The aim of the article is to compare two classifications systems of engineering-geological environment sustainability in terms of its permeability evaluated on the basis of permeability coefficient. The first evaluated classification assumes a permeable environment to be a positive characteristic in the engineering-geological assessment, while the other considers an impermeable environment as favourable. The four fine-grained soil materials were selected, as they had very similar, almost identical grains-size distribution, but different microstructure characterized by grains sphericity, angularity, and roughness. At the same time, the influence of changes in the density of soil materials  was analysed. Permeability coefficient was determined using six methods . The laboratory method falling head test (FHT) was taken as a reference test that reflected the actual water flow through the soil. It was found that with an increase in grain angularity and roughness (and a decrease in sphericity), the permeability coefficient was decreasing and this trend culminated along with gradual compaction. Moreover, the research shows that unsuitable methods may classify soil materials into wrong engineering-geological permeability classes, which may have negative consequences during engineering-geological or geotechnical assessment and cause subsequent problems in foundation engineering. An important factor in engineering geology is the permeability of the geological environment ,4,5,6,7 There are many methods of determining the permeability coefficient, but they may be generally divided into in-situ tests , laboratory tests , empirical and predictive methods. Each of these groups has its advantages and disadvantages. In-situ tests return the most reliable results, however they are costly and technically difficult—erroneous results may be influenced by incomplete recognition of the geological structure of the layer. In laboratory tests, the challenge lies in preparing representative samples for testing, and sample size is limited ,16. EmpiThis problem is addressed in the article via demonstrating the differences based on the analysis of four soil materials of more or less identical grain-size distribution (grain size), but varying microstructure. The differences obtained in the permeability coefficient values mean that the permeability of the geological environment may not be determined correctly. A serious problem of engineering-geological investigations is the fact that the permeability coefficient is often determined based on empirical formulae, but not on more suitable laboratory, microscopic or in-situ methods. However, this may have severe consequences in the field of engineering geology, geotechnics, and foundation engineering. Wrong verdicts may often lead to inappropriate engineering design decisions, wrong redevelopment or foundation engineering projects, which is a negative phenomenon with a number of technical, economic and safety impacts.The permeability of the geological environment influences many engineering structures, especially these where the presence of water may have fatal consequences, such as water reservoirs, dams, or other water management structures. The permeability of the geological environment has been investigated by a number of authors, and the most common criterion was the character of the engineering structure. For example, Chen et al. , Stark eIn case of water flow through the soil, its microstructure reflecting the particle shape and their roughness is also very significant. It determines the capability to retain bound water as well as water movement . AlthougThe aim of the article was to determine the soil permeability coefficient of four fine-grained soils, taking into account their particle shape properties such as sphericity, angularity, and roughness under various density conditions. Apart from studying the effect of grains’ surface on the permeability coefficient, we also assessed different methods in determining the permeability coefficient, namely in terms of different purpose classifications of engineering environment permeability to determine suitability or unsuitability of a particular project. We assumed that the determination of permeability coefficient is influenced by the very method, but also by the permeability classification system, because it determines the relevant limits of the characteristic. It has certain consequences during the implementation of an engineering structure, where permeability plays a significant role. The study has important implications for engineering geology, geotechnics, foundation and civil engineering, as it demonstrated substantial differences in the determination of the permeability coefficient depending on the methods used to meet the boundary conditions of fine-grained soils. Using microscopic techniques and laboratory methods, much more accurate determination of permeability coefficient was obtained than in the empirical approaches.