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18 pages, 3959 KiB  
Article
A High Efficiency Discontinuous Galerkin Method for 3D Ground-Penetrating Radar Simulation
by Shuyang Xue, Changchun Yin, Jing Li, Jiao Zhu and Wuyang Liu
Remote Sens. 2025, 17(2), 228; https://doi.org/10.3390/rs17020228 - 9 Jan 2025
Viewed by 422
Abstract
As an effective geophysical tool, ground penetrating radar (GPR) is widely used for environmental and engineering detections. Numerous numerical simulation algorithms have been developed to improve the computational efficiency of GPR simulations, enabling the modeling of complex structures. The discontinuous Galerkin method is [...] Read more.
As an effective geophysical tool, ground penetrating radar (GPR) is widely used for environmental and engineering detections. Numerous numerical simulation algorithms have been developed to improve the computational efficiency of GPR simulations, enabling the modeling of complex structures. The discontinuous Galerkin method is a high efficiency numerical simulation algorithm which can deal with complex geometry. This method uses numerical fluxes to ensure the continuity between elements, allowing Maxwell’s equations to be solved within each element without the need to assemble a global matrix or solve large systems of linear equations. As a result, memory consumption can be significantly reduced, and parallel solvers can be applied at the element level, facilitating the construction of high-order schemes to enhance computational accuracy. In this paper, we apply the discontinuous Galerkin (DG) method based on unstructured meshes to 3D GPR simulation. To verify the accuracy of our algorithm, we simulate a full-space vacuum and a cuboid in a homogeneous medium and compare results, respectively, with the analytical solutions and those from the finite-difference method. The results demonstrate that, for the same error level, the proposed DG method has significant advantages over the FDTD method, with less than 20% of the memory consumption and calculation time. Additionally, we evaluate the effectiveness of our method by simulating targets in an undulating subsurface, and further demonstrate its capability for simulating complex models. Full article
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19 pages, 2784 KiB  
Article
Integrated Extraction of Root Diameter and Location in Ground-Penetrating Radar Images via CycleGAN-Guided Multi-Task Neural Network
by Xihong Cui, Shupeng Li, Luyun Zhang, Longkang Peng, Li Guo, Xin Cao, Xuehong Chen, Huaxiang Yin and Miaogen Shen
Forests 2025, 16(1), 110; https://doi.org/10.3390/f16010110 - 9 Jan 2025
Viewed by 168
Abstract
The diameter of roots is pivotal for studying subsurface root structure geometry. Yet, directly obtaining these parameters is challenging due their hidden nature. Ground-penetrating radar (GPR) offers a reproducible, nondestructive method for root detection, but estimating diameter from B-Scan images remains challenging. To [...] Read more.
The diameter of roots is pivotal for studying subsurface root structure geometry. Yet, directly obtaining these parameters is challenging due their hidden nature. Ground-penetrating radar (GPR) offers a reproducible, nondestructive method for root detection, but estimating diameter from B-Scan images remains challenging. To address this, we developed the CycleGAN-guided multi-task neural network (CMT-Net). It comprises two subnetworks, YOLOv4-Hyperbolic Position and Diameter (YOLOv4-HPD) and CycleGAN. The YOLOv4-HPD is obtained by adding a regression header for predicting root diameter to YOLOv4-Hyperbola, which achieves the ability to simultaneously accurately locate root objects and estimate root diameter. The CycleGAN is used to solve the problem of the lack of a real root diameter training dataset for the YOLOv4-HPD model by migrating field-measured data domains to simulated data without altering root diameter information. We used simulated and field data to evaluate the model, showing its effectiveness in estimating root diameter. This study marks the first construction of a deep learning model for fully automatic root location and diameter extraction from GPR images, achieving an “Image Input–Parameter Output” end-to-end pattern. The model’s validation across various dataset scales opens the way for estimating other root attributes. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
22 pages, 6944 KiB  
Article
Synthetic GPR84 Agonists in Colorectal Cancer: Effective in THP-1 Cells but Ineffective in BMDMs and MC38 Mouse Tumor Models
by Marlene Schwarzfischer, Maria Rae Walker, Michele Curcio, Nader M. Boshta, Arnaud Marchand, Erik Soons, Doris Pöhlmann, Marcin Wawrzyniak, Yasser Morsy, Silvia Lang, Marianne Rebecca Spalinger, Matthias Versele and Michael Scharl
Int. J. Mol. Sci. 2025, 26(2), 490; https://doi.org/10.3390/ijms26020490 - 9 Jan 2025
Viewed by 216
Abstract
Tumor-associated macrophages (TAMs) in the colorectal cancer (CRC) microenvironment promote tumor progression but can be reprogrammed into a pro-inflammatory state with anti-cancer properties. Activation of the G protein-coupled receptor 84 (GPR84) is associated with pro-inflammatory macrophage polarization, making it a potential target for [...] Read more.
