bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemistry of Reduced Graphene Oxide: Implications for the Electrophysical Properties of Segregated Graphene–Polymer Composites
Authors: Rabchinskii Maxim K. , Shiyanova Kseniya A. , Brzhezinskaya Maria , Gudkov Maksim V. , Saveliev Sviatoslav D. , Stolyarova Dina Yu. , Torkunov Mikhail K. , Chumakov Ratibor G. , Vdovichenko Artem Yu. , Cherviakova Polina D. , Novosadov Nikolai I. , Nguen Diana Z. , Ryvkina Natalia G. , Shvidchenko Alexander V. , Prasolov Nikita D. , Melnikov Valery P.
Published: October 2024

Conductive polymer composites (CPCs) with nanocarbon fillers are at the high end of modern materials science, advancing current electronic applications. Herein, we establish the interplay between the chemistry and electrophysical properties of reduced graphene oxide (rGO), separately and as a filler for CPCs with the segregated structure conferred by the chemical composition of the initial graphene oxide (GO). A set of experimental methods, namely X-ray photoelectron spectroscopy (XPS), ultraviolet-visible spectroscopy, van der Paw and temperature-dependent sheet resistance measurements, along with dielectric spectroscopy, are employed to thoroughly examine the derived materials. The alterations in the composition of oxygen groups along with their beneficial effect on nitrogen doping upon GO reduction by hydrazine are tracked with the help of XPS. The slight defectiveness of the graphene network is found to boost the conductivity of the material due to facilitating the impact of the nitrogen lone-pair electrons in charge transport. In turn, a sharp drop in material conductivity is indicated upon further disruption of the π-conjugated network, predominantly governing the charge transport. Particularly, the transition from the Mott variable hopping transport mechanism to the Efros–Shklovsky one is signified. Finally, the impact of rGO chemistry and physics on the electrophysical properties of CPCs with the segregated structure is evaluated. Taken together, our results give a hint at how GO chemistry manifests the properties of rGO and the CPC derived from it, offering compelling opportunities for their practical applications.
A Qualitative Study of Rural Plant-Based Eaters’ Knowledge and Practices for Nutritional Adequacy
Authors: Leonetti Michelle , Kolodinsky Jane , Trubek Amy , Belarmino Emily H.
Published: October 2024

(1) Background: Healthful plant-based diets, especially those rich in minimally processed plant-based foods such as fruits, vegetables, and whole grains, have been associated with a lower risk of diet-related chronic disease. However, individuals who limit or avoid animal products may be at risk of nutrient deficiencies, especially related to Vitamin B12, Vitamin D, omega-3 fatty acids, calcium, iron, iodine, zinc, and protein. Such deficiencies can result in both short- and long-term health challenges. We used qualitative methods to explore plant-based eaters’ knowledge and awareness of these eight nutrients of concern in diets that limit or exclude animal-source foods. (2) Methods: We conducted interviews with 28 rural flexitarian, pescatarian, vegan, or vegetarian adults in Vermont, USA. (3) Results: The participants positively viewed the healthfulness of plant-based diets, but many recognized limitations to accessing Vitamin B12, Vitamin D, and omega-3 fatty acids. They shared the strategies used to meet their needs including eating a varied diet, seeking out specific foods, and taking dietary supplements. Analyses identified gaps in the participants’ knowledge related to nutrient bioavailability, food sources of specific nutrients, and the importance of zinc and iodine. Vegans—the highest-risk group—generally presented as the most knowledgeable. The participants noted a lack of local plant-based nutrition expertise. (4) Conclusions: Addressing the identified knowledge gaps and challenges to dietary adequacy, especially among those who limit, but do not fully omit, animal-source foods, may support plant-based nutrition.
The Role of Dietary Ingredients and Herbs in the Prevention of Non-Communicable Chronic Liver Disease
Authors: Maćków Monika , Dziubyna Tomasz , Jamer Tatiana , Slivinskyi Dmytro , Pytrus Tomasz , Neubauer Katarzyna , Zwolińska-Wcisło Małgorzata , Stawarski Andrzej , Piotrowska Ewa , Nowacki Dorian
Published: October 2024