ζC0 that expresses the variability of sphericity, angularity, and roughness Zięba .The first formula  falls in10 ≤ 5 mm [With regard to the range of applicability of individual formulae, the following equations were selected. From the first group we chose the empirical formula Kozeny-Carman based on the boundary condition of ranges of applicability-silts, sands, and gravelly sands ,41. From0 ≤ 5 mm . From gr0 ≤ 5 mm .k took into account the amount of water flow V through the sample cross-section F in time t at given hydraulic gradient i.The fourth method  used to measure the permeability coefficient belonged to the group of laboratory tests . The calγ, dynamic viscosity of water μ (both at 10 °C), area of the SEM image A , cross-section area of pore i (Ai) and hydraulic radius of pore i . However, this method recognizes the total pore space area (not effective), so it does not reflect the influence of the microstructure of the soil particles.The fifth and sixth methods were from the group of SEM methods. The fifth applied method (Kozłowski method—SEM K) is based on the analysis of scanning electron microscope SEM images . The forC0) was introduced as a parameter reducing the value of pores cross-section area to obtain the effective pore space area. The same assumption was made for the determination of the effective porosity.Therefore, in the sixth method SEM K-Z, the authors modified Kozłowski’s method based on an empirical analysis of effective pore diameter with reference to the soil microstructure . The totIn both SEM methods, the permeability coefficient was determined on the basis of image analysis. Thirty SEM photographs of each variant (different soils with different density index) were analysed. The geometrical parameters of pore spaces were identified using ImageJ software . Based oBased on the results of the tests and calculations of the permeability coefficient , the evaThe results imply that higher permeability values in the first soil material and a gradual decreasing trend from the second through the fourth soil material . This isIn principle, we can observe in the results that the first two empirical methods (Kozeny-Carman and Slichter) show lower permeability coefficients from 40% to 97% than the reference laboratory method FHT. The third empirical Seelheim Formula presents significantly higher values than the reference method, except for two values in the first anthropogenic soil material. The microscopic Kozlowski method SEM K mostly shows significantly higher values than the reference method (in the order of hundreds per cent), except for the first anthropogenic soil material (GM), where the values are higher by only 9 to 13%. The best compatibility was achieved using the sixth method (SEM K-Z), where the maximum difference with the reference method was below 15%, but the majority of the measured values had a difference of a single digit value .D = 10%) and the most compacted state (density index ID = 90%) was 75% in GM. On the other hand, in SK the difference was 73% and the decreasing trend continued in other soil materials, i.e., 64% in SG and 66% in FA. If we evaluate the values of permeability coefficient based on the different methods applied , the folA similar trend of decreasing values based on density index was also observed in the Slichter Formula . In Seelheim Formula the trend differed. Density did not seem to play any role in the soil materials and the hydraulic permeability value stayed unchanged. In falling head test (fourth method) the trend of permeability coefficient was similar to the first and second empirical formulae, i.e., the values fell along with higher compaction, but the percentage difference between the first and last value was slightly smaller  than in the first and second methods. In the microscopic methods the trend was similar in the fact that the permeability coefficient values fell from the least to the most compacted state. However, the differences in values of the four soil materials had a reversed course than in the previous case. This means that differences in values grew . The fourth and fifth methods had analogous percentage differences between changes in the states of compaction.The acquired results imply that the most suitable method out of the six applied to determine permeability coefficient was the fourth method of reference laboratory falling head test, as it reflected the changes in the state of compaction between the least and most compacted state. At the same time, it most closely reflected changes between concave grains of GM and most angular grains of FA. Fly ash had the best boundary conditions to achieve best state of compaction. Thus, GM had the worst boundary conditions to achieve the best state of compaction. All the facts are presented in the trend of permeability coefficient changes d.The second most suitable method was the modified Kozlowski method (SEM K-Z) f. SimilaThe third most useful was the Kozlowski method (SEM K). Although it reflected the process of gradual compaction of the different soil materials, the permeability coefficient values only partially accounted for the difference in grain shapes. Despite the fact that materials with more concave grains should have a higher permeability coefficient than materials with more angular grains e, this wThe fourth in terms of suitability was the Kozeny-Carman Formula a and the50 and did not reflect the process of gradual compaction of soil materials or the grain shape characteristic. Consequently, all soils have exactly the same permeability coefficient at each compaction, which is inconsistent with the actual water flow in the analysed materials.The last, i.e., the