Tumor-associated macrophages (TAMs) in the colorectal cancer (CRC) microenvironment promote tumor progression but can be reprogrammed into a pro-inflammatory state with anti-cancer properties. Activation of the G protein-coupled receptor 84 (GPR84) is associated with pro-inflammatory macrophage polarization, making it a potential target for CRC therapy. This study evaluates the effects of the GPR84 agonists 6-OAU and ZQ-16 on macrophage activation and anti-cancer efficacy. GPR84 expression on THP-1 macrophages and murine BMDMs was analyzed using flow cytometry. Macrophages were treated with 6-OAU or ZQ-16, and pro-inflammatory cytokine levels, reactive oxygen species (ROS) production, and phagocytosis were assessed using qPCR and functional assays. Anti-cancer effects were tested in a subcutaneous MC38 tumor model, with oral or intraperitoneal agonist administration. Pharmacokinetics and compound stability were also evaluated. In THP-1 macrophages, 6-OAU increased pro-inflammatory cytokines and ROS production, with ZQ-16 showing similar effects. However, neither agonist induced pro-inflammatory responses, ROS production, or phagocytosis in murine macrophages. In vivo, both agonists failed to inhibit tumor growth in the MC38 model despite systemic exposure. Current GPR84 agonists lack efficacy in promoting anti-cancer macrophage activity, limiting their potential as CRC therapies. Full article
(This article belongs to the Section Molecular Oncology)
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22 pages, 17029 KiB  
Article
Cross-Line Fusion of Ground Penetrating Radar for Full-Space Localization of External Defects in Drainage Pipelines
by Yuanjin Fang, Feng Yang, Xu Qiao, Maoxuan Xu, Liang Fang, Jialin Liu and Fanruo Li
Remote Sens. 2025, 17(2), 194; https://doi.org/10.3390/rs17020194 - 8 Jan 2025
Viewed by 253
Abstract
Drainage pipelines face significant threats to underground safety due to external defects. Ground Penetrating Radar (GPR) is a primary tool for detecting such defects from within the pipeline. However, existing methods are limited to single or multiple axial scan lines, which cannot provide [...] Read more.
Drainage pipelines face significant threats to underground safety due to external defects. Ground Penetrating Radar (GPR) is a primary tool for detecting such defects from within the pipeline. However, existing methods are limited to single or multiple axial scan lines, which cannot provide the precise spatial coordinates of the defects. To address this limitation, this study introduces a novel GPR-based drainage pipeline inspection robot system integrated with multiple sensors. The system incorporates MEMS-IMU, encoder modules, and ultrasonic ranging modules to control the GPR antenna for axial and circumferential scanning. A novel Cross-Line Fusion of GPR (CLF-GPR) method is introduced to integrate axial and circumferential scan data for the precise localization of external pipeline defects. Laboratory simulations were performed to assess the effectiveness of the proposed technology and method, while its practical applicability was further validated through real-world drainage pipeline inspections. The results demonstrate that the proposed approach achieves axial positioning errors of less than 2.0 cm, spatial angular positioning errors below 2°, and depth coordinate errors within 2.3 cm. These findings indicate that the proposed approach is reliable and has the potential to support the transparency and digitalization of urban underground drainage networks. Full article
(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)
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17 pages, 8730 KiB  
Article
Mycobacteria Exploit Host GPR84 to Dampen Pro-Inflammatory Responses and Promote Infection in Macrophages
by Reziya Wumaier, Ke Zhang, Jing Zhou, Zilu Wen, Zihan Chen, Geyang Luo, Hao Wang, Juliang Qin, Bing Du, Hua Ren, Yanzheng Song, Qian Gao and Bo Yan
Microorganisms 2025, 13(1), 110; https://doi.org/10.3390/microorganisms13010110 - 8 Jan 2025
Viewed by 370
Abstract
Tuberculosis (TB) remains the major cause of mortality and morbidity, causing approximately 1.3 million deaths annually. As a highly successful pathogen, Mycobacterium tuberculosis (Mtb) has evolved numerous strategies to evade host immune responses, making it essential to understand the interactions between [...] Read more.
