bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Gallibacterium anatis Isolated from Pathological Processes in Domestic Mammals and Birds in the Czech Republic
Authors: Bzdil Jaroslav , Šlosárková Soňa , Fleischer Petr , Zouharová Monika , Matiašovic Ján
Published: March 2024

Gallibacterium anatis, recognized as a resident and opportunistic pathogen primarily in poultry, underwent investigation in unwell domestic mammals and birds. The study encompassed the mapping and comparison of G. anatis isolates, evaluation of their genetic diversity, and determination of their susceptibility to antimicrobials. A total of 11,908 clinical samples were analyzed using cultivation methods and MALDI-TOF. Whole-genome sequencing was performed on seven calf isolates and six hen isolates. Among mammals, G. anatis was exclusively detected in 22 young dairy calves, while among domestic birds, it was found in 35 individuals belonging to four species. Pathological observations in calves were predominantly localized in the digestive tract, whereas in birds, multi-organ infections and respiratory system infections were most prevalent. Distinct groups of genes were identified solely in calf isolates, and conversely, those unique to hen isolates were also recognized. Novel alleles in the multilocus sequence typing scheme genes and previously unidentified sequence types were observed in both calf and hen isolates. Antimicrobial susceptibility exhibited variation between bird and calf isolates. Notably, G. anatis isolates from calves exhibited disparities in genotype and phenotype compared to those from hens. Despite these distinctions, G. anatis isolates demonstrated the capability to induce septicemia in both species.
Transient Receptor Potential Ankyrin 1 (TRPA1) Modulation by 4-Hydroxynonenal (4-HNE) in Pancreatic Adenocarcinoma Cell Lines: Putative Roles for Therapies
Authors: Piciu Florentina , Domocos Dan , Chiritoiu Gabriela , Chiritoiu-Butnaru Marioara , Mernea Maria , Popescu Cezar Gabriel , Mihai Dragos Paul , Galateanu Bianca , Hudita Ariana , Babes Alexandru , Cucu Dana
Published: March 2024

Background: Transient receptor potential channels (TRP) are overexpressed in some pancreatic adenocarcinoma (PDAC) patients and cell lines, settling them as putative therapeutic targets in this disease. Reactive oxygen species (ROS), with levels increased in PDAC, modulate some members of the TRP family renamed “redox channels”. Here, we investigate the direct effects of 4-hydroxinonenal (4-HNE) on TRPA1, natively expressed in PDAC cell lines and in association with cell migration and cell cycle progression. Methods: We performed microfluorimetry experiments, while the activation of resident membrane channels was investigated using confocal microscopy. We applied a prospective molecular docking of 4-HNE using Autodock and AutoDock Tools4. Also, we simulated the diffusion of 4-HNE through the membrane from the extracellular space with the Permeability of Molecules across Membranes (PerMM) web server. The analysis of cell migration was performed using the wound healing assay, and cell cycle progression was acquired using a Beckman Coulter CytoFlex flow cytometer. Results: Our results show, for the first time in PDAC, that 4-HNE diffuses through the cell membrane and rapidly activates Ca2+ uptake in PDAC cells. This process depends on TRPA1 activation, as 4-HNE forms a covalent binding with a pocket-like region within the intracellular N-terminal of the channel, shaped by the cysteine residues 621, 641, and 665. The activation of TRPA1 by 4-HNE inhibits cell migration and induces cell cycle arrest in the G2/M phase. Conclusions: Our study brings new insights into the effects of 4-HNE, highlighting the activation of the TRPA1 channel, a druggable, putative target for PDAC-expressing tumors.
Selenium-Induced Enhancement in Growth and Rhizosphere Soil Methane Oxidation of Prickly Pear
Authors: Wang Yiming , Xie Xuechong , Chen Huijie , Zhang Kai , Zhao Benliang , Qiu Rongliang
Published: March 2024

