bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring
Authors: Gude Vinayaka , Corns Steven
Published: November 2022

Asphyxiation associated with metabolic acidosis is one of the common causes of fetal deaths. The paper aims to develop a feature extraction and prediction algorithm capable of identifying most of the features in the SISPORTO software package and late and variable decelerations. The resulting features were used for classification based on umbilical cord pH data. The algorithms developed here were used to predict cord pH levels. The prediction system assists the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology, which uses fetal heart rate and uterine activity, to identify acidosis. This paper introduces a forecasting model based on deep learning to predict heart rate and uterine contractions, integrated with the classification algorithm, resulting in a robust tool for predictive fetal monitoring. The hybrid algorithm resulted in a model capable of providing future conditions of the fetus, which obstetricians can use for diagnosis and planning interventions. The ensemble classification algorithm had a test accuracy of 85% (n = 24) in predicting fetal acidosis on the features extracted from the cardiotocography data. When integrated with the classification model, the results from the prediction model (long short-term memory network) can effectively identify fetal acidosis 2 or 4 min in the future.
Umbilical Cord Prolapse—Interesting CTG Traces
Authors: Botezatu Radu , Gica Nicolae , Peltecu Gheorghe , Panaitescu Anca Maria
Published: November 2022

Umbilical cord prolapse can be a life-threatening obstetrical event involving the fetus due to sudden oxygenated blood flow obstruction. These types of events most often happen in labor and are associated with obstetric maneuvers. Rarely, a clinical examination can diagnose the condition, but the situation is usually detected secondary to an abnormal cardiotocography trace. We present several clinical cases where a CTG trace was used to infer umbilical cord prolapse.
How the COVID-19 Pandemic Affected Attendance at a Tertiary Orthopedic Center Emergency Department: A Comparison between the First and Second Waves
Authors: Carlicchi Eleonora , Di Sabato Maria Eugenia , Cincotta Antonino , Accetta Riccardo , Aliprandi Alberto , Albano Domenico , Sconfienza Luca Maria , Messina Carmelo
Published: November 2022

Italy was the first European country to face the SARS-CoV-2 virus (COVID-19) pandemic in 2020. The country quickly implemented strategies to contain contagions and re-organize medical resources. We evaluated the COVID-19 effects on the activity of a tertiary-level orthopedic emergency department (ED) during the first and second pandemic waves. We retrospectively collected and compared clinical radiological data of ED admissions during four periods: period A, first pandemic wave; period B, second pandemic wave; period C, three months before the COVID-19 outbreak; period D, same timeframe of the first wave but in 2019. During period A, we found a reduction in ED admissions (−68.2% and −59.9% compared with periods D and C) and a decrease in white codes (non-urgent) (−7.5%) compared with pre-pandemic periods, with a slight increase for all other codes: +6.3% green (urgent, not critical), +0.8% yellow (moderately critical) and +0.3% red (highly urgent, risk of death). We observed an increased rate of fracture diagnosis in period A: +14.9% and +13.3% compared with periods D and C. Our study shows that the COVID-19 pandemic caused a drastic change in the ED patient flow and clinical radiological activity, with a marked reduction in admissions and an increased rate of more severe triage codes and diagnosed fractures.
Study of the Natural Crystalline Lens Characteristics Using Dual-Energy Computed Tomography
Authors: Sachs Jeffrey R. , Nahmias Javier A. , Hiatt Kevin D. , Bomar James G. , West Thomas G. , Bunch Paul M. , Benayoun Marc D. , Lack Chris , Thompson Atalie C.
Published: November 2022

There is a paucity of radiologic literature regarding age-related cataract, and little is known about any differences in the imaging appearance of the natural crystalline lens on computed tomography (CT) exams among different demographic groups. In this retrospective review of 198 eyes in 103 adults who underwent dual-energy computed tomography (DECT) exams of the head, regions of interest spanning 3–5 mm were placed over the center of the lens, and the x-ray attenuation of each lens was recorded in Hounsfield Units (HU) at 3 energy levels: 40 keV, 70 keV, and 190 keV. Generalized estimating equations (GEEs) were used to assess the association of clinical or demographic data with lens attenuation. The mean HU values were significantly lower for the older vs. younger group at 40 keV (GEE p-value = 0.022), but there was no significant difference at higher energy levels (p > 0.05). Mean HU values were significantly higher for females vs. males and non-whites vs. non-Hispanic whites at all 3 energy levels in bivariate and multivariable analyses (all p-value < 0.05). There was no significant association between lens attenuation and either diabetes or smoking status. The crystalline lens of females and non-whites had higher attenuation on DECT which may suggest higher density or increased concentration of materials like calcium and increased potential for cataract formation. Given the large scope of cataracts as a cause of visual impairment and the racial disparities that exist in its detection and treatment, further investigation into the role of opportunistic imaging to detect cataract formation is warranted.
Altered PTPN22 and IL10 mRNA Expression Is Associated with Disease Activity and Renal Involvement in Systemic Lupus Erythematosus
Authors: Román-Fernández Ilce Valeria , Machado-Contreras Jesús René , Muñoz-Valle José Francisco , Cruz Alvaro , Salazar-Camarena Diana Celeste , Palafox-Sánchez Claudia Azucena
Published: November 2022

