Search Results (1,987)

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

This paper proposes a novel automatic recognition method for distinguishing voids and severe loosening in road structures based on features of ground-penetrating radar (GPR) B-scan images. By analyzing differences in image texture, the intensity and clarity of top reflection interfaces, and the regularity of internal waveforms, a set of discriminative features is constructed. Based on these features, we develop the FKS-GPR dataset, a high-quality, manually annotated GPR dataset collected from real road environments, covering diverse and complex background conditions. Compared to datasets based on simulations, FKS-GPR offers higher practical relevance. An improved ACF-YOLO network is then designed for automatic detection, and the experimental results show that the proposed method achieves superior accuracy and robustness, validating its effectiveness and engineering applicability. Full article

Accurate prediction of landslide-induced displacement is essential for the structural integrity and operational safety of buildings and infrastructure situated in geologically unstable regions. This study introduces a novel hybrid predictive framework that synergistically integrates Gaussian Process Regression (GPR) with Temporal Convolutional Neural Networks (TCNs), herein referred to as the GTCN model, to forecast displacement at building monitoring points subject to landslide activity. The proposed methodology is validated using time-series monitoring data collected from the slope adjacent to the Zhongliang Reservoir in Wuxi County, Chongqing, an area where slope instability poses a significant threat to nearby structural assets. Experimental results demonstrate the GTCN model’s superior predictive performance, particularly under challenging conditions of incomplete or sparsely sampled data. The model proves highly effective in accurately characterizing both abrupt fluctuations within the displacement time series and capturing long-term deformation trends. Furthermore, the GTCN framework outperforms comparative hybrid models based on Gated Recurrent Units (GRUs) and GPR, with its advantage being especially pronounced in data-limited scenarios. It also exhibits enhanced capability for temporal feature extraction relative to conventional imputation-based forecasting strategies like forward-filling. By effectively modeling both nonlinear trends and uncertainty within displacement sequences, the GTCN framework offers a robust and scalable solution for landslide-related risk assessment and early warning applications. Its applicability to building safety monitoring underscores its potential contribution to geotechnical hazard mitigation and resilient infrastructure management. Full article

Background/Objectives: While ocular albinism (OA) is usually associated with reduced vision, nystagmus, and foveal hypoplasia, there is phenotypic variability in iris and fundus hypopigmentation. Hemizygous pathogenic/likely pathogenic (P/LP) variants in GPR143 at X: 151.56–151.59 have been shown in the literature to be associated with OA. The purpose of this study was to report the case of a Hispanic male with X-linked inherited OA associated with a hemizygous GPR143 variant and to review the literature relating to genotype–phenotype associations with GPR143 and OA. Methods: After consent to an IRB-approved protocol, a 14-year-old Hispanic male patient with OA and his parents underwent whole genome sequencing (WGS) in 2023. Two maternal uncles with nystagmus underwent targeted variant testing in 2024. A literature review of reported GPR143 variants was completed. Results: A male with reduced visual acuity, infantile-onset nystagmus, foveal hypoplasia, and iris hypopigmentation was identified to have the variant GPR143, c.455+3A>G, which was also present in his mother and two affected maternal uncles. This variant has been previously identified in other Hispanic patients of Mexican descent. Additionally, 127 variants were identified in the literature and reported to be associated with OA. All patients had reduced visual acuity (average 0.71 ± 0.23 logMAR), 99% had nystagmus, 97% foveal hypoplasia, 79% fundus hypopigmentation, and 71% iris hypopigmentation. Of those patients with reported optotype best corrected visual acuity (BCVA), eight (9%) had VA from 20/25 to 20/40, 24 (24%) had VA from 20/50 to 20/80, and 63 (67%) had VA from 20/100 to 20/200. The most frequent type of variant was missense (31%, n = 39). Frameshift and nonsense variants were associated with the lowest rates of iris hypopigmentation (50% [n = 11] and 44% [n = 8], respectively; p = 0.0068). Conclusions: This case represents phenotypic variability of GPR143-associated OA and highlights the importance of repeat genetic testing and independent analyses of test results for accurate variant classification, particularly in non-White and Hispanic patients. Further studies in more diverse populations are needed to better develop genotype–phenotype associations for GPR143-associated OA. Full article