As an essential element for plants, animals, and humans, selenium (Se) has been shown to participate in microbial methane oxidation. We studied the growth response and rhizosphere methane oxidation of an economic crop (prickly pear, Rosa roxburghii Tratt) through three treatments (Se0.6 mg/kg, Se2.0 mg/kg, and Se10 mg/kg) and a control (Se0 mg/kg) in a two-month pot experiment. The results showed that the height, total biomass, root biomass, and leaf biomass of prickly pear were significantly increased in the Se0.6 and Se2.0 treatments. The root-to-shoot ratio of prickly pear reached a maximum value in the Se2 treatment. The leaf carotenoid contents significantly increased in the three treatments. Antioxidant activities significantly increased in the Se0.6 and Se2 treatments. Low Se contents (0.6, 2 mg/kg) promoted root growth, including dry weight, length, surface area, volume, and root activity. There was a significant linear relationship between root and aboveground Se contents. The Se translocation factor increased as the soil Se content increased, ranging from 0.173 to 0.288. The application of Se can improve the state of rhizosphere soil’s organic C and soil nutrients (N, P, and K). Se significantly promoted the methane oxidation rate in rhizosphere soils, and the Se10 treatment showed the highest methane oxidation rate. The soil Se gradients led to differentiation in the growth, rhizosphere soil properties, and methane oxidation capacity of prickly pear. The root Se content and Se translocation factor were significantly positively correlated with the methane oxidation rate. Prickly pear can accumulate Se when grown in Se-enriched soil. The 2 mg/kg Se soil treatment enhanced growth and methane oxidation in the rhizosphere soil of prickly pear.
Development of Sustainable and Active Food Packaging Materials Composed by Chitosan, Polyvinyl Alcohol and Quercetin Functionalized Layered Clay
Authors: Wang Chengyu , Mao Long , Zheng Bowen , Liu Yujie , Yao Jin , Zhu Heping
Published: March 2024

In order to solve the problems of insufficient active functions (antibacterial and antioxidant activities) and the poor degradability of traditional plastic packaging materials, biodegradable chitosan (CS)/polyvinyl alcohol (PVA) nanocomposite active films reinforced with natural plant polyphenol-quercetin functionalized layered clay nanosheets (QUE-LDHs) were prepared by a solution casting method. In this study, QUE-LDHs realizes a combination of the active functions of QUE and the enhancement effect of LDHs nanosheets through the deposition and complexation of QUE and copper ions on the LDHs. Infrared and thermal analysis results revealed that there was a strong interface interaction between QUE-LDHs and CS/PVA matrix, resulting in the limited movement of PVA molecules and the increase in glass transition temperature and melting temperature. With the addition of QUE-LDHs, the active films showed excellent UV barrier, antibacterial, antioxidant properties and tensile strength, and still had certain transparency in the range of visible light. As QUE-LDHs content was 3 wt%, the active films exhibited a maximum tensile strength of 58.9 MPa, representing a significant increase of 40.9% compared with CS/PVA matrix. Notably, the UV barrier (280 nm), antibacterial (E. coli) and antioxidant activities (DPPH method) of the active films achieved 100.0%, 95.5% and 58.9%, respectively. Therefore, CS/PVA matrix reinforced with QUE-LDHs has good potential to act as an environmentally and friendly active packaging film or coating.
Development of Hydroxyapatite/Polycaprolactone Composite Biomaterials for Laser Powder Bed Fusion: Evaluation of Powder Characteristics, Mechanical Properties and Biocompatibility
Authors: Yao Hongbo , Zhu Wei , Zhu Xiaolong , Yi Xiner , Yao Jinchao , Yuan Xun , Chen Feng , Han Xiaoxiao
Published: March 2024

Hydroxyapatite/polycaprolactone (HA/PCL) composites have been extensively explored in laser powder bed fusion (L-PBF) for bone tissue engineering. However, conventional mechanical mixing methods for preparing composite powders often yield inhomogeneous compositions and suboptimal flowability. In this study, HA/PCL powders were prepared and optimized for L-PBF using the modified emulsion solvent evaporation method. The morphology, flowability and thermal and rheological properties of the powders were systematically investigated, along with the mechanical and biological properties of the fabricated specimens. The HA/PCL powders exhibited spherical morphologies with a homogeneous distribution of HA within the particles. The addition of small amounts of HA (5 wt% and 10 wt%) enhanced the processability and increased the maximum values of the elastic modulus and yield strength of the specimens from 129.8 MPa to 166.2 MPa and 20.2 MPa to 25.1 MPa, respectively, while also improving their biocompatibility. However, excessive addition resulted in compromised sinterability, thereby affecting both mechanical and biological properties.
Structure–Property Relationships in PVDF/SrTiO3/CNT Nanocomposites for Optoelectronic and Solar Cell Applications
Authors: Taha Taha Abdel Mohaymen , Alanazi Sultan Saud , El-Nasser Karam S. , Alshammari Alhulw H. , Ismael Ali
Published: March 2024

