 18 dB, which is an improvement of 8% to 17% in comparison with four existing schemes.
DiffuD2T: Empowering Data-to-Text Generation with Diffusion
Authors: Gong Heng , Feng Xiaocheng , Qin Bing
Published: May 2023

Surrounded by structured data, such as medical data, financial data, knowledge bases, etc., data-to-text generation has become an important natural language processing task that can help people better understand the meaning of those data by providing them with user-friendly text. Existing methods for data-to-text generation show promising results in tackling two major challenges: content planning and surface realization, which transform structured data into fluent text. However, they lack an iterative refinement process for generating text, which can enable the model to perfect the text step-by-step while accepting control over the process. In this paper, we explore enhancing data-to-text generation with an iterative refinement process via diffusion. We have four main contributions: (1) we use the diffusion model to improve the prefix tuning for data-to-text generation; (2) we propose a look-ahead guiding loss to supervise the iterative refinement process for better text generation; (3) we extract content plans from reference text and propose a planning-then-writing pipeline to give the model content planning ability; and (4) we conducted experiments on three data-to-text generation datasets and both automatic evaluation criteria (BLEU, NIST, METEOR, ROUGEL, CIDEr, TER, MoverScore, BLEURT, and BERTScore) and human evaluation criteria (Quality and Naturalness) show the effectiveness of our model. Our model can improve the competitive prefix tuning method by 2.19% in terms of a widely-used automatic evaluation criterion BLEU (BiLingual Evaluation Understudy) on WebNLG dataset with GPT-2 Large as the pretrained language model backbone. Human evaluation criteria also show that our model can improve the quality and naturalness of the generated text across all three datasets.
Limits on Cooperative Positioning for a Robotic Swarm with Time of Flight Ranging over Two-Ray Ground Reflection Channel
Authors: Staudinger Emanuel , Pöhlmann Robert , Dammann Armin , Zhang Siwei
Published: May 2023

Autonomous robotic swarms are envisioned for a variety of applications—for example, space exploration, search and rescue, and disaster management. Important features of a robotic swarm include its ability to share information within the network, to sense spatio-temporal processes such as gas distributions, and to collaboratively enhance its navigation. In environments without infrastructure, the swarm elements can cooperatively estimate their position, e.g., based on the time of flight of exchanged radio signals. Cooperative positioning performance depends on the radio propagation environment. Free-space path loss is commonly used for performance assessment, which is an optimistic assumption. In this work, we investigate the limits to cooperative positioning and ranging based on the time of flight of radio signals over the more realistic two-ray ground reflection channel. We show that we obtain a ranging bias caused by the radio signal component reflected from the ground, and that the ranging error becomes bias-limited. In the positioning domain, we investigate how the ranging bias affects the cooperative positioning performance. As a result, we gain in cooperation, but the achievable positioning performance is significantly worsened by the ranging bias. As a conclusion, the two-ray ground reflection model should be considered to obtain realistic cooperative positioning limits.
Deep Learning-Based Time Delay Estimation Using Ground Penetrating Radar
Authors: Lin Feng , Sun Meng , Mao Shiyu , Wang Bin
Published: May 2023

Time delay estimation (TDE) is of great interest for the thickness estimation of pavement using ground penetrating radar (a non-destructive testing tool that uses electromagnetic waves to probe civil engineering material), which determines the difference between the times of arrival of two incoming signals or backscattered echoes. However, conventional TDE methods suffer performance degradation because of limited resolution for thin layers and highly correlated backscattered echoes. In this paper, a deep neural network (DNN)-based TDE method is proposed. Firstly, a new DNN is constructed to classify and train the backscattered echoes; then, the time delays of the backscattered echoes can be estimated through the proposed DNN. The proposed method is based on the data processing of the backscattered echoes, which is more robust to the noise than conventional subspace-based methods (MUSIC, ESPRIT) and compressive sensing-based methods (OMP). The proposed method can directly process coherent backscattered echoes without decorrelation procedures, compared with MUSIC and ESPRIT. In addition, the proposed method is more powerful in resolving the close backscattered echoes than that of OMP. Simulation results show the efficiency of the proposed method in terms of signal-to-noise ratio (SNR) and BΔτ products. (The BΔτ products indicate the resolution of GPR, B is the frequency bandwidth of GPR and Δτ is the time delay between two incoming signals or backscattered echoes).