Background: Liver diseases are among the most commonly diagnosed conditions, with the main risk factors being inappropriate lifestyles, including poor diet, excessive alcohol consumption, low physical activity and smoking, including electronic cigarettes. Non-communicable chronic liver diseases also often develop as a result of accompanying overweight and obesity, as well as type 2 diabetes. Methods: The literature on risk factors for non-communicable chronic liver diseases, which show a high strong influence on their occurrence, was analysed. Results: Measures to prevent non-communicable chronic liver disease include the selection of suitable food ingredients that have proven protective effects on the liver. Such ingredients include dietary fibre, probiotics, herbs, various types of polyphenols and fatty acids (omega-3). Conclusions: Because of their liver-protective effects, nutritionists recommend consuming vegetables, fruits, herbs and spices that provide valuable ingredients with anti-inflammatory and anti-cancer effects. These components should be provided with food and, in the case of probiotics, supplementation appears to be important. As a preventive measure, a diet rich in these nutrients is therefore recommended, as well as one that prevents overweight and other diseases that can result in liver disease.
Energy Availability and Body Composition in Professional Athletes: Two Sides of the Same Coin
Authors: Palazzo Roberto , Parisi Tommaso , Rosa Sara , Corsi Marco , Falconi Edoardo , Stefani Laura
Published: October 2024

Background/Objectives: Energy availability (EA) is essential for maintaining physiological functions, significantly influencing athletes’ health and performance. Nutritional behaviors, however, vary across sports. This study aims to assess EA levels in athletes from different disciplines, focusing on the relationship between EA and body composition in endurance athletes compared to rugby players. Methods: This study involved 18 endurance athletes (15 men, 3 women) and 36 rugby players (all men). Data were gathered through interviews, questionnaires, and bioimpedance analysis. Energy intake (EI) was measured with a 24 h dietary recall, and exercise energy expenditure (EEE) was calculated using the IPAQ questionnaire. EA was calculated as EA = (EI − EEE)/fat-free mass (FFM), with results categorized into clinical, subclinical, and optimal ranges. Results: The endurance group had a lower average FFM (57.81 kg) compared to the rugby players (67.61 kg). EA was also significantly lower in endurance athletes (11.72 kcal/kg FFM) than in rugby players (35.44 kcal/kg FFM). Endurance athletes showed more restrictive nutritional behavior with lower EI and higher EEE, but both groups maintained body composition within normal ranges. Conclusions: Endurance athletes exhibit greater nutritional restrictions compared to rugby players, though their body composition remains healthy. Further research is required to investigate the long-term effects of low EA on performance, injury risk, and potential impairment when EA falls below the optimal threshold of 45 kcal/kg FFM/day.
Association between Serum Fatty Acids Profile and MetScore in Women with Severe Obesity
Authors: Oliveira Emilly Santos , Kattah Fabiana Martins , Lima Glaucia Carielo , Horst Maria Aderuza , Figueiredo Nayra , Lima Gislene Batista , Whitton Renata Guimarães Moreira , de Souza Gabriel Inacio de Morais Honorato , Oyama Lila Missae , Silveira Erika Aparecida , Corgosinho Flávia Campos
Published: October 2024

Background: Metabolic syndrome (MetS) is a set of conditions associated with an increased cardiovascular risk. Several serum fatty acids (FAs) seem to play an essential role in the development of cardiometabolic diseases and mortality. Thus, it is imperative to explore the impact of FAs on MetS parameters, using an early MetS screening tool such as MetScore, which is readily available in clinical practice. Aim: The aim of this study was to assess the potential correlation between serum FAs and cardiovascular risk using a MetScore. Methods: This cross-sectional study involved 41 women with severe obesity. The MetScore was calculated, and participants were categorized into high- and low-cardiovascular-risk groups based on the median MetScore value. Gas chromatography was used to quantify serum FAs. Generalized Linear Models were used to compare group means. The association was assessed through simple logistic regression, and an adjusted logistic regression was conducted to validate the association between Metscore and serum FAs. Results: The high-cardiovascular-risk group exhibited elevated values of HOMA-IR, palmitic, oleic, cis-vaccenic, and monounsaturated fatty acids, as well as the SCD-18C, indicating a heightened cardiovascular risk. Conversely, HDL-c, QUICK, gamma-linolenic, and eicosatetraenoic fatty acids showed lower values compared to the low-risk group. Conclusions: Women with severe obesity and high cardiovascular risk have lower values of some omega-3 and omega-6 FAs, considered cardioprotective and anti-inflammatory, and have higher lipogenic activity and FAs, correlated with high cardiovascular risk. These findings emphasize the need to address lipid metabolism in this population as a therapeutic target to reduce cardiovascular risk. Future research should explore clinical interventions that modulate fatty acid metabolism to mitigate cardiometabolic complications.
Increased Incidence and Clinical Features of Septic Arthritis in Patients Aged 80 Years and above: A Comparative Analysis with Younger Cohorts
Authors: Alexandersson Hanna , Dehlin Mats , Jin Tao
Published: October 2024

