bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Avrami-Based Exponential Model for Predicting Fiber-Reinforced Polymer Bar Service Life: A Comparison with Existing Models Using a Large Database
Authors: Wang Tuanjie , Razaqpur Abdul Ghani , Chen Shaoliang , Zhou Shiqiang
Published: October 2024

The fiber-reinforced polymers (FRP) bar is a promising solution to problems caused by steel rebar corrosion in concrete. To assess the service life of the FRP bar based on accelerated test results, it is crucial to have a reliable model. Here, a modified exponential (MEP) model is proposed based on the Avrami equation. The Avrami equation provides a theoretical foundation for the empirical exponential (EP) model and does not a priori fix the power of the exposure time to one. A database containing 903 data points from 74 groups of test specimens is assembled to compare the reliability of the MEP model vis-a-vis the EP, single logarithmic, double logarithmic, and power function models. The combination of Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (R2) criteria is proposed for assessing model reliability. It is shown that in certain cases the combined criteria, versus R2 alone, significantly increase the number of test groups meeting the acceptable performance limit. Observed test data aberrations are found to have minor influence on the results of the EP model, but they significantly influence the results of the other four models. The EP model generally predicts the lowest activation energy and the smallest strength retention for similar groups of bars, while the predicted values of the other four models exhibit a relatively small difference. The difference between the predicted strength retention values of the EP and MEP models shows an increasing trend with the increase of the absolute value of (1 − n), where n is the power of the exposure time in the MEP model.
Effect of Proteins on the Vulcanized Natural Rubber Crosslinking Network Structure and Mechanical Properties
Authors: Wang Yueqiong , Su Shiqi , Liu Hongchao , Wang Rui , Liao Lusheng , Peng Zheng , Li Jihua , Wu Haijun , He Dongning
Published: October 2024

Proteins are important factors affecting the properties of natural rubber. Therefore, investigating the effect of free and bonded proteins on the structure and mechanical properties of the vulcanized crosslinking network of natural rubber would provide a theoretical basis for the production of high mechanical resistance natural rubber. Herein, natural rubbers with different protein contents and types were prepared by high-speed centrifugation. And, the effects on their network structure, vulcanization, tensile strength, tearing strength and dynamic mechanical properties were investigated. The results showed that the reduction in protein content led to the decrement in the entanglement networks, crosslinking density and tensile and tear strengths of the vulcanized natural rubber. Moreover, the bonded proteins had an obvious influence on the vulcanization process, while free proteins played an important role in the crosslink densities. These results reveal that both bonded and free proteins are involved in the vulcanization process and the construction of the vulcanized crosslinking network structure of natural rubber, which enhances the mechanical properties such as the modulus and tensile strength of vulcanized natural rubber.
Lignin-Based N-Carbon Dots Anchoring NiCo2S4/Graphene Hydrogel Exhibits Excellent Performance as Anodes for Hybrid Supercapacitor
Authors: Cui Linlin , Xu Hanping , Zhang Long , Jin Xiaojuan
Published: October 2024

A NiCo2S4/N-CDs/RGO ternary composite hydrogel was prepared via a one-step hydrothermal method, utilizing lignin-based nitrogen-doped carbon dots as a bridge connecting NiCo2S4 and graphene. The specific capacitance of NiCo2S4/N-CDs/RGO significantly outperforms that of the GH and NiCo2S4/RGO electrodes, achieving 1050 F g−1. The 3D mesh porous hydrogel structure mitigates NiCo2S4 nanoparticle aggregation, providing a larger specific surface area for enhanced charge storage. The abundant functional groups of N-CDs interact with Ni (II) and Co (III) cations, favoring NiCo2S4 particle synthesis. Additionally, an assembled solid-state asymmetric supercapacitor employing NiCo2S4/N-CDs/RGO as the positive electrode exhibited excellent energy density (68.4 Wh kg−1) and cycle stability (82% capacitance retention after 10,000 constant current charge–discharge cycles).
Comprehensive Study of Sustainable Pressurized Liquid Extractions to Obtain Bioavailable Antioxidant Phenolic Compounds from Grape Seed By-Products
Authors: Nieto Juan Antonio , Santoyo Susana , de Sá Marta , Baoshan Sun , Reglero Guillermo , Jaime Laura
Published: October 2024