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with very heterogeneous clinical behavior between affected individuals. Therefore, the search for biomarkers clinically useful for the diagnosis, prognosis, and monitoring of the disease is necessary. Here, we determined the association between PTPN22, IL10, OAS2, and CD70 mRNA expression with the clinical characteristics and with the serum levels of IL-10, IFN-γ, and IL-17 in SLE patients. Forty patients with SLE and 34 control subjects (CS) were included, mRNA expression was determined by real-time qPCR and cytokine levels were quantified by a multiplex bead-based immunoassay. Compared to CS, SLE patients showed increased IL10 mRNA and high IL-10 and IL-17 serum levels; in contrast, PTPN22 mRNA and IFN-γ were decreased. PTPN22 and IL10 gene expression was negatively correlated with Mex-SLEDAI score and were notably downregulated in SLE patients with lupus nephritis. Interestingly, SLE patients with renal damage were the ones with the lowest levels of PTPN22 and IL10 mRNA and the highest SLEDAI scores. No associations were observed for OAS2 and CD70 mRNA and IL-10, IL-17, and IFN-γ. In conclusion, we suggest that the assessment of IL10 and PTPN22 mRNA could be useful for monitoring disease activity in SLE patients showing renal involvement.
Analysis of the Clinical Impact of the BioFire FilmArray Meningitis Encephalitis Panel on Antimicrobial Use and Duration of Therapy at an Academic Medical Center
Authors: Markovich Kylie , Wingler Mary Joyce B. , Stover Kayla R. , Barber Katie E. , Wagner Jamie L. , Cretella David A.
Published: November 2022

The purpose of this study was to assess the clinical impact of the BioFire FilmArray Meningitis/Encephalitis (ME) panel on antimicrobial use and clinical outcomes. This retrospective, quasi-experiment evaluated adult and pediatric patients with suspected ME, evidenced by cerebrospinal fluid (CSF) culture. Hospital-acquired meningitis patients and patients who received antimicrobials >48 h prior to lumbar puncture were excluded. The primary endpoint was days of antimicrobial therapy pre- and post-implementation of the ME panel. Secondary endpoints included total length of stay, 30-day readmission, and individual days of antimicrobial therapy. Two hundred and sixty-four total adult and pediatric patients were included. Antimicrobial days of therapy had a median of 3 days (IQR 0–5) in the pre vs. post group with a median of 2 days (2–5) (p = 0.099). Days of therapy for acyclovir were significantly decreased in the post group (median 2 days [IQR 1–3] vs. 3 days [IQR 2.5–4.5], p = 0.0002). There were no significant differences in the secondary endpoints. Overall, implementation of the ME panel impacted the duration of antimicrobials, particularly acyclovir; however, opportunities for further education regarding antimicrobial de-escalation and utilization of the panel were identified. Antimicrobial stewardship program intervention is critical to maximize benefit of this rapid diagnostic test.
Enhancing Drones for Law Enforcement and Capacity Monitoring at Open Large Events
Authors: Royo Pablo , Asenjo Àlex , Trujillo Juan , Çetin Ender , Barrado Cristina
Published: November 2022

Police tasks related with law enforcement and citizen protection have gained a very useful asset in drones. Crowded demonstrations, large sporting events, or summer festivals are typical situations when aerial surveillance is necessary. The eyes in the sky are moving from the use of manned helicopters to drones due to costs, environmental impact, and discretion, resulting in local, regional, and national police forces possessing specific units equipped with drones. In this paper, we describe an artificial intelligence solution developed for the Castelldefels local police (Barcelona, Spain) to enhance the capabilities of drones used for the surveillance of large events. In particular, we propose a novel methodology for the efficient integration of deep learning algorithms in drone avionics. This integration improves the capabilities of the drone for tasks related with capacity control. These tasks have been very relevant during the pandemic and beyond. Controlling the number of persons in an open area is crucial when the expected crowd might exceed the capacity of the area and put humans in danger. The new methodology proposes an efficient and accurate execution of deep learning algorithms, which are usually highly demanding for computation resources. Results show that the state-of-the-art artificial intelligence models are too slow when utilised in the drone standard equipment. These models lose accuracy when images are taken at altitudes above 30 m. With our new methodology, these two drawbacks can be overcome and results with good accuracy (96% correct segmentation and between 20% and 35% mean average proportional error) can be obtained in less than 20 s.
Point Cloud Geometry Compression Based on Multi-Layer Residual Structure
Authors: Yu Jiawen , Wang Jin , Sun Longhua , Wu Mu-En , Zhu Qing
Published: November 2022

Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that is a big burden for efficient communication. However, it is difficult to efficiently compress such sparse, disordered, non-uniform and high dimensional data. Therefore, this work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture. Specifically, a multi-layer residual module is designed on a sparse convolution-based autoencoders that progressively down-samples the input point clouds and reconstructs the point clouds in a hierarchically way. It effectively constrains the accuracy of the sampling process at the encoder side, which significantly preserves the feature information with a decrease in the data volume. Compared with the state-of-the-art geometry-based point cloud compression (G-PCC) schemes, our approach obtains more than 70–90% BD-Rate gain on an object point cloud dataset and achieves a better point cloud reconstruction quality. Additionally, compared to the state-of-the-art PCGCv2, we achieve an average gain of about 10% in BD-Rate.
Diffusion and Velocity Correlations of the Phase Transitions in a System of Macroscopic Rolling Spheres
Authors: Vega Reyes Francisco , Rodríguez-Rivas Álvaro , González-Saavedra Juan F. , López-Castaño Miguel A.
Published: November 2022

We study an air-fluidized granular monolayer composed of plastic spheres which roll on a metallic grid. The air current is adjusted so that the spheres never lose contact with the grid and so that the dynamics may be regarded as pseudo two dimensional (or two dimensional, if the effects of the sphere rolling are not taken into account). We find two surprising continuous transitions, both of them displaying two coexisting phases. Moreover, in all the cases, we found the coexisting phases display a strong energy non-equipartition. In the first transition, at a weak fluidization, a glass phase coexists with a disordered fluid-like phase. In the second transition, a hexagonal crystal coexists with the fluid phase. We analyze, for these two-phase systems, the specific diffusive properties of each phase, as well as the velocity correlations. Surprisingly, we find a glass phase at a very low packing fraction and for a wide range of granular temperatures. Both phases are also characterized by strong anticorrelated velocities upon a collision. Thus, the dynamics observed for this quasi two-dimensional system unveil phase transitions with peculiar properties, very different from the predicted behavior in well-know theories for their equilibrium counterparts.
A Perspective on Four Emerging Threats to Sustainability and Sustainable Development
Authors: Gounden Kimendren , Mwangi Festus Maina , Mohan Turup Pandurangan
Published: November 2022

Plastics are a precious, versatile set of materials. The accumulation of plastic waste threatens the environment. Recycling plastic waste can produce many new products. The many opportunities for using plastic waste create pressure for a strategy to develop or improve current waste management systems to reduce the negative impact on humans, fauna and flora. The objective of this review paper is to consider an opportunity to recycle plastic; to convert plastic waste into plastic sand bricks. This would reduce the impact of the four emerging crises (plastic pollution, unemployment, the shortage of affordable housing and climate change) identified in South Africa as a threat to sustainability. This paper reviews studies utilising plastic waste to manufacture materials for the construction industry. The feasibility of using plastic waste to manufacture bricks revealed high compressive strength, low water absorption and weighed considerably lower compared to traditional bricks. Plastic sand bricks, therefore, can provide a solution that can be used to curb the four emerging crises and contribute to sustainability.
A Project-Based Instruction Approach to Improving Student Lunar Phases Learning Outcomes: A Quantitative Inquiry
Authors: Cole Merryn , Yang Hongwei , Wilhelm Jennifer Anne
Published: November 2022

We investigated how students’ lunar phases learning outcomes were affected by student and teacher demographic characteristics (gender, race/ethnicity, spatial thinking ability, and content knowledge). The study identified moderately strong correlations both between students’ spatial thinking ability and understanding of lunar phases, as well as between the pre-to-post-intervention scores of the two measures. Multilevel modeling showed significant predictors of learning outcomes from both student and teacher variables. This study furthers works on establishing a connection between student learning outcomes and the content knowledge and spatial ability for themselves as well as their teachers, and shows promise for a project-based instruction approach in aiding in lunar phases understanding.
STEM 1, 2, 3: Levelling Up in Primary Schools
Authors: Way Jennifer , Preston Christine , Cartwright Katherin
Published: November 2022

Recent research advocating the educational value of engaging primary students in authentic integrated STEM inquiry projects also identifies challenges for teachers aspiring to high-quality student-learning experiences. Teachers require support to integrate disciplines, prioritise processes and reasoning over curriculum content, and increase student autonomy in purposeful STEM projects. Within the context of a year-long professional learning program in Australia, an innovative tri-level approach to skill-building for teachers and their students has been developed. The three levels of ‘STEM Skills’, ‘Design Process’ and ‘Integrated STEM Projects’ are intended to successively escalate demands on time, resources and pedagogical change while promoting the development of the dispositions and skills needed to engage in sustained inquiry projects. The teacher participants (n = 11) came from five schools who had just concluded the STEM Academy professional learning program. Semi-structured interviews guided teacher reflection. Data analysis combined inductive and deductive processes to thematize meaning and revealed ways in which the tri-level approach to STEM education supported the development of STEM integration practices. The findings suggest the efficacy of the tri-level approach and its potential value beyond the context in which it was developed.
Evidence-Based Optimization of Classroom Teaching Units Using 3D Printers for Designing Models—From the 2D Picture to the 3D Flower Model
Authors: Bonorden Marcel , Papenbrock Jutta
Published: November 2022