Tuberculosis (TB) remains the major cause of mortality and morbidity, causing approximately 1.3 million deaths annually. As a highly successful pathogen, Mycobacterium tuberculosis (Mtb) has evolved numerous strategies to evade host immune responses, making it essential to understand the interactions between Mtb and host cells. G-protein-coupled receptor 84 (GPR84), a member of the G-protein-coupled receptor family, contributes to the regulation of pro-inflammatory reactions and the migration of innate immune cells, such as macrophages. Its role in mycobacterial infection, however, has not yet been explored. We found that GPR84 is induced in whole blood samples from tuberculosis patients and Mycobacterium marinum (Mm)-infected macrophage models. Using a Mm-wasabi infection model in mouse tails, we found that GPR84 is an important determinant of the extent of tissue damage. Furthermore, from our studies in an in vitro macrophage Mm infection model, it appears that GPR84 inhibits pro-inflammatory cytokines expression and increases intracellular lipid droplet (LD) accumulation, thereby promoting intracellular bacterial survival. Our findings suggest that GPR84 could be a potential therapeutic target for host-directed anti-TB therapeutics. Full article
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18 pages, 7374 KiB  
Article
Lin28b-let-7 Modulates mRNA Expression of GnRH1 Through Multiple Signaling Pathways Related to Glycolysis in GT1-7 Cells
by Yujing Xie, Xin Li, Meng Wang, Mingxing Chu and Guiling Cao
Animals 2025, 15(2), 120; https://doi.org/10.3390/ani15020120 - 7 Jan 2025
Viewed by 295
Abstract
Lin28b and let-7 miRNA regulate mammalian pubertal initiation and Gonadotropin-releasing hormone (GnRH) production. However, it remains unclear which signaling pathways Lin28b regulates to modulate GnRH production. In this study, the mRNA expression levels of Lin28b and let-7 in the pubertal and juvenile goat [...] Read more.
Lin28b and let-7 miRNA regulate mammalian pubertal initiation and Gonadotropin-releasing hormone (GnRH) production. However, it remains unclear which signaling pathways Lin28b regulates to modulate GnRH production. In this study, the mRNA expression levels of Lin28b and let-7 in the pubertal and juvenile goat hypothalamus and pituitary gland were detected, and Lin28b expression in the pubertal hypothalamus decreased significantly compared with that in juvenile tissues. It was predicted that Lin28b might inhibit GnRH1 expression, which was verified in the GnRH-producing cell model GT1-7 cells. Lin28b inhibited GnRH1 expression and promoted Kiss1/Gpr54 signaling. The pyruvate content and the expression of Hif1a and Hk2, which were related to glycolysis, were also promoted by Lin28b overexpression. Additionally, 77 differentially expressed miRNAs (DEMIs) in Lin28b-overexpressed GT1-7 cells were identified. Bioinformatics analysis and mRNA expression of the target genes of DEMIs revealed that the MAPK and PI3K-AKT-mTOR signaling pathways were key pathways that involved the regulatory effect of Lin28b on GnRH. In GT1-7 cells, GnRH1 expression was suppressed by blocking mTOR signaling with rapamycin, which was rescued by Lin28b overexpression. These results indicate that Lin28b-let-7 regulates GnRH1 expression through several pathways, including the Kiss1/Gpr54, MAPK, and mTOR signaling pathways, which are all related to glucose metabolism and provide new insights into the molecular mechanism of the regulatory role of Lin28b on GnRH production. Full article
(This article belongs to the Section Small Ruminants)
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23 pages, 3201 KiB  
Article
Machine Learning Approach for Prediction and Reliability Analysis of Failure Strength of U-Shaped Concrete Samples Joined with UHPC and PUC Composites
by Sadi I. Haruna, Yasser E. Ibrahim and Ibrahim Khalil Umar
J. Compos. Sci. 2025, 9(1), 23; https://doi.org/10.3390/jcs9010023 - 6 Jan 2025
Viewed by 714
Abstract
To meet the increasing demand for resilient infrastructure in seismic and high-impact areas, accurate prediction and reliability analysis of the performance of composite structures under impact loads is essential. Conventional techniques, including experimental testing and high-quality finite element simulation, require considerable time and [...] Read more.
