Search Results (1,758)

Community drinking water sources are increasingly contaminated by various point and non-point sources, with emerging organic contaminants and microbial strains posing health risks and disrupting ecosystems. This study explores the use of zinc oxide nanoparticles (ZnO-NPs) as a non-specific agent to address groundwater contamination and combat microbial resistance effectively. The ZnO-NPs were synthesized via a green chemistry approach, employing a sol-gel method with lemon peel aqueous extract. The catalyst was characterized using techniques including XRD, ATR-FTIR, SEM-EDAX, UV-DRS, BET, and Raman spectroscopy. ZnO-NPs were then tested for photodegradation of quinoline yellow dye (QY) under sunlight irradiation, as well as for their antibacterial and antioxidant properties. The ZnO-NP photocatalyst showed significant photoactivity, attributed to effective separation of photogenerated charge carriers. The efficiency of sunlight dye photodegradation was influenced by catalyst dosage (0.1–0.6 mg L⁻¹), pH (3–11), and initial QY concentration (10–50 mg L⁻¹). The study developed a first-order kinetic model for ZnO-NPs using the Langmuir–Hinshelwood equation, yielding kinetic constants of equilibrium adsorption and photodegradation of Kc = 6.632 × 10⁻² L mg⁻¹ and kH = 7.104 × 10⁻² mg L⁻¹ min⁻¹, respectively. The results showed that ZnO-NPs were effective against Gram-positive bacterial strains and showed moderate antioxidant activity, suggesting their potential in wastewater disinfection to achieve sustainable development goals. A potential antibacterial mechanism of ZnO-NPs involving interactions with microbial cells is proposed. Additionally, Gaussian Process Regression (GPR) combined with an improved Lévy flight distribution (FDB-LFD) algorithm was used to model QY photodegradation by ZnO-NPs. The ARD-Exponential kernel function provided high accuracy, validated through residue analysis. Finally, an innovative MATLAB-based application was developed to integrate the GPR_FDB-LFD model and FDB-LFD algorithm, streamlining optimization for precise photodegradation rate predictions. The results obtained in this study show that the GPR and FDB-LFD approaches offer efficient and cost-effective methods for predicting dye photodegradation, saving both time and resources. Full article

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process. Full article

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively. Full article

Sorghatmesh mosque is a historical structure that was constructed in Cairo, Egypt, by Prince Saif El-Din Sorghatmesh in 1356. A dual-frequency ground-penetrating radar (GPR) with 250–700 MHz was used to investigate the subsurface of the Sorghatmesh mosque for restoration purposes. A total of 37 lines were surveyed on the ground floor of the mosque. The subsurface utilities were detected, and the status of the concrete base and the medium of the ground floor were assessed. A set of subsurface anomalies were detected and interpreted within the ground floor area of the mosque. In order to validate the interpretation, a trial pit was drilled on the ground floor, allowing for the visual inspection of the subsurface, and a Georadar numerical simulation was carried out to study the responses of the subsurface materials and conditions. For a better comprehension of the results, the ground floor area was categorized into five zones where the GPR interpretations between survey lines are almost similar. This work not only demonstrates the effectiveness of GPR as a non-invasive investigation tool but also highlights the potential of integrating advanced technologies into cultural heritage preservation by offering refined methodologies and insights for future research and restoration efforts. Full article

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target’s polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems. Full article

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R²_adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R²_adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, B_f: 0.998 to 1.047; accuracy factor, A_f: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology. Full article

Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing the cultivation practices of Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis for determining chlorophyll content are labor-intensive and time-consuming, making them unsuitable for large-scale dynamic monitoring and high-throughput phenotyping. To accurately quantify chlorophyll content in Ginkgo seedlings under different nitrogen levels, this study employed a hyperspectral imaging camera to capture canopy hyperspectral images of seedlings throughout their annual growth periods. Reflectance derived from pure leaf pixels of Ginkgo seedlings was extracted to construct a set of spectral parameters, including original reflectance, logarithmic reflectance, and first derivative reflectance, along with spectral index combinations. A one-dimensional convolutional neural network (1D-CNN) model was then developed to estimate chlorophyll content, and its performance was compared with four common machine learning methods, including Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF). The results demonstrated that the 1D-CNN model outperformed others with the first derivative spectra, achieving higher CV-R² and lower RMSE values (CV-R² = 0.80, RMSE = 3.4). Furthermore, incorporating spectral index combinations enhanced the model’s performance, with the 1D-CNN model achieving the best performance (CV-R² = 0.82, RMSE = 3.3). These findings highlight the potential of the 1D-CNN model in strengthening the chlorophyll estimations, providing strong technical support for the precise cultivation and the fertilization management of Ginkgo seedlings. Full article

The endocannabinoid signalling system (ECS) plays a critical role from the very beginning of embryogenesis. Accordingly, the ECS is engaged early on in nervous system development, starting from neurulation, supported by the identification of ECS components—both receptors and enzymes controlling endocannabinoid metabolism—at these early stages. In particular, regarding the brain, the ECS is involved in the tightly regulated sequence of events that comprise brain development, from neurogenesis to neuronal migration, morphological guidance for neuronal connectivity, and synaptic circuitry refinement. The importance of this broad role of the ECS across various brain development processes is further underscored by the growing understanding of the consequences of cannabis exposure at different developmental stages. Despite the considerable knowledge we have on the role of the ECS in brain development, significant gaps in our understanding remain, particularly regarding the long-term impact and underlying mechanisms of cannabis exposure at different developmental stages. This review provides an overview of the current state of knowledge on the role of the ECS throughout brain development, from embryogenesis to adulthood, and discusses the impact of cannabis exposure, especially during adolescence—a critical period of circuitry maturation and refinement coinciding with an increased risk of cannabis use. Full article