Civil engineering structures with damage, defects, or subsurface utilities create a high-contrast exploration environment. These anomalies of interest exhibit different electromagnetic properties from the surrounding medium, and ground-penetrating radar (GPR) has the potential to accurately locate and map their three-dimensional (3D) distributions. However, full-waveform inversion (FWI) for GPR data struggles to simultaneously reconstruct high-resolution 3D images of both permittivity and conductivity models. Considering the magnitude and sensitivity disparities of the model parameters in the inversion of GPR data, this study proposes a 3D dual-parameter FWI algorithm for GPR with a composite model constraint strategy. It balances the gradient updates of permittivity and conductivity models through performing total variation (TV) regularization and minimum support gradient (MSG) regularization on different parameters in the inversion process. Numerical experiments show that TV regularization can optimize permittivity reconstruction, while MSG regularization is more suitable for conductivity inversion. The TV+MSG composite model constraint strategy improves the accuracy and stability of dual-parameter inversion, providing a robust solution for the 3D imaging of subsurface anomalies with high-contrast features. These outcomes offer researchers theoretical insights and a valuable reference when investigating scenarios with high-contrast environments. Full article

This study aims to investigate the processes associated with mass movements and their relationship with the behavior of the Amazon River delta-estuary (ADE) wetlands. The methodological approach involves using water spectral indices and ground-penetrating radar (GPR) to diagnose areas of soil water saturation and characterize regions affected by mass movements in Amazonian cities. It also involves identifying areas of critical saturation content and consequent mass movements. Analysis of risk and land use data revealed that the affected areas coincide with zones of high susceptibility to mass movements induced by water. The results showed the following: the accumulated annual precipitation ranged from 70.07 ± 55.35 mm·month⁻¹ to 413.34 ± 127.51 mm·month⁻¹; the response similarity across different sensors obtained an accuracy greater than 90% for NDWI, MNDWI, and AWEI for the same targets; and a landfill layer with a thickness variation between 1 and 2 m defined the mass movement concentration in Abaetetuba city. The interaction between infiltration, water saturation, and human-induced land alteration suggests that these areas act as wetlands with unstable dynamics. The analysis methodology developed for this study aimed to address this scenario by systematically mapping areas with mass movement potential and high-water saturation. Due to the absence of geological and geotechnical data, remote sensing was employed as an alternative, and in situ ground-penetrating radar (GPR) evaluation was suggested as a means of investigating the causes of a previously observed movement. Full article

Kynurenic acid (KYNA), a tryptophan metabolite, possesses immunomodulatory properties, although the molecular mechanism of this action has not yet been resolved. In the present study, the effects of KYNA on the secretion of selected cytokines and chemokines by macrophages derived from the human THP-1 cell line are investigated. Furthermore, the involvement of the aryl hydrocarbon receptor (AhR) and the G protein-coupled receptor 35 (GPR35) in mediating the effects of KYNA was examined. In lipopolysaccharide (LPS)-stimulated THP-1-derived macrophages, KYNA significantly reduced IL-6 and CCL-2, but increased IL-10 and M-CSF levels. AhR antagonist CH-223191 reduced the KYNA influence on IL-6, CCL-2, and M-CSF production, while the GPR35 antagonist, ML-145, blocked KYNA-induced IL-10 production. Furthermore, it was shown that THP-1 derived macrophages were capable of synthesizing and releasing KYNA and that its production was increased in the presence of LPS. These findings suggest that THP-1-derived macrophages are a source of KYNA and that KYNA modulates inflammatory responses predominantly through AhR and GPR35 receptors. Our study provides further evidence for the involvement of macrophages in immunomodulatory processes that are dependent on AhR and GPR35 receptors, as well as the potential role of KYNA in these phenomena. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

