bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effects of Potato Presentation on Vegetable Intake in School-Aged Children: A Cross-Over Study
Authors: Hernandez Sanchez Mayra G. , Bellini Sarah , Christensen William F. , Jefferies Laura K. , LeCheminant James D. , Patten Emily V. , Redelfs Alisha H. , Stokes Nathan , Wang Jacklyn , Rennick Micaela , Anderson Kelsey , Hunt Joli , Ahlborn Gene J.
Published: October 2023

Vegetables are an essential component of a healthy dietary pattern in children; however, their consumption is often insufficient due to lack of preference. To address this, the influence of combining vegetables (mixed peas and carrots—MPACs) with potatoes, a generally liked food, on overall vegetable consumption among children aged 7–13 years was explored. The research involved a cross-over study design with 65 participants who completed five lunchtime meal conditions, each with different combinations of MPACs and potatoes versus a control (MPACs with a wheat roll). The meals were provided in a cafeteria setting, and plate waste was used to measure vegetable consumption. Anthropometric data and other variables were also measured. Notably, self-reported hunger did not significantly differ between conditions. Meal condition was a significant predictor of MPACs (F = 5.20; p = 0.0005), with MPAC consumption highest when combined with shaped potato faces in the same bowl (+8.77 g compared to serving MPACs and shaped potato faces in separate bowls) and lowest when combined with diced potatoes in the same bowl (−2.85 g compared to serving MPACs and diced potatoes in separate bowls). The overall model for MPAC consumption was influenced by age, height z-score, body fat percentage z-score, and condition (likelihood ratio = 49.1; p < 0.0001). Age had the strongest correlation with vegetable consumption (r = 0.38), followed by male gender, height z-score (r = 0.30), and body fat z-score (r = −0.15). The results highlight the positive impact of combining potatoes with vegetables in school meals, particularly when using shaped potato faces. These findings emphasize the potential of potatoes as a valuable vegetable option in promoting healthier eating habits among children. Additionally, future research could explore the impact of different potato combinations and investigate other factors influencing meal consumption in school settings.
Applying the Adjusted Chinese Dietary Balance Index-16 to Assess the Dietary Quality of Chinese Postpartum Lactating Mothers
Authors: Jiang Junyue , Huang Jiating , Su Yanyan , Wang Yu
Published: October 2023

A balanced diet is considered necessary in maternal recovery and neonatal development; however, the dietary quality of lactating mothers in China has not been systematically evaluated in different regions and stages of lactation. In addition, the release of the Chinese Dietary Guidelines in 2022 implies that the dietary index method needs to be adjusted accordingly. In this study, the adjusted Chinese Dietary Balance Index-16 (DBI-16) was used to assess the dietary quality of lactating women, referred to as the Dietary Balance Index for lactating women (DBI-L). This study is part of the MUAI study, in which dietary intake and demographic characteristics of lactating mothers from six cities in China and at different stages of lactation were obtained through a self-administered questionnaire and a food frequency questionnaire; 2532 puerperal women were included. According to the DBI-L, 66.2% of participants had inadequate dietary intake (79.1% vegetables, 79.1% fruits, 86.7% dairy products, 39.7% soybeans, and 69.4% fish products, respectively), 57.8% had excessive intake (76.0% cereals, 64.4% meat, and 29.1% eggs, respectively) and 92.2% had unbalanced dietary consumption. Dietary quality was optimal for mothers in the first month after delivery, and the dietary quality of mothers in economically developed places such as Shanghai and Guangzhou was significantly better than that in less developed places such as Lanzhou and Changchun. The dietary quality of lactating women in China is imbalanced, with excessive and inadequate dietary intake. The country should strengthen nutritional interventions for lactating mothers, especially in economically underdeveloped regions.
Strong Negative Association between Cesarean Delivery and Early Initiation of Breastfeeding Practices among Vietnamese Mothers—A Secondary Analysis of the Viet Nam Sustainable Development Goal Indicators on Children and Women Survey
Authors: Nguyen Tam Thi Thanh , Nishino Kimihiro , Le Lan Thi Huong , Inthaphatha Souphalak , Yamamoto Eiko
Published: October 2023

