Search

Background and purpose: The baseline glucose-to-potassium ratio (GPR) is associated with poor outcomes in patients with acute brain injury and intracranial hemorrhage. However, the impact of serum GPR on clinical outcomes after endovascular thrombectomy (EVT) is unclear. This study aimed to evaluate the association between the GPR at admission and functional outcomes at 90 days after EVT. Methods: We retrospectively reviewed our database for patients with acute ischemic stroke involving an anterior circulation large-vessel occlusion who received EVT between October 2019 and December 2021. The baseline serum GPR was measured after admission. The primary outcome was a 90-day poor outcome, which was defined as a modified Rankin scale score of 3–6. Results: A total of 273 patients (mean age, 70.9 ± 11.9 years; 161 men) were finally included for analyses. During the 90-day follow-up, 151 patients (55.3%) experienced an unfavorable outcome. After adjusting for demographic characteristics and other potential confounders, the increased GPR was significantly associated with a higher risk of a 90-day poor outcome (odds ratio, 1.852; 95% confidence interval, 1.276–2.688, p = 0.001). Similar results were observed when the GPR was analyzed as a categorical variable. In addition, the restricted cubic spline observed a positive and linear association between the GPR and poor outcomes at 90 days (p = 0.329 for linearity; p = 0.001 for linearity). Conclusion: Our study found that ischemic stroke patients with the higher GPR at admission were more likely to have an unfavorable prognosis at 3 months, suggesting that GPR may be a potential prognostic biomarker for ischemic stroke after EVT. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features. [ABSTRACT FROM AUTHOR]

Coal–rock interface recognition is one of the key challenges in advancing unmanned rapid tunneling systems. To address this, a method based on variations in ground-penetrating radar (GPR) reflection wave amplitude is proposed for identifying coal-rock interfaces in soft coal seams. A mathematical model derived from Maxwell's equations calculates the coal seam area based on the reflected wave amplitude. Experiments using a 400 MHz radar antenna show that the method achieves identification errors of 3.58 cm, 3.74 cm, and 4.23 cm for coal-to-rock ratios of 1:2, 1:1, and 2:1, respectively. In practical applications within a coal mine excavation face, the identification error is 6.64 cm. This method provides crucial data for drum height adjustment and cutting arm speed regulation in unmanned tunneling systems. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the rockfall potential of the steep rock slope above the Sumela Monastery (Trabzon, Türkiye) by using Ground Penetrating Radar (GPR) and surface fracture analysis on discontinuity. In this context, studies were carried out on 10 risky blocks determined after the investigations made by a team of industrial mountaineering and observations on the orthophotos. The discontinuity analysis data on the rock masses containing of mainly andesites and basalts were combined with discontinuities observed in the ground penetrating radar sections (radargrams) and the risk status of the blocks was revealed by using the location and characteristics of the dominant discontinuities. By using GPR data, discontinuities with 4 main sets were detected in 3 blocks at a depth of 2.0–2.5 m from the surface and the loose rocks were removed from the slopes using a combination of manual and mechanical techniques, including the use of lifting jacks and jackhammers, as well as explosives. In another block where GPR measurements were taken, a main fracture was detected from the surface to a depth of 2 m, but the continuity of the fracture to a deeper depth was not observed. Thus, this block was reinforced together by wrapping them with steel nets. Ground penetrating radar measurements could not be taken from the other six blocks because they did not have smooth surfaces. The stability of these blocks, which are smaller than the others, was assessed using observational data collected in situ by industrial climbers. In addition, the monastery was opened to visitors after the other small blocks on the slope that were at risk of falling were dropped. It has been demonstrated that fracture-crack systems of rocks on steep and weathered rock slopes, which are difficult to apply in rock engineering studies, can be successfully visualized using GPR measurement. [ABSTRACT FROM AUTHOR]

Time-lapse ground-penetrating radar (GPR) surveys, combined with automated infiltration experiments, provide a non-invasive approach for investigating the distribution of infiltrated water within the soil medium and creating three-dimensional images of the wetting bulb. This study developed and validated an experimental protocol aimed at quantifying and visualizing water distribution fluxes in layered soils under both unsaturated and saturated conditions. The 3D images of the wetting bulb significantly enhanced the interpretation of infiltration data, enabling a detailed analysis of water movement through the layered system. We used the infiltrometer data and the Beerkan Estimation of Soil Transfer parameters (BEST) method to determine soil capacitive indicators and evaluate the physical quality of the upper soil layer. The field survey involved conducting time-lapse GPR surveys alongside infiltration experiments between GPR repetitions. These experiments included both tension and ponding tests, designed to sequentially activate the soil matrix and the full pore network. The results showed that the soil under study exhibited significant soil aeration and macroporosity (represented by AC and pMAC), while indicators related to microporosity (such as PAWC and RFC) were notably low. The RFC value of 0.55 m3 m−3 indicated the soil's limited capacity to retain water relative to its total pore volume. The PAWC value of 0.10 m3 m−3 indicated a scarcity of micropores ranging from 0.2 to 30 μm in diameter, which typically hold water accessible to plant roots within the total porosity. The saturated soil hydraulic conductivity, Ks, values ranged from 192.2 to 1031.0 mm h−1, with a mean of 424.4 mm h−1, which was 7.9 times higher than the corresponding unsaturated hydraulic conductivity measured at a pressure head of h = −30 mm (K−30). The results indicated that the upper soil layer supports root proliferation and effectively drains excess water to the underlying limestone layer. However, this layer has limited capacity to store and supply water to plant roots and acts as a restrictive barrier, promoting non-uniform downward water movement, as revealed by the 3D GPR images. The observed difference in hydraulic conductivity between the two layers suggests that surface ponding and overland flow are generated through a saturation excess mechanism. Water percolating through the soil can accumulate above the limestone layer, creating a shallow perched water table. During extreme rainfall events, this water table may rise, leading to the complete saturation of the soil profile. [ABSTRACT FROM AUTHOR]