Few investigations have been conducted to evaluate pressurized liquid extraction (PLE) technology as a sustainable method for the recovery of phenolic compounds of grape seed by-products. In this study, PLE combined with an experimental design was evaluated for optimizing the sustainable extraction of phenolic compounds from grape seed by-products. The solvent ethanol content (X1, 0–100%), temperature (X2, 20–100 °C) and time (X3, 1–11 min) were studied as independent experimental factors. Yield, TPC, antioxidant activity and phenolic composition were analyzed as optimized dependent variables. Two optimal extraction conditions at different temperatures (20 °C and 100 °C) were found, but thermal degradations at 100 °C allowed for selecting the optimal condition as 75% ethanol, 11 min and 20 °C. The optimal extracts showed high phenolic content (TPC = 350.80 ± 3.97 mg GAE/g extract) and antioxidant activity (ABTS, 9.31 ± 0.33 mmol Trolox/g extract), mainly composed of polymeric and mono-oligomeric flavan-3-ols. The digestion process reduced the TPC and antioxidant activity due to the low bioaccessibility of the flavan-3-ols, mainly as catechin, epicatechin and polymeric proanthocyanidin losses during the digestion process. However, increases in the antioxidant activity of the basolateral side (DDPH, 0.061 ± 0.000 mmol Trolox/g extract) were determined after in vitro transepithelial transport, which is a consequence of bioavailable catechin and epicatechin and reduced amounts of dimer B2, dimer B1, epicatechin gallate and gallic acid. Consequently, PLE combined with hydroalcoholic solvents at a low temperature resulted in a valuable methodology to obtain sustainable extracts from grape seed by-products (contributing to the circular economy), containing bioavailable phenolic compounds, which are able to increase the antioxidant status.
Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints
Authors: Tsai Wen-Hsien , Chen Hsiang-Ching , Chang Shuo-Chieh , Chan Kai-Cheng
Published: October 2024

The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.
Quantum-Based Maximum Likelihood Detection in MIMO-NOMA Systems for 6G Networks
Authors: Urgelles Helen , Garcia-Roger David , Monserrat Jose F.
Published: October 2024

As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.
Characterization of Heavy Minerals and Their Possible Sources in Quaternary Alluvial and Beach Sediments by an Integration of Microanalytical Data and Spectroscopy (FTIR, Raman and UV-Vis)
Authors: Surour Adel A. , El-Tohamy Amira M.
Published: October 2024

Quaternary stream sediments and beach black sand in north-western Saudi Arabia (namely Wadi Thalbah, Wadi Haramil and Wadi Al Miyah) are characterized by the enrichment of heavy minerals. Concentrates of the heavy minerals in two size fractions (63–125 μm and 125–250 μm) are considered as potential sources of “strategic” accessory minerals. A combination of mineralogical, geochemical and spectroscopic data of opaque and non-opaque minerals is utilized as clues for provenance. ThO2 (up to 17.46 wt%) is correlated with UO2 (up to 7.18 wt%), indicating a possible uranothorite solid solution in zircon. Hafnoan zircon (3.6–5.75 wt% HfO2) is a provenance indicator that indicates a granitic source, mostly highly fractionated granite. In addition, monazite characterizes the same felsic provenance with rare-earth element oxides (La, Ce, Nd and Sm amounting) up to 67.88 wt%. These contents of radionuclides and rare-earth elements assigned the investigated zircon and monazite as “strategic” minerals. In the bulk black sand, V2O5 (up to 0.36 wt%) and ZrO2 (0.57 wt%) are correlated with percentages of magnetite and zircon. Skeletal or star-shaped Ti-magnetite is derived from the basaltic flows. Mn-bearing ilmenite, with up to 5.5 wt% MnO, is derived from the metasediments. The Fourier-transform infrared transmittance (FTIR) spectra indicate lattice vibrational modes of non-opaque silicate heavy minerals, e.g., amphiboles. In addition, the FTIR spectra show O-H vibrational stretching that is related to magnetite and Fe-oxyhydroxides, particularly in the magnetic fraction. Raman data indicate a Verwey transition in the spectrum of magnetite, which is partially replaced by possible ferrite/wüstite during the measurements. The Raman shifts at 223 cm−1 and 460 cm−1 indicate O-Ti-O symmetric stretching vibration and asymmetric stretching vibration of Fe-O bonding in the FeO6 octahedra, respectively. The ultraviolet-visible-near infrared (UV-Vis-NIR) spectra confirm the dominance of ferric iron (Fe3+) as well as some Si4+ transitions of magnetite (226 and 280 nm) in the opaque-rich fractions. Non-opaque heavy silicates such as hornblende and ferrohornblende are responsible for the 192 nm intensity band.
Hidden Archaeological Remains in Heterogeneous Vegetation: A Crop Marks Study in Fortified Settlements of Northwestern Iberian Peninsula
Authors: Peña-Villasenín Simón , Gil-Docampo Mariluz , Ortiz-Sanz Juan
Published: October 2024