The optical properties of polyvinylidene fluoride (PVDF) polymer nanocomposite films incorporating SrTiO3/carbon nanotubes (CNTs) as nanofillers are investigated. PVDF/SrTiO3/CNTs films were prepared by the solution casting technique. X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) analyses confirmed the incorporation of SrTiO3/CNTs into the PVDF matrix. The addition of nanofillers influenced the crystalline structure, morphology, and optical properties of the films. SEM images showed spherulite morphology, which is a spherical aggregate of crystalline polymer chains. The addition of a SrTiO3/CNTs nanofiller modified the polymer’s electronic structure, causing a variation in the energy gap. The addition of SrTiO3/CNTs at 0.1 wt% increased the band gap, refractive index, and nonlinear optical properties of the PVDF films. These improvements indicate the potential of these nanocomposite films in optoelectronic applications such as solar cells, image sensors, and organic light-emitting diodes.
The Catholic Church in Fragile Democracies: An Influencer, a Moral Guide, or a Judge? A Case Study from the Peruvian Catholic Church
Authors: Piccone-Camere Carlos , Lecaros Véronique
Published: March 2024

This paper navigates the complex roles that the Catholic Church assumes in fragile democracies, exploring whether it functions as an influencer, a moral guide, or a judge in shaping public policies and societal values using the Peruvian context as a focal reference. The authors begin by providing an overview of the global religious landscape, highlighting the diverse and polarized trends within the ecclesial institution. In the second part of this paper, this study delves into the homilies of two influential Peruvian prelates, Mgr. Cipriani and Mgr. Castillo, representing opposing theological currents within the Catholic Church, offering a pertinent exploration of how religious institutions engage with evolving international and local trends in the context of democracy.
Applicability Comparison of GIS-Based RUSLE and SEMMA for Risk Assessment of Soil Erosion in Wildfire Watersheds
Authors: Shin Seung Sook , Park Sang Deog , Kim Gihong
Published: March 2024

The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate.
RETRACTED: Yan et al. Forecasting the Landslide Blocking River Process and Cascading Dam Breach Flood Propagation by an Integrated Numerical Approach: A Reservoir Area Case Study. Remote Sens. 2023, 15, 4669
Authors: Yan Jianhua , Xing Xiansen , Li Xiaoshuang , Zhu Chun , Han Xudong , Zhao Yong , Chen Jianping
Published: March 2024

The journal retracts the article titled “Forecasting the Landslide Blocking River Process and Cascading Dam Breach Flood Propagation by an Integrated Numerical Approach: A Reservoir Area Case Study” [...]
A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios
Authors: Kong Qingyang , Ye Shengbo , Liang Xiao , Li Xu , Liu Xiaojun , Fang Guangyou , Si Guixing
Published: March 2024

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.
MFIL-FCOS: A Multi-Scale Fusion and Interactive Learning Method for 2D Object Detection and Remote Sensing Image Detection
Authors: Zhang Guoqing , Yu Wenyu , Hou Ruixia
Published: March 2024

Object detection is dedicated to finding objects in an image and estimate their categories and locations. Recently, object detection algorithms suffer from a loss of semantic information in the deeper feature maps due to the deepening of the backbone network. For example, when using complex backbone networks, existing feature fusion methods cannot fuse information from different layers effectively. In addition, anchor-free object detection methods fail to accurately predict the same object due to the different learning mechanisms of the regression and centrality of the prediction branches. To address the above problem, we propose a multi-scale fusion and interactive learning method for fully convolutional one-stage anchor-free object detection, called MFIL-FCOS. Specifically, we designed a multi-scale fusion module to address the problem of local semantic information loss in high-level feature maps which strengthen the ability of feature extraction by enhancing the local information of low-level features and fusing the rich semantic information of high-level features. Furthermore, we propose an interactive learning module to increase the interactivity and more accurate predictions by generating a centrality-position weight adjustment regression task and a centrality prediction task. Following these strategic improvements, we conduct extensive experiments on the COCO and DIOR datasets, demonstrating its superior capabilities in 2D object detection tasks and remote sensing image detection, even under challenging conditions.
Spatial-Pooling-Based Graph Attention U-Net for Hyperspectral Image Classification
Authors: Diao Qi , Dai Yaping , Wang Jiacheng , Feng Xiaoxue , Pan Feng , Zhang Ce
Published: March 2024