Early initiation of breastfeeding (EIBF) involves feeding a newborn with the mother’s breast milk within the first hour of delivery. The prevalence of EIBF in Vietnam has recently shown a downward trend. The present study aimed to demonstrate the current prevalence of EIBF practices and identify factors associated with EIBF among Vietnamese mothers with children under 24 months of age. This study was a secondary analysis of data from the Viet Nam Sustainable Development Goal Indicators on Children and Women (SDGCW) survey 2020–2021. The study participants included 1495 mothers extracted from the SDGCW dataset. Descriptive statistics and logistic regression analyses were performed. The prevalence of EIBF practice was 25.5% among all mothers, 31.9% among vaginal-delivery mothers groups, and 9.0% among cesarean-section mothers groups. Factors negatively associated with EIBF were younger age (0.18 times), cesarean delivery (0.25 times), and absence of skin-to-skin contact with newborns immediately after birth (0.43 times). The prevalence of EIBF among Vietnamese mothers was found to be substantially low, especially among those who underwent cesarean delivery. EIBF should be promoted among younger mothers and those who underwent cesarean delivery.
Thermosensitive Polyurethane-Based Hydrogels as Potential Vehicles for Meloxicam Delivery
Authors: Plugariu Ioana-Alexandra , Gradinaru Luiza Madalina , Avadanei Mihaela , Rosca Irina , Nita Loredana Elena , Maxim Claudia , Bercea Maria
Published: October 2023

Meloxicam (MX) is a nonsteroidal anti-inflammatory drug (NSAID) used mainly to reduce pain, inflammation, and fever. In the present study, thermosensitive polyurethane (PU)-based hydrogels with various excipients (PEG, PVP, HPC, and essential oil) were prepared and loaded with MX. Rheological investigations were carried out on the PU-based formulations in various shear regimes, and their viscoelastic characteristics were determined. The average size of the PU micelles was 35.8 nm at 37 °C and slightly increased at 37 nm in the presence of MX. The zeta potential values of the hydrogels were between −10 mV and −11.5 mV. At pH = 6 and temperature of 37 °C, the formulated PU-based hydrogels loaded with MX could deliver significant amounts of the active substance, between 60% and 80% over 24–48 h and more than 90% within 2 weeks. It was found that anomalous transport phenomena dominated MX’s release mechanism from the PU-based networks. The results are encouraging for further studies aiming to design alternative carriers to commercial dosage forms of nonsteroidal anti-inflammatory drugs.
Mechanisms of Regulation of the Expression of miRNAs and lncRNAs by Metformin in Ovarian Cancer
Authors: Alfaro Ignacio , Vega Margarita , Romero Carmen , Garrido Maritza P.
Published: October 2023

Ovarian cancer (OC) is one of the most lethal gynecological malignancies. The use of biological compounds such as non-coding RNAs (ncRNAs) is being considered as a therapeutic option to improve or complement current treatments since the deregulation of ncRNAs has been implicated in the pathogenesis and progression of OC. Old drugs with antitumoral properties have also been studied in the context of cancer, although their antitumor mechanisms are not fully clear. For instance, the antidiabetic drug metformin has shown pleiotropic effects in several in vitro models of cancer, including OC. Interestingly, metformin has been reported to regulate ncRNAs, which could explain its diverse effects on tumor cells. In this review, we discuss the mechanism of epigenetic regulation described for metformin, with a focus on the evidence of metformin-dependent microRNA (miRNAs) and long non-coding RNA (lncRNAs) regulation in OC.
Recent Advances in Long-Acting Drug Delivery and Formulations
Authors: Al Fatease Adel , Abdelkader Hamdy
Published: October 2023

Conventional immediate-release delivery systems are simple, industrially reproducible, acceptable, and easy-to-use by most patients [...]
Phylogenomics of Southern European Taxa in the Ranunculus auricomus Species Complex: The Apple Doesn’t Fall Far from the Tree
Authors: Bradican John Paul , Tomasello Salvatore , Boscutti Francesco , Karbstein Kevin , Hörandl Elvira
Published: October 2023