Background and purpose: The baseline glucose-to-potassium ratio (GPR) is associated with poor outcomes in patients with acute brain injury and intracranial hemorrhage. However, the impact of serum GPR on clinical outcomes after endovascular thrombectomy (EVT) is unclear. This study aimed to evaluate the association between the GPR at admission and functional outcomes at 90 days after EVT. Methods: We retrospectively reviewed our database for patients with acute ischemic stroke involving an anterior circulation large-vessel occlusion who received EVT between October 2019 and December 2021. The baseline serum GPR was measured after admission. The primary outcome was a 90-day poor outcome, which was defined as a modified Rankin scale score of 3-6. Results: A total of 273 patients (mean age, 70.9 ± 11.9 years; 161 men) were finally included for analyses. During the 90-day follow-up, 151 patients (55.3%) experienced an unfavorable outcome. After adjusting for demographic characteristics and other potential confounders, the increased GPR was significantly associated with a higher risk of a 90-day poor outcome (odds ratio, 1.852; 95% confidence interval, 1.276-2.688, p = 0.001). Similar results were observed when the GPR was analyzed as a categorical variable. In addition, the restricted cubic spline observed a positive and linear association between the GPR and poor outcomes at 90 days (p = 0.329 for linearity; p = 0.001 for linearity). Conclusion: Our study found that ischemic stroke patients with the higher GPR at admission were more likely to have an unfavorable prognosis at 3 months, suggesting that GPR may be a potential prognostic biomarker for ischemic stroke after EVT. [ABSTRACT FROM AUTHOR]

The site of Amasya-Oluz Höyük was discovered during a surface survey conducted in 1999, with archaeological excavations commencing in 2007. The site has revealed cultural layers and cities belonging to significant civilizations. Through archaeological excavations at Oluz Höyük, significant findings that could reshape the religious history of Anatolia have been uncovered. In the settlement dating back to the 5th century BC, important architectural complexes related to the Fire Cult (associated with early Zoroastrianism) of the Persian Settlement have been discovered. As civil and religious structures of the Persian Settlement continue to be unearthed at the höyük, these 2,500-year-old findings are expected to add new dimensions to the religious history of the period. The richness of the archaeological artifacts uncovered so far at Oluz Höyük is evidence that the mound is a significant archaeological heritage site in Anatolia. According to the "Charter for the Protection and Management of the Archaeological Heritage" prepared by ICOMOS and adopted in Lausanne in 1990, "the protection of the archaeological heritage must be based on the fullest and most detailed knowledge possible of its nature and characteristics" (Article 4). In this context, Ground Penetrating Radar (GPR) surveys were conducted in the southern section of Trench A, located at the top of the mound, near the western edge of the religious structure remains and in the area thought to be either a civil structure or part of a temple. Two- and three-dimensional subsurface images were obtained from these surveys. The GPR studies revealed high-amplitude anomalies that displayed linear, circular, right-angled, and geometrically structured forms. Archaeological excavations were conducted in this area, uncovering the foundations of a grain storage facility that had been affected by fire. [ABSTRACT FROM AUTHOR]

Cryptography is used by cryptocurrencies to shift money without the intervention of centralized financial institutions. They are decentralized digital assets. On rapidly changing exchanges like those for crypto currencies, it is a tremendously taxing procedure for people to keep track of many simultaneous instantaneous price changes. As a solution to this, computer software that can make fast and objective decisions by constantly observing can replace humans. In this study, the closing price of Bitcoin (BTC), which has the highest volume in the crypto money system, is analyzed. In the study, in which the Gaussian Process Regression (GPR) model and the SMOTE method were used, data belonging to BTC for the period between 25/07/2010 and 05/06/2022 were used as the data set. Opening price, highest-lowest price, volume, dollar index and some indicators used in technical analysis were used as input parameters. The kfold method was followed in the separation of training and test data. The data is divided into 5 subsets with kfold. The mean MAPE value was found to be 1887, and the mean R2 value was found to be 0.99977 in the models with SMOTE. In addition, the GPR model and the GPR model functions that were applied to the SMOTE method were compared by excluding the opening price, which was the price that was highest-lowest, from the data. It was carried out to determine which model performed better. [ABSTRACT FROM AUTHOR]

The Greek marble statue of the Auriga of Mozia Island, in Sicily, is the most important artwork displayed at the Whitaker Foundation Archaeological Museum. It underwent geophysical investigations twice, in 2012 and 2021, to assess the marble's degradation. The 2012 investigation prepared the statue for transfer to the Paul Getty Museum in New York and repositioning on an anti-seismic pedestal. The 2021 investigation evaluated potential new damage before another transfer. Both investigations utilized 3D ultrasonic tomography (UST) to detect degraded marble areas and ground-penetrating radar (GPR) to identify internal discontinuities, such as fractures or lesions, and locate metal pins that were previously inserted to reassemble the statue and its pedestal. Results from the UST indicate an average marble velocity of approximately 4700 m/s, suggesting good mechanical strength, with some areas showing lower velocities (~3000 m/s) within the material's variability range. The GPR profiles demonstrated internal signal homogeneity, excluding internal fracture surfaces or lesions, and confirmed the presence of metallic pins. This study highlights the effectiveness of integrating UST and GPR for non-invasive diagnostics of marble sculptures, providing detailed insights into the marble's condition and identifying hidden defects or damage. [ABSTRACT FROM AUTHOR]

The main purpose of this study is the subsurface investigation of two complex geological environments focusing on the improvement of data acquisition and processing parameters regarding electric and seismic tomographic techniques. Two different study areas, in central–east Peloponnese and SE Attica, were selected, where detailed geological mapping and surface geophysical survey were carried out. The applied geophysical survey included the application of electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and ground penetrating radar (GPR). The geoelectrical measurements were acquired with different arrays and electrode configurations. Moreover, various types of seismic sources were used at seventeen shot locations along the seismic arrays. For the processing of geoelectrical data, clustered datasets were created, increasing the depth of investigation and discriminatory capability. The seismic data processing included the following: (a) the creation of synthetic models and seismic records to determine the effectiveness and capabilities of the technique, (b) spectral analysis of the seismic records to determine the optimal seismic source type and (c) inversion of the field data to create representative subsurface velocity models. The results of the two techniques successfully delineated the complex subsurface structure that characterizes these two geological environments. The application of the ERT combined with the SRT are the two dominant, high-resolution techniques for the elucidation of complex subsurface structures. [ABSTRACT FROM AUTHOR]