This study explores the mechanical behavior of polymer and composite specimens fabricated using fused deposition modeling (FDM), focusing on three material configurations: acrylonitrile butadiene styrene (ABS), carbon fiber-reinforced polyphthalamide (PPA/Cf), and a sandwich-structured composite. A systematic experimental plan was developed using the Box–Behnken design (BBD) to investigate the effects of material type (MT), infill pattern (IP), and printing direction (PD) on tensile and flexural strength. Experimental results showed that the PPA/Cf material with a “Cross” IP printed “Flat” yielded the highest mechanical performance, achieving a tensile strength of 75.8 MPa and a flexural strength of 102.3 MPa. In contrast, the lowest values were observed in ABS parts with a “Grid” pattern and “Upright” orientation, recording 37.8 MPa tensile and 49.5 MPa flexural strength. Analysis of variance (ANOVA) results confirmed that all three factors significantly influenced both outputs (p < 0.001), with MT being the most dominant factor. Machine learning (ML) algorithms, Bayesian linear regression (BLR), and Gaussian process regression (GPR) were employed to predict mechanical performance. GPR achieved the best overall accuracy with R² = 0.9935 and MAPE = 11.14% for tensile strength and R² = 0.9925 and MAPE = 12.96% for flexural strength. Comparatively, the traditional BBD yielded slightly lower performance with MAPE = 13.02% and R² = 0.9895 for tensile strength. Validation tests conducted on three unseen configurations clearly demonstrated the generalization capability of the models. Based on actual vs. predicted values, the GPR yielded the lowest average prediction errors, with MAPE values of 0.54% for tensile and 0.45% for flexural strength. In comparison, BLR achieved 0.79% and 0.60%, while BBD showed significantly higher errors at 1.76% and 1.32%, respectively. Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

Artificial intelligence (AI) has emerged as a powerful tool for advancing the interpretation of ground penetrating radar (GPR) data, offering solutions to long-standing challenges in manual analysis, such as subjectivity, inefficiency, and limited scalability. This review investigates recent developments in AI-driven techniques for GPR interpretation, with a focus on machine learning, deep learning, and hybrid approaches that incorporate physical modeling or multimodal data fusion. We systematically analyze the application of these techniques across various domains, including utility detection, infrastructure monitoring, archeology, and environmental studies. Key findings highlight the success of convolutional neural networks in hyperbola detection, the use of segmentation models for stratigraphic analysis, and the integration of AI with robotic and real-time systems. However, challenges remain with generalization, data scarcity, model interpretability, and operational deployment. We identify promising directions, such as domain adaptation, explainable AI, and edge-compatible solutions for practical implementation. By synthesizing current progress and limitations, this review aims to bridge the gap between theoretical advancements in AI and the practical needs of GPR practitioners, guiding future research towards more reliable, transparent, and field-ready systems. Full article

Surrogate modeling has been rapidly evolving in the field of aerospace engineering, further reducing the cost of computational analyses. These models often require large amounts of information to learn the underlying process, which is at odds with obtaining and using the highest-fidelity data. This study assesses the efficacy of multi-fidelity modeling (MFM) to improve simulation accuracy while reducing computational cost. A database of hovering rotor simulations with perturbations of the rotor design and operating conditions was first generated using two different fidelity levels of the OVERFLOW 2.4D Computational Fluid Dynamics software. MFM was then used to quantify the effectiveness of this approach for the development of accurate surrogate models. Multi-fidelity models based on Gaussian Process Regression (GPR) were derived for hovering rotor performance prediction given the geometric rotor blade inputs that currently include twist, planform, airfoil, and the collective pitch angle. The MFM approach was consistently more accurate at predicting the hold-out test data than the surrogate model with high-fidelity data alone. An MFM using just 20% of the available high-fidelity training data was as accurate as a solely high-fidelity model trained on 80% of the available data, representing an approximate fourfold reduction in computational cost. Full article