This study evaluates the effectiveness of multispectral imaging via Unmanned Aerial Systems (UAS), in combination with advanced digital image processing techniques, for the detection and mapping of archaeological sites within diverse landscapes. The research focuses on six case studies located in the northwest of the Iberian Peninsula, a region marked by complex vegetation patterns and varying topography. The primary objective is to assess the potential of these non-invasive remote sensing techniques in identifying crop marks associated with buried structures from ancient, fortified settlements. By means of Principal Component Analysis (PCA) and vegetation indices, the study aims to pinpoint areas of interest that may indicate the presence of archaeological features, while effectively distinguishing them from modern disturbances or natural terrain variations. The research encountered several challenges, including seasonal variations in crop conditions and recent land-use changes. The methodology successfully identified distinct archaeological features. In some instances, natural vegetation variability, typically seen as an obstacle, enhanced the visibility of crop marks, aiding in the detection of underlying structures. These results offer a cost-effective and scalable option for preliminary archaeological surveys, particularly in refining survey methodologies and guiding future excavation efforts aimed at uncovering and preserving ancient, fortified settlements in the region.
Active Fault Interpretation in the Northern Segment of the Red River Fault Based on Multisource Remote Sensing Data
Authors: Guo Long , He Zhongtai , Ren Zhikun , Li Xingao , Li Linlin
Published: October 2024

High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection and ranging (LiDAR) data, high-resolution GaoFen-7 (GF-7) remote sensing image data, and historical aerial photographs, and a high-resolution digital elevation model (DEM) was generated based on the airborne LiDAR data and GF-7 data. According to the remote sensing interpretation, the main active faults were identified. We subsequently verified the faults in the field and constrained the geographic locations. The current activity was confirmed to be dominantly normal faulting, with some dextral strike-slip components, and the latest active age was the Late Holocene. It reflects the coordination of structural deformation between the rotation of the secondary block and the sliding of the boundary fault within the Sichuan–Yunnan Block. The results show that airborne LiDAR and GF-7 remote sensing data have a great application value in providing high-resolution topographic and geomorphologic data for the study of active structures. The comprehensive application of multisource remote sensing data can greatly improve the reliability of active fault interpretations and provide a reference for follow-up research within the study area.
Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing
Authors: Leditznig Thomas , Klug Hermann
Published: October 2024

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.
Comparative Study of GPR Acquisition Methods for Shallow Buried Object Detection
Authors: Smogavec Primož , Pongrac Blaž , Sarjaš Andrej , Kafedziski Venceslav , Dončov Nabojša , Gleich Dušan
Published: October 2024

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features.
A Validation of OLCI Sentinel-3 Water Products in the Baltic Sea and an Evaluation of the Effect of System Vicarious Calibration (SVC) on the Level-2 Water Products
Authors: O’Kane Sean , McCarthy Tim , Fealy Rowan , Kratzer Susanne
Published: October 2024

The monitoring of coastal waters using satellite data, from sensors such as Sentinel-3 OLCI, has become a vital tool in the management of these water environments, especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, the latest Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region through a matchup analysis. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI Case-2 (neural network) products from Sentinel-3’s processing baseline, listed as follows: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. These products have not yet been validated to such an extent in the region. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. The results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3 h validation window. This is the first time the effects of these SVC gains on the Level-2 OLCI water products has been isolated and quantified in the study region. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data in order to improve the Level-2 water product retrieval in the region. The results of this study are important for end users and the water authorities making use of the satellite water products in the Baltic Sea region.
Impacts of GCP Distributions on UAV-PPK Photogrammetry at Sermeq Avannarleq Glacier, Greenland
Authors: Zhao Haiyan , Li Gang , Chen Zhuoqi , Zhang Shuhang , Zhang Baogang , Cheng Xiao
Published: October 2024

Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured at Sermeq Avannarleq glacier in western Greenland from 4 August 2021 to the 6th, covering approximately 85 km2, with the furthest distance being 13.22 km away from the coastline. Benefited by the meandering coastline, 11 roving stations roughly uniformly distributed on bare rock were surveyed with the RTK technique. PPK-geotagged images were processed in Agisoft Metashape Professional to derive the DSMs, utilizing eight different configurations of GCP distributions that gradually extended longitudinally (along the glacier flow direction) to the upper part of the glacier. The accuracy of DSMs was evaluated by referring to the validation points (VPs) that were not employed in the Bundle Block Adjustment (BBA). The results indicated that the RMSE values of the easting, northing, and height of the reconstruction model georeferenced by only PPK geotagging (no GCPs applied) were 0.038 m, 0.031 m, and 0.146 m, respectively. Applying four GCPs located at one side of the region but with both longitudinal and lateral distribution improved the RMSE values in easting, northing, and vertical to 0.037 m, 0.031 m, and 0.081 m, respectively, and these values were stable even when distributing four GCPs evenly or when increasing the number of GCPs to eleven. Moreover, the cross-validation with ICESat-2 and ArcticDEM performed only at an off-glacier region also suggested that vertical accuracy shows significant improvements for every configuration of GCPs compared to the reconstruction model optimized only by PPK, but such improvements were not obvious if the number of GCPs exceeded four. Moreover, no elevation ramps appeared in the UAV DSM, even for the GCP configuration with only two GCPs distributed at the terminus. Therefore, combining PPK with only a few GCPs but distributing in both directions of the surveying region can offer a viable solution for obtaining glacier DSMs at the coastline with decimeter-level accuracy.
Advancing Forest Degradation and Regeneration Assessment Through Light Detection and Ranging and Hyperspectral Imaging Integration
Authors: Almeida Catherine Torres de , Galvão Lênio Soares , Ometto Jean Pierre H. B. , Jacon Aline Daniele , Pereira Francisca Rocha de Souza , Sato Luciane Yumie , Silva-Junior Celso Henrique Leite , Brancalion Pedro H. S. , Aragão Luiz Eduardo Oliveira e Cruz de
Published: October 2024

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts.
Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction
Authors: Li Hongcheng , Xia Geming , Yu Chaodong , Zhang Yuze , Li Hongfeng
Published: October 2024

The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its “understand before transmit” approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter.
IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms
Authors: Gorges Tom , Davidson Padraig , Boeschen Myriam , Hotho Andreas , Merz Christian
Published: October 2024

Airtime is crucial for high-rotation tricks in snowboard halfpipe performance, significantly impacting trick difficulty, the primary judging criterion. This study aims to enhance the detection of take-off and landing events using inertial measurement unit (IMU) data in conjunction with machine learning algorithms since manual video-based methods are too time-consuming. Eight elite German National Team snowboarders performed 626 halfpipe tricks, recorded by two IMUs at the lateral lower legs and a video camera. The IMU data, synchronized with video, were labeled manually and segmented for analysis. Utilizing a 1D U-Net convolutional neural network (CNN), we achieved superior performance in all of our experiments, establishing new benchmarks for this binary segmentation task. In our extensive experiments, we achieved an 80.34% lower mean Hausdorff distance for unseen runs compared with the threshold approach when placed solely on the left lower leg. Using both left and right IMUs further improved performance (83.37% lower mean Hausdorff). For data from an algorithm-unknown athlete (Zero-Shot segmentation), the U-Net outperformed the threshold algorithm by 67.58%, and fine-tuning on athlete-specific (Few-Shot segmentation) runs improved the lower mean Hausdorff to 78.68%. The fine-tuned model detected takeoffs with median deviations of 0.008 s (IQR 0.030 s), landing deviations of 0.005 s (IQR 0.020 s), and airtime deviations of 0.000 s (IQR 0.027 s). These advancements facilitate real-time feedback and detailed biomechanical analysis, enhancing performance and trick execution, particularly during critical events, such as take-off and landing, where precise time-domain localization is crucial for providing accurate feedback to coaches and athletes.
Enhancing Grapevine Node Detection to Support Pruning Automation: Leveraging State-of-the-Art YOLO Detection Models for 2D Image Analysis
Authors: Oliveira Francisco , da Silva Daniel Queirós , Filipe Vítor , Pinho Tatiana Martins , Cunha Mário , Cunha José Boaventura , dos Santos Filipe Neves
Published: October 2024

Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed us to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection in images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70% and 86.5% with inference times of around 89 ms for an input size of 1280 × 1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.
A Novel Hybrid XAI Solution for Autonomous Vehicles: Real-Time Interpretability Through LIME–SHAP Integration
Authors: Tahir H. Ahmed , Alayed Walaa , Hassan Waqar Ul , Haider Amir
Published: October 2024

The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP). Our framework combines the precision and globality of SHAP and low computational requirements of LIME, creating a balanced approach for onboard deployment with enhanced transparency. We evaluate the proposed framework on three different state-of-the-art models: ResNet-18, ResNet-50, and SegNet-50 on the KITTI dataset. The results demonstrate that our hybrid approach consistently outperforms traditional approaches by achieving a fidelity rate of more than 85%, interpretability factor of more than 80%, and consistency of more than 70%, surpassing the conventional methods. Furthermore, the inference time of our proposed framework with ResNet-18 was 0.28 s; for ResNet-50, it was 0.571 s; and that for SegNet was 3.889 s with XAI layers. This is optimal for onboard computations and deployment. This research establishes a strong foundation for the deployment of XAI in safety-critical AV with balanced tradeoffs for real-time decision-making.
Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots
Authors: Yang Jingwen , Li Xin , Wang Xin , Fu Leiyang , Li Shaowen
Published: October 2024

To address the issue of accurately recognizing and locating picking points for tea-picking robots in unstructured environments, a visual positioning method based on RGB-D information fusion is proposed. First, an improved T-YOLOv8n model is proposed, which improves detection and segmentation performance across multi-scale scenes through network architecture and loss function optimizations. In the far-view test set, the detection accuracy of tea buds reached 80.8%; for the near-view test set, the mAP0.5 values for tea stem detection in bounding boxes and masks reached 93.6% and 93.7%, respectively, showing improvements of 9.1% and 14.1% over the baseline model. Secondly, a layered visual servoing strategy for near and far views was designed, integrating the RealSense depth sensor with robotic arm cooperation. This strategy identifies the region of interest (ROI) of the tea bud in the far view and fuses the stem mask information with depth data to calculate the three-dimensional coordinates of the picking point. The experiments show that this method achieved a picking point localization success rate of 86.4%, with a mean depth measurement error of 1.43 mm. The proposed method improves the accuracy of picking point recognition and reduces depth information fluctuations, providing technical support for the intelligent and rapid picking of premium tea.
GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection
Authors: Zhang Ruifang , Deng Bohan , Cheng Xiaohui , Zhao Hong
Published: October 2024

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model’s parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model’s adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset’s Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model’s parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos.
Road Defect Identification and Location Method Based on an Improved ML-YOLO Algorithm
Authors: Li Tianwen , Li Gongquan
Published: October 2024