Phosphate has been proven to be effective in remediating soils contaminated with potentially toxic elements (PTEs); however, the potential release of phosphorus (P) through runoff and the impact on PTEs’ transport in this process have never been assessed. A rainfall simulation study was conducted to investigate P runoff loss and its impact on the stability of arsenic (As) and cadmium (Cd) after applying potassium dihydrogen phosphate (PDP), superphosphate (SSP), and ground phosphate rock (GPR) in soil trays packed with As–Cd-contaminated soil. The phosphorus loss through runoff and sedimentary phases followed the order of SSP > PDP > GPR > control. Phosphate fertilizers’ application reduced the mobility of As and Cd. In the first rainfall, the enrichment ratios (ERs) of As and Cd in the sedimentary phase after PDP, SSP, and GPR treatment were 0.12, 0.04, and 0.08 and 0.24, 0.16, and 0.07 units lower than the control, respectively. The <53 μm fraction in the sedimentary phase accounted for 53.06–75.95%, and phosphate fertilizers significantly enhanced the As and Cd stability in this fraction. The XPS analysis showed that the conversion of As(III) to As(V) and the generation of Cd–phosphate compounds were important reasons for enhancing As and Cd stability. This study demonstrated that PDP might be capable of the remediation of As–Cd contamination with the least release of P to watersheds. Full article

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR. Full article

Leaf chlorophyll content (LCC) is a key indicator in representing the photosynthetic capacity of Populus deltoides (Populus deltoides Marshall). Unmanned aerial vehicle (UAV) hyperspectral imagery provides an effective approach for LCC estimation, but the issue of band redundancy significantly impacts model accuracy and computational efficiency. Commonly used single feature selection algorithms not only fail to balance computational efficiency with optimal set search but also struggle to combine different regression algorithms under dynamic set conditions. This study proposes an ensemble feature selection framework to enhance LCC estimation accuracy using UAV hyperspectral data. Firstly, the embedded algorithm was improved by introducing the SHapley Additive exPlanations (SHAP) algorithm into the ranking system. A dynamic ranking strategy was then employed to remove bands in steps of 10, with LCC models developed at each step to identify the initial band subset based on estimation accuracy. Finally, the wrapper algorithm was applied using the initial band subset to search for the optimal band subset and develop the corresponding model. Three regression algorithms including gradient boosting regression trees (GBRT), support vector regression (SVR), and gaussian process regression (GPR) were combined with this framework for LCC estimation. The results indicated that the GBRT-Optimal model developed using 28 bands achieved the best performance with R² of 0.848, RMSE of 1.454 μg/cm² and MAE of 1.121 μg/cm². Compared with a model performance that used all bands as inputs, this optimal model reduced the RMSE value by 24.37%. In addition to estimating biophysical and biochemical parameters, this method is also applicable to other hyperspectral imaging tasks. Full article

Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal. However, the cycle-skipping problem has become an urgent focus of this method because of the nonlinearity of the inversion problem. To mitigate the issue of local minima, the optimal transport problem has been introduced into full-waveform inversion in this study. The Wasserstein distance derived from the optimal transport problem is defined as the mismatch function in the FWI objective function, replacing the $L_2$ norm. In this study, the Wasserstein distance is computed by using entropy regularization and the Sinkhorn algorithm to reduce computational complexity and improve efficiency. Additionally, this study presents the objective function for dual-parameter full-waveform inversion of ground-penetrating radar, with the Wasserstein distance as the mismatch function. By normalizing with the Softplus function, the electromagnetic wave signals are adjusted to meet the non-negativity and mass conservation assumptions of the Wasserstein distance, and the convexity of the method has been proven. A multi-scale frequency-domain Wasserstein distance full-waveform inversion method based on the Softplus normalization approach is proposed, enabling the simultaneous inversion of relative permittivity and conductivity from ground-penetrating radar data. Numerical simulation cases demonstrate that this method has low initial model dependency and low noise sensitivity, allowing for high-precision inversion of relative permittivity and conductivity. The inversion results show that it, in particular, significantly improves the accuracy of conductivity inversion. Full article

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing. Full article

This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This study was carried out on the Tower of the Cathedral “Santa Iglesia Catedral de Santa María” in Murcia, Spain. The novel distribution of a continuous nonlinear profile along the outer and inner perimeters of the Tower allowed us to obtain a 3D ERT model of the subsoil, even under its load-bearing walls. This nonlinear configuration of the electrodes allowed us to reach adequate investigation depths in buildings with limited interior and exterior space for data collection without disturbing the historic structure. The ERT results were compared with GPR measurements and with information from archaeological excavations conducted in 1999 and 2009. The geometry and distribution of the cavities in the entire foundation slab of the Tower were determined, verifying the proposed procedure. This methodology allows the acquisition of a detailed understanding of the singularities of the foundations of protected historic towers in urban areas with limited space, reducing time and costs and avoiding the use of destructive techniques, with the aim of implementing a more efficient and effective strategy for the protection of other tower foundations. Full article

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained. Full article