least suitable, was the Seelheim Formula c. This mThe permeability coefficient is an important parameter in the assessment of soil environment, both in engineering geology and geotechnics. Thus, the research study aims to point at its importance in permeability assessment and in the choice of a suitable method of its determination. In the four case studies, the most suitable method was the reference laboratory FHT method. If permeability coefficient is not considered, wrong decisions may be taken in foundation engineering or other uses of engineering-geological environment. The study showed that the assessment of permeability coefficient using different methods led to different classifications of permeability, which may also result in wrong decisions. From the point of view of engineering practice, a correct description of the phenomenon of water flow in soil and a precise determination of the permeability coefficient that characterises it is particularly important. Therefore, it is crucial to choose an appropriate method to determine this parameter ,19,45. TTo date, numerous studies have been carried out showing significant differences between results obtained with different empirical formulae, as well as empirical formulae and laboratory and field studies ,46,47,48There have been few studies comparing different methods of determining permeability coefficient with consideration of particle shape characteristic and porosity. In their paper, Cabalar and Akbulut compared the values of permeability coefficient for sands with different gradation and shape, with the use of SEM images and simple indices classifying particle shape . SEM imaIn the literature reviewed, the authors obtained results that cannot be compared with one another, nor can the tendency for individual empirical formulae to over- or underestimate results in comparison with laboratory methods be clearly identified. Few methods for determining permeability coefficient  can be applied to a wide range of grain sizes, hence global analysis and comparisons between studies are problematic. Microscopic methods may be considered as an alternative to empirical formulae for determining the permeability coefficient. Pioneer studies in this field were carried out by Kozłowski (SEM K method) , but theIn connection with engineering-geological and civil engineering practice, and especially with foundation engineering, soil classifications for different purposes or different properties are crucial. It is important to consider permeability  of foundIn fact, both assessment approaches are used, but more frequently permeable soil is considered more suitable. Research shows that permeable soils such as gravel and sandy soils are more suitable in terms of load-bearing capacity ,52 and sIf we evaluate the determination of permeability coefficient, it proves to be a highly sensitive topic when one sample is considered suitable using one classification method and simultaneously unsuitable  using another classification method. In terms of practicality, this may mean that we may select an inappropriate approach during foundation engineering or other interference with the geological environment.It is more common to assess permeable geological environment as a suitable foundation engineering soil . ImportaThis type of classification is suitable for all types of structures, where permeable soils represent appropriate load-bearing foundation soil because of more fitting physical-mechanical properties in terms of load-bearing capacity. It corresponds to the fact that permeable soils have better capacity to drain water and thus are subjected to fewer changes in the volume, which brings more positive connections in load-bearing capacity and settlement. On the contrary, low permeable fine-grained soils, which are the subject of the four case studies, represent more problematic foundation soil . In particular, problems are encountered if they are found in soft or slushy consistencies. The anthropogenic soil material of glass microbeads is a certain exception as it is more permeable and thus conditionally suitable.The second, less common approach is when impermeable engineering-geological environment is considered a suitable foundation soil for a given purpose. This way, the impermeable engineering-geological environment of fine-grained soils may be more suitable foundation soil. Therefore, sandy silt from Krakowiany, sandy silt from Graniczna, and fly ash are conditionally suitable foundation soils—see the yellow colour in Using this opposite approach , the appThe study points at the fact that the permeability of the engineering-geological environment perceived through permeability coefficient is an important scholarly topic with a number of boundary conditions that limit or enable its application in engineering geology, geotechnics, and foundation engineering.The results show that grain sphericity, angularity, and roughness in fine-grained soils are very important parameters of foundation soils affecting the permeability expressed by permeability coefficient. Clearly, if we have soils of analogous grain-size distribution but different grain shape and roughness, the permeability (permeability coefficient) increases along with a rise in the sphericity and concaveness and a fall in grain angularity and