Background: This study aimed to determine the incidence of septic arthritis across adult age groups in Västra Götaland Region (VGR) of Sweden, while also comparing disease characteristics among different age groups with hematogenous septic arthritis. Methods: Using ICD-10 codes for septic arthritis from 2016 to 2019, we identified 955 patients in VGR. We reviewed the medical records of 216 adult patients with hematogenous septic arthritis and compared data across age groups. Results: The overall incidence of septic arthritis in adults was 4 per 100,000 persons annually, rising to 14 per 100,000 in those ≥80 years. The median age of the 216 patients was 71. The comparison across age groups (18–64, 65–79, and ≥80) showed significantly longer hospital stays and higher mortality rate in the older groups. CRP levels were higher in the middle age group, SF-WBC counts were lower in the youngest age group, and synovial fluid crystals were more common in the oldest. No differences were found in joint involvement or the organisms isolated. Conclusion: The incidence of septic arthritis is 6.5 times higher in patients aged ≥ 80 compared to those under 65, highlighting the need to consider age-related differences in disease management.
Ocular Toxoplasmosis: Advances in Toxoplasma gondii Biology, Clinical Manifestations, Diagnostics, and Therapy
Authors: Miyagaki Miki , Zong Yuan , Yang Mingming , Zhang Jing , Zou Yaru , Ohno-Matsui Kyoko , Kamoi Koju
Published: October 2024

Toxoplasma gondii, an obligate intracellular parasite, is a globally prevalent pathogen capable of infecting a wide range of warm-blooded animals, including humans. Ocular toxoplasmosis (OT), a severe manifestation of T. gondii infection, can lead to potentially blinding complications. This comprehensive review delves into the current understanding of T. gondii biology, exploring its complex life cycle, diverse transmission routes, and strain diversity. This article provides an in-depth analysis of the clinical manifestations of OT, which can result from both congenital and acquired infections, presenting a spectrum of signs and symptoms. The review examines various diagnostic strategies employed for OT, including clinical examination, multimodal imaging techniques such as fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA), as well as laboratory tests including serology and molecular methods. Despite extensive research, the specific mechanisms underlying ocular involvement in T. gondii infection remain elusive, and current diagnostic options have limitations. Moreover, the treatment of active and recurrent OT remains a challenge. While existing therapies, such as antimicrobial agents and immunosuppressants, can control active infections, they do not offer a definitive cure or completely prevent recurrence. The clinical endpoints for the management of active and recurrent OT are also not yet well-established, and the available treatment methods carry the potential for adverse effects. This article highlights the need for future research to elucidate the pathogenesis of OT, investigate genetic factors influencing susceptibility to infection, and develop more sensitive and specific diagnostic tools. Enhancing global surveillance, implementing robust prevention strategies, and fostering multidisciplinary collaborations will be crucial in reducing the burden of OT and improving patient outcomes. This comprehensive review aims to provide a valuable resource for clinicians, researchers, and policymakers, contributing to a better understanding of T. gondii infection and its impact on ocular health.
Toscana Virus in Wild-Caught Sand Flies in Portugal, Findings from the National Vector Surveillance Network, 2023
Authors: Amaro Fátima , Zé-Zé Líbia , Osório Hugo Costa , Soares Patrícia , Silva Manuel , Freitas Inês Campos , Alves Maria João
Published: October 2024