To meet the increasing demand for resilient infrastructure in seismic and high-impact areas, accurate prediction and reliability analysis of the performance of composite structures under impact loads is essential. Conventional techniques, including experimental testing and high-quality finite element simulation, require considerable time and resources. To address these issues, this study investigated individual and hybrid models including support vector regression (SVR), Gaussian process regression (GPR), and improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) models for predicting the failure strength of composite concrete developed by combining normal concrete (NC) with ultra-high performance concrete (UHPC) and polyurethane-based polymer concrete (PUC), considering different surface treatments and subjected to various static and impact loads. An experimental dataset was utilized to train the ML models and perform the reliability analysis on the impact dataset. Key parameters included compressive strength (Cfc), flexural load of the U-shaped specimens (P), density (ρ), first crack strength (N1), and splitting tensile strength (ft). Results revealed that all the developed models had high prediction accuracy, achieving NSE values above acceptable thresholds greater than 90% across all the datasets. Statistical errors such as RMSE, MAE, and PBIAS were calculated to fall within acceptable limits. Hybrid IEPANN appeared to be the most effective model, demonstrating the highest NSE value of 0.999 and the lowest RMSE, PBIAS, and MAE values of 0.0013, 0.0018, and 0.001, respectively. The reliability analysis revealed that impact times (N1 and N2) reduced as the survival probability increased. Full article
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12 pages, 1050 KiB  
Article
Rebar Recognition Using Multi-Hyperbolic Attention in Faster R-CNN
by Chuan Li, Nianbiao Cai, Tong Pu, Xi Yang, Hao Liu and Lulu Wang
Appl. Sci. 2025, 15(1), 367; https://doi.org/10.3390/app15010367 - 2 Jan 2025
Viewed by 417
Abstract
Rebar constitutes a crucial element within tunnel lining structures, where its precise arrangement plays a pivotal role in determining both structural stability and load-bearing capacity. Due to the rebar’s high dielectric constant approaching infinity, radar signal reflections are intensified, manifesting as distinct hyperbolic [...] Read more.
Rebar constitutes a crucial element within tunnel lining structures, where its precise arrangement plays a pivotal role in determining both structural stability and load-bearing capacity. Due to the rebar’s high dielectric constant approaching infinity, radar signal reflections are intensified, manifesting as distinct hyperbolic patterns within radar imagery. By performing convolutional operations, these hyperbolic features of rebar can be effectively extracted from radar images. Building upon the feature extraction capabilities of the ResNet50 model, this study introduces a Deformable Attention to Capture Salient Information (DAS) mechanism, employing deformable and separable convolutions to enhance rebar localization and concentrate on hyperbolic features resulting from multiple reflections. Before the Region Proposal Network (RPN) and region of interest (ROI) pooling stages in Faster R-CNN, this study integrates a hyperbolic attention (HAT) module. Through refined distance metrics, the hyperbolic attention mechanism enhances the network’s Precision in identifying rebar hyperbolic features within feature maps. To ensure robustness across diverse conditions, this study utilizes a simulated public dataset for tunnel linings, alongside real data from the Husa Tunnel, to create a comprehensive ground-penetrating radar image dataset for tunnel linings. Experimental results indicate that the proposed model achieved an Average Precision of 94.93%, reflecting a 3.14% increase compared to the baseline model. Lastly, in a random selection of 50 radar images for testing, the model achieved a rebar detection accuracy of 93.46%, representing an enhancement of 0.94% over the baseline model. Full article
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17 pages, 4426 KiB  
Article
Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression
by Paolo Di Barba, Arash Ghafoorinejad, Maria Evelina Mognaschi, Fabrizio Dughiero, Michele Forzan and Elisabetta Sieni
Algorithms 2025, 18(1), 10; https://doi.org/10.3390/a18010010 - 2 Jan 2025
Viewed by 265
Abstract
In this paper, a multi-physics case study belonging to the class of induction heating problem is considered. Finite Element Analysis is used to evaluate the temperature along a line on a graphite disk heated by two power inductors. In order to build a [...] Read more.