roughness. Also, along with a rise in angularity (lower sphericity and concaveness) and roughness, the permeability decreases, manifesting in lower permeability coefficient. The difference in the permeability coefficient between the most spherical (least angular and rough) grains of glass microbeads was four-fold in comparison with the most angular and roughest fly ash. Moreover, the difference increases further, along with higher compaction, which means that at higher compaction, the differences in grain shapes are even more important. When evaluating the effect of compaction on the permeability coefficient values and thus on permeability of the selected fine-grained soil materials, it may be stated that the effect of compaction was studied using four states of compaction . Using the laboratory method, we identified that there was a similar efficiency of compaction in the most angular and roughest FA, where the difference between the minimum and maximum compaction was 37%, which manifested itself in lower permeability coefficient values. Similar effects occurred in the most concave grains too (GM). In the two remaining materials, the difference between the minimum and maximum compaction states was 41% (SK) and 49% (SG). Using the modified microscopic method, we found that the efficiency of compaction increased gradually  along with changes in the grain shape and roughness from the least angular and least rough to the most angular and roughest shapes, which was demonstrated in a gradual decrease of the permeability coefficient and permeability.Another assessment criterion in the study was the choice of methods in determining the permeability coefficient in selected fine-grained soils with analogous grain-size distributions. FHT was adopted as the reference method, because in this laboratory test the permeability coefficient was determined based on water flow through the soil. Using FHT, the permeability coefficient values reflected the differences in the soil material grain shape as well as states of compactness expressed as density index. Comparable results were achieved using the modified Kozlowski method, while using the four remaining approaches we observe a number of drawbacks described above.Another aspect of the study assessment was to point at the differences in the permeability coefficient values on particular classifications of engineering-geological environment permeability when determining the suitability or unsuitability of rock/soil massive for a particular purpose. The results show that the influence is clear and it is vital to approach the choice of permeability coefficient assessment methods in a sensitive manner and take into account also the grain shape and roughness. Also, it is important to consider effective porosity and density index. This is because all these boundary conditions have specific influence on the purpose classification systems of engineering-geological environment permeability used in engineering geology, geotechnics, and foundation engineering. The results prove that all the boundary conditions may manifest themselves in altered classification category of suitable, conditionally suitable or unsuitable engineering-geological environment for a particular purpose. This may have negative consequences in foundation engineering as these purpose classifications are important tools in foundation engineering processes. The study proved that certain permeability of engineering-geological environment may constitute a suitable engineering-geological environment for one purpose, but conditionally suitable or unsuitable for another.


Glaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test’s innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field. RetiNerveNet uses information from the more objective Spectral-Domain Optical Coherence Tomography (SDOCT). RetiNerveNet attempts to trace-back the arcuate convergence of the retinal nerve fibers, starting from the Retinal Nerve Fiber Layer (RNFL) thickness around the optic disc, to estimate individual age-corrected 24-2 SAP values. Recursive passes through the proposed network sequentially yield estimates of the visual locations progressively farther from the optic disc. While all the methods used for our experiments exhibit lower performance for the advanced disease group (possibly due to the “floor effect” for the SDOCT test), the proposed network is observed to be more accurate than all the baselines for estimating the individual visual field values. We further augment the proposed network to additionally predict the SAP Mean Deviation values and also facilitate the assignment of higher weightage to the underrepresented groups in the data. We then study the resulting performance trade-offs of the RetiNerveNet on the early, moderate and severe disease groups. Defined as a chronic and progressive optic neuropathy, it is characterized by the progressive loss of retinal ganglion cells, resulting in characteristic changes in the optic disc and consequent visual loss2. Although irreversible, adequate treatment can slow or even prevent further damage caused by the disease3. This highlights the need for early diagnosis and accurate prediction of glaucoma progression. In clinical practice, investigation and monitoring of patients with glaucoma involves evaluation of the visual