In the process of using multi-channel ground-penetrating radar (GPR) for underground cavity exploration, the acquired 3D data include reflection data from underground cavities or various underground objects (structures). Reflection data from unspecified structures can interfere with the identification process of underground cavities. This study aims to identify underground cavities using a C-GAN model with an applied ResBlock technique. This deep learning model demonstrates excellent performance in the image domain and can automatically classify the presence of cavities by analyzing 3D GPR data, including reflection waveforms (A-scan), cross-sectional views (B-scan), and plan views (C-scan) measured from the ground under roads. To maximize the performance of the C-GAN model, a background filtering algorithm (BFA) was developed and applied to enhance the visibility and clarity of underground cavities. To verify the performance of the developed BFA, 3D data collected from roads in Seoul, Republic of Korea, using 3D GPR equipment were transformed, and the C-GAN model was applied. As a result, it was confirmed that the recall, an indicator of cavity prediction, improved by approximately 1.15 times compared to when the BFA was not applied. This signifies the verification of the effectiveness of the BFA. This study developed a special algorithm to distinguish underground cavities. This means that in the future, not only the advancement of separate equipment and systems but also the development of specific algorithms can contribute to the cavity exploration process. [ABSTRACT FROM AUTHOR]

Continuous measurements of soil moisture in the deeper parts of the unsaturated zone remain an important challenge. This study examines the development of an integrated system for the continuous and 3-D monitoring of the vadose zone processes in a cost- and energy-efficient way. This system comprises TDR, ERT and GPR geophysical techniques. Their capacities to adequately image subsurface moisture changes with continuous and time-lapse measurements are assessed during an artificial infiltration experiment conducted in a characteristic urban site with anthropogenic fills and much compaction. A 3-D array was designed for each method to expand the information of a single TDR probe and obtain a broader image of the subsurface. Custom spatial TDR probes installed in boreholes made with a percussion drilling instrument were used for soil moisture measurements. Moisture profiles along the probes were estimated with a numerical one-dimensional inversion model and a standard calibration equation. High conductivity water used during all infiltration tests led to the detection of the flow by all techniques. Preferential flow was present throughout the experiment and imaged sufficiently by all methods. Overall, the integrated approach conceals each method's weaknesses and provides a reliable 3-D view of the subsurface. The results suggest that this approach can be used to monitor the unsaturated zone at even greater depths. [ABSTRACT FROM AUTHOR]

Machine learning in non-destructive testing (NDT) offers significant potential for efficient daily data analysis and uncovering previously unknown relationships in persistent problems. However, its successful application heavily depends on the availability of a diverse and well-labeled training dataset, which is often lacking, raising questions about the transferability of trained algorithms to new datasets. To examine this issue closely, the authors applied classifiers trained with laboratory Ground Penetrating Radar (GPR) data to categorize on-site moisture damage in layered building floors. The investigations were conducted at five different locations in Germany. For reference, cores were taken at each measurement point and labeled as (i) dry, (ii) with insulation damage, or (iii) with screed damage. Compared to the accuracies of 84 % to 90 % within the laboratory training data (504 B-Scans), the classifiers achieved a lower overall accuracy of 53 % for on-site data (72 B-Scans). This discrepancy is mainly attributable to a significantly higher dynamic of all signal features extracted from on-site measurements compared to laboratory training data. Nevertheless, this study highlights the promising sensitivity of GPR for identifying individual damage cases. In particular the results showing insulation damage, which cannot be detected by any other non-destructive method, revealed characteristic patterns. The accurate interpretation of such results still depends on trained personnel, whereby fully automated approaches would require a larger and diverse on-site data set. Until then, the findings of this work contribute to a more reliable analysis of moisture damage in building floors using GPR and offer practical insights into applying machine learning to non-destructive testing for civil engineering (NDT-CE). [ABSTRACT FROM AUTHOR]

Coarse roots and the root plate play an important role in tree resistance to uprooting. In this study, a qualitative mechanistic model was developed to analyze coniferous tree resistance to uprooting in relation to tree morphological characteristics. The sizes of the crown, stem, and root plate of twenty sample spruces and twenty sample Korean pines were individually measured for this purpose. Using Ground Penetrating Radar (GPR), the coarse root distribution and root plate size were detected. In the qualitative mechanistic model, a larger crown area increased the overturning moment, while higher DBH and root plate mass increased the resistance moment. The resistance coefficient (Rm) was calculated by comparing resistive and overturning moments, classifying samples into three uprooting hazard levels. Trees with smaller crown areas, larger stems, and root plates tend to have higher resistance to uprooting, as indicated by higher Rm values. This qualitative mechanistic model provides a useful tool for assessing coniferous standing tree uprooting resistance. [ABSTRACT FROM AUTHOR]

The demand for nondestructive testing techniques (NDTs) that can be implemented continuously and cost-effectively for large-scale study purposes is increasing. Ground penetrating radar (GPR) as an NDT method permits the estimation of pavement materials characteristics without disrupting the serviceability of the system. This research used typical pavement materials for constructing load-bearing layers (base and subbase) for the GPR laboratory tests. A 2 GHz GPR antenna was applied to execute the tests by changing three essential variables of the material: water content, compaction and clay content. Machine learning and interactive methods were innovatively utilised to model the collected data. As a result, a multivariate non-linear empirical function is proposed by the interactive procedure. Furthermore, the machine learning modelling (SVM method) with R2 of 0.91 indicates promising results to evaluate the pavement layers' properties. Machine learning can enhance the speed and accuracy of analysis when faced with multi-variables and extensive data. [ABSTRACT FROM AUTHOR]