The implementation of digital tools into science education is a major demand of various stakeholders, such as teachers, schools and ministries of education. However, teaching innovations and the introduction of new competences need to be carefully tested and optimized for successful and sustainable application and learning success. Our aim was to develop and establish an easily adaptable teaching unit comprising the aspects of 3D printing from computer-aided modeling to slicing, printing and post-processing, which is linked to curricular learning content. The original teaching concept developed with a small group of students has been adapted to the conditions in large groups and full-size 9th grade school classes. With an increased sample size, it was now possible to investigate and analyze the teaching approach with respect to student’s motivation, learning success as well as the quality and acceptance of the teaching–learning arrangement for designing and 3D printing flower models. The goal of the study was to further optimize the existing teaching tool based on the evaluation of the student experience. While the exploration of this teaching approach ties into the current discourse of innovative biology teaching, the efficacy is evidenced by results that indicate a positive impact on student’s motivation and a high learning success regarding computer-aided modeling and 3D printing. As a result, the teaching-revised concept reported in this article is based on the students’ evaluation and can be provided as well-tested teaching material for schools.
Teaching Environmental Themes within the “Scientific Awakening” Course in Moroccan Primary School: Approaches, Methods and Difficulties
Authors: El Batri Bouchta , Maskour Lhoussaine , Ksiksou Jamal , Jeronen Eila , Ismaili Jalal , Alami Anouar , Lachkar Mohammed
Published: November 2022

In addition to identifying the pedagogical approaches favoured by teachers in environmental education, the study aims to reveal the impact of the teaching methods and tools used as well as the teaching difficulties encountered on the effectiveness of teachers’ pedagogical action, particularly in the “Scientific Awakening” course.The study concerns a sample of 636 primary school teachers from the urban and rural areas of the Fez-Meknes Regional Academy of Education in Morocco. The data was collected using a 37-item questionnaire covering the following variables: the teaching methods adopted, the preferred pedagogical approaches, the teaching material used, and the teaching difficulties encountered. The study fits under a descriptive correlational design. The most used teaching methods were the teacher-centred oral methods (dialogue and demonstration method) lacking learner-centred activities (working in small groups, discovery method). The least used ones were laboratory experiments and ICT-based demonstrations. The study shows that teachers who use active methods are the most aware of difficulties in teaching environmental issues and were the most capable of effectively solving learning problems and achieving pedagogical objectives. The study shows that Moroccan primary school teachers need in-service training for the adoption of a systemic and interdisciplinary pedagogical approach. In addition, to address the issue of the alarming failure to complete the “Scientific Awakening” program, we recommend continuous training for the benefit of teachers. This training should cover the appropriate active methods to effectively complete this program. Finally, the study underlined the need to solve the problem of the enormous lack of teaching tools. Certainly, this shortage significantly influences the pedagogical action of teachers regardless of the pedagogical methods adopted.
Human Perception Intelligent Analysis Based on EEG Signals
Authors: Geng Bingrui , Liu Ke , Duan Yiping
Published: November 2022

The research on brain cognition provides theoretical support for intelligence and cognition in computational intelligence, and it is further applied in various fields of scientific and technological innovation, production and life. Use of the 5G network and intelligent terminals has also brought diversified experiences to users. This paper studies human perception and cognition in the quality of experience (QoE) through audio noise. It proposes a novel method to study the relationship between human perception and audio noise intensity using electroencephalogram (EEG) signals. This kind of physiological signal can be used to analyze the user’s cognitive process through transformation and feature calculation, so as to overcome the deficiency of traditional subjective evaluation. Experimental and analytical results show that the EEG signals in frequency domain can be used for feature learning and calculation to measure changes in user-perceived audio noise intensity. In the experiment, the user’s noise tolerance limit for different audio scenarios varies greatly. The noise power spectral density of soothing audio is 0.001–0.005, and the noise spectral density of urgent audio is 0.03. The intensity of information flow in the corresponding brain regions increases by more than 10%. The proposed method explores the possibility of using EEG signals and computational intelligence to measure audio perception quality. In addition, the analysis of the intensity of information flow in different brain regions invoked by different tasks can also be used to study the theoretical basis of computational intelligence.
A Non-Destructive Method for Hardware Trojan Detection Based on Radio Frequency Fingerprinting
Authors: Mi Siya , Zhang Zechuan , Zhang Yu , Hu Aiqun
Published: November 2022