field using Standard Automated Perimetry (SAP), and evaluation of the optic disc and Retinal Nerve Fiber Layer (RNFL) using Optical Coherence Tomography (OCT).Glaucoma is the leading cause of irreversible blindness in the world, and it is estimated that over 70 million people are affected by it4. It is most commonly performed using the 24-2 Swedish Interactive Threshold Algorithm5 of the Humphrey perimeter , by presenting luminous stimuli with varying intensities at 54 different locations of the visual field and recording the patient’s response to the stimuli. The patient is typically required to press a switch each time they are able to see a presented stimulus. The dimmest stimulus which is detected at least 50% of the time by the patient, in each visual field location, is recorded using a logarithmic scale, where higher values represent lower brightness, and therefore better results. Although SAP is the mainstay strategy for visual field testing, there are issues associated with the testing strategy. Due to the interactive nature of the test, there is typically a learning curve for the patient to comprehend the test instructions. Therefore, for many patients, the first few tests cannot be relied on. Further, a high level of attention is demanded of the patient during the entire duration of the test. Fatigue and lack of attention often leads to artifacts and to unreliable results. Finally, even for well-trained patients, there is high test-retest variability associated with SAP, which can preclude or delay the diagnosis or detection of disease progression7.Currently, SAP is the most frequently used method for detecting visual loss due to glaucoma8.With the advent of a family of better ocular imaging technologies, namely the Spectral-Domain OCT (SDOCT), it has become possible to image the retina with near-histologic definition, with an axial resolution of a few micrometers. Using the SDOCT peripapillary scan, it is possible to measure the thickness of the RNFL at evenly spaced points on a circle centered around the opening of the optic disc. In contrast to SAP, SDOCT is fast, highly reproducible, and does not require any response from the patient. However, while SDOCT is capable of accurately measuring structural changes in the retina and the optic disc, it is not a direct assessment of visual function. Moreover, the SDOCT also suffers from the well known “floor effect”, where extreme structural damage beyond a certain point is no longer detectable, possibly due to the presence of residual tissues and/or due to the failure of segmentation methods11. Such estimates can potentially be used as proxies for visual function, particularly for patients who are incapable of undergoing SAP. Deep learning techniques, Convolutional Neural Networks (CNNs) in particular12, are capable of using spatial information to identify underlying relationships that may not be easily discerned by conventional methods. Some existing studies14, have used such deep learning techniques to estimate SAP summary metrics like Mean Deviation  using information acquired with SDOCT. Other works17, more closely related to our own, have attempted to estimate pointwise sensitivities for all the locations tested by SAP, based on the SDOCT thickness values. Some studies19 have even attempted to predict the SAP sensitivities based on the raw images obtained from SDOCT. However, these previous investigations have not made use of the known topographic characteristics of the RNFL when attempting to estimate SAP data20.Previous works have shown that it is indeed possible to draw conclusions about the visual function based on the structural information acquired with SDOCT22. Since our aim is to estimate visual function at different points on the retina based on the RNFL thicknesses around the optic disc, we must essentially trace-back the path along which the retinal axon fibers converge to the optic disc. We hypothesize that this approach will allow us to relate the amount of nerve tissue around the optic disc (represented by the RNFL thickness) to the appropriate SAP locations likely to be affected by it, thus improving the estimates. To this end, we propose RetiNerveNet (a.k.a. RetiNN), a deep fully convolutional neural network architecture that is inspired by this structure of retinal axon fibers. To the best of our knowledge, RetiNerveNet is the first method that incorporates elements of the structure of the retinal ganglion axon fibers for the task of estimating all the individual visual field values based on SDOCT information. Additionally, we attempt to further augment the RetiNerveNet by training it to simultaneously predict the individual age-corrected SAP values  as well as the MD values accurately and also by assigning higher weightage to the underrepresented parts of the data . We also study the trade-off on performance among the early, moderate and severe disease groups, resulting from tuning the weights for the two outcomes (TD and MD) as well as those for the underrepresented groups in the data.The RNFL is comprised of the axons of the retinal ganglion cells which are responsible for carrying visual stimuli to the optic nerve. The ganglion cells converge to the optic disc in a characteristic arcuate pattern, deviating radially from the fovea, without crossing into the opposite