bluuebunny/crossref_metadata_2025 huggingface dataset:
Lifetime Evaluation of Left Ventricular Structure and Function in Male C57BL/6J Mice after Gamma and Space-Type Radiation Exposure
Authors: Brojakowska Agnieszka , Jackson Cedric J. , Bisserier Malik , Khlgatian Mary K. , Grano Cynthia , Blattnig Steve R. , Zhang Shihong , Fish Kenneth M. , Chepurko Vadim , Chepurko Elena , Gillespie Virginia , Dai Ying , Lee Brooke , Garikipati Venkata Naga Srikanth , Hadri Lahouaria , Kishore Raj , Goukassian David A.
Published: March 2023

The lifetime effects of space irradiation (IR) on left ventricular (LV) function are unknown. The cardiac effects induced by space-type IR, specifically 5-ion simplified galactic cosmic ray simulation (simGCRsim), are yet to be discovered. Three-month-old, age-matched, male C57BL/6J mice were irradiated with 137Cs gamma (γ; 100, 200 cGy) and simGCRsim (50 and 100 cGy). LV function was assessed via transthoracic echocardiography at 14 and 28 days (early), and at 365, 440, and 660 (late) days post IR. We measured the endothelial function marker brain natriuretic peptide in plasma at three late timepoints. We assessed the mRNA expression of the genes involved in cardiac remodeling, fibrosis, inflammation, and calcium handling in LVs harvested at 660 days post IR. All IR groups had impaired global LV systolic function at 14, 28, and 365 days. At 660 days, 50 cGy simGCRsim-IR mice exhibited preserved LV systolic function with altered LV size and mass. At this timepoint, the simGCRsim-IR mice had elevated levels of cardiac fibrosis, inflammation, and hypertrophy markers Tgfβ1, Mcp1, Mmp9, and βmhc, suggesting that space-type IR may induce the cardiac remodeling processes that are commonly associated with diastolic dysfunction. IR groups showing statistical significance were modeled to calculate the Relative Biological Effectiveness (RBE) and Radiation Effects Ratio (RER). The observed dose-response shape did not indicate a lower threshold at these IR doses. A single full-body IR at doses of 100–200 cGy for γ-IR, and 50–100 cGy for simGCRsim-IR decreases the global LV systolic function in WT mice as early as 14 and 28 days after exposure, and at 660 days post IR. Interestingly, there is an intermediate time point (365 days) where the impairment in LV function is observed. These findings do not exclude the possibility of increased acute or degenerative cardiovascular disease risks at lower doses of space-type IR, and/or when combined with other space travel-associated stressors such as microgravity.
IFN-Inducible SerpinA5 Triggers Antiviral Immunity by Regulating STAT1 Phosphorylation and Nuclear Translocation
Authors: Wang Congcong , Liu Yajie , Liu Xinglai , Zhao Jin , Lang Bing , Wu Fan , Wen Ziyu , Sun Caijun
Published: March 2023

Deeply understanding virus-host interactions is a prerequisite for developing effective strategies to control frequently emerging infectious diseases, which have become a serious challenge for global public health. The type I interferon (IFN)-mediated JAK/STAT pathway is well known for playing an essential role in host antiviral immunity, but the exact regulatory mechanisms of various IFN-stimulated genes (ISGs) are not yet fully understood. We herein reported that SerpinA5, as a novel ISG, played a previously unrecognized role in antiviral activity. Mechanistically, SerpinA5 can upregulate the phosphorylation of STAT1 and promote its nuclear translocation, thus effectively activating the transcription of IFN-related signaling pathways to impair viral infections. Our data provide insights into SerpinA5-mediated innate immune signaling during virus-host interactions.
CircPLXNA2 Affects the Proliferation and Apoptosis of Myoblast through circPLXNA2/gga-miR-12207-5P/MDM4 Axis
Authors: Dong Xu , Xing Jiabao , Liu Qingchun , Ye Mao , Zhou Zhen , Li Yantao , Huang Rongqin , Li Zhenhui , Nie Qinghua
Published: March 2023