Hardware Trojans (HTs) pose a security threat to the Internet of Things (IoT). Attackers can take control of devices in IoT through HTs, which seriously jeopardize the security of many systems in transportation, finance, healthcare, etc. Since subtle differences in the circuit are reflected in far-field signals emitted by the system, the detection of HT status can be performed by monitoring the radio frequency fingerprinting (RFF) of the transmitting signals. For the detection of HTs, a non-destructive detection method based on RFF is proposed in this paper. Based on the proposed method, the detection of HTs can be achieved without integrating additional devices in the receiver, which reduces associated costs and energy consumption. QPSK and triangular-wave signals are measured and identified via experimentation, and the results validate the proposed method. For identifying the presence and operating state of Trojan, the average accuracy achieved measures as high as 98.7%. Notably, with regard to capturing the moment of Trojan activation in the AES encryption circuit, the accuracy of the proposed method is 100% and can provide warning of the threat in a timely manner.
Information and Communication Technologies Combined with Mixed Reality as Supporting Tools in Medical Education
Authors: Pregowska Agnieszka , Osial Magdalena , Dolega-Dolegowski Damian , Kolecki Radek , Proniewska Klaudia
Published: November 2022

The dynamic COVID-19 pandemic has destabilized education and forced academic centers to explore non-traditional teaching modalities. A key challenge this creates is in reconciling the fact that hands-on time in lab settings has been shown to increase student understanding and peak their interests. Traditional visualization methods are already limited and topics such as 3D molecular structures remain difficult to understand. This is where advances in Information and Communication Technologies (ICT), including remote meetings, Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR, so-called Metaverse) offer vast potential to revolutionize the education landscape. Specifically, how MR merges real and virtual life in a uniquely promising way and offers opportunities for entirely new educational applications. In this paper, we briefly overview and report our initial experience using MR to teach medical and pharmacy students. We also explore the future usefulness of MR in pharmacy education. MR mimics real-world experiences both in distance education and traditional laboratory classes. We also propose ICT-based systems designed to run on the Microsoft HoloLens2 MR goggles and can be successfully applied in medical and pharmacy coursework. The models were developed and implemented in Autodesk Maya and exported to Unity. Our findings demonstrate that MR-based solutions can be an excellent alternative to traditional classes, notably in medicine, anatomy, organic chemistry, and biochemistry (especially 3D molecular structures), in both remote and traditional in-person teaching modalities. MR therefore has the potential to become an integral part of medical education in both remote learning and in-person study.
Chatter Detection in Variable Cutting Depth Side Milling Using VMD and Vibration Characteristics Analysis
Authors: Zhao Na , Su Yingxin , Wang Shijuan , Xia Min , Liu Changfu
Published: November 2022

Chatter is a key factor affecting tool wear, workpiece surface quality and cutting efficiency. When milling thin-walled parts, it is difficult to extract the chatter frequency band due to the time-varying characteristics of the dynamic parameters of the machining system. Variational mode decomposition (VMD) shows good performance in signal processing, but the decomposition result of this algorithm is limited by the influence of initialization parameters. Therefore, this paper proposes a scheme to determine the number of VMD decomposition layers based on the number of Fourier transform frequency peaks. The feasibility of the scheme is verified by the simulation signal and experiment signal. The results show that taking the peak number of the spectrum as the decomposition level of VMD, the spectrum distribution of the decomposed intrinsic mode function (IMF) is clear, and the frequency band containing rich chatter information can be effectively extracted.
Unambiguous Direction Estimation and Localization of Two Unresolved Targets via Monopulse Radar
Authors: Rezaei Habib , Sebt Mohammad Ali , Zarei Nadali , Saadati Moghadam Goudarz
Published: November 2022

Traditional monopulse radar cannot resolve two closely spaced targets present in one resolution cell (range and Doppler) by means of the monopulse ratio. This study presents a closed-form solution to resolve the directions of arrival of two unresolved targets using a single snapshot of four independent channels in phase comparison monopulse radar. If both targets have the same elevation or same azimuth direction, the proposed scheme cannot estimate their directions. To estimate the direction of such targets, an extra antenna is required. The impact of input noise power, the targets’ direction, and phase difference of the targets’ signal on the accuracy of angle estimation are also explained. The numerical simulation result validates the effectiveness of the presented scheme.
A Semantic Mapping Method of Relation Representation Enhancement for Few-Shot Knowledge Graph Completion
Authors: He Haitao , Niu Haoran , Feng Jianzhou
Published: November 2022