half of the retina see Fig. , eventuaThe proposed RetiNerveNet is a deep fully convolutional neural network consisting of two similarly structured and separate sub-networks modeling the inferior and superior hemiretinae. Each sub-network, in turn, consists of four main blocks. The first convolutional block is meant to extract coarse structural information from the fine-grained RNFL thickness values. The second block is a Recursive Progression Layer  visual field values are modeled as linear functions of all the inferior (superior) RNFL thickness values, having 19,968 learnable parameters. We also compare against a fully connected neural network model in which both the superior and inferior sub-networks consist of two hidden layers with 32 nodes each and ReLU activations24. An inspection of Table We compare the performance of the contending methods to reconstruct the SAP visual field values based on the SDOCT RNFL thickness values. The results are presented in Table As there is imbalance in the number of tests across MD values, naïvely training the RetiNerveNet results in uneven performance across the early (or no) disease, moderate disease, and advanced disease groups. Moreover, the basic RetiNerveNet also does not have a way to control how accurately the model predicts the individual TD values, as opposed to the overall MD values. Therefore, to explore the trade-offs among the groups as well as that between the TD and MD values, we make two modifications to the basic RetiNerveNet architecture. Firstly, we incorporate an additional output which simultaneously predicts the MD value alongside the age-corrected SAP outcomes. Further, we divide the data into 4 intervals based on the MD values, viz. (MD > − 6 dB), (− 6 dB ≥ MD > − 16 dB), (− 16 dB ≥ MD > − 26 dB), and (− 26 dB > MD), as shown in Fig. 20. The MAEs for estimating the sectoral average deviations and the MD values for all the models are presented in Table While RetiNerveNet is trained to predict the individual age-corrected SAP values , we also investigate the ability of RetiNerveNet to estimate the sectoral average deviations, which are averages of the age-corrected SAP values for the different sectors of the visual field14 in Table We also compare the R18, we postulate that building our network to mimic the arcuate structure of the axons of the retinal ganglion cells can help improve performance for this task. The fact that the proposed architecture performs better than a number of baselines in Table 25. Moreover, comparison with the results reported by Christopher et al.14 shows that our results are characterized by more uniform performance on all the sectors of the visual field. The results from the former article seem to imply that rich modalities like an enface image or an RNFL thickness map are required to achieve good performance on this task. Our results, on the contrary, show that much of the information needed to estimate visual function may be extracted from the RFNL thickness vector, using a more sophisticated model like RetiNerveNet. However, it is important to acknowledge that the proposed techniques  exhibit high MAE for the advanced disease group. While the high variability of SAP thresholds for the advanced disease group27 may be a possible reason behind this, the nature of the error indicates that it is more likely to be due to the bias in measurements owing to the floor effect for the SDOCT test8.We propose RetiNerveNet, a deep fully convolutional neural architecture for obtaining estimates of SAP visual field values based on RNFL thickness values obtained from the more objective SDOCT tests. Unlike existing works with similar aim28 may also have affected our results. Additionally, the floor effect for the SDOCT test results in loss of information  which cannot be recovered by deep learning methods. Finally, properties of the dataset we worked with may have influenced our design choices. For example, predicting the TD values instead of the SAP thresholds was found to be more effective. However, this may be specific to our dataset as a previous study9 has reported much lower errors on a different dataset, while predicting the SAP sensitivity thresholds. An interesting future direction of research may be to investigate how our proposed architecture may perform in conjunction with segmentation-free RNFL thickness extraction systems based on deep learning28.Since we undertake supervised deep learning in this study, the main limitations of our work are that the predictions from our models can only be as good as visual field estimates obtained from SAP, which are known to be noisy. Moreover, as the RNFL thickness values were obtained from the SDOCT scans using the conventional SDOCT software, errors inherent to that process29. We excluded patients who underwent procedures such as panretinal photocoagulation or suffered from other diseases like retinal detachment, optic neuritis, proliferative diabetic retinopathy, etc. that could impact the RNFL thickness measurements from SDOCT, or could impact the SAP visual fields. Patients younger than 18 years were also excluded. This study was approved and informed consent was waived (due to the retrospective nature of the work) by the Duke University Institutional Review Board. All methods adhered to the tenets of the Declaration