Modern climatic changes have an impact on the bearing capacity of permafrost soils at the base of the foundations of buildings and structures in the urbanized territories of the Arctic and Subarctic. The activation of cryogenic processes leads to the destruction of infrastructure and to social, economic, and environmental consequences for the population. Based on the results for the geothermy of frozen and thawing soil, and on the georadar profiling of the city of Anadyr, it was concluded that the main risks of permafrost degradation are associated with the spread of hydrogenic melting zones. Maps of the soil temperature in imaginary cross-sections with depths of 3, 5, and 10 m were compiled, along with maps of the capacity of thawing soils, the permafrost aquifer, and the dangerous spread zones for exogenous cryogenic processes. The total area of talik zones with a depth of 6 m or more in the urban area was 2.34 km2, or 67% of the built-up area. The system of permafrost monitoring in the territory of Anadyr was substantiated, and is based on monitoring the boundaries of talik zones. It consists of an automated network of observations of the ground temperature in 35 wells at the boundary and in the center of 20 zones of the dangerous development of exogenous cryogenic processes, as well as 12 control GPR profiles at the intersection of linear hydrogenic taliks. [ABSTRACT FROM AUTHOR]

Purpose: SRS MapCHECK (SMC) is a commercially available patient‐specific quality assurance (PSQA) tool for stereotactic radiosurgery (SRS) applications. This study investigates the effects of degree of modulation, location off‐axis, and low dose threshold (LDT) selection on gamma pass rates (GPRs) between SMC and treatment planning system, Analytical Anisotropic Algorithm (AAA), or Vancouver Island Monte Carlo (VMC++ algorithm) system calculated dose distributions. Methods: Volumetric‐modulated arc therapy (VMAT) plans with modulation factors (MFs) ranging from 2.7 to 10.2 MU/cGy were delivered to SMC at isocenter and 6 cm off‐axis. SMC measured dose distributions were compared against AAA and VMC++ via gamma analysis (3%/1 mm) with LDT of 10% to 80% using SNC Patient software. Results: Comparing on‐axis SMC dose against AAA and VMC++ with LDT of 10%, all AAA‐calculated plans met the acceptance criteria of GPR ≥ 90%, and only one VMC++ calculated plan was marginally outside the acceptance criteria with pass rate of 89.1%. Using LDT of 80% revealed decreasing GPR with increasing MF. For AAA, GPRs reduced from 100% at MF of 2.7 MU/cGy to 57% at MF of 10.2 MU/cGy, and for VMC++ calculated plans, the GPRs reduced from 89% to 60% in the same MF range. Comparison of SMC dose off‐axis against AAA and VMC++ showed more pronounced reduction of GPR with increasing MF. For LDT of 10%, AAA GPRs reduced from 100% to 83% in the MF range of 2.7 to 9.8 MU/cGy, and VMC++ GPR reduced from 100% to 91% in the same range. With 80% LDT, GPRs dropped from 100% to 42% for both algorithms. Conclusions: MF, dose calculation algorithm, and LDT selections are vital in VMAT‐based SRT PSQA. LDT of 80% enhances sensitivity of gamma analysis for detecting dose differences compared to 10% LDT. To achieve better agreement between calculated and SMC dose, it is recommended to limit the MF to 4.6 MU/cGy on‐axis and 3.6 MU/cGy off‐axis. [ABSTRACT FROM AUTHOR]

Data synergy involves acquiring and combining data from different sensors to achieve better problem analysis and research results. For more comprehensive data analysis, the sensors are not only mounted on one platform. Still, they should also be compatible with software and hardware, e.g. for the same timestamp registration by different sensors. The aim of this article is to propose the synergy between various remote sensing sensors including the ground penetrating radar (GPR), LiDAR (Light Detection and Ranging) sensor and three photogrammetric RGB cameras for damage detection in a pavement in a park alley. The data were acquired with a low-cost platform, in the Pole Mokotowskie Park in Warsaw, Poland. Three drives were made along the same path with the platform, so it was possible to assess the repeatability of the data. Based on the GPR data, orthophotomap, and digital terrain model (DTM) from images, an analysis of the cracks in the pavement was done. The paper proves additive value from the synergy of data collected for the alley also in the form of a common visualization of acquired data. Results presented in the article showed that using mobile mapping platform and technologies describing the situation above and below the ground level enable a more detailed analysis and inspection of the damages in the park alley.

This study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a vital non-destructive testing (NDT) technology that can be employed for detecting the backfill grouting of shield tunnels. To achieve intelligent analysis of GPR data and overcome the subjectivity of traditional data processing methods, the CatBoost & BO-TPE model was constructed for regressing the grouting thickness based on GPR waveforms. A full-scale model test and corresponding numerical simulations were carried out to collect GPR data at 400 and 900 MHz, with known backfill grouting thickness. The model test helps address the limitation of not knowing the grout body condition in actual field detection. The data were then used to create machine learning datasets. The method of feature selection was proposed based on the analysis of feature importance and the electromagnetic (EM) propagation law in mediums. The research shows that: (1) the CatBoost & BO-TPE model exhibited outstanding performance in both experimental and numerical data, achieving R2 values of 0.9760, 0.8971, 0.8808, and 0.5437 for numerical data and test data at 400 and 900 MHz. It outperformed extreme gradient boosting (XGBoost) and random forest (RF) in terms of performance in the backfill grouting thickness regression; (2) compared with the full-waveform GPR data, the feature selection method proposed in this paper can promote the performance of the model. The selected features within the 5–30 ns of the A-scan can yield the best performance for the model; (3) compared to GPR data at 900 MHz, GPR data at 400 MHz exhibited better performance in the CatBoost & BO-TPE model. This indicates that the results of the machine learning model can provide feedback for the selection of GPR parameters; (4) the application results of the trained CatBoost & BO-TPE model in engineering are in line with the patterns observed through traditional processing methods, yet they demonstrate a more quantitative and objective nature compared to the traditional method.