Phlebotomine sand flies play a crucial role in both human and veterinary medicine, acting as vectors for Leishmania parasites and most known phleboviruses. In Portugal, the REVIVE program, a comprehensive national surveillance network under the Ministry of Health, has included sand fly surveys since 2016. REVIVE aims to identify existing sand fly species in the country, determine which pathogens are circulating among them, and provide actionable insights for prevention and control measures when necessary. In this way, annually, from May to October, health technicians collect sand flies across mainland Portugal with CDC light traps. The collected sand flies are sent to the Centre for Vectors and Infectious Diseases Research for species identification and molecular screening of pathogens. On 21 September 2023, Toscana virus (TOSV), a well-known phlebovirus in the Mediterranean region due to its capacity to cause neurological disease, was detected in a pool of 30 sand flies collected in Algarve, the southernmost region of Portugal. A 668 bp partial sequence of the nucleoprotein gene shows similarity with TOSV strains from Spain. To our knowledge, this is the first detection of TOSV in its vector in this country, having previously only been reported in vertebrate hosts. These findings highlight the important role of ongoing surveillance efforts in monitoring and understanding the dynamics of sand fly-borne diseases in Portugal.
Sustainable Starch-Based Films from Cereals and Tubers: A Comparative Study on Cherry Tomato Preservation
Authors: Figueroa-Lopez Kelly J. , Villabona-Ortíz Ángel , Ortega-Toro Rodrigo
Published: October 2024

Biodegradable films are sustainable alternatives to conventional plastics, particularly in food preservation, where the barrier and mechanical properties are crucial for maintaining the physicochemical, microbiological, and sensory qualities of the product. This study evaluated films made from starches of corn, potato, cassava, yam, and wheat to determine their effectiveness in preserving cherry tomatoes. Amylose content, a key factor influencing the crystallinity and properties of the films, varied among the sources, with wheat starch having the highest (28.2%) and cassava the lowest (18.3%). The wheat starch film emerged as the best formulation, exhibiting the highest tensile strength and the lowest water vapor permeability (4.1 ± 0.3 g∙mm∙m−2∙h−1∙KPa−1), contributing to superior barrier performance. When applied to cherry tomatoes, the films based on wheat and corn starch showed the least moisture loss over fifteen days, highlighting their potential in fresh food preservation. These results suggest that starch-based films, specifically those rich in amylose, have significant potential as biodegradable packaging materials for food product conservation.
CDP-MVS: Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation
Authors: Liu Zitian , Chen Zhao , Zhang Xiaoli , Cheng Shihan
Published: October 2024

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys.
An Improved Adaptive Grid-Based Progressive Triangulated Irregular Network Densification Algorithm for Filtering Airborne LiDAR Data
Authors: Zheng Jinjun , Xiang Man , Zhang Tao , Zhou Ji
Published: October 2024

Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively.
Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors
Authors: Peña Angela , Alvarez Edwin L. , Ayala Valderrama Diana M. , Palacio Carlos , Bermudez Yosmely , Paredes-Madrid Leonel
Published: October 2024

Recently, there has been a huge increase in the different ways to manufacture polymer-based sensors. Methods like additive manufacturing, microfluidic preparation, and brush painting are just a few examples of new approaches designed to improve sensor features like self-healing, higher sensitivity, reduced drift over time, and lower hysteresis. That being said, we believe there is still a lot of potential to boost the performance of current sensors by applying modeling, classification, and machine learning techniques. With this approach, final sensor users may benefit from inexpensive computational methods instead of dealing with the already mentioned manufacturing routes. In this study, a total of 96 specimens of two commercial brands of Force Sensing Resistors (FSRs) were characterized under the error metrics of drift and hysteresis; the characterization was performed at multiple input voltages in a tailored test bench. It was found that the output voltage at null force (Vo_null) of a given specimen is inversely correlated with its drift error, and, consequently, it is possible to predict the sensor’s performance by performing inexpensive electrical measurements on the sensor before deploying it to the final application. Hysteresis error was also studied in regard to Vo_null readings; nonetheless, a relationship between Vo_null and hysteresis was not found. However, a classification rule base on k-means clustering method was implemented; the clustering allowed us to distinguish in advance between sensors with high and low hysteresis by relying solely on Vo_null readings; the method was successfully implemented on Peratech SP200 sensors, but it could be applied to Interlink FSR402 sensors. With the aim of providing a comprehensive insight of the experimental data, the theoretical foundations of FSRs are also presented and correlated with the introduced modeling/classification techniques.
Identifying the Vertical Stratification of Sediment Samples by Visible and Near-Infrared Spectroscopy
Authors: Fan Pingping , Jia Zongchao , Qiu Huimin , Wang Hongru , Gao Yang
Published: October 2024