CircRNAs are newly identified special endogenous RNA molecules that covalently close a loop by back-splicing with pre-mRNA. In the cytoplasm, circRNAs would act as molecular sponges to bind with specific miRNA to promote the expression of target genes. However, knowledge of circRNA functional alternation in skeletal myogenesis is still in its infancy. In this study, we identified a circRNA–miRNA–mRNA interaction network in which the axis may be implicated in the progression of chicken primary myoblasts’ (CPMs) myogenesis by multi-omics (i.e., circRNA-seq and ribo-seq). In total, 314 circRNA–miRNA–mRNA regulatory axes containing 66 circRNAs, 70 miRNAs, and 24 mRNAs that may be relevant to myogenesis were collected. With these, the circPLXNA2-gga-miR-12207-5P-MDM4 axis aroused our research interest. The circPLXNA2 is highly differentially expressed during differentiation versus proliferation. It was demonstrated that circPLXNA2 inhibited the process of apoptosis while at the same time stimulating cell proliferation. Furthermore, we demonstrated that circPLXNA2 could inhibit the repression of gga-miR-12207-5p to MDM4 by directing binding to gga-miR-12207-5p, thereby restoring MDM4 expression. In conclusion, circPLXNA2 could function as a competing endogenous RNA (ceRNA) to recover the function of MDM4 by directing binding to gga-miR-12207-5p, thereby regulating the myogenesis.
Rapid, Easy, and Reliable Identification of Nocardia sp. by MALDI-TOF Mass Spectrometry, VITEK®-MS IVD V3.2 Database, Using Direct Deposit
Authors: Hodille Elisabeth , Prudhomme Clémence , Dumitrescu Oana , Benito Yvonne , Dauwalder Olivier , Lina Gérard
Published: March 2023

The reference methods for Nocardia identification are based on gene sequencing. These methods are time-consuming and not accessible for all laboratories. Conversely, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry is easy to use and widely available in clinical laboratories, but for Nocardia identification, the VITEK®-MS manufacturer recommends a tedious step of colony preparation that is difficult to integrate into a laboratory workflow. This study aimed to evaluate Nocardia identification by MALDI-TOF VITEK®-MS using direct deposit with the VITEK®-PICKMETM pen and a formic acid-based protein extraction directly onto the bacterial smear on a 134 isolates collection; this identification was compared to the results from molecular reference methods. For 81.3% of the isolates, VITEK®-MS delivered an interpretable result. The overall agreement with the reference method was 78.4%. Taking only the species included in the VITEK®-MS in vitro diagnostic V3.2 database into account, the overall agreement was significantly higher, 93.7%. VITEK®-MS rarely misidentified isolates (4/134, 3%). Among the 25 isolates that produced no result with the VITEK®-MS, 18 were expected, as Nocardia species were not included in the VITEK®-MS V3.2 database. A rapid and reliable Nocardia identification using direct deposit by VITEK®-MS is possible by combining the use of the VITEK®-PICKMETM pen and a formic acid-based protein extractiondirectly onto the bacterial smear.
Effect of Sea Level Rise and Access Channel Deepening on Future Tidal Power Plants in Buenaventura Colombia
Authors: Rueda-Bayona Juan Gabriel , García Vélez José Luis , Parrado-Vallejo Daniel Mateo
Published: March 2023

The evolution of tidal stream turbines is increasing the feasibility of future tidal plants in shallow depth areas with mid-tidal ranges (<5 m). However, extreme events such as changes in bathymetry due to the access channel deepening of coastal ports and sea level rise modify hydrodynamics and might affect the infrastructure and energy production of tidal energy converters. This research focused on Buenaventura Bay to analyze the effect of these extreme events on marine currents through calibrated-validated numerical modeling. Several monitored points were analyzed, and the results highlighted that the bay has potential for implementing tidal stream turbines because of the reported velocities between 0.25 and 2 m/s. The sea level rise increased 11.39% and access channel deepening reduced by 17.12% the velocity currents of the bay, respectively. These findings convert Buenaventura Bay to a candidate for implementing third generation tidal stream turbines and motivate future research for implementing tidal power systems in crucial areas such as the Colombian Pacific, where communities face restrictions in accessing affordable and clean energy.
Roving Multiple Camera Array with Structure-from-Motion for Coastal Monitoring
Authors: Godfrey Samantha , Cooper James R. , Plater Andrew J.
Published: March 2023

Regular monitoring is essential for vulnerable coastal locations such as areas of landward retreat. However, for coastal practitioners, surveying is limited by budget, specialist personnel/equipment and weather. In combination structure-from-motion and multi-view stereo (SfM-MVS) has helped to improve accessibility to topographic data acquisition. Pole-mounted cameras with SfM-MVS have gained traction but to guarantee coverage and reconstruction quality, greater understanding of camera position and interaction is required. This study uses a multi-camera array for image acquisition and reviews processing procedures in Agisoft Photoscan (Metashape). The camera rig was deployed at three sites and results were verified against a terrestrial laser scanner (TLS) and independent precision estimates. The multi-camera approach provided effective image acquisition ~11 times faster than the TLS. Reconstruction quality equalled (>92% similarity) the TLS, subject to processing parameters. A change in the image alignment parameter demonstrated a significant influence on deformation, reducing reprojection error by~94%. A lower densification parameter (‘High’) offered results ~4.39% dissimilar from the TLS at 1/8th of the processing time of other parameters. Independent precision estimates were <8.2 mm for x, y and z dimensions. These findings illustrate the potential of multi-camera systems and the influence of processing on point cloud quality and computation time.
Advanced Technologies for Maritime and Underwater Archaeology
Authors: Prieto Javier , Bruno Fabio , Lagudi Antonio , Ricca Michela , La Russa Mauro Francesco , Papatheodorou George , Mišković Nikola
Published: March 2023