In this paper, a multi-physics case study belonging to the class of induction heating problem is considered. Finite Element Analysis is used to evaluate the temperature along a line on a graphite disk heated by two power inductors. In order to build a surrogate field model of the device, i.e., to compute the temperature profile on the disk, given the amplitudes and frequencies of the supply currents, three methods have been used (Support Vector Regression (SVR), fully connected Neural Network (NN) and Gaussian Process Regression (GPR)). In turn, to solve the inverse problem, i.e., to identify frequencies and currents of the two coils, given a prescribed temperature profile, two approaches have been implemented. The former is an optimization approach based on a multi-objective formulation, solved by means of the NSGA-II algorithm; the latter is a two-step procedure, based on fully connected Deep Neural Networks (DNNs), solving an optimal design problem first and, subsequently, an optimal control problem. Full article
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22 pages, 26127 KiB  
Article
Defect Recognition: Applying Time-Lapse GPR Measurements and Numerical Approaches
by Enas Abdelsamei, Diaa Sheishah, Mohamed Aldeep, Csaba Tóth and György Sipos
Eng 2025, 6(1), 5; https://doi.org/10.3390/eng6010005 - 1 Jan 2025
Viewed by 414
Abstract
Roads are critical components of infrastructure, and assessing their quality is essential to ensure the safe transport of people and goods, which in turn supports economic prosperity. Various factors, such as subsurface conditions, moisture content, and temperature, influence road performance and can degrade [...] Read more.
Roads are critical components of infrastructure, and assessing their quality is essential to ensure the safe transport of people and goods, which in turn supports economic prosperity. Various factors, such as subsurface conditions, moisture content, and temperature, influence road performance and can degrade their efficiency as transportation networks. While surface road defects can often be identified through visual inspection, information about subsurface extensions, their impact on structural integrity, and potential risks remain concealed. This study aimed to perform a comparative analysis of dielectric permittivity (ε) using time-lapse Ground Penetrating Radar (GPR) measurements on pre- and post-renovated road sections. This study also sought to evaluate the effectiveness of this approach for road assessment and to employ forward modeling for a deeper understanding of road defects and their associated hazards. Results revealed that the pre-renovated road section exhibited significant fluctuations in dielectric values, ranging from 3.13 to 15.9. In contrast, the post-renovated section showed consistent values within a narrow range of 5 to 6.6. Different crack types were classified, and the mean ε for each visually identified crack type was calculated. Despite the higher frequency of transverse cracks compared to other defects, longitudinal cracks exhibited the highest mean dielectric value (~10.3), while alligator cracks had the lowest (~8.33). Numerical simulations facilitated accurate interpretation of the defects identified in the road section, providing insights into their nature and associated risks. The methodology used for crack classification and numerical simulation can be applied to other road sections globally, offering a standardized approach to road assessment and maintenance planning. Full article
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71 pages, 7585 KiB  
Systematic Review
Unmanned Aerial Geophysical Remote Sensing: A Systematic Review
by Farzaneh Dadrass Javan, Farhad Samadzadegan, Ahmad Toosi and Mark van der Meijde
Remote Sens. 2025, 17(1), 110; https://doi.org/10.3390/rs17010110 - 31 Dec 2024
Viewed by 1011
Abstract
Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in [...] Read more.
Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Geophysical Surveys Based on UAV)
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24 pages, 6819 KiB  
Article
Three-Dimensional Reconstruction of Road Structural Defects Using GPR Investigation and Back-Projection Algorithm
by Lutai Wang, Zhen Liu, Xingyu Gu and Danyu Wang
Sensors 2025, 25(1), 162; https://doi.org/10.3390/s25010162 - 30 Dec 2024
Viewed by 403
Abstract
Ground-Penetrating Radar (GPR) has demonstrated significant advantages in the non-destructive detection of road structural defects due to its speed, safety, and efficiency. This paper proposes a three-dimensional (3D) reconstruction method for GPR images, integrating the back-projection (BP) imaging algorithm to accurately determine the [...] Read more.
Ground-Penetrating Radar (GPR) has demonstrated significant advantages in the non-destructive detection of road structural defects due to its speed, safety, and efficiency. This paper proposes a three-dimensional (3D) reconstruction method for GPR images, integrating the back-projection (BP) imaging algorithm to accurately determine the size, location, and other parameters of road structural defects. Initially, GPR detection images were preprocessed, including direct wave removal and wavelet denoising, followed by the application of the BP algorithm to effectively restore the defect’s location and size. Subsequently, a 3D data set was constructed through interpolation, and the effective reflection data were extracted by using a clustering algorithm. This algorithm distinguished the effective reflection data from the background data by determining the distance threshold between the data points. The 3D imaging of the defect was then performed in MATLAB. The proposed method was validated using both gprMax simulations and laboratory test models. The experimental results indicate that the correlation between the reconstructed and actual defects was approximately 0.67, demonstrating the method’s efficacy in accurately achieving the 3D reconstruction of road structural defects. Full article
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17 pages, 3346 KiB  
Article
The Use of 3D Printing Filaments to Build Moisture Sensors in Porous Materials
by Magdalena Paśnikowska-Łukaszuk, Joanna Szulżyk-Cieplak, Magda Wlazło, Jarosław Zubrzycki, Ewa Łazuka, Arkadiusz Urzędowski and Zbigniew Suchorab
Materials 2025, 18(1), 115; https://doi.org/10.3390/ma18010115 - 30 Dec 2024
Viewed by 336
Abstract
This study explores the application of materials used in 3D printing to manufacture the housings of non-invasive sensors employed in measurements using a TDR (Time Domain Reflectometry) meter. The research investigates whether sensors designed with 3D printing technology can serve as viable alternatives [...] Read more.
This study explores the application of materials used in 3D printing to manufacture the housings of non-invasive sensors employed in measurements using a TDR (Time Domain Reflectometry) meter. The research investigates whether sensors designed with 3D printing technology can serve as viable alternatives to conventional invasive and non-invasive sensors. This study focuses on innovative approaches to designing humidity sensors, utilizing Fused Deposition Modeling (FDM) technology to create housings for non-invasive sensors compatible with TDR devices. The paper discusses the use of 3D modeling technology in sensor design, with particular emphasis on materials used in 3D printing, notably polylactic acid (PLA). Environmental factors, such as moisture in building materials, are characterized, and the need for dedicated sensor designs is highlighted. The software utilized in the 3D modeling and printing processes is also described. The Materials and Methods Section provides a detailed account of the construction process for the non-invasive sensor housing and the preparation for moisture measurement in silicate materials using the designed sensor. A prototype sensor was successfully fabricated through 3D printing. Using the designed sensor, measurements were conducted on silicate samples soaked in aqueous solutions with water absorption levels ranging from 0% to 10%. Experimental validation involved testing silicate samples with the prototype sensor to evaluate its effectiveness. The electrical permittivity of the material was calculated, and the root-mean-square error (RMSE) was determined using classical computational methods and machine learning techniques. The RMSE obtained using the classical method was 0.70. The results obtained were further analyzed using machine learning models, including Gaussian Process Regression (GPR) and Support Vector Machine (SVM). The GPR model achieved an RMSE of 0.15, while the SVM model yielded an RMSE of 0.25. These findings confirm the sensor’s effectiveness and its potential for further research and practical applications. Full article
(This article belongs to the Special Issue 3D-Printed Composite Structures: Design, Properties and Application)
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21 pages, 29111 KiB  
Article
GPR in Damage Identification of Concrete Elements—A Case Study of Diagnostics in a Prestressed Bridge
by Piotr Łaziński, Marcin Jasiński, Mateusz Uściłowski, Dawid Piotrowski and Łukasz Ortyl
Remote Sens. 2025, 17(1), 35; https://doi.org/10.3390/rs17010035 - 26 Dec 2024
Viewed by 482
Abstract
Effective placement and compaction of the concrete mixture within the spans of prestressed bridges are essential for the proper anchoring and prestressing of tendons. The high density of reinforcement and location of the cable ducts present significant challenges, increasing the risk of void [...] Read more.