Few-shot knowledge graph completion (FKGC) tasks involve determining the authenticity of triple candidates using a small number of reference triples with a given relation. Intuitively, the expression of relation features contributes to the close correlation among the triples with the same relation. Therefore, the relation features are not comprehensive enough, leading to the fact that the triples cannot learn sufficient association information for the FKGC task. In this paper, an enhanced relation semantic representation model is constructed for associative reference triples from both aspects of external structure and internal semantics. On the one hand, as the structure around the triple is helpful to understand the relation semantics implicated in the triple, a graph convolution network with attention and relation features is proposed to obtain the graph structure features. Furthermore, the local structure information of triples can be used to learn the deep relation semantics. On the other hand, entity information can enhance the perception of the relation semantics in the triple. Afterward, in order to associate the triples with the same relation by enhanced relation semantics, a semantic mapping method is proposed, which uses shared merged variables to map the relation, entity, and graph structure features into the same embedding space. Finally, a prototype network based on attention convolution is established to extract the relation prototype representation, and then classify query triples to achieve the purpose of completing the knowledge graph. Through experimental verification, the proposed model achieves excellent performance on two datasets commonly used in the FKGC.
Utilizing Parallel Superconducting Element as a Novel Approach of Flux-Coupled Type SFCL to Limit DC Current in the System
Authors: Aurangzeb Muhammad , Xin Ai , Iqbal Sheeraz , Aymen Flah , Jasiński Michał , Jasińska Laura
Published: November 2022

To lessen the amount of energy lost during transmission, electricity is increasingly being sent using high-voltage lines. Transmission loss in a DC system is lower than in an AC system over long distances. The DC system can improve the efficiency of long-distance transmission by connecting power grids with different requirements. The DC method is becoming popular since it helps to keep the grid stable. Managing and blocking DC flow is crucial to system functionality. In this study, we explore the operation of a flux-coupled type superconducting fault current limiter (SFCL) in a DC system, where the two windings are connected in parallel to limit the fault current flow. A flux-coupled type SFCL is built by connecting two coils in parallel and a superconducting element (SE) in series with the secondary coil. The functions of an SFCL of the flux-coupled kind are equivalent in both direct and alternating current systems. Because of the opposing magnetic fluxes produced by the two coils, the voltage generated by the parallel connection of the coils is always zero. Inadequate SE leads to an increase in resistance, inhibiting the cancellation of opposing magnetic fluxes and hence a loss in power. Connecting the two coils in series allows voltage to be generated while the fault current is limited. To further validate the performance of SFCL with varying resistance and inductance, the system is additionally tested on the IEEE 39 bus system. The MATLAB/SIMULINK software suite is used to run the test system.
A Low-Cost and Do-It-Yourself Device for Pumping Monitoring in Deep Aquifers
Authors: Aliagas Carles , Pérez-Foguet Agustí , Meseguer Roc , Millán Pere , Molina Carlos
Published: November 2022

Water crises due to climate change, high population growth and increasing demands from industry and agriculture claim for increasing efficiency and universalizing water resources management strategies and techniques. Water monitoring helps providing necessary evidences for making sound decisions about managing water resources both now and in the future. In this work, a low cost and “do it yourself” communication device is proposed to record water production and energy consumption of electric pumpings from deep boreholes/wells, and to predict the impact of the ongoing and previous pumpings in the evolution of the water level in the aquifer. The proposal incorporates an edge-computing approach for the simulation of the aquifer response in real-time. Computation of results of interest is performed at the sensor, minimizing communication requirements and ensuring almost immediate results. An approximated solution to physically based modeling of aquifer response is computed thanks to the a priori expression of the water level time evolution in a reduced basis. The accuracy is enough to detect deviations from expected behaviour. The energy consumption of the device is very much reduced with respect to that of a full modelling, which can be computed off-line for calibrating reduced model parameters and perform detailed analyses. The device is tested in a real scenario, in a mountain subbasin of the Ebro river in Spain, obtaining a good trade-off between performance, price, and energy consumption.
The DLR ThermoFluid Stream Library
Authors: Zimmer Dirk , Meißner Michael , Weber Niels
Published: November 2022

This paper introduces the DLR Thermofluid Stream Library: a free open-source library for the robust modeling of complex thermofluid architectures. Designed to be easy to use, easy to adapt, and enriched by a number of examples, this library contains the fundamental components for many different applications, such as thermal management of electric cars, power plants, or building physics. Different from many previous software implementations, the library exploits a new computational concept on the basis of the fluid inertance that derives the mass flow balance in such a manner that large non-linear equations systems in implicit form can be avoided. Hence, there is a reliable and efficient computational scheme for simulation and initialization. Although this scheme is explained in detail in previous publications, this paper focuses on the implementational aspects of the library, explaining its structure, the underlying equations of key components and providing basic examples. In addition to the practical value of the library, we also aim to display the underlying thought driving the design of the software.
Computer Vision-Based Approach for Automatic Detection of Dairy Cow Breed
Authors: Gupta Himanshu , Jindal Parul , Verma Om Prakash , Arya Raj Kumar , Ateya Abdelhamied A. , Soliman Naglaa. F. , Mohan Vijay
Published: November 2022