The taxonomic status of many Southern European taxa of the Ranunculus auricomus complex remains uncertain despite this region’s proximity to the native ranges of the sexual progenitor species of the complex. We investigated whether additional sexual progenitor species are present in the Mediterranean region. Utilizing target enrichment of 736 single-copy nuclear gene regions and flow cytometry, we analyzed phylogenomic relationships, the ploidy level, and the reproductive mode in representatives of 16 populations in Southern Europe, with additional sequence data from herbarium collections. Additionally, phased sequence assemblies from suspected nothotaxa were mapped to previously described sexual progenitor species in order to determine hybrid ancestry. We found the majority of Mediterranean taxa to be tetraploid, with hybrid populations propagating primarily via apomixis. Phylogenomic analysis revealed that except for the progenitor species, the Mediterranean taxa are often polyphyletic. Most apomictic taxa showed evidence of mixed heritage from progenitor species, with certain progenitor genotypes having mapped more to the populations from adjacent geographical regions. Geographical trends were found in phylogenetic distance, roughly following an east-to-west longitudinal demarcation of the complex, with apomicts extending to the southern margins. Additionally, we observed post-hybridization divergence between the western and eastern populations of nothotaxa in Southern Europe. Our results support a classification of apomictic populations as nothotaxa, as previously suggested for Central Europe.
Thermomechanical Properties of Virgin and Recycled Polypropylene—High-Density Polyethylene Blends
Authors: Jones Hannah , McClements Jake , Ray Dipa , Hindle Colin S. , Kalloudis Michail , Koutsos Vasileios
Published: October 2023

This paper provides evidence and discusses the variability in the thermomechanical behaviour of virgin and recycled polypropylene/high-density polyethylene blends without the addition of other components, which is sparse in the literature. Understanding the performance variability in recycled polymer blends is of critical importance in order to facilitate the re-entering of recycled materials to the consumer market and, thus, contribute towards a circular economy. This is an area that requires further research due to the inhomogeneity of recycled materials. Therefore, the thermal and mechanical properties of virgin and recycled polypropylene/high-density polyethylene blends were investigated systematically. Differential scanning calorimetry concludes that both the recycled and virgin blends are immiscible. Generally, recycled blends have lower overall crystallinity and melting temperatures compared with virgin blends while, remarkably, their crystallisation temperatures are compared favourably. Dynamical mechanical analysis showed little variation in the storage modulus of recycled and virgin blends. However, the alpha and beta relaxation temperatures are lower in recycled blends due to structural deterioration. Deterioration in the thermal and mechanical properties of recycled blends is thought to be caused by the presence of contaminants and structural degradation during reprocessing, resulting in shorter polymeric chains and the formation of imperfect crystallites. The tensile properties of recycled blends are also affected by the recycling process. The Young’s modulus and yield strength of the recycled blends are inferior to those of virgin blends due to the deterioration during the recycling process. However, the elongation at break of the recycled blends is higher compared with the virgin blends, possibly due to the plasticity effect of the low-molecular-weight chain fragments.
Antibacterial Effect and Therapy of Chronic Skin Defects Using the Composite Bioscaffold Polycaprolactone/GelitaSpon/Povidone-Iodine in Domestic Dogs
Authors: Šišková Barbora , Kožár Martin , Staroňová Radka , Shepa Ivan , Hajdučková Vanda , Hudecová Patrícia , Kaduková Michaela , Schnitzer Marek
Published: October 2023

Chronic wounds and the failure of conventional treatment are relatively common in veterinary medicine. Recently, there has been a growing interest in alternative therapeutic approaches and the utilization of biodegradable materials. Their potential application in wound therapy may offer a novel and more suitable option compared to conventional treatment methods. Biodegradable materials can be classified into two main categories: natural, synthetic, and a combination of both, which have the potential to have synergistically enhanced properties. In this study, four domestic dogs with clinical symptoms of chronic wounds were enrolled. These wounds underwent treatment utilizing a novel biodegradable composite material composed of gelatin sponge combined with two electrospun layers of polycaprolactone (PCL) along with polyvinylpyrrolidone (PVP) fibers containing povidone-iodine complex (PVP-I). The initial phase of the study was dedicated to evaluating the antibacterial properties of iodine against Staphylococcus aureus and Escherichia coli. On average, wound healing in domestic dogs took 22 days from the initial treatment, and iodine concentrations demonstrated a significant antibacterial effect against Escherichia coli and Staphylococcus aureus. Based on the favorable outcomes observed in wound management, we believe that the utilization of a blend of natural and synthetic biodegradable materials holds promise as an effective wound therapy option.
Enhancing the Piezoelectric Properties of 3D Printed PVDF Using Concurrent Torsional Shear Strain
Authors: Han Pu , Tofangchi Alireza , Carr Derek , Zhang Sihan , Hsu Keng
Published: October 2023