In recent years, graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image (HSI) classification owing to their exceptional representation capabilities. However, the high computational requirements of GCNs have led most existing GCN-based HSI classification methods to utilize superpixels as graph nodes, thereby limiting the spatial topology scale and neglecting pixel-level spectral–spatial features. To address these limitations, we propose a novel HSI classification network based on graph convolution called the spatial-pooling-based graph attention U-net (SPGAU). Specifically, unlike existing GCN models that rely on fixed graphs, our model involves a spatial pooling method that emulates the region-growing process of superpixels and constructs multi-level graphs by progressively merging adjacent graph nodes. Inspired by the CNN classification framework U-net, SPGAU’s model has a U-shaped structure, realizing multi-scale feature extraction from coarse to fine and gradually fusing features from different graph levels. Additionally, the proposed graph attention convolution method adaptively aggregates adjacency information, thereby further enhancing feature extraction efficiency. Moreover, a 1D-CNN is established to extract pixel-level features, striking an optimal balance between enhancing the feature quality and reducing the computational burden. Experimental results on three representative benchmark datasets demonstrate that the proposed SPGAU outperforms other mainstream models both qualitatively and quantitatively.
Assessing Interactions between Nitrogen Supply and Leaf Blast in Rice by Hyperspectral Imaging
Authors: Maina Angeline Wanjiku , Becker Mathias , Oerke Erich-Christian
Published: March 2024

Mineral nitrogen (N) supply reportedly increases rice susceptibility to the fungal pathogen Magnaporthe oryzae causing blast disease. These biotic and abiotic factors cause changes in spectral reflectance of leaves; however, the effects of N × pathogen interactions on spectral characteristics of rice have not been studied. In this study, hyperspectral imaging was used to assess the effect of N supply on symptoms of rice leaf blast under greenhouse conditions. Three rice genotypes differing in blast susceptibility grown at low, medium, and high N supply were inoculated at the four-leaf stage with three M. oryzae isolates differing in virulence. The reflectance spectra (400 to 1000 nm) of healthy and symptomatic leaves were analyzed using the spectral angle mapper algorithm for supervised classification. Mineral N supply increased the contents of chlorophyll and total N. The number and area of lesions and total blast severity varied depending on rice genotype—M. oryzae isolate interactions and the amount of mineral N applied. The reflectance spectra of healthy tissue and of blast symptom subareas differed with N supply; rice genotypes differed in the response to N supply. Infected plants at high mineral N supply could be distinguished from those at low N supply due to higher differences in the spectra of symptom subareas. Results reveal the potential (and limitations) of hyperspectral imaging for quantifying N effects on rice leaves, disease severity, and symptom expression. The impact of these findings on plant phenotyping and remote sensing under field conditions is discussed.
SAR-CDSS: A Semi-Supervised Cross-Domain Object Detection from Optical to SAR Domain
Authors: Luo Cheng , Zhang Yueting , Guo Jiayi , Hu Yuxin , Zhou Guangyao , You Hongjian , Ning Xia
Published: March 2024

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets.
A Satellite-Drone Image Cross-View Geolocalization Method Based on Multi-Scale Information and Dual-Channel Attention Mechanism
Authors: Gong Naiqun , Li Liwei , Sha Jianjun , Sun Xu , Huang Qian
Published: March 2024

Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. The key to successful cross-view geolocalization lies in extracting crucial spatial structure information across different scales in the image. Recent studies improve image matching accuracy by introducing an attention mechanism to establish global associations among local features. However, existing methods primarily focus on using single-scale features and employ a single-channel attention mechanism to correlate local convolutional features from different locations. This approach inadequately explores and utilizes multi-scale spatial structure information within the image, particularly lacking in the extraction and utilization of locally valuable information. In this paper, we propose a cross-view image geolocalization method based on multi-scale information and a dual-channel attention mechanism. The multi-scale information includes features extracted from different scales using various convolutional slices, and it extensively utilizes shallow network features. The dual-channel attention mechanism, through successive local and global feature associations, effectively learns depth discriminative features across different scales. Experimental results were conducted using existing satellite and drone image datasets, with additional validation performed on an independent self-made dataset. The findings indicate that our approach exhibits superior performance compared to existing methods. The methodology presented in this paper exhibits enhanced capabilities, especially in the exploitation of multi-scale spatial structure information and the extraction of locally valuable information.
Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification
Authors: Chang Chein-I , Kuo Yi-Mei , Ma Kenneth Yeonkong
Published: March 2024

Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue. Density peak clustering (DPC) provides an effective means for this purpose, referred to as DPC-based BS (DPC-BS). It uses two indicators, cluster density and cluster distance, to rank all bands for BS. This paper reinterprets cluster density and cluster distance as band local density (BLD) and band distance (BD) and also introduces a new concept called band prominence value (BPV) as a third indicator. Combining BLD and BD with BPV derives new band prioritization criteria for BS, which can extend the currently used DPC-BS to a new DPC-BS method referred to as band density prominence clustering (BDPC). By taking advantage of the three key indicators of BDPC, i.e., cut-off band distance bc, k nearest neighboring-band local density, and BPV, two versions of BDPC can be derived called bc-BDPC and k-BDPC, both of which are quite different from existing DPC-based BS methods in three aspects. One is that the parameter bc of bc-BDPC and the parameter k of k-BDPC can be automatically determined by the number of clusters and virtual dimensionality (VD), respectively. Another is that instead of using Euclidean distance, a spectral discrimination measure is used to calculate BD as well as inter-band correlation. The most important and significant aspect is a novel idea that combines BPV with BLD and BD to derive new band prioritization criteria for BS. Extensive experiments demonstrate that BDPC generally performs better than DPC-BS as well as many current state-of-the art BS methods.
Inspecting Pond Fabric Using Unmanned Aerial Vehicle-Assisted Modeling, Smartphone Augmented Reality, and a Gaming Engine
Authors: Shih Naai-Jung , Tasi Yun-Ting , Qiu Yi-Ting , Hsu Ting-Wei
Published: March 2024

Historical farm ponds have been designed, maintained, and established as heritage sites or cultural landscapes. Has their gradually evolving function resulted in changes to the landscape influenced by their degenerated nature and the new urban fabric? This study aimed to assess the interaction between urban fabrics and eight farm ponds in Taoyuan by determining the demolition ratio of ponds subject to the transit-oriented development (TOD) of infrastructure and to evaluate land cover using historical maps, unmanned aerial vehicle (UAV)-assisted 3D modeling, smartphone augmented reality (AR), and a gaming engine to inspect and compare well-developed or reactivated ponds and peripheries. A 46% reduction in pond area around Daxi Interchange was an important indicator of degeneration in the opposite direction to TOD-based instrumentation. Three-dimensional skyline analysis enabled us to create an urban context matrix to be used in the simulations. Nearly 55 paired AR comparisons were made with 100 AR cloud-accessed models from the Augment® platform, and we produced a customized interface to align ponds with landmark construction or other ponds using Unreal Engine®. Smartphone AR is a valuable tool for situated comparisons and was used to conduct analyses across nine categories, from buildings and infrastructure to the intensity and stage of development. The gaming engine handled large point models with high detail and was supported by a customized blueprint. We found that 3D virtual dynamics highlighted the evolving interstitial space and role substitution of the agricultural fabric. This combination of heterogeneous platforms provides a practical method of preserving heritage and enables conflict resolution through policy and TOD instrumentation.
Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle
Authors: Ding Yi , Che Jiaxing , Zhou Zhiming , Bian Jingyuan
Published: March 2024

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target’s position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR’s real-time point cloud data are filtered based on position–direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target’s position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function.
Research on a Visual/Ultra-Wideband Tightly Coupled Fusion Localization Algorithm
Authors: Jiang Pin , Hu Chen , Wang Tingting , Lv Ke , Guo Tingfeng , Jiang Jinxuan , Hu Wenwu
Published: March 2024

