Search

This paper introduces an alternative approach for detecting cavities in reinforced concrete walls using Ground Penetrating Radar (GPR) A-scan data. GPR, leveraging electromagnetic waves, is extensively applied for cavity detection within structures. The nature of electromagnetic waves, significantly influenced by reflective media and attenuating through them, requires specialized analysis methods for data interpretation. Traditional methods often involve identifying and eliminating overlapping reflection patterns or adjusting signal magnitude at specific depths to isolate peak signals from the target object's surface, which can be subjective and complex. To overcome these challenges, this study proposes quantitatively assessing the presence of cavities by analyzing the integral area of A-scan data within suspected ranges. Observations indicate a substantial difference in reflection patterns between areas with and without cavities, showcasing the potential of this approach for quantitative cavity detection. This approach offers a more objective and quantitative basis for identifying cavities in reinforced concrete structures.

The paper deals with a combined time–depth conversion strategy able to improve the reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty hypogeal rooms, and similar targets. The combined time–depth conversion accounts for the propagation velocity of the electromagnetic waves both in free space and in the embedding medium, and it allows better imaging and interpretation of the underground scenario. To assess the strategy's effectiveness, ground penetrating radar (GPR) data referred to as an experimental test in controlled conditions are accounted for and processed by two different approaches to achieve focused images of the scenario under test. The first approach is based on a classical migration algorithm, while the second one faces the imaging as a linear inverse scattering approach. The results corroborate that the combined time–depth conversion improves the imaging in both cases. [ABSTRACT FROM AUTHOR]

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.

In this paper, the linear sampling method is introduced for cavity detection in a two dimensional (2D) piezoelastic media using measurement of data on the boundary. This method is categorized as a qualitative approach to image the geometrical features of unknown targets. Although this method has been used in the context of inverse problems such as acoustics, electromagnetism and elastostatic, there is no specific attempt to apply this method to identification of cavities in piezoelastic media. This study emphasizes the implementation of the linear sampling method coupled with the finite element method (FEM). A set of numerical simulations on two-dimensional problems is presented to highlight many effective features of the proposed qualitative identification method. [ABSTRACT FROM AUTHOR]

Abstract The present study deals with the evaluation of a three-mixed array dataset for the detection of subsurface cavities using conceptual air-filled cavity model sets at different depths. Cavity models were simulated using the forward modelling technique to generate synthetic apparent resistivity data for three common individual arrays. These arrays are dipole–dipole (DD), pole–dipole (PD), and Wenner–Schlumberger (WS). The synthetically apparent resistivity data obtained from two different individual arrays were merged to form a high-resolution single model. Consequently, three possible mixed arrays datasets can be obtained: the dipole–dipole-Wenner–Schlumberger (DD+WS), pole–dipole, and Wenner–Schlumberger (PD+WS), and dipole–dipole and pole–dipole (DD+PD). The synthetically apparent resistivity data for both the individual and mixed arrays were inverted using Res2dinv software based on the robust constrain inversion technique to obtain a 2D resistivity model section. The inverted resistivity sections were evaluated in terms of their recovering ability of the model’s parameters (e.g. resistivity, and geometry). The results show that the individual arrays can resolve the location and dimensions of the cavity within reasonable accuracy only at a depth not exceeding 6 m below the surface. On the other hand, a significant resolution enhancement in model resistivity with increasing depth was observed when the mixed arrays were used. The (DD+WS) mixed arrays dataset brings up better model resistivity and shows closer parameters to the true actual model among the other mixed arrays. So it is strongly recommended for cavity detection studies.