Underwater cultural heritage (UCH) is a crucial asset for our knowledge of human history and traditions [...]
A Novel Fault Diagnosis Method Based on SWT and VGG-LSTM Model for Hydraulic Axial Piston Pump
Authors: Zhu Yong , Su Hong , Tang Shengnan , Zhang Shida , Zhou Tao , Wang Jie
Published: March 2023

Since the hydraulic axial piston pump is the engine that drives hydraulic transmission systems, it is widely utilized in aerospace, marine equipment, civil engineering, and mechanical engineering. Operating safely and dependably is crucial, and failure poses a major risk. Hydraulic axial piston pump malfunctions are characterized by internal concealment, challenging self-adaptive feature extraction, and blatant timing of fault signals. By completely integrating the time-frequency feature conversion capability of synchrosqueezing wavelet transform (SWT), the feature extraction capability of VGG11, as well as the feature memory capability of the long short-term memory (LSTM) model, a novel intelligent fault identification method is proposed in this paper. First, the status data are transformed into two dimensions in terms of time and frequency by using SWT. Second, the depth features of the time–frequency map are obtained and dimensionality reduction is carried out by using the deep feature mining capability of VGG11. Third, LSTM is added to provide the damage identification model for long-term memory capabilities. The Softmax layer is utilized for the intelligent evaluation of various damage patterns and health state. The proposed method is utilized to identify and diagnose five typical states, including normal state, swash plate wear, sliding slipper wear, loose slipper, and center spring failure, based on the externally observed vibration signals of a hydraulic axial piston pump. The results indicate that the average test accuracy for five typical state signals reaches 99.43%, the standard deviation is 0.0011, and the average test duration is 2.675 s. The integrated model exhibits improved all-around performance when compared to LSTM, LeNet-5, AlexNet, VGG11, and other typical models. The proposed method is validated to be efficient and accurate for the intelligent identification of common defects of hydraulic axial piston pumps.
Exfoliating Whales–Sandy Bottom Contact Behaviour of Humpback Whales
Authors: Meynecke Jan-Olaf , Gustafon Johan , Cade David E.
Published: March 2023

Cetaceans are known for their intelligence and display of complex behaviours including object use. For example, bowhead whales (Balaena mysticetus) are known to rub on rocks and some humpback whale (Megaptera novaeangliae) populations undertake lateral bottom feeding. Such underwater behaviour is difficult to observe but can play a critical role in the whales’ survival and well-being. Distinguishing social behaviours from those which serve a specific function remains challenging due to a lack of direct observations and detailed descriptions of such behaviours. A CATS (Customized Animal Tracking Solutions) suction cup tag with on board video and a 3D inertial measurement unit was deployed on three different humpback whales to assess their behaviour in the Gold Coast bay, Australia. Here, we present evidence of humpback whales (tagged and untagged individuals) performing bottom contact with prolonged rolling on sandy substrate. In addition, we showed that fish were actively feeding from the whales’ skin during this behaviour. We detail the behaviour and discuss possible drivers, with a focus on cetacean innovation, possible ectoparasite removal, and habitat preferences.
Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection
Authors: Wang Zhuo , Chen Haojie , Qin Hongde , Chen Qin
Published: March 2023

In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.
Sulfoxide-Containing Bisabolane Sesquiterpenoids with Antimicrobial and Nematicidal Activities from the Marine-Derived Fungus Aspergillus sydowii LW09
Authors: Yang Xiao , Yu Hongjia , Ren Jinwei , Cai Lei , Xu Lijian , Liu Ling
Published: March 2023

Phytopathogens, such as phytopathogenic bacteria, fungi, and nematodes, have caused great losses of crops every year, seriously threatening human health and agricultural production. Moreover, marine-derived fungi are abundant sources of structurally unique and bioactive secondary metabolites that could be potential candidates for anti-phytopathogenic drugs. One new sulfoxide-containing bisabolane sesquiterpenoid aspersydosulfoxide A (1) and nine known analogues (2–10) were isolated from the marine-derived A. sydowii LW09. The absolute configuration of the sulfur stereogenic center in 1 was determined by electronic circular dichroism (ECD) calculations. Compound 5 showed inhibition activity against Pseudomonas syringae, with a minimum inhibitory concentration (MIC) value of 32 μg/mL, whereas, compounds 2, 7, and 8 showed antibacterial activities toward Ralstonia solanacarum, with the same MIC value at 32 μg/mL. Meanwhile, compounds 3, 7, and 8 inhibited the fungal spore germination of Fusarium oxysporum, with the half maximal effective concentration (EC50) values of 54.55, 77.16, and 1.85 μg/mL, respectively, while compounds 2, 3, 7, and 8 inhibited the fungal spore germination of Alternaria alternata, which could be induced by vacuolization of germ tubes, with EC50 values of 34.04, 44.44, 26.02, and 46.15 μg/mL, respectively. In addition, compounds 3, 7, and 8 exhibited nematicidal activities against Meloidogyne incognita second-stage juveniles (J2s). In addition, compound 8 possessed the strongest nematicidal activity of nearly 80% mortality at 60 h with the half lethal concentration (LC50) values of 192.40 μg/mL. Furthermore, compounds 3, 7, and 8 could paralyze the nematodes and then impair their pathogenicity.
Revisiting the Determinants of Consumption: A Bayesian Model Averaging Approach
Authors: Deniz Pinar , Stengos Thanasis
Published: March 2023