Ground penetrating radar (GPR) is one of the most useful non-destructive techniques for locating underground objects. Advancements in this technology have facilitated the development of new sensors over the past decade. In this paper, an accuracy assessment of the location of underground objects using various GPR antennas is presented. To achieve the stated goals, measurements of 5 concrete slabs, reinforced with steel bars of various diameters and located at variable depths were taken. The experiment includes the usage of three GPR antennas to assess the format, characteristics, and differences of extracted data. This set of antennas from different manufacturers varied in terms of operating frequency. Additional lidar data from TLS (terrestrial laser scanning) was utilized in the methodology to provide precise surface measurements and therefore, external orientation of the surveyed data. The experiment allowed for the determination of vertical and horizontal accuracy for three tested antennas and the assessment of increasing errors value with greater depth of the measured items, which is important for surveying accuracy forecasting.

When it comes to hydropower dam safety, continuous and comprehensive monitoring is increasingly important. Especially for aging dams, this can pose a difficult challenge that benefits from a multimethod analysis. Here, we present the use and suitability of a geophysical method, Ground Penetrating Radar (GPR), for the non-invasive assessment of two distinct types of hydropower dams in Romania: Herculane (a concrete arch dam) and Gura Apelor (an embankment dam with a rockfill and clay core). Unlike traditional monitoring methods for dam safety in Romania, which might provide an incomplete overview, GPR offers a broader, non-destructive approach to evaluating some elements of dam integrity. Here, we present the results of surveys carried out with a 200 MHz antenna on the crests of both dams. The aim was to conduct a rapid assessment of the crest condition and identify the potential damage to the crest that may elude standard monitoring techniques. The surveys provide an imaging indicative of the structural integrity, although this is more challenging in the embankment dam, and additionally we provide significant information regarding the deformations in the upper layers. This complements data from routine topo-geodetical surveys, offering a potential explanation for the vertical displacements observed therein. We highlight several areas of potential deformation as well as degradation in subsurface structures such as rebars. The results underscore the value of GPR in supplementing established dam monitoring methods, highlighting its effectiveness in different contexts and dam types, as well as its potential in shaping future standards for dam safety management in Romania. [ABSTRACT FROM AUTHOR]

Cemented subsoils, commonly referred to as caliche, pose a regular challenge for agricultural development in arid and semi-arid regions like coastal southern Peru. These subsurface features restrict root penetration, limit water infiltration and hinder essential soil processes, ultimately reducing crop yields and agricultural productivity. Accurate and efficient mapping of caliche is important for optimizing land-use planning and implementing sustainable agricultural practices. This study presents the application of near-surface geophysical techniques for mapping caliche deposits in the context of agricultural development at the future Majes II site in the Arequipa region of southern Peru. Specifically, we employed high-frequency ground-penetrating radar (GPR) and frequency-domain electromagnetics (FDEM) at a testbed on the Majes II site to evaluate their ability to delineate the extent, thickness, and depth of caliche within the local geology. GPR offers high-resolution imaging, effectively capturing sharp contrasts between caliche and surrounding materials, providing detailed information on the thickness (approximately 0.4 m) and the depth (up to 1.5 m) of the caliche layers. FDEM provides valuable insights into the presence of caliche at a faster rate of data acquisition and processing, enabling rapid assessment of the extent of caliche deposits, although with the tradeoff of lower resolution and depth information. We demonstrate that these two geophysical methods can be used separately or in an integrated manner for collaborative interpretation at the Majes II site to inform land management decisions, including identifying areas with favorable conditions for crop production and implementing targeted interventions to mitigate the adverse effects of caliche on agricultural productivity. [ABSTRACT FROM AUTHOR]

Existing machine learning models for carbonation in fly ash blended concrete, developed and validated using accelerated or combined datasets, lack validation against natural carbonation processes in concrete. Furthermore, the reliability and accuracy of these models are directly influenced by the sets of input variables used in model training. This research specifically aims to investigate the reliability and accuracy of machine learning models, trained with accelerated datasets, in predicting the natural carbonation process. Additionally, this study introduces Gaussian process regression (GPR) as an alternative modeling tool, due to its potential for enhanced prediction accuracy. The results of this study indicate that GPR can satisfactorily predict natural carbonation progress in fly ash concrete. Moreover, the study carried out a model interpretation to provide intuitive feature contributions in the black‐box model. The results indicate that the accuracy of the natural carbonation process evaluation of models trained with accelerated carbonation dataset is less than 0.2 R2 value, indicating the inappropriateness of using accelerated carbonation data to train reliable models. Additionally, the model interpretation shows a significant dependency of the natural carbonation process on the binder's chemical composition, highlighting key chemical factors influencing carbonation. [ABSTRACT FROM AUTHOR]

The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics. [ABSTRACT FROM AUTHOR]

Borehole ground‐penetrating radar (BGPR) measurements allow for the detection of objects and structures in the subsurface and are often applied to the detection of unexploded ordnance (UXO). If omnidirectional borehole antennas in reflection mode are used for the measurement, the localization of UXO is only possible if the data from a multitude of boreholes are analysed. Data analysis is usually still done by manual picking of reflections. We propose novel approaches to process and visualize data from BGPR measurements in a more advanced and appealing manner. Therein, the reflected energy recorded in the radargrams is projected back to all potential reflection points in the three‐dimensional space around the boreholes. If the projection direction is considered, we obtain a vectorized energy projection image. Superposition of projected energy yields an easy‐to‐grasp indicator of possible locations of UXO and of regions of interest that ought to be investigated in more detail. These approaches have been applied to synthetic data and to data measured on a test site with buried UXO. The results show that energy projection is a useful tool for BGPR data visualization, although the result is dependent on data pre‐processing. The proposed methods provide novel representations of BGPR data based on an objective algorithm which will at least complement the conventional methods. [ABSTRACT FROM AUTHOR]