A pseudo-3D electrical resistivity tomography (ERT) survey has been carried out to detect cavities at the Neolithic/Palaeolithic site of Grotta Scaloria, close to Manfredonia, Apulia, Italy. Scaloria Cave has a rich history of archaeological research of more than 80 years and is one of the most important Neolithic complexes in the Mediterranean. Synthetic data modelling allowed to check the adequacy of the geophysical method and to develop a proper experimental setup at the survey design stage. Indeed, the results of the field data inversion revealed high resistivity anomalies which can be related to cavities and provided a good definition of the main geological structures and boundaries. Moreover, the results suggest that unknown and speleological unexplored cavities are still present at the Scaloria Cave study site. These findings may provide further insights on pseudo-3D ERT applicability, particularly for cavity detection. Furthermore, the approach used in this study yields fruitful information for further archaeological survey design and for the interpretation of ERT investigations targeting similar geological features and structures. [ABSTRACT FROM AUTHOR]

The present study deals with the evaluation of a three-mixed array dataset for the detection of subsurface cavities using conceptual air-filled cavity model sets at different depths. Cavity models were simulated using the forward modelling technique to generate synthetic apparent resistivity data for three common individual arrays. These arrays are dipole–dipole (DD), pole–dipole (PD), and Wenner–Schlumberger (WS). The synthetically apparent resistivity data obtained from two different individual arrays were merged to form a high-resolution single model. Consequently, three possible mixed arrays datasets can be obtained: the dipole–dipole-Wenner–Schlumberger (DD+WS), pole–dipole, and Wenner–Schlumberger (PD+WS), and dipole–dipole and pole–dipole (DD+PD). The synthetically apparent resistivity data for both the individual and mixed arrays were inverted using Res2dinv software based on the robust constrain inversion technique to obtain a 2D resistivity model section. The inverted resistivity sections were evaluated in terms of their recovering ability of the model’s parameters (e.g. resistivity, and geometry). The results show that the individual arrays can resolve the location and dimensions of the cavity within reasonable accuracy only at a depth not exceeding 6 m below the surface. On the other hand, a significant resolution enhancement in model resistivity with increasing depth was observed when the mixed arrays were used. The (DD+WS) mixed arrays dataset brings up better model resistivity and shows closer parameters to the true actual model among the other mixed arrays. So it is strongly recommended for cavity detection studies.Article highlights: • Comparison between three traditional arrays and mixed array datasets in delineation of the geometry of subsurface cavities using numerical simulation was made. • The resolution of the obtained resistivity model can be enhanced by using mixed array datasets. • The numerical simulations are considered an effective tool for predicting several scenarios for studying the electrical response of any subsurface structures. [ABSTRACT FROM AUTHOR]

The paper deals with a combined time–depth conversion strategy able to improve the reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty hypogeal rooms, and similar targets. The combined time–depth conversion accounts for the propagation velocity of the electromagnetic waves both in free space and in the embedding medium, and it allows better imaging and interpretation of the underground scenario. To assess the strategy’s effectiveness, ground penetrating radar (GPR) data referred to as an experimental test in controlled conditions are accounted for and processed by two different approaches to achieve focused images of the scenario under test. The first approach is based on a classical migration algorithm, while the second one faces the imaging as a linear inverse scattering approach. The results corroborate that the combined time–depth conversion improves the imaging in both cases.

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. Results pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder’s capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. Conclusions We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

We present a novel method for underground cavity detection using cross-borehole ground-penetrating radar (GPR). Modeling the soil-cavity system as a filter allows us to estimate the group dispersion of the GPR signal induced by propagation through a cavity. We adopt a hypothesis-testing approach based on the observed group dispersion and propose a method which allows us to detect cavities with high probability. The proposed method is validated on both simulated and real measurements. Geologic clutter that causes conventional methods to fail has negligible impact on the efficacy of the proposed method, and the proposed method may be combined with conventional methods to provide a more robust and effective cavity-detection solution. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Both underground cavities and anomalies are important issues which may cause damage to ground strata and subsequently to their overlaying structures due to subsidence and excessive settlement. Thus, the first step of stabilising the ground and preventing undesired damages is detecting these cavities. In this paper, multichannel analysis of surface waves (MASW) test as a general surface wave method is utilised for obtaining the properties of the ground and the coherence function of two adjacent signals to establish an approach for detecting multiple underground anomalies. In this regard, MASW tests were performed in five numerical models of homogeneous half-space with multiple cavities. The extent of the signals' coherence of acquired seismic data of each couple of adjacent signals was plotted to delineate the cavity and anomaly location. The results of coherence values showed a distinguishable-coherence zone in the spectrum. Two main categories of cavities and buried concrete masses were investigated via the coherence function, indicating a decline in the cavity position and a rise in the concrete mass position. Eventually, to test the validity of the method, two sets of real data were employed. The results of both numerical and experimental data showed the accuracy of the method. [ABSTRACT FROM AUTHOR]

Background: Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. Results: pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. Conclusions: We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications. [ABSTRACT FROM AUTHOR]

Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein–protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein–protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein–ligand docking. The proposed strategy will be useful for many protein–protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.