This study revisits the widely researched area of the consumption function using Bayesian Model Averaging (BMA) for a panel of EU countries to deal with the uncertainty of potential determinants, using the convergence club analysis to construct homogeneous groups by income. BMA suggests that income is the only variable that is found to be a strong determinant across different country groups, whereas other variables have varying importance for different country groups.
Pricing Multi-Asset Bermudan Commodity Options with Stochastic Volatility Using Neural Networks
Authors: Hoshisashi Kentaro , Yamada Yuji
Published: March 2023

It has been recognized that volatility in commodity markets fluctuates significantly depending on the demand–supply relationship and geopolitical risk, and that risk and financial management using multivariate derivatives are becoming more important. This study illustrates an application of multi-layered neural networks for multi-dimensional Bermudan option pricing problems assuming a multi-asset stochastic volatility model in commodity markets. In addition, we aim to identify continuation value functions for these option pricing problems by implementing smooth activation functions in the neural networks and evaluating their accuracy compared with other activation functions or regression techniques. First, we express the underlying asset dynamics using the multi-asset stochastic volatility model with mean reversion properties in the commodity market and formulate the multivariate Bermudan commodity option pricing problem. Subsequently, we apply multi-layer perceptrons in the neural network to represent the continuation value functions of Bermudan commodity options, wherein the entire neural network is trained using the least-squares Monte Carlo simulation method. Finally, we perform numerical experiments and demonstrate that applications of neural networks for Bermudan options in a multi-dimensional commodity market achieve sufficient accuracy with regard to various aspects, including changing the exercise dates, the number of layers/neurons, and the dimension of the problem.
Carbon Tax and Tourism Employment: Is There An Interplay?
Authors: Rotar Laura Juznik
Published: March 2023

The impact of the climate change response on the labour market is an important question for policymakers, while the net positive effect of green policies on the labour market is seen as one of the arguments in favour of a green transition. This is particularly important for the tourism labour market, which was severely hit by the COVID-19 pandemic. This study examined the effect of carbon taxes on tourism employment for European countries that have levied a carbon tax over the past thirty years. A macroeconomic panel data regression model ex-post study was applied by contrasting the obtained results via a robustness check. The estimation results indicate a slightly positive and significant association between the carbon tax and tourism employment, which was additionally tested by considering revenue recycling, early adopters of the carbon tax, and a higher carbon tax compared to countries with a lower carbon tax. We cannot conclude that these factors matter for tourism employment, proving the robustness of the results. Revenue-neutral carbon taxation, policies to address the skills gap, push and pull incentives, and active labour market policies to facilitate the quick re-integration of jobseekers into employment are viewed as pivotal to ensure a smoother transition toward a sustainable tourism labour market.
Homo sapiens—A Species Not Designed for Space Flight: Health Risks in Low Earth Orbit and Beyond, Including Potential Risks When Traveling beyond the Geomagnetic Field of Earth
Authors: Hart David A.
Published: March 2023

Homo sapiens and their predecessors evolved in the context of the boundary conditions of Earth, including a 1 g gravity and a geomagnetic field (GMF). These variables, plus others, led to complex organisms that evolved under a defined set of conditions and define how humans will respond to space flight, a circumstance that could not have been anticipated by evolution. Over the past ~60 years, space flight and living in low Earth orbit (LEO) have revealed that astronauts are impacted to varying degrees by such new environments. In addition, it has been noted that astronauts are quite heterogeneous in their response patterns, indicating that such variation is either silent if one remained on Earth, or the heterogeneity unknowingly contributes to disease development during aging or in response to insults. With the planned mission to deep space, humans will now be exposed to further risks from radiation when traveling beyond the influence of the GMF, as well as other potential risks that are associated with the actual loss of the GMF on the astronauts, their microbiomes, and growing food sources. Experimental studies with model systems have revealed that hypogravity conditions can influence a variety biological and physiological systems, and thus the loss of the GMF may have unanticipated consequences to astronauts’ systems, such as those that are electrical in nature (i.e., the cardiovascular system and central neural systems). As astronauts have been shown to be heterogeneous in their responses to LEO, they may require personalized countermeasures, while others may not be good candidates for deep-space missions if effective countermeasures cannot be developed for long-duration missions. This review will discuss several of the physiological and neural systems that are affected and how the emerging variables may influence astronaut health and functioning.
Application and Research Progress of Covalent Organic Frameworks for Solid-State Electrolytes in Lithium Metal Batteries
Authors: Qiao Yufeng , Zeng Xiaoyue , Wang Haihong , Long Jianlin , Tian Yanhong , Lan Jinle , Yu Yunhua , Yang Xiaoping
Published: March 2023