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0–50 cm (the upper layer), 50–100 cm (the middle layer), and 100–160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR.
Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning
Authors: Senol Nurettin Selcuk , Rasheed Amar , Baza Mohamed , Alsabaan Maazen
Published: October 2024

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms—Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks.
A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion
Authors: Ren Bin , Ren Pengyu , Luo Wenfa , Xin Jingze
Published: October 2024

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.
Recent Advances in Nanozyme-Based Sensing Technology for Antioxidant Detection
Authors: Cao Xin , Liu Tianyu , Wang Xianping , Yu Yueting , Li Yangguang , Zhang Lu
Published: October 2024

Antioxidants are substances that have the ability to resist or delay oxidative damage. Antioxidants can be used not only for the diagnosis and prevention of vascular diseases, but also for food preservation and industrial production. However, due to the excessive use of antioxidants, it can cause environmental pollution and endanger human health. It can be seen that the development of antioxidant detection technology is important for environment/health maintenance. It is found that traditional detection methods, including high performance liquid chromatography, gas chromatography, etc., have shortcomings such as cumbersome operation and high cost. In contrast, the nanozyme-based detection method features advantages of low cost, simple operation, and rapidity, which has been widely used in the detection of various substances such as glucose and antioxidants. This article focuses on the latest research progress of nanozymes for antioxidant detection. Nanozymes for antioxidant detection are classified according to enzyme-like types. Different types of nanozyme-based sensing strategies and detection devices are summarized. Based on the summary and analysis, one can find that the development of commercial nanozyme-based devices for the practical detection of antioxidants is still challenging. Some emerging technologies (such as artificial intelligence) should be fully utilized to improve the detection sensitivity and accuracy. This article aims to emphasize the application prospects of nanozymes in antioxidant detection and to provide new ideas and inspiration for the development of detection methods.
The Square-Root Unscented and the Square-Root Cubature Kalman Filters on Manifolds
Authors: Clemens Joachim , Wellhausen Constantin
Published: October 2024

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands.
Validation of Automated Countermovement Vertical Jump Analysis: Markerless Pose Estimation vs. 3D Marker-Based Motion Capture System
Authors: Aleksic Jelena , Kanevsky Dmitry , Mesaroš David , Knezevic Olivera M. , Cabarkapa Dimitrije , Bozovic Branislav , Mirkov Dragan M.
Published: October 2024

This study aimed to validate the automated temporal analysis of countermovement vertical jump (CMJ) using MMPose, a markerless pose estimation framework, by comparing it with the gold-standard 3D marker-based motion capture system. Twelve participants performed five CMJ trials, which were simultaneously recorded using the marker-based system and two smartphone cameras capturing both sides of the body. Key kinematic points, including center of mass (CoM) and toe trajectories, were analyzed to determine jump phases and temporal variables. The agreement between methods was assessed using Bland–Altman analysis, root mean square error (RMSE), and Pearson’s correlation coefficient (r), while consistency was evaluated via intraclass correlation coefficient (ICC 3,1) and two-way repeated-measures ANOVA. Cohen’s effect size (d) quantified the practical significance of differences. Results showed strong agreement (r > 0.98) with minimal bias and narrow limits of agreement for most variables. The markerless system slightly overestimated jump height and CoM vertical velocity, but ICC values (ICC > 0.91) confirmed strong reliability. Cohen’s d values were near zero, indicating trivial differences, and no variability due to recording side was observed. Overall, MMPose proved to be a reliable alternative for in-field CMJ analysis, supporting its broader application in sports and rehabilitation settings.
Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems
Authors: Li Ni , Deng Honggui , Xu Fuxin , Zheng Yitao , Qu Mingkang , Fu Wanqing , Zhou Nanqing
Published: October 2024