Extrusion-based polymer 3D printing induces shear strains within the material, influencing its rheological and mechanical properties. In materials like polyvinylidene difluoride (PVDF), these strains stretch polymer chains, leading to increased crystallinity and improved piezoelectric properties. This study demonstrates a 400% enhancement in the piezoelectric property of extrusion-printed PVDF by introducing additional shear strains during the printing process. The continuous torsional shear strains, imposed via a rotating extrusion nozzle, results in additional crystalline β-phases, directly impacting the piezoelectric behavior of the printed parts. The effect of the nozzle’s rotational speed on the amount of β-phase formation is characterized using FTIR. This research introduces a new direction in the development of polymer and composite 3D printing, where in-process shear strains are used to control the alignment of polymer chains and/or in-fill phases and the overall properties of printed parts.
Recent Study of Separation and Identification of Micro- and Nanoplastics for Aquatic Products
Authors: Xu Jin , Wu Gan , Wang Hao , Ding Zhaoyang , Xie Jing
Published: October 2023

Micro- and nanoplastics (MNPs) are polymeric compounds widely used in industry and daily life. Although contamination of aquatic products with MNPs exists, most current research on MNPs focuses on environmental, ecological, and toxicological studies, with less on food safety. Currently, the extent to which aquatic products are affected depends primarily on the physical and chemical properties of the consumed MNPs and the content of MNPs. This review presents new findings on the occurrence of MNPs in aquatic products in light of their properties, carrier effects, chemical effects, seasonality, spatiality, and differences in their location within organisms. The latest studies have been summarized for separation and identification of MNPs for aquatic products as well as their physical and chemical properties in aquatic products using fish, bivalves, and crustaceans as models from a food safety perspective. Also, the shortcomings of safety studies are reviewed, and guidance is provided for future research directions. Finally, gaps in current knowledge on MNPs are also emphasized.
The Priesthood of the Believers: Quakers and the Abolition of Slavery
Authors: Strehle Stephen
Published: October 2023

Quakers became the first group in history to develop a consciousness about slavery and spearheaded the early movement in America and Britain that led to its abolition. Why did they develop this consciousness? What was the spiritual matrix that moved them to denounce a well-accepted and well-established practice that existed in most cultures from time out of mind? The following article helps answer this question. It particularly accents their radical emphasis upon egalitarianism—an emphasis that began in Christianity with the teachings of Jesus and Paul and came to the forefront in Protestantism with Martin Luther’s teaching on the priesthood of the believers. The Quakers followed the doctrine of equality in the Bible, particularly stressing the monogenetic origin of humans in the book of Genesis and the universal redemption of Christ for the fallen race of Adam. They took this egalitarian message much further than others through deconstructing Luther’s priesthood of the believers and rejecting the hierarchical structure of the church in toto, including any professional clergy that would administer the sacraments or preach an authoritative word from the Bible. All Christians were equal before God and received the same immediate instruction from the Holy Spirit, no matter what their race, gender, or position in life. This decided emphasis upon the priesthood of believers made Quakers treat everyone the same and led them to question the inferior status of blacks and a degrading institution like slavery.
Human Rights and Religions: An Overview on a Controversial Relationship
Authors: Ruiz Andrés Rafael
Published: October 2023

If Human Rights (HR) and religions are controversial concepts on their own, the relationship between the two can be fraught with complexity. However, the interest in jointly exploring the realities that underlie the two categories together has led to a long academic tradition of research on the relationship between religions and HR. In this article, based on a review of the state-of-the-art and from a post-secular perspective, we will approach the issue raised from two key points that will be in continuous interaction throughout the text. On the one hand, I will examine the interaction between religions and the polygenesis of HR, a vexed issue that has aroused growing attention in the academic community and that has marked the debate on the polyvalence of fundamental rights. On the other hand, I will analyze the studies that currently attempt to explore the potential contribution of religions to the promotion of HR in the present, but also pointing out the contradictions and ambiguities that arise in this endeavor. These two keys will show us the limits of the relationship between HR and religions, but also the opportunities that the explorations of the common substrate between the two offer cooperation between secular and religious actors in the joint promotion of Human Rights.
The Divinity of Jesus and Ibn ʿArabī: A Study Based on Jesus’s Chapter in Fuṣūṣ al-ḥikam
Authors: Toosi Javad Fakhkhar
Published: October 2023