Purpose: Identification of individual cow breeds may offer various farming opportunities for disease detection, disease prevention and treatment, fertility and feeding, and welfare monitoring. However, due to the large population of cows with hundreds of breeds and almost identical visible appearance, their exact identification and detection become a tedious task. Therefore, the automatic detection of cow breeds would benefit the dairy industry. This study presents a computer-vision-based approach for identifying the breed of individual cattle. Methods: In this study, eight breeds of cows are considered to verify the classification process: Afrikaner, Brown Swiss, Gyr, Holstein Friesian, Limousin, Marchigiana, White Park, and Simmental cattle. A custom dataset is developed using web-mining techniques, comprising 1835 images grouped into 238, 223, 220, 212, 253, 185, 257, and 247 images for individual breeds. YOLOv4, a deep learning approach, is employed for breed classification and localization. The performance of the YOLOv4 algorithm is evaluated by training the model on different sets of training parameters. Results: Comprehensive analysis of the experimental results reveal that the proposed approach achieves an accuracy of 81.07%, with maximum kappa of 0.78 obtained at an image size of 608 × 608 and an intersection over union (IoU) threshold of 0.75 on the test dataset. Conclusions: The model performed better with YOLOv4 relative to other compared models. This places the proposed model among the top-ranked cow breed detection models. For future recommendations, it would be beneficial to incorporate simple tracking techniques between video frames to check the efficiency of this work.
Dense Vehicle Counting Estimation via a Synergism Attention Network
Authors: Jin Yiting , Wu Jie , Wang Wanliang , Wang Yibin , Yang Xi , Zheng Jianwei
Published: November 2022

Along with rising traffic jams, accurate counting of vehicles in surveillance images is becoming increasingly difficult. Current counting methods based on density maps have achieved tremendous improvement due to the prosperity of convolution neural networks. However, as highly overlapping and sophisticated large-scale variation phenomena often appear within dense images, neither traditional CNN methods nor fixed-size self-attention transformer methods can implement exquisite counting. To relieve these issues, in this paper, we propose a novel vehicle counting approach, namely the synergism attention network (SAN), by unifying the benefits of transformers and convolutions to perform dense counting assignments effectively. Specifically, a pyramid framework is designed to adaptively utilize the multi-level features for better fitting in counting tasks. In addition, a synergism transformer (SyT) block is customized, where a dual-transformer structure is equipped to capture global attention and location-aware information. Finally, a Location Attention Cumulation (LAC) module is also presented to explore the more efficient and meaningful weighting regions. Extensive experiments demonstrate that our model is very competitive and reached new state-of-the-art performance on TRANCOS datasets.
Intra-Cluster Federated Learning-Based Model Transfer Framework for Traffic Prediction in Core Network
Authors: Li Pengyu , Shi Yingji , Xing Yanxia , Liao Chaorui , Yu Menghan , Guo Chengwei , Feng Lei
Published: November 2022

Accurate prediction of cellular traffic will contribute to efficient operations and management of mobile network. With deep learning, many studies have achieved exact cellular traffic prediction. However, the reality is that quite a few subnets in the core network do not have sufficient computing power to train their deep learning model, which we call subnets (LCP-Nets) with limited computing power. In order to improve the traffic prediction efficiency of LCP-Nets with the help of deep learning and the subnets (ACP-Nets) with abundant computing power under the requirement of privacy protection, this paper proposes an intra-cluster federated learning-based model transfer framework. This framework customizes models for LCP-Nets, leveraging transferring models trained by ACP-Nets. Experimental results on the public dataset show that the framework can improve the efficiency of LCP-Nets traffic prediction.
Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review
Authors: Abayomi-Alli Olusola O. , Damaševičius Robertas , Qazi Atika , Adedoyin-Olowe Mariam , Misra Sanjay
Published: November 2022

The aim of this systematic literature review (SLR) is to identify and critically evaluate current research advancements with respect to small data and the use of data augmentation methods to increase the amount of data available for deep learning classifiers for sound (including voice, speech, and related audio signals) classification. Methodology: This SLR was carried out based on the standard SLR guidelines based on PRISMA, and three bibliographic databases were examined, namely, Web of Science, SCOPUS, and IEEE Xplore. Findings. The initial search findings using the variety of keyword combinations in the last five years (2017–2021) resulted in a total of 131 papers. To select relevant articles that are within the scope of this study, we adopted some screening exclusion criteria and snowballing (forward and backward snowballing) which resulted in 56 selected articles. Originality: Shortcomings of previous research studies include the lack of sufficient data, weakly labelled data, unbalanced datasets, noisy datasets, poor representations of sound features, and the lack of effective augmentation approach affecting the overall performance of classifiers, which we discuss in this article. Following the analysis of identified articles, we overview the sound datasets, feature extraction methods, data augmentation techniques, and its applications in different areas in the sound classification research problem. Finally, we conclude with the summary of SLR, answers to research questions, and recommendations for the sound classification task.
Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network
Authors: Zuo Kun , Su Xiaofeng
Published: November 2022