We present a novel method for underground cavity detection using cross-borehole ground-penetrating radar (GPR). Modeling the soil-cavity system as a filter allows us to estimate the group dispersion of the GPR signal induced by propagation through a cavity. We adopt a hypothesis-testing approach based on the observed group dispersion and propose a method which allows us to detect cavities with high probability. The proposed method is validated on both simulated and real measurements. Geologic clutter that causes conventional methods to fail has negligible impact on the efficacy of the proposed method, and the proposed method may be combined with conventional methods to provide a more robust and effective cavity-detection solution.

Finding efficient and less expensive techniques for different aspects of culvert inspection is in great demand. This study assesses the potential of infrared thermography (IRT) to detect the presence of cavities in the soil around a culvert, specifically for cavities adjacent to the pipe of galvanized culverts. To identify cavities, we analyze thermograms, generated via long pulse thermography, using absolute thermal contrast, principal components thermography, and a statistical approach along with a combination of different pre- and post-processing algorithms. Using several experiments, we evaluate the performance of IRT for accomplishing the given task. Empirical results show a promising future for the application of this approach in culvert inspection. The size and location of cavities are among the aspects that can be extracted from analyzing thermograms. The key finding of this research is that the proposed approach can provide useful information about a certain type of problem around a culvert pipe which may indicate the early stage of the cavity formation. Becoming aware of this process in earlier stages will certainly help to prevent any costly incidents later. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulations have been recorded on the complex between the edema factor (EF) of Bacilllus anthracis and calmodulin (CaM), starting from a structure with the orthosteric inhibitor adefovir bound in the EF catalytic site. The starting structure has been destabilized by alternately suppressing different co-factors, such as adefovir ligand or ions, revealing several long-distance correlations between the conformation of CaM, the geometry of the CaM/EF interface, the enzymatic site and the overall organization of the complex. An allosteric communication between CaM/EF interface and the EF catalytic site, highlighted by these correlations, was confirmed by several bioinformatics approaches from the literature. A network of hydrogen bonds and stacking interactions extending from the helix V of of CaM, and the residues of the switches A, B and C, and connecting to catalytic site residues, is a plausible candidate for the mediation of allosteric communication. The greatest variability in volume between the different MD conditions was also found for cavities present at the EF/CaM interface and in the EF catalytic site. The similarity between the predictions from literature and the volume variability might introduce the volume variability as new descriptor of allostery.

Biological processes are regulated mainly by the binding of small molecules into cavities distributed throughout the biomolecular structure. Computational tools to detect these cavities have an essential role in rational drug design. With the exponential availability of high-order 3D atomic structures and large sets of atomic models, the tools must balance accuracy and speed. In this sense, we developed parKVFinder, a parallelized software for geometry-based cavity detection. Here, we described its functionalities and presented its easy-to-use PyMOL plugin and command-line interface. Finally, we demonstrated parKVFinder by identifying an important HIV-1 protease cavity and compared it with other geometry-based programs.