The most impressive Muslim mystic, Muḥyī al-Dīn Ibn ʿArabī (d. 638/1240), devotes a chapter in his book Fuṣūṣ al-ḥikam to Jesus. He emphasizes the divinity of Jesus and offers a distinctive viewpoint. In addition to two different expressions regarding Jesus’s divinity, by which some deny this divinity and some affirm it, similar to the Bible’s differences, he specifically focuses on the duality of reality (taḥqīq) and illusion (tawahhum) in relation to Jesus. Accordingly, Ibn ʿArabī views the divinity of Jesus as related to his aspect of reality (the identity that emerged in his human form) and regards the denial of his divinity as related to his aspect of illusion (his human form). This research investigates Ibn ʿArabī’s views on the divinity of Jesus. It adds that the evidence confirming Jesus’s divinity is not based on the general theory of the unity of being but instead points directly to Jesus’s divinity. The epistemological position of Ibn ʿArabī toward the theory of the “indwelling (ḥulūl) of God in Jesus” is discussed in light of his significant phrases.
Improving HJ-1B/IRS LST Retrieval of the Generalized Single-Channel Algorithm with Refined ERA5 Atmospheric Profile Database
Authors: Zhang Guoqin , Li Dacheng , Li Hua , Xu Zhaopeng , Hu Zhiheng , Zeng Jian , Yang Yi , Jia Hui
Published: October 2023

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products.
Wind Field Digital Twins Sandbox System for Transmission Towers
Authors: Zhang Chenshuo , Li Yunpeng , Feng Chun , Zhang Yiming
Published: October 2023

Given the digitalization trends within the field of engineering, we propose a practical approach to engineering digitization. This method is established based on a physical sandbox model, camera equipment and simulation technology. We propose an image processing modeling method to establish high-precision continuous mathematical models of transmission towers. The calculation of the wind field is realized by using wind speed calculations, a load-wind-direction-time algorithm and the Continuum-Discontinuum Element Method (CDEM). The sensitivity analysis of displacement- and acceleration-controlled transmission tower loads under two different wind direction conditions is conducted. The results show that the digital model exhibits a proportional relationship with the physical dimensions of the transmission tower model. The error between the numerical simulation results and the experimental results falls within a reasonable range. Nodes at higher positions of the transmission tower experience significantly higher forces compared to those at lower positions, and the structural forms with larger windward projected areas yield similar simulation results. The proposed digital twin system can help monitor the performance of structural bodies and assess the disaster degree in extreme conditions. It can guide specific maintenance and repair tasks.
Research on Key Technologies for the Static Measurement of Railway Track Smoothness
Authors: Lao Dabao , Wang Fang , Quan Yongbin , Liu Yukun
Published: October 2023

In this study, a static railway track smoothness detection system based on laser reference, which can measure various track smoothness parameters by using multiple sensors, is proposed. Furthermore, in order to improve the measurement accuracy and stability of the system, this paper also conducted three key analyses based on the static track measurement system. By using a liquid double-wedge automatic compensation device to compensate the horizontal angle of the beam, a mathematical model of liquid double-wedge automatic compensation was established. Then, by using an optical ring grating system to ring-grate and characterize the laser spot, the collimation efficiency of the system was improved when measuring at long distances. For the special ring grating spot image, an adaptive image processing algorithm was proposed, which can achieve sub-pixel-level positioning accuracy. This study also conducted a field measurement experiment, comparing the experimental data obtained via the static track measurement system with the results of existing track measurement products, and verifying that the static track measurement system has high measurement accuracy and stability.
L-DIG: A GAN-Based Method for LiDAR Point Cloud Processing under Snow Driving Conditions
Authors: Zhang Yuxiao , Ding Ming , Yang Hanting , Niu Yingjie , Feng Yan , Ohtani Kento , Takeda Kazuya
Published: October 2023