Covalent organic frameworks (COFs) are a class of crystalline porous organic polymers with periodic networks that are constructed from small molecular units via covalent bonds, which have low densities, high porosity, large specific surface area, and ease of functionalization. The one-dimension nanochannels in COFs offer an effective means of transporting lithium ions while maintaining a stable structure over a wide range of temperatures. As a new category of ionic conductors, COFs exhibit unparalleled application potential in solid-state electrolytes. Here, we provide a comprehensive summary of recent applications and research progress for COFs in solid-state electrolytes of lithium metal batteries and discuss the possible development directions in the future. This review is expected to provide theoretical guidance for the design of high-performance solid-state electrolytes.
The Effect of Antioxidants on Dentin Bond Strength after Application of Common Endodontic Irrigants: A Systematic Review
Authors: Gascón Regina , Forner Leopoldo , Llena Carmen
Published: March 2023

The purpose of this systematic review was to evaluate how an antioxidant treatment would influence the immediate bond strength of dental adhesives to endodontically treated dentine. Scientific evidence was researched in five databases using selected descriptors. Two independent reviewers conducted the investigation and assessed the data and methodological quality of the studies. Inclusion and exclusion criteria were specified for article selection; only studies in English and published between 2001 and 2022 were evaluated. A total of 18 studies were selected and analysed. According to most studies, the irrigation procedure and substrate preparation in endodontic therapy may affect the immediate bond strength. Antioxidants can improve bond strength and adhesion following the use of the most frequently used irrigants in root canal treatments. Therefore, antioxidant treatment can be considered a proper technique to enhance the bond strength of endodontically treated teeth.
Composites in Vehicles Brake Systems-Selected Issues and Areas of Development
Authors: Borawski Andrzej , Mieczkowski Grzegorz , Szpica Dariusz
Published: March 2023

Modern composite materials, thanks to their excellent properties, are widely used [...]
Microstructure and Tribo-Behavior of WC–Cr3C2–Ni Coatings by Laser Cladding and HVAF Sprayed: A Comparative Assessment
Authors: Zhang Ziying , Li Weizhou , Yang Ruixia , Zhao Xiaolian , Zhang Houan
Published: March 2023

SK5 steel is the base material used for the preparation of the wrinkle scraper, whose service life strongly affects the working efficiency and economic benefits. In this work, WC–Cr3C2–Ni coating was deposited on the SK5 steel substrate by using High-velocity air fuel spray (HVAF) and Laser cladding (LC) processes respectively, named HVAF-WC coating and LC-WC coating. The microstructure and wear resistance of both coatings were analyzed, and were compared with the substrate sample. Results showed that the coatings were adhesive well onto the substrate. More WC with fine crystals is retained in HVAF-WC coating due to low flame flow temperature, while WC of LC-WC coating is characterized by columnar crystals. The wear rate of HVAF-WC and LC-WC coating was 4.00 × 10−7 mm3/(N•m) and 3.47 × 10−6 mm3/(N•m), respectively, which was two and one orders of magnitude lower than SK5 steel with 3.54 × 10−5 mm3/Nm. HVAF-WC coating exhibited the best wear resistance because of significant fine grain strengthening, which wear mechanism is mainly dominated by abrasive wear. Thus, it was thought that HVAF-WC coating is more effective ways to improve the wear resistance of SK5 steel, comparing with LC-WC coating.
Research on Deterioration Mechanism and High-Precision Modelling of the Core Loss for Amorphous Alloys after Wire-Cut Electric Discharge Machining
Authors: Yang Xinyu , Qiu Shuheng , Wang Yuheng , Zhao Pengfei , Gao Yunpeng , Wang Haifeng , Zhang Chi
Published: March 2023

Amorphous alloys (AAs) have the advantage of low core loss. Thus, they can be used in high-speed motor applications. However, compared with the nominal performances, the performance of the wire-cut electric discharge machine (W-EDM)-processed AA iron core changes significantly, which limits its popularization. This paper focuses on the performance degradation mechanism of the AA ribbon caused by W-EDM and establishes a modified core loss model after machining. First, a 308 × 15 mm ribbon-shaped AA sample machined by W-EDM was prepared. The characterization and analysis of the magnetic properties, phase, magnetic domain, nano-indentation, micro-morphology, and composition were carried out. In this paper, by analysing the variation in the magnetic domain distribution based on domain width and nano-mechanical properties, it is proposed that the performance degradation range of AA ribbons processed by W-EDM is within 1 mm from the edge. By comparing the microscopic morphology and chemical composition changes in the affected and the unaffected area, this paper presents a mechanism for the property deterioration of W-EDM-processed AA ribbons based on electrochemical corrosion. Finally, a modified loss model for W-EDM-processed AAs is established based on the division of the affected area. This model can significantly improve the accuracy of core loss estimation in the medium- and high-frequency bands commonly used in high-speed motors.
Microstructural Effects on Irradiation Creep of Reactor Core Materials
Authors: Griffiths Malcolm
Published: March 2023