This study proposes a 3D attitude estimation algorithm using the RMPE algorithm coupled with a deep neural network that combines human pose estimation and action recognition, which provides a new idea for basketball auxiliary training. Compared with the traditional single-action recognition method, the present method makes the recognition accuracy better and the display effect more intuitive. The flipped classroom teaching mode based on this algorithm is applied to the college sports basketball optional course to explore the influence of this teaching mode on the classroom teaching effect. Compared with the evaluation index of action recognition, the experimental results of various action recognition methods and datasets are compared and analyzed, and it is verified that the method has a good recognition effect. The values of Topi and Top5 of the proposed method are 42.21% and 88.77%, respectively, which are 10.61% and 35.09% higher than those of the Kinetics-skeleton dataset. However, compared with the NTU RGM dataset, the recognition rate of Topi is significantly reduced. Compared with the traditional single-action recognition method, this method has better recognition accuracy and a more intuitive display effect. The fusion method of human posture estimation and motion recognition provides a new idea for basketball auxiliary training.
Cost-Sensitive Multigranulation Approximation in Decision-Making Applications
Authors: Yang Jie , Kuang Juncheng , Liu Qun , Liu Yanmin
Published: November 2022

A multigranulation rough set (MGRS) model is an expansion of the Pawlak rough set, in which the uncertain concept is characterized by optimistic and pessimistic upper/lower approximate boundaries, respectively. However, there is a lack of approximate descriptions of uncertain concepts by existing information granules in MGRS. The approximation sets of rough sets presented by Zhang provide a way to approximately describe knowledge by using existing information granules. Based on the approximation set theory, this paper proposes the cost-sensitive multigranulation approximation of rough sets, i.e., optimistic approximation and pessimistic approximation. Their related properties were further analyzed. Furthermore, a cost-sensitive selection algorithm to optimize the multigranulation approximation was performed. The experimental results show that when multigranulation approximation sets and upper/lower approximation sets are applied to decision-making environments, multigranulation approximation produces the least misclassification costs on each dataset. In particular, misclassification costs are reduced by more than 50% at each granularity on some datasets.
Research on a Sowing Depth Detection System Based on an Improved Adaptive Kalman Filtering Method
Authors: Zhao Naichen , Zhao Bin , Yi Shujuan , Zhou Zheng , Che Gang
Published: November 2022

The current real-time detection of sowing depth has the problems of low detection accuracy and poor reliability. After analyzing the movement mechanism of the sowing monomer parallel four-bar linkage, an improved adaptive Kalman filtering method for sowing depth detection is proposed. The method uses two MPU6050 attitude sensors. The difference between the rotation angle of the parallel four-bar linkage and the attitude angle of the sowing monomer is used as the relative rotation angle of the parallel four-bar linkage relative to the sowing monomer. The sowing monomer parallel four-bar linkage has the characteristics of up–down translation, and sowing depth changes can be obtained by converting the angle data. By using moving average filter, particle filter, Kalman filter, and improved adaptive Kalman filter to fuse the data of MPU6050 in two positions and perform MATLAB simulation, the average mean squared errors of the above four filtering algorithms are 0.12645, 0.05545, 0.03785, and 0.0189, respectively. In the end, the experiment of sowing depth detection was carried out, and it was found that the improved adaptive Kalman filter algorithm detects the smallest sowing depth mean squared error, which is 0.0636, and the algorithm can track data changes better. At the same time, it is more adaptable to noise and obtains an ideal filtering effect.
A Compact 10–14.5 GHz Quadrature Hybrid with Digitally Reconfigurable I/Q Phase in SiGe BiCMOS Process
Authors: Chen Zhe , Xu Xiaojie , Hou Debin , Zhou Peigen , Yan Pinpin , Chen Jixin
Published: November 2022

In this article, the development of a compact 10–14.5 GHz quadrature hybrid with digitally reconfigurable I/Q phase in 130 nm SiGe BiCMOS process is presented. Thanks to the switched capacitance loaded on I/Q path of the quadrature hybrid, the I/Q phase difference can be optimized and digitally reconfigured. The equivalent model is analyzed with even/odd mode theory, and the ABCD matrix is used for the circuit derivation. In order to obtain high coupling coefficient, the broadside coupled line sections are utilized, and compact hybrid size can be realized accordingly. Measured results show that the compact quadrature hybrid has optimized phase difference of 90 ± 1.0° and amplitude difference less than ±0.5 dB for 10–14.5 GHz, with an ultra-compact size of 460 µm × 151 µm, or 0.031λ0 × 0.011λ0. Meanwhile, with the seven reconfigurable phase states, the quadrature hybrid I/Q phase can be digitally reconfigured for a range of 3 degrees to compensate the I/Q phase imbalance in the quadrature system, without DC power consumption.