LiDAR point clouds are significantly impacted by snow in driving scenarios, introducing scattered noise points and phantom objects, thereby compromising the perception capabilities of autonomous driving systems. Current effective methods for removing snow from point clouds largely rely on outlier filters, which mechanically eliminate isolated points. This research proposes a novel translation model for LiDAR point clouds, the ‘L-DIG’ (LiDAR depth images GAN), built upon refined generative adversarial networks (GANs). This model not only has the capacity to reduce snow noise from point clouds, but it also can artificially synthesize snow points onto clear data. The model is trained using depth image representations of point clouds derived from unpaired datasets, complemented by customized loss functions for depth images to ensure scale and structure consistencies. To amplify the efficacy of snow capture, particularly in the region surrounding the ego vehicle, we have developed a pixel-attention discriminator that operates without downsampling convolutional layers. Concurrently, the other discriminator equipped with two-step downsampling convolutional layers has been engineered to effectively handle snow clusters. This dual-discriminator approach ensures robust and comprehensive performance in tackling diverse snow conditions. The proposed model displays a superior ability to capture snow and object features within LiDAR point clouds. A 3D clustering algorithm is employed to adaptively evaluate different levels of snow conditions, including scattered snowfall and snow swirls. Experimental findings demonstrate an evident de-snowing effect, and the ability to synthesize snow effects.
Physical Demands in the Worst-Case Scenarios of Elite Futsal Referees Using a Local Positioning System
Authors: Martinez-Torremocha Gemma , Sanchez-Sanchez Javier , Alonso-Callejo Antonio , Martin-Sanchez Maria Luisa , Serrano Carlos , Gallardo Leonor , Garcia-Unanue Jorge , Felipe Jose Luis
Published: October 2023

The aim of this study is to analyze the worst-case scenarios of professional futsal referees during the first and second half of official matches in the Spanish Futsal Cup using a Local Positioning System (LPS) for monitoring their movement patterns. Eight professional futsal referees (40 ± 3.43 years; 1.80 ± 0.03 m; 72.84 ± 4.01 kg) participated in the study. The external load (total distance, high-speed running distance and efforts, sprint distance and efforts, and accelerations and decelerations distances) of the referees was monitored and collected using an LPS. The results revealed significant differences in the worst-case scenarios of the futsal referees during the match according to the time window analyzed (p < 0.05). The longest time windows (120 s, 180 s, and 300 s) showed lower relative total distances in the worst-case scenarios (p < 0.05). The high-speed running distances were significatively higher in the first half for the 120 s (+2.65 m·min−1; ES: 1.25), 180 s (+1.55 m·min−1; ES: 1.28), and 300 s (+0.95 m·min−1; ES: 1.14) time windows (p < 0.05). No differences were found between the first and second half for the high-intensity deceleration distance (p > 0.05). These results will serve to prepare the referees in the best conditions for the competition and adapt the training plans to the worst-case scenarios.
LWR-Net: Robust and Lightweight Place Recognition Network for Noisy and Low-Density Point Clouds
Authors: Zhang Zhenghua , Chen Guoliang , Shu Mingcong , Wang Xuan
Published: October 2023

Point cloud-based retrieval for place recognition is essential in robotic applications like autonomous driving or simultaneous localization and mapping. However, this remains challenging in complex real-world scenes. Existing methods are sensitive to noisy, low-density point clouds and require extensive storage and computation, posing limitations for hardware-limited scenarios. To overcome these challenges, we propose LWR-Net, a lightweight place recognition network for efficient and robust point cloud retrieval in noisy, low-density conditions. Our approach incorporates a fast dilated sampling and grouping module with a residual MLP structure to learn geometric features from local neighborhoods. We also introduce a lightweight attentional weighting module to enhance global feature representation. By utilizing the Generalized Mean pooling structure, we aggregated the global descriptor for point cloud retrieval. We validated LWR-Net’s efficiency and robustness on the Oxford robotcar dataset and three in-house datasets. The results demonstrate that our method efficiently and accurately retrieves matching scenes while being more robust to variations in point density and noise intensity. LWR-Net achieves state-of-the-art accuracy and robustness with a lightweight model size of 0.4M parameters. These efficiency, robustness, and lightweight advantages make our network highly suitable for robotic applications relying on point cloud-based place recognition.
Gradient Statistics-Based Multi-Objective Optimization in Physics-Informed Neural Networks
Authors: Vemuri Sai Karthikeya , Denzler Joachim
Published: October 2023