The processes that control irradiation creep are dependent on the temperature and the rate of production of freely migrating point defects, affecting both the microstructure and the mechanisms of mass transport. Because of the experimental difficulties in studying irradiation creep, many different hypothetical models have been developed that either favour a dislocation slip or a mass transport mechanism. Irradiation creep mechanisms and models that are dependent on the microstructure, which are either fully or partially mechanistic in nature, are described and discussed in terms of their ability to account for the in-reactor creep behaviour of various nuclear reactor core materials. A rate theory model for creep of Zr-2.5Nb pressure tubing in CANDU reactors incorporating the as-fabricated microstructure has been developed that gives good agreement with measurements for tubes manufactured by different fabrication routes having very different microstructures. One can therefore conclude that for Zr-alloys at temperatures < 300 °C and stresses < 150 MPa, diffusional mass transport is the dominant creep mechanism. The most important microstructural parameter controlling irradiation creep for these conditions is the grain structure. Austenitic alloys follow similar microstructural dependencies as Zr-alloys, but up to higher temperature and stress ranges. The exception is that dislocation slip is dominant in austenitic alloys at temperatures < 100 °C because there are few barriers to dislocation slip at these low temperatures, which is linked to the enhanced recombination of irradiation-induced point defects.
Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms
Authors: Kadnár Milan , Káčer Peter , Harničárová Marta , Valíček Jan , Tóth František , Bujna Marián , Kušnerová Milena , Mikuš Rastislav , Boržan Marian
Published: March 2023

The production of parts by pressing and subsequent welding is commonly used in the automotive industry. The disadvantage of this method of production is that inaccuracies arising during pressing significantly affect the final dimension of the part. However, this can be corrected by the choice of the technological parameters of the following operation—welding. Suitably designed parameters make it possible to partially eliminate inaccuracies arising during pressing and thus increase the overall applicability of this technology. The paper is focused on the upper arm geometry of a car produced in this manner. There have been two neural networks proposed in which the optimal welding parameters are determined based on the stamped dimensions and the desired final dimensions. The Levenberg–Marquardt back-propagation algorithm and the Bayesian regularised back-propagation algorithm were used as the learning algorithm for ANNs in multi-layer feed-forward networks. The outputs obtained from the neural networks were compared with a linear prediction model based on a on the design of experiment methodology. The mean absolute percentage error of the linear regression model on the entire dataset was 3 × 10−3%. A neural network with Levenberg–Marquardt back-propagation learning algorithm had a mean absolute percentage error of 4 × 10−3. Similarly, a neural network with a Bayesian regularised back-propagation learning algorithm had a mean absolute percentage error of 3 × 10−3%.
The Viability of a Grid of Autonomous Ground-Tethered UAV Platforms in Agricultural Pest Bird Control
Authors: Trethowan Joshua , Wang Zihao , Wong K. C.
Published: March 2023

Pest birds are a salient problem in agriculture all around the world due to the damage they can cause to commercial or high-value crops. Recent advancements in Unmanned Aerial Vehicles (UAVs) have motivated the use of drones in pest bird deterrence, with promising success already being demonstrated over traditional bird control techniques. This paper presents a novel bird deterrence solution in the form of tethered UAVs, which are attached and arranged in a grid-like fashion across a vineyard property. This strategy aims to bypass the power and endurance limitations of untethered drones while still utilising their dynamism and scaring potential. A simulation model has been designed and developed to assess the feasibility of different UAV arrangements, configurations, and strategies against expected behavioural responses of incoming bird flocks, despite operational and spatial constraints imposed by a tether. Attempts at quantifying bird persistence and relative effort following UAV-induced deterrence are also introduced through a novel bird energy expenditure model. This aims to serve as a proxy for selecting control techniques that reduce future foraging missions. The simulation model successfully isolated candidate configurations, which were able to deter both single and multiple incoming bird flocks using a centralised multi-UAV control strategy. Overall, this study indicates that a grid of autonomous ground-tethered UAV platforms is viable as a bird deterrence solution in agriculture, a novel solution not seen nor dealt with elsewhere to the authors’ knowledge.
Analyzing Both Fractional Porous Media and Heat Transfer Equations via Some Novel Techniques
Authors: Albalawi Wedad , Shah Rasool , Shah Nehad Ali , Chung Jae Dong , Ismaeel Sherif M. E. , El-Tantawy Samir A.
Published: March 2023