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed.
A Case Study on the Integration of Powerline Communications and Visible Light Communications from a Power Electronics Perspective
Authors: Loose Felipe , Garcia-Meré Juan Ramón , Rosanelli Adrion Andrei , Barriquello Carlos Henrique , Alvárez José Antonio Fernandez , Rodríguez Juan , Lamar Diego González
Published: October 2024

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure.
Design of a Capacitive Tactile Sensor Array System for Human–Computer Interaction
Authors: Fei Fei , Jia Zhenkun , Wu Changcheng , Lu Xiong , Li Zhi
Published: October 2024

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from “0” to “9”. These results show the potential of the capacitive sensor array for advanced human–computer interaction applications.
Voice Quality Evaluation in a Mobile Cellular Network: In Situ Mean Opinion Score Measurements
Authors: Leonte Sorin , Pastrav Andra , Zamfirescu Ciprian , Puschita Emanuel
Published: October 2024

This article aims to test, measure and evaluate the quality of voice calls made in a mobile cellular network. A set of drive tests were conducted, during which logs were collected using specialized measurement terminals equipped with a dedicated voice evaluation application. Three different scenarios were considered: the first scenario consisted of a series of mobile-to-mobile calls in a circuit-switched (CS) domain over the GSM network, the second scenario involved similar calls using the VoLTE service in a packet-switched (PS) domain of a 4G network, and the third scenario employed an over-the-top (OTT) media service type via the WhatsApp application in the same PS domain of the 4G network. The measurement results highlight the user experience in each scenario and compare the voice quality evaluated through the Mean Opinion Score (MOS) across the CS and PS domains. The originality of this work consists of in situ measurements performed in Bucharest, Romania, providing detailed, context-specific insights regarding the network performance that can drive local improvements and support policy and investment decisions.
Vertical Microfluidic Trapping System for Capturing and Simultaneous Electrochemical Detection of Cells
Authors: Bató Lilia , Fürjes Péter
Published: October 2024

Electrochemical impedance spectroscopy (EIS) is a non-invasive and label-free method widely used for characterizing cell cultures and monitoring their structure, behavior, proliferation and viability. Microfluidic systems are often used in combination with EIS methods utilizing small dimensions, controllable physicochemical microenvironments and offering rapid real-time measurements. In this work, an electrode array capable of conducting EIS measurements was integrated into a multichannel microfluidic chip which is able to trap individual cells or cell populations in specially designed channels comparable to the size of cells. An application-specific printed circuit board (PCB) was designed for the implementation of the impedance measurement in order to facilitate connection with the device used for taking EIS spectra and for selecting the channels to be measured. The PCB was designed in consideration of the optical screening of trapped cells in parallel with the EIS measurements which allows the comparison of EIS data with optical signals. With continuous EIS measurement, the filling of channels with cell suspension can be followed. Yeast cells were trapped in the microfluidic system and EIS spectra were recorded considering each individual channel, which allows differentiating between the number of trapped cells.
Ultrasonic Array-Based Multi-Source Fusion Indoor Positioning Technology
Authors: Li Cong , Zhang Chenning , Chen Bing , Xu Shaojian , Xu Luping , Yan Bo
Published: October 2024

Underground mining involves numerous risks, such as collapses, gas leaks, and explosions, posing significant threats to worker safety. In this work, we develop an indoor localization system that uses Bluetooth for coarse positioning and ultrasonic arrays for precision calibration. This system is particularly useful for automated mining operations in underground environments where satellite positioning signals are unavailable. The indoor localization system consists of ultrasonic receiver arrays and an improved multi-transmitter-multi-receiver algorithm, enabling accurate localization within the mining environment. Geometric Dilution of Precision (GDOP) analysis is incorporated to optimize the network layout, and an inertial navigation module is integrated to track the posture of moving objects, enabling precise trajectory determination over large areas, such as coal mines. In the experiment, three traditional methods were compared, and the proposed tracking approach demonstrated a positioning accuracy within 10 cm, reducing error by 20% compared to conventional techniques. This high-precision indoor localization method proves beneficial for underground mining applications.
A Kernel-Based Calibration Algorithm for Chromatic Confocal Line Sensors
Authors: Qin Ming , Xiong Xiao , Xiao Enqiao , Xia Min , Gao Yimeng , Xie Hucheng , Luo Hui , Zhao Wenhao
Published: October 2024