Modeling and simulation of complex non-linear systems are essential in physics, engineering, and signal processing. Neural networks are widely regarded for such tasks due to their ability to learn complex representations from data. Training deep neural networks traditionally requires large amounts of data, which may not always be readily available for such systems. Contrarily, there is a large amount of domain knowledge in the form of mathematical models for the physics/behavior of such systems. A new class of neural networks called Physics-Informed Neural Networks (PINNs) has gained much attention recently as a paradigm for combining physics into neural networks. They have become a powerful tool for solving forward and inverse problems involving differential equations. A general framework of a PINN consists of a multi-layer perceptron that learns the solution of the partial differential equation (PDE) along with its boundary/initial conditions by minimizing a multi-objective loss function. This is formed by the sum of individual loss terms that penalize the output at different collocation points based on the differential equation and initial and boundary conditions. However, multiple loss terms arising from PDE residual and boundary conditions in PINNs pose a challenge in optimizing the overall loss function. This often leads to training failures and inaccurate results. We propose advanced gradient statistics-based weighting schemes for PINNs to address this challenge. These schemes utilize backpropagated gradient statistics of individual loss terms to appropriately scale and assign weights to each term, ensuring balanced training and meaningful solutions. In addition to the existing gradient statistics-based weighting schemes, we introduce kurtosis–standard deviation-based and combined mean and standard deviation-based schemes for approximating solutions of PDEs using PINNs. We provide a qualitative and quantitative comparison of these weighting schemes on 2D Poisson’s and Klein–Gordon’s equations, highlighting their effectiveness in improving PINN performance.
Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network
Authors: Zheng Xiaoyang , Chen Lei , Yu Chengbo , Lei Zijian , Feng Zhixia , Wei Zhengyuan
Published: October 2023

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.
AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden
Authors: Fisher Oliver J. , Rady Ahmed , El-Banna Aly A. A. , Emaish Haitham H. , Watson Nicholas J.
Published: October 2023

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20–82.66%) and semi-supervised learning (81.39–85.26%), active learning models were able to achieve higher accuracy (82.85–85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.
Reconstruction of Water-Filled Pipe Ultrasonic Guided Wave Signals in the Distance Domain by Orthogonal Matching Pursuit Based on Dispersion and Multi-Mode
Authors: Wang Yuemin , Tang Binghui , Gong Ruqing , Zhou Fan , Chen Ang
Published: October 2023

Ultrasonic guided waves (UGWs) in water-filled pipes are subject to more severe dispersion and attenuation than vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called orthogonal matching pursuit based on dispersion and multi-mode (DMOMP) was proposed, which utilizes the second-order asymptotic solution of dispersion curves and the conversion characteristics of asymmetric UGWs in the defect contact stage to reconstruct the dispersive signals and converts the time-domain dispersive signals to distance-domain non-dispersive signals by dispersion compensated time-distance mapping. The synthesized simulation results indicate that DMOMP not only exhibits higher reconstruction accuracy compared to OMP, but also reveals more accurate and stable mode recognition and localization compared to DOMP, which only considers the dispersion under perturbation and noise. In addition, the UGW testing experimental results of water-filled pipes verify the effectiveness of DMOMP, the localization accuracies of three feature signals (defct 1, defct 2 and end echo) with DMOMP are 99.10%, 98.72% and 98.36%, respectively, and the average localization accuracy of DMOMP is as high as 98.73%.
Effects of Functional Electrical Stimulation on Gait Characteristics in Healthy Individuals: A Systematic Review
Authors: Aout Thomas , Begon Mickael , Jegou Baptiste , Peyrot Nicolas , Caderby Teddy
Published: October 2023

Background: This systematic review aimed to provide a comprehensive overview of the effects of functional electrical stimulation (FES) on gait characteristics in healthy individuals. Methods: Six electronic databases (PubMed, Embase, Epistemonikos, PEDro, COCHRANE Library, and Scopus) were searched for studies evaluating the effects of FES on spatiotemporal, kinematic, and kinetic gait parameters in healthy individuals. Two examiners evaluated the eligibility and quality of the included studies using the PEDro scale. Results: A total of 15 studies met the inclusion criteria. The findings from the literature reveal that FES can be used to modify lower-limb joint kinematics, i.e., to increase or reduce the range of motion of the hip, knee, and ankle joints. In addition, FES can be used to alter kinetics parameters, including ground reaction forces, center of pressure trajectory, or knee joint reaction force. As a consequence of these kinetics and kinematics changes, FES can lead to changes in spatiotemporal gait parameters, such as gait speed, step cadence, and stance duration. Conclusions: The findings of this review improve our understanding of the effects of FES on gait biomechanics in healthy individuals and highlight the potential of this technology as a training or assistive solution for improving gait performance in this population.
Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics Modelling
Authors: Yin Yi , Tian Yingtao , Ding Jialuo , Mitchell Tim , Qin Jian
Published: October 2023