Effective placement and compaction of the concrete mixture within the spans of prestressed bridges are essential for the proper anchoring and prestressing of tendons. The high density of reinforcement and location of the cable ducts present significant challenges, increasing the risk of void formation and structural irregularities, which can lead to failures during the prestressing process. Ground Penetrating Radar (GPR) emerges as a pivotal non-destructive testing method for diagnosing such complex prestressed structures. Utilizing high-frequency electromagnetic waves, GPR accurately detects and maps anomalies within hardened concrete, enabling precise identification of defect locations and their dimensions. The detailed imaging provided by GPR facilitates the development of targeted repair strategies and allows for the exclusion of concrete voids through selective invasive inspections in designated boreholes. This study presents the use of GPR for the investigation of anomalies and damage in prestressing tendons of a newly built concrete bridge. It underscores the critical role of GPR in enhancing the diagnostic and maintenance programs for prestressed bridge structures, thereby improving their overall integrity and longevity. Full article
(This article belongs to the Section Engineering Remote Sensing)
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21 pages, 3504 KiB  
Article
G Protein-Coupled Receptor 17 Inhibits Glucagon-like Peptide-1 Secretion via a Gi/o-Dependent Mechanism in Enteroendocrine Cells
by Jason M. Conley, Alexander Jochim, Carmella Evans-Molina, Val J. Watts and Hongxia Ren
Biomolecules 2025, 15(1), 9; https://doi.org/10.3390/biom15010009 - 25 Dec 2024
Viewed by 553
Abstract
Gut peptides, including glucagon-like peptide-1 (GLP-1), regulate metabolic homeostasis and have emerged as the basis for multiple state-of-the-art diabetes and obesity therapies. We previously showed that G protein-coupled receptor 17 (GPR17) is expressed in intestinal enteroendocrine cells (EECs) and modulates nutrient-induced GLP-1 secretion. [...] Read more.
Gut peptides, including glucagon-like peptide-1 (GLP-1), regulate metabolic homeostasis and have emerged as the basis for multiple state-of-the-art diabetes and obesity therapies. We previously showed that G protein-coupled receptor 17 (GPR17) is expressed in intestinal enteroendocrine cells (EECs) and modulates nutrient-induced GLP-1 secretion. However, the GPR17-mediated molecular signaling pathways in EECs have yet to be fully deciphered. Here, we expressed the human GPR17 long isoform (hGPR17L) in GLUTag cells, a murine EEC line, and we used the GPR17 synthetic agonist MDL29,951 together with pharmacological probes and genetic approaches to quantitatively assess the contribution of GPR17 signaling to GLP-1 secretion. Constitutive hGPR17L activity inhibited GLP-1 secretion, and MDL29,951 treatment further inhibited this secretion, which was attenuated by treatment with the GPR17 antagonist HAMI3379. MDL29,951 promoted both Gi/o and Gq protein coupling to mediate cyclic AMP (cAMP) and calcium signaling. hGPR17L regulation of GLP-1 secretion appeared to be Gq-independent and dependent upon Gi/o signaling, but was not correlated with MDL29,951-induced whole-cell cAMP signaling. Our studies revealed key signaling mechanisms underlying the role of GPR17 in regulating GLP-1 secretion and suggest future opportunities for pharmacologically targeting GPR17 with inverse agonists to maximize GLP-1 secretion. Full article
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