It has been increasingly obvious in recent decades that fractional calculus (FC) plays a key role in many disciplines of applied sciences. Fractional partial differential equations (FPDEs) accurately model various natural physical phenomena and many engineering problems. For this reason, the analytical and numerical solutions to these issues are seriously considered, and different approaches and techniques have been presented to address them. In this work, the FC is applied to solve and analyze the time-fractional heat transfer equation as well as the nonlinear fractional porous media equation with cubic nonlinearity. The idea of solving these equations is based on the combination of the Yang transformation (YT), the homotopy perturbation method (HPM), and the Adomian decomposition method (ADM). These combinations give rise to two novel methodologies, known as the homotopy perturbation transform method (HPTM) and the Yang tranform decomposition method (YTDM). The obtained results show the significance of the accuracy of the suggested approaches. Solutions in various fractional orders are found and discussed. It is noted that solutions at various fractional orders lead to an integer-order solution. The application of the current methodologies to other nonlinear fractional issues in other branches of applied science is supported by their straightforward and efficient process. In addition, the proposed solution methods can help many plasma physics researchers in interpreting the theoretical and practical results.
Hamilton–Jacobi Inequality Adaptive Robust Learning Tracking Controller of Wearable Robotic Knee System
Authors: Jerbi Houssem , Al-Darraji Izzat , Tsaramirsis Georgios , Ladhar Lotfi , Omri Mohamed
Published: March 2023

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties.
A New Blow-Up Criterion to a Singular Non-Newton Polytropic Filtration Equation
Authors: Li Qingwei , Liao Menglan
Published: March 2023

In this paper, a singular non-Newton polytropic filtration equation under the initial-boundary value condition is revisited. The finite time blow-up results were discussed when the initial energy E(u0) was subcritical (E(u0)d), with d being the potential depth by using the potential well method and some differential inequalities. The goal of this paper is to give a finite time blow-up result if E(u0) is independent of d. Moreover, the explicit upper bound of the blow-up time is obtained by the classical Levine’s concavity method, and the precise lower bound of the blow-up time is derived by applying an interpolation inequality.
Computational Analysis of Hemodynamic Indices Based on Personalized Identification of Aortic Pulse Wave Velocity by a Neural Network
Authors: Gamilov Timur , Liang Fuyou , Kopylov Philipp , Kuznetsova Natalia , Rogov Artem , Simakov Sergey
Published: March 2023

Adequate personalized numerical simulation of hemodynamic indices in coronary arteries requires accurate identification of the key parameters. Elastic properties of coronary vessels produce a significant effect on the accuracy of simulations. Direct measurements of the elasticity of coronary vessels are not available in the general clinic. Pulse wave velocity (AoPWV) in the aorta correlates with aortic and coronary elasticity. In this work, we present a neural network approach for estimating AoPWV. Because of the limited number of clinical cases, we used a synthetic AoPWV database of virtual subjects to train the network. We use an additional set of AoPWV data collected from real patients to test the developed algorithm. The developed neural network predicts brachial–ankle AoPWV with a root-mean-square error (RMSE) of 1.3 m/s and a percentage error of 16%. We demonstrate the relevance of a new technique by comparing invasively measured fractional flow reserve (FFR) with simulated values using the patient data with constant (7.5 m/s) and predicted AoPWV. We conclude that patient-specific identification of AoPWV via the developed neural network improves the estimation of FFR from 4.4% to 3.8% on average, with a maximum difference of 2.8% in a particular case. Furthermore, we also numerically investigate the sensitivity of the most useful hemodynamic indices, including FFR, coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) to AoPWV using the patient-specific data. We observe a substantial variability of all considered indices for AoPWV below 10 m/s and weak variation of AoPWV above 15 m/s. We conclude that the hemodynamic significance of coronary stenosis is higher for the patients with AoPWV in the range from 10 to 15 m/s. The advantages of our approach are the use of a limited set of easily measured input parameters (age, stroke volume, heart rate, systolic, diastolic and mean arterial pressures) and the usage of a model-generated (synthetic) dataset to train and test machine learning methods for predicting hemodynamic indices. The application of our approach in clinical practice saves time, workforce and funds.
COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models
Authors: Alvo Mayer , Mu Jingrui
Published: March 2023

Since December 2019, many statistical spatial–temporal methods have been developed to track and predict the spread of the COVID-19 pandemic. In this paper, we analyzed the COVID-19 dataset which includes the number of biweekly infected cases registered in Ontario from March 2020 to the end of June 2021. We made use of Bayesian Spatial–temporal models and Area-to-point (ATP) and Area-to-area (ATA) Poisson Kriging models. With the Bayesian models, spatial–temporal effects and government intervention effects on infection risk are considered while the ATP Poisson Kriging models are used to display the spread of the pandemic over space.
Community Evolution Analysis Driven by Tag Events: The Special Perspective of New Tags
Authors: Yang Jing , Wang Jun , Gao Mengyang
Published: March 2023