Estimates of radar attenuation in the shallow Martian subsurface are retrieved from RIMFAX soundings along the Perseverance rover traverse. Specifically, analyzed data is from the Hawksbill Gap area during the rover's first drives onto the Jezero Western Fan Front. The centroid frequency‐shift method is employed to quantify attenuation in terms of the constant‐Q approximation. Results are then compared with the amplitude decay method, which—in order to calculate attenuation—requires propagation velocities retrieved from radargram analysis. By verifying that results from two separate analyses are consistent, we ensure that quantified radar properties are well constrained. First estimate of constant‐Q is 78.8 ± 11.6. For a subsurface propagation velocity of 0.113 m/ns, that equals an attenuation of −2.1 ± 0.4 dB/m at the RIMFAX 675 MHz center frequency. Results are consistent with dry sedimentary rocks, and are distinguishable from the magmatic lithologies on Jezero Crater Floor. Plain Language Summary: This study presents first estimates of radar attenuation at the Jezero Western Fan Front. Measurements were made with the RIMFAX payload instrument on the Mars 2020 Perseverance rover mission, acquired along the rover drive path. Results indicate low signal losses in the subsurface that are consistent with dry sedimentary rocks, as observed on the surface by other payload instruments. Maximum imaging depths increase compared to imaging over magmatic lithologies on Jezero Crater Floor. By using separate methods of analysis (the centroid frequency‐shift method and the amplitude decay method), we reliably quantify attenuation and maximum penetration depths at the Western Fan Front, and observe differences to the Crater Floor lithologies. Key Points: The first estimate of radar attenuation at the Jezero Western Fan Front is on average −2.1 dB/mFor an average propagation velocity of 0.113 m/ns, returned power and time‐frequency analyses yield similar resultsRadar properties are consistent with dry sedimentary rocks and are distinguishable from the magmatic lithologies on Jezero Crater Floor [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the GPR signal, and its spectrum and the spectrum of the active signal are aliased, it is difficult to separate and suppress the noise by traditional filtering methods. In this paper, we propose a deep learning GPR image noise suppression method based on a recursive guided and dual multi-scale self-attention mechanism neural network (RG-DMSA-NN), which uses a recursive guidance module and a dual multi-scale self-attention mechanism module to improve the feature extraction ability of the image and enhance the robustness and generalization ability in image noise suppression. Through the application of noise suppression on the synthesized test data and the GPR data actually collected by the Macao Science and Technology Museum, the advantages of this method over the traditional filtering, DnCNN and UNet noise suppression methods are demonstrated. [ABSTRACT FROM AUTHOR]

Data synergy involves acquiring and combining data from different sensors to achieve better problem analysis and research results. For more comprehensive data analysis, the sensors are not only mounted on one platform. Still, they should also be compatible with software and hardware, e.g. for the same timestamp registration by different sensors. The aim of this article is to propose the synergy between various remote sensing sensors including the ground penetrating radar (GPR), LiDAR (Light Detection and Ranging) sensor and three photogrammetric RGB cameras for damage detection in a pavement in a park alley. The data were acquired with a low-cost platform, in the Pole Mokotowskie Park in Warsaw, Poland. Three drives were made along the same path with the platform, so it was possible to assess the repeatability of the data. Based on the GPR data, orthophotomap, and digital terrain model (DTM) from images, an analysis of the cracks in the pavement was done. The paper proves additive value from the synergy of data collected for the alley also in the form of a common visualization of acquired data. Results presented in the article showed that using mobile mapping platform and technologies describing the situation above and below the ground level enable a more detailed analysis and inspection of the damages in the park alley. [ABSTRACT FROM AUTHOR]

This research aims to determine the brittleness index (BI) and engineering properties of limestone specimens. In addition, this study evaluates the effect of moisture on the developed models to predict the BI and shear wave velocity (Vs), based on the point load index (Is50), dry and saturated tensile strength (Ts-d and Ts-s), and porosity. Gaussian process regression (GPR), multilayer feedforward neural network (MFFNN), and multiple linear regression (MLR) predictive models were utilized. Microscopic examination of the limestone specimens revealed that calcite is the predominant mineral. It was observed that samples with higher calcite content exhibited greater brittleness and strength properties while having lower porosity. The results obtained from the MLR analysis demonstrated that it is possible to accurately forecast the brittleness index (BI), as well as the dry and saturated shear wave velocities (Vs-d and Vs-s) at the specific sites under investigation. The moisture effect on developed models showed that Vs prediction in dry conditions (Vs-d) was less accurate compared to the saturated conditions (Vs-s). Conversely, the relationships developed for estimating the BI in dry conditions exhibited higher accuracy. The analysis of all model assumptions using MLR indicated that the models could be reliably utilized. However, the MFFNN and GPR methods were found to be more conservative in estimating these properties. Moreover, the study identified the best transfer function and training algorithm for predicting Vs and BI. The evaluation metrics, such as R² and RMSE revealed that GPR demonstrated higher precision compared to MFFNN and MLR. [ABSTRACT FROM AUTHOR]

A survey was conducted to investigate buried archaeological remains at Tingambato, a pre‐Tarascan classic‐epiclassic archaeological site located in the north‐central part of the State of Michoacán in western Mexico, using ground‐penetrating radar (GPR) and electrical resistivity tomography (ERT). The aim of this study was to detect the foundations (ancient buried walls) and cavities (tombs), define the geometry of the foundations and correlate construction style and depth with relative chronological buried structures. The survey was carried out on two grids of 15 m × 37 m (Zona Verde) and 10.25 m × 36.5 m (Ballgame court), using a 200 MHz antenna for GPR and Schlumberger‐Wenner arrays for ERT. GPR 3D acquisition was carried out along parallel lines spaced 0.25 m apart in a single direction. In the first area, considering the geometric shape found at a depth of 1.35 m, we can assume the existence of a buried structure, probably wall remains. In the second area, a very diffractive zone coincides with a resistive anomaly (> 2000 ohm·m). In order to refine the GPR results, synthetic modelling and a comparison with real traces were carried out. The 1D GPR modelling allows us to precise the presence of a cavity with a rectangular cross section. Since the type of volcaniclastic avalanche deposits of the subsoil do not allow the formation of cavities of that size, we infer that it may be the remains of a tomb or an anthropogenic cavity. [ABSTRACT FROM AUTHOR]