of Helsinki for research involving human participants, and the study was in accordance with the regulations of the Health Insurance Portability and Accountability Act.Our retrospective study is based on cross-sectional data about paired SDOCT and SAP tests, from the Duke Glaucoma Registry, a database of electronic medical and research records at the Vision, Imaging, and Performance Laboratory at Duke University. Healthy individuals as well as patients having or suspected of having glaucoma were included in the study. Diagnosis of glaucoma or suspect was based on International Classification of Diseases (ICD) codes5). We excluded unreliable tests with more than 33% fixation losses or more than 15% false-positive errors. Out of the 54 visual field values obtained using the 24-2, the two values corresponding to points around the blind spot were removed, resulting in 52 sensitivity threshold values being obtained for each SAP test. The locations in the superior hemiretina are responsible for inferior vision (and vice versa). The visual fields were also corrected for age to obtain the TD values, which were to be predicted, for the 52 locations. We choose to predict the TD values instead of directly predicting the SAP thresholds as we observed the former approach to yield slightly better results in our initial experiments.The SAP visual field tests were performed using the 24-2 Swedish Interactive Threshold Algorithm (SITA) protocol . RNFL thickness values at 768 evenly spaced points on a circle having a diameter of 3.45 mm, positioned around the center of Bruch’s membrane opening (the opening of the optic disc), was obtained directly form the SDOCT software. Tests having a quality score lower than 15 were excluded, according to the manufacturer recommendations.22. Our task is the regression problem of estimating the 52 visual field values (excluding the 2 points around the blind spot) from the SAP test as a function of 768 RNFL thickness values obtained from the SDOCT test. To be able to do this, we must essentially trace-back the path along which the retinal axon fibers converge to the optic disc , are effective for distilling coarse information from finer details while preserving spacial correlation12. Hence, we refrain from using fully connected layers  and instead constrain RetiNerveNet to entirely consist of 1D convolutional layers . SAP measures the visual function at 52 locations on the retina, with the 26 locations in the superior hemiretina being responsible for inferior vision (and vice versa) due to lateral inversion in the eye. SDOCT, on the other hand, scans the nerve tissue along a circle around the optic disc.Within each hemiretina, the axons converge to the optic disc in a characteristic arcuate pattern. As can be seen from Fig. 31. Therefore, in order to ease the training of our proposed RetiNerveNet, we incorporate convolutional skip connections with appropriate filter size and strides in the first and third blocks of RetiNerveNet. It is important to notice that the first , third, and fourth convolutional blocks are not responsible for modeling any spatial movement. Spatial movement along the retina is meant to be modeled solely by the RPL.The structure of the inferior sub-network is further detailed in Fig. t-th pass through the RPL.We trace-back the arcuate convergence of the axons fibers as multiple recursive passes through a single convolutional layer (with linear activation). In other words, the first pass through the RPL is meant to map from the SDOCT circle to the region of the retina corresponding to the first 5 visual field points nearest to the blind spot. Thereafter, farther movement away from the optic disc is modeled as subsequent passes through the same convolutional layer. To be precise, we implicitly require that the additional increment in the curvature of the axons  be captured by applying the same transformation to the output obtained after the previous pass. Thus, the second recursive pass is meant to map  to the region corresponding to the next 4 visual field points closest to the optic disc, and so on see Fig. . Due to While the output after the four blocks of the superior (or inferior) sub-network is always 5-dimensional, only the first 5, 4, 4, 4, 4, 4, and 1 dimensions of the output for the respective passes are deemed to be points in the visual field, as the rest are deemed to have progressed outside the region tested by SAP. Therefore, after 7 recursive passes through the RPL (and the subsequent part of the sub-network), the entire inferior (or superior) half of the visual field (consisting of 26 points) is estimated.N is the number of training SDOCT-SAP pairs, i-th SDOCT-SAP pair, j-th SAP location, j-th SAP location for the i-th SDOCT-SAP pair, t-th recursive pass  is calculated on estimated individual visual field points, while the second loss is calculated on the estimated MD value. The loss on the estimated visual field values ismentclass2pt{minimThe RetiNerveNet has three tunable parameters