The presence of natural voids and fractures (weak zones) in subsurface gypsiferous soil and gypsum, within the University of Al-Anbar, western Iraq. It causes a harsher problem for civil engineering projects. Electrical resistivity technique is applied as an economic decipher for investigation underground weak zones. The inverse models of the Dipole-dipole and Pole-dipole arrays with aspacing of 2 m and an n-factor of 6 clearly show that the resistivity contrast between the anomalous part of the weak zone and the background. The maximum thickness and shape are well defined from 2D imaging with Dipole-dipole array, the maximum thickness ranges between 9.5 to 11.5 m. It is concluded that the 2D imaging survey is a useful technique and more effective for determining and mapping subsurface weak zones (voids, fracture and cavities), when taken in consideration using the suitable a-spacing and n-factor for each electrode array, especially with the Dipole-dipole array which provided the best imaging of the subsurface shape of the weak zones. [ABSTRACT FROM AUTHOR]

Glycogen synthase kinase-3 beta (GSK-3β) is an enzyme pertinently linked to neurodegenerative diseases since it is associated with the regulation of key neuropathological features in the central nervous system. Among the different kinds of inhibitors of this kinase, the allosteric ones stand out due to their selective and subtle modulation, lowering the chance of producing side effects. The mechanism of GSK-3β allosteric modulators may be considered still vague in terms of elucidating a well-defined binding pocket and a bioactive pose for them. In this context, we propose to reinvestigate and reinforce such knowledge by the application of an extensive set of in silico methodologies, such as cavity detection, ligand 3D shape analysis and docking (with robust validation of corresponding protocols), and molecular dynamics. The results here obtained were consensually consistent in furnishing new structural data, in particular by providing a solid bioactive pose of one of the most representative GSK-3β allosteric modulators. We further applied this to the prospect for new compounds by ligand-based virtual screening and analyzed the potential of the two obtained virtual hits by quantum chemical calculations. All potential hits achieved will be subsequently tested by in vitro assays in order to validate our approaches as well as to unveil novel chemical entities as GSK-3β allosteric modulators.

Abstract Background Protein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity. Results We solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them. Conclusion This article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein.

This paper is devoted to the application of the Combined Resistivity Sounding and Profiling electrode configuration (CRSP) to detect underground cavities. Electrical resistivity surveying is among the most favorite geophysical methods due to its nondestructive and economical properties in a wide range of geosciences. Several types of the electrode arrays are applied to detect different certain objectives. In one hand, the electrode array plays an important role in determination of output resolution and depth of investigations in all resistivity surveys. On the other hand, they have their own merits and demerits in terms of depth of investigations, signal strength, and sensitivity to resistivity variations. In this article several synthetic models, simulating different conditions of cavity occurrence, were used to examine the responses of some conventional electrode arrays and also CRSP array. The results showed that CRSP electrode configuration can detect the desired objectives with a higher resolution rather than some other types of arrays. Also a field case study was discussed in which electrical resistivity approach was conducted in Abshenasan expressway (Tehran, Iran) U-turn bridge site for detecting potential cavities and/or filling loose materials. The results led to detect an aqueduct tunnel passing beneath the study area.

Finding efficient and less expensive techniques for different aspects of culvert inspection is in great demand. This study assesses the potential of infrared thermography (IRT) to detect the presence of cavities in the soil around a culvert, specifically for cavities adjacent to the pipe of galvanized culverts. To identify cavities, we analyze thermograms, generated via long pulse thermography, using absolute thermal contrast, principal components thermography, and a statistical approach along with a combination of different pre- and post-processing algorithms. Using several experiments, we evaluate the performance of IRT for accomplishing the given task. Empirical results show a promising future for the application of this approach in culvert inspection. The size and location of cavities are among the aspects that can be extracted from analyzing thermograms. The key finding of this research is that the proposed approach can provide useful information about a certain type of problem around a culvert pipe which may indicate the early stage of the cavity formation. Becoming aware of this process in earlier stages will certainly help to prevent any costly incidents later.