This article deals with the study of land slide situation on the proposed path of the gas pipe line from Imam zadeh Hashem to Rudehen in Tehran province, using geophysical studies with 3 geophysical methods of one dimentional geoelectrical method, 2 dimentional Geo electrical Induced Polarization and Resistivity (IP & RS) method, and Ground Peneteration Radar (GPR) method. For this purpose, geophysical studies were carried out in the study area (in 70 kilometers east of Tehran and near Rodhan) as follows. In order to measure the changes of Geoelectrical resistivity in the designated place, to check the ground conditions for the gas pipeline in the Imam zadeh Hashem project, to separate the underground layers based on their Geoelectrical resistivity and to diagnose possible fractures, the number of 20 electrical soundings in an irregular network were recorded in the land slide area along ten profiles. After the approximate identification of the land slide area, 2D study was done in two profiles with a pole - dipole arrangement of 380 meters with induction polarization and resistivity (IP-RS) method. In order to obtain more information near the surface at the same time and also to investigate the extension to the depth, 5-meter electrode intervals with a maximum of 8 jumps and 10-meter intervals from 4 to 13 jumps have been used The depth of investigation will be about 50 meters. Moreover, Ground Peneteration Radar (GPR) method was performed with the length of 1000 meters in 27 profiles. GPR studies have been conducted with the aim of investigating and determining the location of loosened areas and possible holes in the range. Data acquisition was done using 80 MHz antenna. The results of data acquisition for a total area of 1000 meters have been processed by professional software and interpreted by taking into account the environmental conditions and taking into account all the complications in the route of the conducted surveys. Based on conducted studies, the land slide areas were determined and using Ground Peneteration Radar (GPR), underground cavities were located. With the help of the conducted studies, the range of the main land slide resulting from geophysical studies is determined and land slide prone surfaces are actually formed on the slope of land stratification and the mechanism of slides is planar movement. The main problem is related to the surface prone to upper slippage. Therefore, consolidation and stabilization of anomalous areas were investigated using stability analysis software. [ABSTRACT FROM AUTHOR]

This study aims to develop a damage-detection algorithm based on the electromagnetic wave properties inside a reinforced concrete structure. The proposed method involves employing two algorithms based on data measured using ground-penetrating radar—a common electromagnetic wave method in civil engineering. The possible defect area was identified based on the energy dissipated by the damage in the frequency-wavenumber domain, with the damage localized using the calculated relative permittivity of the measurements. The proposed method was verified through a finite difference time-domain-based numerical analysis and a testing slab with artificial damage. As a result of verification, the proposed method quickly identified the presence of damage inside the concrete, especially for honeycomb-like defects located at the top of the rebar. This study has practical significance in scanning structures over a large area more quickly than other non-destructive testing methods, such as ultrasonic methods. [ABSTRACT FROM AUTHOR]

Economic growth in South Africa over the last decade has been disappointing. Even before the emergence of the Covid-19 pandemic, the economy was underperforming. South Africa needs high and sustained growth to reduce the rate of unemployment, which has remained stubbornly high at over 30% for many years. Fund flows may provide capital to spur this needed growth. Fund flows are a critical issue in emerging markets like South Africa as sudden reversals have also been associated with severe destabilizing effects. These concerns and the substantial growth in fund flows to South Africa in the past two decades motivate this study. We examine the relationships among fund flows, uncertainty measures, economic indicators, and financial market variables in South Africa. We estimate several vector autoregressions to account for the inherent feedback among fund flows and economic and financial variables. We find that the term structure of interest rates (TERM) is negatively related to equity fund flows (EFF) while positively related to bond fund flows (BFF). While BFF and EFF are both negatively related to most uncertainty measures, BFF is more sensitive to downside risk indictors than EFF. We show that the economic and financial development of South Africa is impacted by fund flows with GDP being more strongly impacted by BFF while stock returns are impacted by both BFF and EFF. We also uncover evidence that commodity prices increase BFF. Next, we show that the volatility of the South African equity market immediately reduces equity prices which attracts EFF after a two-to-three-month lag. Finally, we uncover convincing evidence that illiquidity as measured by effective spread reduces EFF to South Africa. The ability to better predict the direction EFF and BFF will allow policy makers to impose circuit breakers to mitigate the adverse impacts of sudden investment reversals. This is particularly relevant to the current situation where additional financial uncertainty due to the energy crisis in South Africa coupled with political instability may increase the likelihood of such reversals. [ABSTRACT FROM AUTHOR]

Based on the amplitude attribute analysis of ground penetrating radar (GPR) data, this paper applies GPR to the identification of internal damages and abnormal objects in stone walls of Tibetan ruins. The corresponding relationship between the characteristics of internal damages and abnormal objects and the radar data is explored through the Tibetan stone walls (TSWs) simulation test. The identification of location and size of the damages and abnormal objects in TSWs is realized based on the Mahalanobis distance abnormal data discrimination method. Using the normal distribution and the noncentral chi-square distribution, the identification law of types of the damages and abnormal objects is constructed, which takes root mean square (RMS) amplitude and interface reflection coefficient as the characteristic values. The field detection results of TSWs at three Tibetan sites are taken as an extension and supplement to the simulation results, and a set of identification atlas for the location, size and type of the internal damages and abnormal objects in TSWs is established. The application results of the identification atlas were verified by the field detection and the results show that the atlas has a high accuracy, which can significantly improve the efficiency of the identification, and can provide a basis for the performance research, protection and maintenance of TSWs. [ABSTRACT FROM AUTHOR]