In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a vision SLAM/UWB tightly coupled localization method and designs a UWB non-line-of-sight error identification method using the displacement increment of the visual odometer. It utilizes the displacement increment of visual output and UWB ranging information as measurement values and applies the extended Kalman filtering algorithm for data fusion. This study utilized the constructed experimental platform to collect images and ultra-wideband ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that the algorithm outperforms individual UWB or loosely coupled combination positioning methods in terms of positioning accuracy. It effectively eliminates non-line-of-sight errors in UWB, improving the accuracy and stability of the combined positioning system.
A TSENet Model for Predicting Cellular Network Traffic
Authors: Wang Jianbin , Shen Lei , Fan Weiming
Published: March 2024

Wireless sensor networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, we present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spatial features of each grid with information from its correlated grids, generating spatial predictions within the spatial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and cellular data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.
Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges
Authors: Enshaeian Alireza , Ghahremani Behzad , Rizzo Piervincenzo
Published: March 2024

Three Pennsylvanian bridges were studied using finite element and wireless sensor technology. A detailed finite element model of each bridge was created using a commercial software in order to calculate the strains generated by a load that simulates the presence of a standard truck. Pristine and damage scenarios were simulated, and the computed strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test performed by companies not involved in the study presented in this article. The comparison demonstrated the accuracy of the model and the presence of a few non-critical anomalies in terms of load redistribution. In addition, the comparison proved the reliability of the wireless sensing system installed on the bridges, although some drift was observed. The structural monitoring program for the three bridges was also evaluated by processing more than two years of data streamed to a repository.
Non-Destructive Testing of a Fiber-Web-Reinforced Polymethacrylimide Foam Sandwich Panel with Terahertz Time-Domain Spectroscopy
Authors: Liu Yu , Hu Yefa , Zhang Jinguang , Liu Haixin , Wan Meng
Published: March 2024

Terahertz (THz) non-destructive testing can detect internal defects in dielectric materials. However, this technology is mainly used for detecting thin and simple structures at present, lacking validations for the detection effectiveness of internal defects in thicker and more complex structures, such as fiber-web-reinforced composite sandwich panels. In this study, samples of fiber-web-reinforced polymethacrylimide foam sandwich panels, which are, respectively, 20 mm and 30 mm thick, were made to detect the internal debonding, inclusion, pore, and crack defects by the THz time-domain spectroscopy system (THz-TDS). The peak-to-peak-imaging algorithm, maximum-amplitude-imaging algorithm, minimum-amplitude-imaging algorithm, pulse-width-imaging algorithm, and time-of-flight-imaging algorithm were used to process and image the collected THz signals. The results showed that the peak-to-peak-imaging algorithm had the best performance. To address the low imaging resolution of THz-TDS, a block-based super-resolution reconstruction method—SSSRGAN—is proposed, which can improve image resolution while maintaining the clear edge contours of defects. The defect-detection results of the samples showed that THz-TDS could detect all pore, debonding, and crack defects, with a minimum size of 3 mm for pores and debonding and a minimum thickness of 1 mm for cracks. The method showed poor detection performance for inclusions with a thickness of 0.053 mm, but could still extract the defect features. Based on the THz-TDS reflection mode measurement principle, the thickness information of the panel, foam core, and web of the samples was calculated: the measurement error was no more than 0.870 mm for Sample #1 and no more than 0.270 mm for Sample #2, demonstrating the accuracy of THz-TDS in measuring the dimensions of sandwich panel structures. In general, THz technology shows potential for detecting internal defects and performing dimensional measurements in complex structures. With the advancement of portable devices and enhancements in detection speed, real-time on-site detection is anticipated in the future.
How Approaching Angle, Bottleneck Width and Walking Speed Affect the Use of a Bottleneck by Individuals
Authors: Boomers Ann Katrin , Boltes Maik , Kersting Uwe G.
Published: March 2024