The S-19 Cave was with its explored depth of 177 m one of the most important caves of the Mt. Kanin massif, but after its discovery in 1974, a huge snow avalanche protection dyke was constructed across the cave entrance. To excavate the buried cave, the accurate location of the cave had to be determined first. Since the entrance coordinates were incorrect and no markers were available, application of geophysical techniques was necessary to do this. A Ground Penetrating Radar (GPR) with special 50 MHz rough terrain antennas was selected as the single suitable geophysical method for the given conditions where thick debris overlay a rugged limestone surface. Nevertheless, it was not possible to directly detect the relatively narrow cave entrance itself due to data resolution limits. However, a historical photo of the area showed that the cave entrance was located in a local depression, which therefore represented the main target of the GPR survey. Seven GPR profiles were measured across the rough and steep surface causing difficulties in traversing the area with sensitive research equipment. In all recorded radargrams a small depression was clearly imaged under debris, and recognized as a topographic feature with the cave entrance. Based on the GPR data interpretation, the exact location for digging was determined and the thickness of debris assessed at 6.5–7 m. A massive excavation by a dredger resulted in a successful opening of the cave entrance, confirming both its geophysically determined location and its estimated depth. The application of an advanced geophysical method was therefore proven successful in providing a solution to this specific case in karst exploration and an important cave was saved.

We applied a supervised machine learning method for the purpose of auto detecting cavities under paved roadway from GPR (Ground Penetrating Radar) images. In cavity detection surveys under paved road way, we typically use vehicle-born GPR systems for the necessity to cover wide survey area without any traffic controls. To detect cavity from massive data acquired by vehicle-borne GPR swiftly, skilled-engineers carefully interpret GPR data considering features based on physics of GPR response. Automatic process based on knowledges of skilled-engineers is required, although automatic anomaly detection has not practically realized cause of un-uniform or complex responses from cavities. We applied machine learning methods to detect cavity anomalies using actual GPR data which were acquired on natural occurred cavities as training data. At the verification with actual survey data, our method was able to detect cavity-caused GPR patterns. This will be helpful for analyzer to narrow down cavity responses, meanwhile there were still too many over detections. We think accumulation of labeled GPR data of cavity will also contribute to improvement of our method. [ABSTRACT FROM AUTHOR]

ABSTRACT: We study the interaction of a seismic wavefield with a spherical acoustic gas‐ or fluid‐filled cavity. The intention of this study is to clarify whether seismic resonances can be expected, a characteristic feature that may help in detecting cavities in the subsurface. This is important for many applications, in particular the detection of underground nuclear explosions, which are to be prohibited by the Comprehensive Test Ban Treaty. To calculate the full seismic wavefield from an incident plane wave that interacts with the cavity, we considered an analytic formulation of the problem. The wavefield interaction consists of elastic scattering and the wavefield interaction between the acoustic and elastic media. Acoustic resonant modes caused by internal reflections in the acoustic cavity show up as spectral peaks in the frequency domain. The resonant peaks coincide with the eigenfrequencies of the un‐damped system described by the particular acoustic medium bounded in a sphere with stiff walls. The filling of the cavity could thus be determined by the observation of spectral peaks from acoustic resonances. By energy transmission from the internal oscillations back into the elastic domain, the oscillations experience damping, resulting in a frequency shift and a limitation of the resonance amplitudes. In case of a gas‐filled cavity, the impedance contrast is still high, which means low damping of the internal oscillations resulting in very narrow resonances of high amplitude. In synthetic seismograms calculated in the surrounding elastic domain, the acoustic resonances of gas‐filled cavities show up as persisting oscillations. However, due to the weak acoustic–elastic coupling in this case, the amplitudes of the oscillations are very low. Due to a lower impedance contrast, a fluid‐filled cavity has a stronger acoustic–elastic coupling, which results in wide spectral peaks of lower amplitudes. In the synthetic seismograms derived in the surrounding medium of fluid‐filled cavities, acoustic resonances show up as strong but fast decaying reverberations. [ABSTRACT FROM AUTHOR]

Background: Protein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity. Results: We solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them. Conclusion: This article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein. [ABSTRACT FROM AUTHOR]

The article addresses the application of electrical resistivity imaging for engineering site investigation in Pishva Hospital, Varamin, Iran. Some aqueduct shafts exist in the study area backfilled by loose materials. The goals of this study are to detect probable aqueduct tunnels and their depth, investigate filling quality in the shafts as well as connection(s) between them. Therefore, three profiles were surveyed by dipoledipole electrode array. Also, to investigate the potentially anomalous areas more accurately, five additional resistivity profiles were measured by a Combined Resistivity Sounding-Profiling array (CRSP). According to the results of 2-D inversion modelling, a main aqueduct tunnel was detected beneath the central part of the site. Finally, the resistivity pattern of the detected aqueduct system passing the investigated area was provided using the obtained results. [ABSTRACT FROM AUTHOR]