Determining precise boundaries and protective zones for historical and cultural objects enables their effective preservation. This article presents the framework for establishing protective zones around historical and cultural heritage sites existing in Ukraine, using the example of the Citadel defensive complex located in Lviv (Ukraine). It proposes general and detailed conceptual models for the combined application of remote and non-invasive methods for investigating historical and cultural heritage sites. It introduces the theory of integrating radar satellite imaging with ground-based georadar imaging. Additionally, it presents a software module that has been developed to analyze collected data on two-dimensional historical and cultural heritage objects, refine their boundaries, and establish protective zones around them. The result of the work is the determination of extremum points of vertical displacement on the territory of the historical and cultural heritage site "Lviv Citadel" in Ukraine using SAR, and the construction of a map of vertical displacements. A classification of these points was carried out, after which two of them were investigated using GPR for anomalies. Artifacts from World War II were discovered at each of these points. Using the developed software module, updated boundaries of the site were constructed, taking into account the underground artifacts and protective zones. [ABSTRACT FROM AUTHOR]

Background: For patient‐specific quality assurance (PSQA) for small targets, the dose resolution can change depending on the characteristics of the dose calculation algorithms. Purpose: This study aimed to evaluate the influence of the dose calculation algorithms Acuros XB (AXB), anisotropic analytical algorithm (AAA), photon Monte Carlo (pMC), and collapsed cone (CC) on a helical diode array using volumetric‐modulated arc therapy (VMAT) for small targets. Materials and methods: ArcCHECK detectors were inserted with a physical depth of 2.9 cm from the surface. To evaluate the influence of the dose calculation algorithms for small targets, rectangular fields of 2×100, 5×100, 10×100, 20×100, 50×100, and 100×100 mm2 were irradiated and measured using ArcCHECK with TrueBeam STx. A total of 20 VMAT plans for small targets, including the clinical sites of 19 brain metastases and one spine, were also evaluated. The gamma passing rates (GPRs) were evaluated for the rectangular fields and the 20 VMAT plans using AXB, AAA, pMC, and CC. Results: For rectangular fields of 2×100 and 5×100 mm2, the GPR at 3%/2 mm of AXB was < 50% because AXB resulted in a coarser dose resolution with narrow beams. For field sizes > 10×100 mm2, the GPR at 3%/2 mm was > 88.1% and comparable for all dose calculation algorithms. For the 20 VMAT plans, the GPRs at 3%/2 mm were 79.1 ± 15.7%, 93.2 ± 5.8%, 94.9 ± 4.1%, and 94.5 ± 4.1% for AXB, AAA, pMC, and CC, respectively. Conclusion: The behavior of the dose distribution on the helical diode array differed depending on the dose calculation algorithm for small targets. Measurements using ArcCHECK for VMAT with small targets can have lower GPRs owing to the coarse dose resolution of AXB around the detector area. [ABSTRACT FROM AUTHOR]

An important geological risk to which many towns in Puglia are exposed is sinking cavities in urban areas. For urban centers, studying, mapping, providing geological and speleological descriptions, classifying, and cataloging the forms and types of cavities is essential because cavities are linked to past local anthropic and natural processes at different sites. These circumstances could lead to the enhancement of existing underground cavities in urban areas through conservation and continuous monitoring. Unfortunately, in many cases, these underground cavities have been used as landfills and subsequently abandoned. In late March 2007, one of these cavities collapsed inside Gallipoli's inhabited center, causing damage to the structures but fortunately not human lives. In the area surrounding the collapsed cavity, a series of geophysical investigations were undertaken using ground penetrating radar in an attempt to delimit the area of collapse and develop possible interventions for restoration. In the same area, these measures were repeated 16 years later in December 2022 due to another collapse. The comparison between data acquired in these two periods shows that there were no strong changes apart from an increased presence of subsoil moisture in 2022. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation of the effect of clutter on the performance of microwave imaging and target identification using ground-penetrating radar (GPR) for buried objects. Signals collected by the GPR were formed to be B-scan and 3D images to carry out the microwave imaging of buried objects. Poles extracted using the short-time matrix pencil method from a late-time response of the received signal collected at a specific position were exploited to identify buried objects. The experiment was set up by constructing a large box filled with sand where an L-shaped metallic sheet was buried. The GPR was installed along with a 2-dimensional scanner above the box to image and identify the buried metallic sheet. In order to investigate the effect of the clutter on the performance of the GPR-based microwave imaging and target identification, small rocks were arranged on the sand surface, and their density was then varied. The experimental results have shown that clutter density significantly affects the sharpness of the B-scan and 3D images. The rocks result in an additional surface layer in the images. Moreover, the results have also shown that the clutter does not affect the performance of the identification of the buried object. From the results, the poles obtained from the GPR scanning inside and outside the buried object region differed. This confirms that the poles of the buried object should be different from those without the object. The poles of the buried object remain constant, although rocks on the sand surface exist since the clutter affects only the early-time response (not the late-time response). With varying clutter densities, the underlying poles were significantly changed, but this does not affect the identification of the buried object. [ABSTRACT FROM AUTHOR]

Machine-learning prediction algorithms have been used in different scenarios by earlier researchers. This article found the application of these algorithms for predictions of the material deposition rate of copper ions through high-speed selective jet electrodeposition (HSSJED). The presented article emphasises the selection of the best suitable machine-learning prediction algorithms among Gaussian Process Regression (GPR), Support Vector Machine (SVM) regression and Linear Regression (LR) for prediction and compares them with each other for selection of the best algorithm which agrees with experimental data. The comparison of performances of these machine-learning algorithms has been done taking root-mean-square error (RMSE), R2, mean-squared error (MSE) and mean absolute error (MAE) as a basic measure for the same, also various graphs have been plotted and discussed for better understanding. The data set was collected by performing the HSSJED experiment on various parameters like electrolyte composition, electrode gap and applied dc potential. K-fold cross-validation method has been adopted for validation. GPR predictions were found better than the other two which also agree with the experimental output as the quantitative comparison has been done and results are discussed. The average values of RMSE, R2, MSE and MAE were 0.057668, 0.96, 0.0032432 and 0.045733 for GPR. [ABSTRACT FROM AUTHOR]