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.
A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging
Authors: Mehri Maroua , Calmon Guillaume , Odille Freddy , Oster Julien , Lalande Alain
Published: October 2023

Recently, deep learning (DL) models have been increasingly adopted for automatic analyses of medical data, including electrocardiograms (ECGs). Large, available ECG datasets, generally of high quality, often lack specific distortions, which could be helpful for enhancing DL-based algorithms. Synthetic ECG datasets could overcome this limitation. A generative adversarial network (GAN) was used to synthesize realistic 3D magnetohydrodynamic (MHD) distortion templates, as observed during magnetic resonance imaging (MRI), and then added to available ECG recordings to produce an augmented dataset. Similarity metrics, as well as the accuracy of a DL-based R-peak detector trained with and without data augmentation, were used to evaluate the effectiveness of the synthesized data. Three-dimensional MHD distortions produced by the proposed GAN were similar to the measured ones used as input. The precision of a DL-based R-peak detector, tested on actual unseen data, was significantly enhanced by data augmentation; its recall was higher when trained with augmented data. Using synthesized MHD-distorted ECGs significantly improves the accuracy of a DL-based R-peak detector, with a good generalization capacity. This provides a simple and effective alternative to collecting new patient data. DL-based algorithms for ECG analyses can suffer from bias or gaps in training datasets. Using a GAN to synthesize new data, as well as metrics to evaluate its performance, can overcome the scarcity issue of data availability.
“My Father Put Me in a Patera So I Could Study”: Key Aspects of Socio-Educational Support for Minors Who Migrate Alone
Authors: Olivares-García María Ángeles , García-Segura Sonia , García-Cabrera María del Mar , Fernández-Caminero Gemma , Martínez Romero Belén María , Arcos Aguilar Blanca
Published: October 2023

The migration process for minors who migrate alone represents a complex reality, one entailing exciting life projects but which often clashes with a host society that at times responds in a way shaped by stereotypes and prejudices. In this regard, the role of public and private institutions responsible for the care and custody of these minors is of the utmost importance. This is the point of departure of this study: to explore the multicultural and inclusive practices that are implemented in the process of socio-educational support for minors who migrate alone. Taking as a reference the closest context, as a case study, and employing a qualitative methodological approach, 16 semi-structured interviews were conducted with professionals from Centers for the Protection of Minors, Institutes of Compulsory Secondary Education (secondary schools), and third-sector entities involved in socio-educational intervention with minors who migrate alone in the province of Cordoba (Spain). The results show, firstly, the complexity of the process behind the socio-educational inclusion of these minors, mainly due to their backgrounds; and, furthermore, the role of the different expectations of the professionals involved with regards to this group’s educational and labor-oriented development, along with the importance of the diversity of socio-educational intervention strategies used, focused on individualized attention and with a comprehensive approach. In conclusion, the findings of this study highlight the importance of tackling the task of socio-educational care in a coordinated manner, without forgetting the cultural backgrounds and previous experiences that these minors have when they join these protection and educational systems. It is, therefore, necessary to continue to implement these inclusive practices that promote the comprehensive development of minors and facilitate their transition to adult life, as should be done with any other minor facing situations of social vulnerability.
School Geography’s Critical Role for a More Sustainable Future: Powerful Knowledge and Praxis
Authors: Windsor Sally , Kriewaldt Jeana
Published: October 2023

In this theoretical article we draw on the concepts of powerful knowledge and powerful pedagogies to argue that the school geography curriculum is key to developing structured teaching programmes for students to extend their knowledge and actions for a sustainable future. We argue that geography education uniquely opens up opportunities for action with its focus on place-based, sensory and multimedia experiences, that centre on students’ schools and their communities. This article posits that, although important, merely identifying geographical powerful knowledge is not enough, teachers must also incorporate geographical “powerful pedagogies”. Geography, as a discipline, holds a critical role when it comes to sustainability and education for the future as it makes the links between people and the environment clearly visible. Yet for Geography to be a discipline that empowers students to navigate their current and future life-worlds, it must encompass action through fieldwork and incorporate dialogue between students, teachers, experts and the public that focus on perspectives and possibilities for praxis—action for the good of humankind. This article situates geography education in the powerful knowledge debate by offering a new synthesis of theories connecting curriculum, practice architectures and praxis/action for the future.