The S-19 Cave was with its explored depth of 177 m one of the most important caves of the Mt. Kanin massif, but after its discovery in 1974, a huge snow avalanche protection dyke was constructed across the cave entrance. To excavate the buried cave, the accurate location of the cave had to be determined first. Since the entrance coordinates were incorrect and no markers were available, application of geophysical techniques was necessary to do this. A Ground Penetrating Radar (GPR) with special 50 MHz rough terrain antennas was selected as the single suitable geophysical method for the given conditions where thick debris overlay a rugged limestone surface. Nevertheless, it was not possible to directly detect the relatively narrow cave entrance itself due to data resolution limits. However, a historical photo of the area showed that the cave entrance was located in a local depression, which therefore represented the main target of the GPR survey. Seven GPR profiles were measured across the rough and steep surface causing difficulties in traversing the area with sensitive research equipment. In all recorded radargrams a small depression was clearly imaged under debris, and recognized as a topographic feature with the cave entrance. Based on the GPR data interpretation, the exact location for digging was determined and the thickness of debris assessed at 6.5-7 m. A massive excavation by a dredger resulted in a successful opening of the cave entrance, confirming both its geophysically determined location and its estimated depth. The application of an advanced geophysical method was therefore proven successful in providing a solution to this specific case in karst exploration and an important cave was saved. [ABSTRACT FROM AUTHOR]

This paper focuses on the impacts of contemporary geomorphological processes on fruition activities in a karst area of Salento (Apulia, southeastern Italy). The work illustrates the results of studies in a sinkhole field at Nociglia, in the Lecce province, recently recognized as a geosite and where shallow phreatic speleogenesis operates close to the water table level with the formation of karst caves, successive roof collapse, formation of wide caverns, and sinkhole development at the surface. All these features threaten the nearby infrastructures including a province road. Salento has a great number of active sinkholes related to natural and anthropogenic cavities. Their presence is at the origin of several problems in the built-up environment, due to increasing population growth and development pressures. In such a context, the detection of cavities, and the assessment of sinkhole hazards present numerous difficulties. To assess the potential danger from sinkholes, it is important to identify and monitor the main factors contributing to the formation process. A multi-disciplinary approach, comprising geological, geomorphological, and geophysical analyses, is necessary to obtain a comprehensive knowledge of these complex phenomena in karst areas. Geophysical methods can be of great help to monitor the processes and for identifying and mapping features related to the underground voids, by detecting contrasts in physical properties, such as density and electrical resistivity, with the surrounding sediments. At the same time, recognition of the presence of sinkholes by geophysical methods has to adapt to the local geological conditions and take advantage of the integration of the several methodologies available. The territory of Nociglia demonstrates that monitoring is essential for the safe exploitation of these type of geomorphosites. [ABSTRACT FROM AUTHOR]

The Karstic thrust edge, a pronounced geomorphologic step, which is a result of the tectonostratigraphic evolution of the active Adria-Dinarides thrust zone, represents a major obstacle for the planned new railway route Divača-Koper. Thus, the geotechnical and structural properties as well as the geometry of the thrust-fault planes in this area are of great importance. Since geological mapping cannot give insight into the subsurface to reveal a complex 3D structure, and the numerous boreholes needed to investigate the area would be too expensive and time consuming, the application of a geophysical method such as ground penetrating radar (GPR) is needed. To test the method for determining near surface features and detecting low-angle inclined thrusts, a low frequency GPR system with 50 MHz rough terrain antenna was used to record 13 GPR profiles along all three floors of the Črnotiče quarry, where the spatial position of the Socerb thrust fault that separates limestones above and flysch layers below is relatively well documented. The profiles were positioned across selected existing boreholes. The GPR results were correlated with borehole data as well as geological mapping results. The GPR provided not only precise information on the geometry of the Socerb thrust fault, but was also very useful for establishing the position of some known as well as several potential cavities, both air- and sediment-filled. In areas further from the thrust-fault zone, where the limestone is less tectonically damaged, it was also possible to determine apparent dip angles of the strata, which after reconstruction matched the true dips gathered from geological mapping. [ABSTRACT FROM AUTHOR]