The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based on an enhanced ML-YOLO algorithm. By refining the YOLOv8 object detection framework, we optimize both the traditional convolutional layers and the spatial pyramid pooling network. Additionally, we incorporate the Convolutional Block Attention to effectively capture channel and spatial features, along with the Selective Kernel Networks that dynamically adapt to feature extraction across varying scales. An optimized target localization algorithm is proposed to achieve high-precision identification and accurate positioning of road defects. Experimental results indicate that the detection accuracy of the improved ML-YOLO algorithm reaches 0.841, with a recall rate of 0.745 and an average precision of 0.817. Compared to the baseline YOLOv8 model, there is an increase in accuracy by 0.13, a rise in recall rate by 0.117, and an enhancement in average precision by 0.116. After the high detection accuracy of road defects was confirmed, generalization experiments were carried out on the improved ML-YOLO model in the public data set. The experimental results showed that compared with the original YOLOv8n, the average precision and recall rate of all types of ML-YOLO increased by 0.075, 0.121, and 0.035 respectively, indicating robust generalization capabilities. When applied to real-time road monitoring scenarios, this algorithm facilitates precise detection and localization of defects while significantly mitigating traffic accident risks and extending roadway service life. A high detection accuracy of road defects was achieved.
A Review of Thermal Detectors of THz Radiation Operated at Room Temperature
Authors: Bielecki Zbigniew , Mikolajczyk Janusz , Wojtas Jacek
Published: October 2024

This article concerns optical detection issues in the terahertz (THz) range. This is a kind of guide to various types of uncooled thermal detectors in the most often applications. Particular attention is paid to the principle of their operation, technology, and practical features. In addition, some detection methods were also characterized by comparing their performances. The article ends with a performance summary of the selected THz thermal detectors.
Light-Sheet Skew Ray-Based Microbubble Chemical Sensor for Pb2+ Measurements
Authors: Zhuang Tingting , Xu Lukui , Khalid Mamoona , Wu Xuan , Du Linqiao , Shahnia Soroush , Codemard Christophe A. , Bai Zhiyong , Wang Ying , Liu Shen , Chen George Y. , Wang Yiping
Published: October 2024

A multimode fiber-based sensor is proposed and demonstrated for the detection of lead traces in contaminated water. The sensing mechanism involves using a light sheet to excite a specific group of skew rays that optimizes light absorption. The sensing region features an inline microbubble structure that funnels the skew rays into a tight ring, thereby intensifying the evanescent field. The sensitivity is further refined by incorporating gold nanoparticles, which amplify the evanescent field strength through localized surface plasmon resonance. The gold nanoparticles are functionalized with oxalic acid to improve specificity for lead ion detection. Experiment results demonstrated the significantly enhanced absorption sensitivity of the proposed sensing method for large center offsets, achieving a detection limit of 0.1305 ng/mL (the World Health Organization safety limit is 10 ng/mL) for concentrations ranging from 0.1 to 10 ng/mL.
Sensor-Acquired Reachable Workspace (RWS) Correlates with Activities of Daily Living (ADL) Function in Stroke as Measured by Functional Independence Measure (FIM) Self-Care
Authors: Chan Vicky , Lee Seungcheol Austin , Lee Jaylen M. , Han Jay J.
Published: October 2024

Optimal upper extremity motor control and range of motion are necessary to achieve even the basic activities of daily living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide an assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, the sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at the 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3 months were noted (r = 0.619, r = 0.661, p < 0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r = 0.690, r = 0.815, p < 0.05). Multiple linear regression analyses revealed a change in average bilateral total RSA of 0.1 unit from admission to the 3-month follow-up correlated with a respective change in the FIM self-care score of 2.011 points (95%CI: 0.663–3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.
Development of an Electromyography Signal Acquisition Prototype and Statistical Validation Against a Commercial Device
Authors: Guzmán-Quezada Erick , Lomeli-Garcia Santiago , Velazco-Garcia Jorge , Jonguitud-Ceballos Maby , Vega-Martinez Adriana , Ojeda-Galvan Juan , Alvarado-Rodríguez Francisco J. , Reyes-Jiménez Fernanda
Published: October 2024