The type, quantity, and scale of social-tagging systems have grown constantly in recent years as users’ interest increases. Tags have important reference value in the study of networked communities since they typically represent user preference. This paper aims to examine how a tagging community evolves and to check the impact of new tags on evolution. Therefore, we proposed an improved evolution model for tag communities where tags constantly accumulate without withdrawal. Based on the model, we conducted an evolution analysis on three different tag communities with the datasets generated from the Delicious bookmarking system, CiteULike, and Douban. The results from Delicious emphasized that new individuals have an enormous influence on the community evolution, for they dominate the Form event, lead the early Split event, indirectly have a hand in the Merge event, and affect existing tags’ transfer when they flood into the system. Moreover, new tags are proved to be more influential in tagging relation data of CiteULike and Douban, where new tags dominate the Split event. The in-depth and detailed depiction of community evolution helps us understand the evolution process of tag communities and the crucial role of new tags.
CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People
Authors: Yadav Harshwardhan , Shah Param , Gandhi Neel , Vyas Tarjni , Nair Anuja , Desai Shivani , Gohil Lata , Tanwar Sudeep , Sharma Ravi , Marina Verdes , Raboaca Maria Simona
Published: March 2023

Cardiovascular diseases (CVDs) are a significant cause of death worldwide. CVDs can be prevented by diagnosing heartbeat sounds and other conventional techniques early to reduce the harmful effects caused by CVDs. However, it is still challenging to segment, extract features, and predict heartbeat sounds in elderly people. The inception of deep learning (DL) algorithms has helped detect various types of heartbeat sounds at an early stage. Motivated by this, we proposed an intelligent architecture categorizing heartbeat into normal and murmurs for elderly people. We have used a standard heartbeat dataset with heartbeat class labels, i.e., normal and murmur. Furthermore, it is augmented and preprocessed by normalization and standardization to significantly reduce computational power and time. The proposed convolutional neural network and bi-directional gated recurrent unit (CNN + BiGRU) attention-based architecture for the classification of heartbeat sound achieves an accuracy of 90% compared to the baseline approaches. Hence, the proposed novel CNN + BiGRU attention-based architecture is superior to other DL models for heartbeat sound classification.
Performance of a New Sixth-Order Class of Iterative Schemes for Solving Non-Linear Systems of Equations
Authors: Moscoso-Martínez Marlon , Chicharro Francisco I. , Cordero Alicia , Torregrosa Juan R.
Published: March 2023

This manuscript is focused on a new parametric class of multi-step iterative procedures to find the solutions of systems of nonlinear equations. Starting from Ostrowski’s scheme, the class is constructed by adding a Newton step with a Jacobian matrix taken from the previous step and employing a divided difference operator, resulting in a triparametric scheme with a convergence order of four. The convergence order of the family can be accelerated to six by setting two parameters, resulting in a uniparametric family. We performed dynamic and numerical development to analyze the stability of the sixth-order family. Previous studies for scalar functions allow us to isolate those elements of the family with stable performance for solving practical problems. In this regard, we present dynamical planes showing the complexity of the family. In addition, the numerical properties of the class are analyzed with several test problems.
Word Game Modeling Using Character-Level N-Gram and Statistics
Authors: Mattiev Jamolbek , Salaev Ulugbek , Kavsek Branko
Published: March 2023

Word games are one of the most essential factors of vocabulary learning and matching letters to form words for children aged 5–12. These games help children to improve letter and word recognition, memory-building, and vocabulary retention skills. Since Uzbek is a low-resource language, there has not been enough research into designing word games for the Uzbek language. In this paper, we develop two models for designing the cubic-letter game, also known as the matching-letter game, in the Uzbek language, consisting of a predefined number of cubes, with a letter on each side of each six-sided cube, and word cards to form words using a combination of the cubes. More precisely, we provide the opportunity to form as many words as possible from the dataset, while minimizing the number of cubes. The proposed methods were created using a combination of a character-level n-gram model and letter position frequency in words at the level of vowels and consonants. To perform the experiments, a novel dataset, consisting of 4.5 k 3–5 letter words, was created by filtering based on child age groups for the Uzbek language, and three more datasets were generated, based on the support of experts for the Russian, English, and Slovenian languages. Experimental evaluations showed that both models achieved good results in terms of average coverage. In particular, the Vowel Priority (VL) approach obtained reasonably high coverage with 95.9% in Uzbek, 96.8% in English, and 94.2% in the Slovenian language in the case of eight cubes, based on the five-fold cross-validation method. Both models covered around 85% of five letter words in Uzbek, English, and Slovenian datasets, while this coverage was even higher (99%) in three letter words in the case of eight cubes.