Background The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed. Results We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work. Conclusions iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug. [ABSTRACT FROM AUTHOR]

Background: The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed. Results: We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work. Conclusions: iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at . [ABSTRACT FROM AUTHOR]

High frequency ground penetrating radar (GPR) is usually applied for cavities detection in a shallow subsurface of karst areas to prevent geotechnical hazards. For specific projects, such as tunnel construction, it is important to detect also larger voids at medium depth range. However, dimensions of classical rigid low frequency antennas seriously limit their applicability in a rough terrain with dense vegetation commonly encountered in a karst. In this study recently developed 50 MHz antennas designed in a tube form were tested to detect cave gallery at the depth between 12 m and 60 m. The Divaca cave was selected because of a wide range of depths under the surface, possibility of unknown galleries in the vicinity and a rough terrain surface typical for Slovenian karst. Seven GPR profiles were measured across the main gallery of the cave and additional four profiles NE of the cave entrance where no galleries are known. Different acquisition and processing parameters were analysed together with the data resolution issues. The main gallery of the cave was clearly imaged in the part where the roof of the gallery is located at the depth from 10 m to 30 m. The width of the open space is mainly around 10 m. Applied system was not able to detect the gallery in the part where it is located deeper than 40 m, but several shallower cavities were discovered which were unknown before. The most important result is that the profiles acquired NE of the cave entrance revealed very clearly the existence of an unknown gallery which is located at the depth between 15 m and 22 m and represents the continuation of the Divaca cave. Access to this gallery is blocked by the sediment fill in the entrance shaft of the cave. The results of the study are important also for future infrastructure projects which will involve construction of tunnels through karstified limestone and for speleological investigations to direct the research efforts. Keywords: ground penetrating radar, cavity detection, spatial resolution, limestone, Divača cave.

For layered half spaces in which the shear wave (S-wave) velocity of layer increases with the layer depth, the subsurface seismic wave field generated by surface sources is dominated by the fundamental mode Rayleigh waves (R-waves). When a cavity full of air is present in the underlying half space, the forward (or incident) R-waves are scattered. The scattered wave field in front of the cavity is dominated by the back-scattered R-waves and the interference fringes are formed. Under the conditions that the length (i.e. horizontal extent) of cavity is enough large relative to the wavelength and the burial depth, the majority of the diffracted waves over the cavity approach the fundamental mode plate waves. The displacement structures (variations of displacement with depth) of the diffracted waves are different from those of R-waves within the burial depth. The differences could result in the decrease of the spectral amplitude over the cavity relative to that in front of the cavity. In the surface wave-field behind the cavity, the diffracted waves under the cavity could cause the increase of the spectral amplitude relative to that over the cavity at some low frequencies. By transforming the spectrum into the wavelength domain, a method is proposed to estimate the horizontal extent and burial depth of a cavity from the spectral changes in the offset-wavelength domain. The feasibility is verified by a preliminary experiment. ∙ The normalized depth of energy distribution in a layered half space is smaller than that in a half space with the Poisson's ratio equal to that of the first layer. ∙The majority of the diffracted waves over a cavity behave as the fundamental mode plate waves under conditions that the length of cavity is large compared to the wavelength and the burial depth. ∙The spectral changes over a cavity are explained using displacement structure differences between R-waves and the diffracted waves. ∙The spectral density over a cavity is weaker than that in front of the cavity. ∙In the wave field behind a cavity, the frequency content relatively increases for the waves with the wavelengths larger than the burial depth. ∙The location and burial depth of cavity can be estimated from the spectral changes in front of and behind a cavity in the offset-wavelength domain. [ABSTRACT FROM AUTHOR]