Electromyography (EMG) stands out as an accessible and inexpensive method for identifying muscle contractions on the surface and within deeper muscle tissues. Using specialized electronic circuits for amplification and filtering can help develop simple but effective systems for detecting and analyzing these signals. However, EMG devices developed by research teams frequently lack rigorous methodologies for validating the quality of the signals they record compared to those obtained by commercial systems that have undergone extensive testing and regulatory approval for market release. This underscores the critical need for standardized validation techniques to reliably assess the performance of experimental devices relative to established commercial equipment. Hence, this study introduces a methodology for the development and statistical validation of a laboratory EMG circuit compared with a professional device available on the market. The experiment simultaneously recorded the muscle electrical activity of 18 volunteers using two biosignal acquisition devices—a prototype EMG and a commercial system—both applied in parallel at the same recording site. Volunteers performed a series of finger and wrist extension movements to elicit myoelectric activity in these forearm muscles. To achieve this, it was necessary to develop not only the EMG signal conditioning board, but also two additional interface boards: one for enabling parallel recording on both devices and another for synchronizing the devices with the task programmatically controlled in Python that the volunteers were required to perform. The EMG signals generated during these tasks were recorded simultaneously by both devices. Subsequently, 22 feature indices commonly used for classifying muscular activity patterns were calculated from two-second temporal windows of the recordings to extract detailed temporal and spatial characteristics. Finally, the Mean Absolute Percentage Error (MAPE) was computed to compare the indices from the prototype with those from the commercial device, using this method as a validation system to assess the quality of the signals recorded by the prototype relative to the commercial equipment. A concordance of 87.6% was observed between the feature indices calculated from the recordings of both devices, suggesting high effectiveness and reliability of the EMG signals recorded by the prototype compared to the commercial device. These results validate the efficacy of our EMG prototype device and provide a solid foundation for the future evaluation of similar devices, ensuring their reliability, accuracy, and suitability for research or clinical applications.
Effects of Whole-Body Electromyostimulation on Metabolic Syndrome in Adults at Moderate-to-High Cardiometabolic Risk—A Systematic Review and Meta-Analysis
Authors: Guretzki Ellen , Kohl Matthias , von Stengel Simon , Uder Michael , Kemmler Wolfgang
Published: October 2024

In the present work, we aimed to determine the effect of whole-body electromyostimulation (WB-EMS) on metabolic syndrome (MetS) as a cluster of cardiometabolic risk factors in people at moderate-to-high cardiometabolic risk. The present meta-analysis is based on a systematic literature search of a recent evidence map, which searched five electronic databases, two registers, and Google Scholar, according to PRISMA, until 31 March 2023. Controlled trials comprising adult cohorts with central obesity that compared the effect of WB-EMS versus controls using a continuous score representing MetS were included. We applied a random-effects meta-analysis and used the inverse heterogeneity model to analyze the data of the five eligible trials identified by our search. Outcome measures were standardized mean differences (SMDs) with 95% confidence intervals (95%-CIs). The risk of bias was determined using the PEDro-Score. In summary, we identified five eligible articles containing 117 participants in the WB-EMS group and 117 participants in the control group. We observed a small effect (SMD: −0.30; 95%-CI: −0.04 to −0.56) in favor of the WB-EMS intervention. The heterogeneity between the trials was very low (I2: 0%); further evidence for risks of small study/publication bias was minimal. The methodologic quality of these studies can be classified as moderate to high. In summary, the present work provides evidence of the favorable effect of WB-EMS on cardiometabolic risk in adults at moderate–high cardiometabolic risk. Considering the time effectiveness of WB-EMS, along with its safety and attractiveness, as indicated by the five studies, WB-EMS can be regarded as a feasible training option for people at cardiometabolic risk.
Optimizing Sustainability Offshore Hybrid Tidal-Wind Energy Storage Systems for an Off-Grid Coastal City in South Africa
Authors: Kangaji Ladislas Mutunda , Raji Atanda , Orumwense Efe
Published: October 2024

South Africa’s extensive marine energy resources present a unique opportunity for advancing sustainable energy solutions. This study focuses on developing a sustainable hybrid power generation system that combines offshore wind and tidal current energy to provide a stable, renewable energy supply for off-grid coastal communities. By addressing the challenges of intermittency and unpredictability in renewable energy sources, the proposed system integrates wind and tidal energy with energy storage and diesel backup to ensure reliability while reducing greenhouse gas emissions and minimizing the environmental footprint. The system is optimized for sustainability, with a configuration of one wind turbine, five tidal turbines, and a diesel generator demonstrated to be the most effective in increasing the renewable energy fraction and lowering the net present cost. Simulations conducted using HOMER Pro version 3.20 software underscore the potential of this hybrid system to support South Africa’s transition to a more sustainable energy future, aligning with national and global sustainability goals. The results emphasize the environmental benefits of combining these renewable energy sources, offering a blueprint for achieving energy security and sustainable development in coastal regions.