Understanding pedestrian dynamics at bottlenecks and how pedestrians interact with their environment—particularly how they use and move in the space available to them—is of safety importance, since bottlenecks are a key point for pedestrian flow. We performed a series of experiments in which participants walked through a bottleneck individually for varying combinations of approaching angle, bottleneck width and walking speed, to investigate the dependence of the movement on safety-relevant influencing factors. Trajectories as well as 3D motion data were recorded for every participant. This paper shows that (1) the maximum amplitude of shoulder rotation is mainly determined by the ratio of the bottleneck width to the shoulder width of the participant, while the direction is determined by the starting angle and the foot position; (2) the ‘critical point’ is not invariant to the starting angle and walking speed; (3) differences between the maximum and minimum speed values arise mainly from the distribution of deceleration patterns; and (4) the position of crossing shifts by 1.75 cm/10 cm, increasing the bottleneck width in the direction of origin.
Thermography Sensor to Assess Motor and Sensitive Neuromuscular Sequels of Brain Damage
Authors: Cabizosu Alessio , Grotto Daniele , López López Alberto , Castañeda Vozmediano Raúl
Published: March 2024

Introduction. The aim of this study was to observe the validity, diagnostic capacity, and reliability of the thermographic technique in the analysis of sensitive and motor sequelae in patients with chronic brain damage. Method. A longitudinal descriptive observational study was performed. Forty-five people with impairment in at least one anatomical region participated in and completed this study. All patients who had become infected by SARS-CoV-2 in the past year were excluded. Thermographic measurement was conducted, and the Modified Ashworth Scale and Pressure Pain Threshold was analyzed. Results. A high correlation between two times of thermography data was observed. The Spearman correlations obtained between the Ashworth score on each leg and the temperature given by thermography were all significant. Discussion and conclusions. Despite the above, the Spearman correlations obtained between the PPT in each leg and the temperature offered by thermography were not significant in any of the measurements. For this reason, thermography is a potential tool for the diagnosis and assessment of neuromuscular motor sequelae, but not for sensitive sequelae, after brain injury. Nevertheless, for the time being, no statistical relationship has been observed between the data reported by thermography and PPT; thus, future studies are needed to further investigate these results.
Estimating Rotational Acceleration in Shoulder and Elbow Joints Using a Transformer Algorithm and a Fusion of Biosignals
Authors: Bai Yu , Guan Xiaorong , He Long , Wang Zheng , Li Zhong , Zhu Meng
Published: March 2024

In the present study, we used a transformer model and a fusion of biosignals to estimate rotational acceleration in elbow and shoulder joints. To achieve our study objectives, we proposed a mechanomyography (MMG) signal isolation technique based on a variational mode decomposition (VMD) algorithm. Our results show that the VMD algorithm delivered excellent performance in MMG signal extraction compared to the commonly used technique of empirical mode decomposition (EMD). In addition, we found that transformer models delivered estimates of joint acceleration that were more precise than those produced by mainstream time series forecasting models. The average R2 values of transformer are 0.967, 0.968, and 0.935, respectively. Finally, we found that using a fusion of signals resulted in more precise estimation performance compared to using MMG signals alone. The differences between the average R2 values are 0.041, 0.053, and 0.043, respectively. Taken together, the VMD isolation method, the transformer algorithm and the signal fusion technique described in this paper can be seen as supplying a robust framework for estimating rotational acceleration in upper-limb joints. Further study is warranted to examine the effectiveness of this framework in other musculoskeletal contexts.
Study on the Equivalence Transformation between Blasting Vibration Velocity and Acceleration
Authors: Yu Chong , Wu Jiajun , Li Haibo , Ma Yongan , Wang Changjian
Published: March 2024

The evaluation of blasting vibrations primarily hinges on two physical quantities: velocity and acceleration. A significant challenge arises when attempting to reference the two types of vibration data in relation to one another, such as different types of seismometers, noise, etc., necessitating a method for their equivalent transformation. To address this, a transformation method is discussed in detail with a case study, and equations for the ratio (Ra) of the particle peak velocity (PPV) to the particle peak acceleration (PPA) are proposed. The findings are twofold: (1) The conventional data conversion processes often suffer from low accuracy due to the presence of trend terms and noise in the signal. To mitigate this, the built-in MATLAB function is used for trend term elimination, complemented by a combined approach that integrates CEEMDAN with WD/WDP for noise reduction. These significantly enhance the accuracy of the transformation. (2) This analysis reveals a positive power function correlation between Ra and the propagation distance of the blast vibrations, contrasted by a negative correlation with the maximum charge per delay. Intriguingly, the Ra values observed in pre-splitting blasting operations are consistently lower than those in bench blasting. The established Ra equations offer a rapid, direct method for assessing the transformation between the PPV and PPA, providing valuable insights for the optimization of blasting design.
eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance
Authors: Alqahtani Abdullah , Sheldon Frederick T.
Published: March 2024

Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual Information Feature Selection (eMIFS) technique that incorporates a normalized hyperbolic function for ransomware early detection models. The normalized hyperbolic function is utilized to address the challenge of perceiving common characteristics among features, particularly when there are insufficient attack patterns contained in the dataset. The Term Frequency–Inverse Document Frequency (TF–IDF) was used to represent the features in numerical form, making it ready for the feature selection and modeling. By integrating the normalized hyperbolic function, we improve the estimation of redundancy coefficients and effectively adapt the MIFS technique for early ransomware detection, i.e., before encryption takes place. Our proposed method, eMIFS, involves evaluating candidate features individually using the hyperbolic tangent function (tanh), which provides a suitable representation of the features’ relevance and redundancy. Our approach enhances the performance of existing MIFS techniques by considering the individual characteristics of features rather than relying solely on their collective properties. The experimental evaluation of the eMIFS method demonstrates its efficacy in detecting ransomware attacks at an early stage, providing a more robust and accurate ransomware detection model compared to traditional MIFS techniques. Moreover, our results indicate that the integration of the normalized hyperbolic function significantly improves the feature selection process and ultimately enhances ransomware early detection performance.
TGLFusion: A Temperature-Guided Lightweight Fusion Method for Infrared and Visible Images
Authors: Yan Bao , Zhao Longjie , Miao Kehua , Wang Song , Li Qinghua , Luo Delin
Published: March 2024

The fusion of infrared and visible images is a well-researched task in computer vision. These fusion methods create fused images replacing the manual observation of single sensor image, often deployed on edge devices for real-time processing. However, there is an issue of information imbalance between infrared and visible images. Existing methods often fail to emphasize temperature and edge texture information, potentially leading to misinterpretations. Moreover, these methods are computationally complex, and challenging for edge device adaptation. This paper proposes a method that calculates the distribution proportion of infrared pixel values, allocating fusion weights to adaptively highlight key information. It introduces a weight allocation mechanism and MobileBlock with a multispectral information complementary module, innovations which strengthened the model’s fusion capabilities, made it more lightweight, and ensured information compensation. Training involves a temperature-color-perception loss function, enabling adaptive weight allocation based on image pair information. Experimental results show superiority over mainstream fusion methods, particularly in the electric power equipment scene and publicly available datasets.
Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals
Authors: Peng Fulai , Zhang Ningling , Chen Cai , Wu Fengxia , Wang Weidong
Published: March 2024

Non-invasive detection of hemoglobin (Hb) concentration is of great clinical value for health screening and intraoperative blood transfusion. However, the accuracy and stability of non-invasive detection still need to be improved to meet clinical requirement. This paper proposes a non-invasive Hb detection method using ensemble extreme learning machine (EELM) regression based on eight-wavelength PhotoPlethysmoGraphic (PPG) signals. Firstly, a mathematical model for non-invasive Hb detection based on the Beer-Lambert law is established. Secondly, the captured eight-channel PPG signals are denoised and fifty-six feature values are extracted according to the derived mathematical model. Thirdly, a recursive feature elimination (RFE) algorithm is used to select the features that contribute most to the Hb prediction. Finally, a regression model is built by integrating several independent ELM models to improve prediction stability and accuracy. Experiments conducted on 249 clinical data points (199 cases as the training dataset and 50 cases as the test dataset) evaluate the proposed method, achieving a root mean square error (RMSE) of 1.72 g/dL and a Pearson correlation coefficient (PCC) of 0.76 (p < 0.01) between predicted and reference values. The results demonstrate that the proposed non-invasive Hb detection method exhibits a strong correlation with traditional invasive methods, suggesting its potential for non-invasive detection of Hb concentration.