In chromatic confocal line sensors, calibration is usually divided into peak extraction and wavelength calibration. In previous research, the focus was mainly on peak extraction. In this paper, a kernel-based algorithm is proposed to deal with wavelength calibration, which corresponds to the mapping relationship between peaks (i.e., the wavelengths) in image space and profiles in physical space. The primary component of the mapping function is depicted using polynomial basis functions, which are distinguished along various dispersion axes. Considering the unknown distortions resulting from field curvature, sensor fabrication and assembly, and even the inherent complexity of dispersion, a typical kernel trick-based nonparametric function element is introduced here, predicated on the notion that similar processes conducted on the same sensor yield comparable distortions.To ascertain the performance with and without the kernel trick, we carried out wavelength calibration and groove fitting on a standard groove sample processed via glass grinding and with a reference depth of 66.14 μm. The experimental results show that depths calculated by the kernel-based calibration algorithm have higher accuracy and lower uncertainty than those ascertained using the conventional polynomial algorithm. As such, this indicates that the proposed algorithm provides effective improvements.
A Parameter-Adaptive Method for Primary Frequency Regulation of Grid-Forming Direct-Drive Wind Turbines
Authors: Hu Siqi , Meng Keqilao , Wu Zikai
Published: October 2024

When wind turbines contribute to system frequency support using virtual synchronous generator (VSG) control, conventional VSG methods often fall short of meeting operational demands, particularly in terms of inertia and frequency support. In this study, considering both the frequency regulation and dynamic performance of VSG, a novel parameter design method that enhances frequency modulation capabilities is proposed in this paper. Initially, VSG control is integrated into the grid-side converter of a direct-drive permanent magnet synchronous generator (D-DPMSG) wind turbine. A small-signal model of the D-DPMSG-VSG active power is then formulated to analyze how the moment of inertia and damping coefficient impact system stability. Subsequently, ensuring that system parameter constraints are met, the key parameters of VSG are adaptively designed to dynamically adjust the system’s frequency and output power during transient responses. Finally, simulation results based on D-DPMSG-VSG in MATLAB/Simulink validated the feasibility, effectiveness, and advantages of the proposed parameter-adaptive VSG control strategy for enhancing the frequency modulation (FM) performance of wind turbines.
UNeXt: An Efficient Network for the Semantic Segmentation of High-Resolution Remote Sensing Images
Authors: Chang Zhanyuan , Xu Mingyu , Wei Yuwen , Lian Jie , Zhang Chongming , Li Chuanjiang
Published: October 2024

The application of deep neural networks for the semantic segmentation of remote sensing images is a significant research area within the field of the intelligent interpretation of remote sensing data. The semantic segmentation of remote sensing images holds great practical value in urban planning, disaster assessment, the estimation of carbon sinks, and other related fields. With the continuous advancement of remote sensing technology, the spatial resolution of remote sensing images is gradually increasing. This increase in resolution brings about challenges such as significant changes in the scale of ground objects, redundant information, and irregular shapes within remote sensing images. Current methods leverage Transformers to capture global long-range dependencies. However, the use of Transformers introduces higher computational complexity and is prone to losing local details. In this paper, we propose UNeXt (UNet+ConvNeXt+Transformer), a real-time semantic segmentation model tailored for high-resolution remote sensing images. To achieve efficient segmentation, UNeXt uses the lightweight ConvNeXt-T as the encoder and a lightweight decoder, Transnext, which combines a Transformer and CNN (Convolutional Neural Networks) to capture global information while avoiding the loss of local details. Furthermore, in order to more effectively utilize spatial and channel information, we propose a SCFB (SC Feature Fuse Block) to reduce computational complexity while enhancing the model’s recognition of complex scenes. A series of ablation experiments and comprehensive comparative experiments demonstrate that our method not only runs faster than state-of-the-art (SOTA) lightweight models but also achieves higher accuracy. Specifically, our proposed UNeXt achieves 85.2% and 82.9% mIoUs on the Vaihingen and Gaofen5 (GID5) datasets, respectively, while maintaining 97 fps for 512 × 512 inputs on a single NVIDIA GTX 4090 GPU, outperforming other SOTA methods.