In this paper, the investigation of laser-induced periodic surface structures (LIPSSs) on a polycrystalline diamond substrate using synthetic optical holography (SOH) is demonstrated. While many techniques for LIPSS detection operate with sample contact and/or require preparation or processing of the sample, this novel technique operates entirely non-invasively without any processing of or contact with the LIPSS sample at all. The setup provides holographic amplitude and phase images of the investigated sample with confocally enhanced and diffraction-limited lateral resolution, as well as three-dimensional surface topography images of the periodic structures via phase reconstruction with one single-layer scan only. Laser-induced periodic surface structures (LIPSSs) are periodic patterns formed on surfaces of different materials  ,3,17 andIn order to enable a non-destructive investigation, all-optical methods are used to visualize LIPSS patterns, such as white-light interferometry (WLI)  or confoIn this paper, we present a new all-optical, non-invasive method for three-dimensional visualization of LIPSS patterns exploiting the benefits of digital holographic microscopy (DHM). Our approach to investigate LIPSSs is based on the principle of synthetic optical holography (SOH)  and combThis technique operates without any mechanical contact with the sample; does not require any preparation, handling or treatment; and entirely preserves the sample in its original condition. It provides amplitude images based on the distribution of back-reflected intensities with a diffraction-limited lateral resolution of about 600 nm for an illumination wavelength of 1064 nm. In contrast to other techniques typically used for imaging LIPSSs, this technique additionally provides refractive index- and height-sensitive phase images of the sample, from which 3D topographic surface images can be extracted. Last but not least, the technical and financial effort is lower than that of the other mentioned techniques typically used for imaging LIPSSs. This makes this technique capable of cost-effective investigation of laser-induced periodic surface structures, while fully preserving the sample in its original condition.In our experiments, LIPSSs were generated on a polycrystalline diamond plate. The laser pulses , with The laser processing parameters were chosen as follows: the pulse fluence was −15cm2/W , while t2≈0.45MW , so thatNikon Eclipse LV100 microscope  ,26,27,28The other part of the beam, used as reference beam, was directed onto a silver mirror that was placed on a piezo translation stage PZM (Newport NPXYZ100SG). While scanning the sample, the reference mirror was shifted step-wise along the optical axis. The reflections of both beams from the reference and sample arms were coupled into a At the photodiode, the interference signal was detected and mapped with the sample position known from the scanning stages by the controller (Newport XPS Q8). Although the reference wave has a constant phase for each detected point (pixel), its phase shifting during the scan creates a virtual tilt of the reference plane in the overall image. This virtual tilt between reference and object creates the off-axis behavior , althougAfter the interference of the virtually tilted reference beam and the object beam from the sample was captured by photodetector PD, it was amplified with a transimpedance amplifier and converted by the analog-digital converter of the controller. Due to the confocal imaging approach, the measured intensities mapped with the stage positions resulted in an off-axis holographic image containing the scanned sample image superimposed with a holographic interference pattern, from which the three-dimensional wavefront of the sample’s reflection could be numerically reconstructed. Consequently, numerical propagation was not mandatory for the image reconstruction.This numerical reconstruction was performed using the angular spectrum method , spectrae sample ,36.(1)ΔWhile lections , the remariation . In casegorithms , which rThe characterization of the setup was performed with a custom test target containing an approximately 120 nm-thick layer of titanium structures on a silicon surface fabricated by electron-beam lithography and evaporation. The Ti structures were grouped in different sizes in sub-micrometer range. The groups contained dots and bars, with the group name indicating the dot width, line width, and distance between the structures. The holographic investigation of the test target with our synthetic optical holographic microscope is shown in The calculated diffraction limit of the system, according to the Rayleigh criterion together with the enhancement factor f 611 nm . The praThe phase map of the investigated test target provides a topographic surface plot with the three-dimensional shape of the test target structures. The lateral dimensions agree well with the known sample information. In order to derive the height quations , this phAdditional spikes and exaggerations in the phase values occurred during numerical reconstruction at sharp edges, as only limited spatial frequencies were taken into account during reconstruction. Their influence on the resulting image is the higher the smaller the imaged structures are. This is particularly notable in the 600 nm structures (red line), which are slightly below the diffraction limit where the modulation depth is also limited, which causes uncertainties when imaging strong edges over small areas. In contrast, the adjacent 700 nm structures appear smoother and more accurate. Other factors influencing the deviation of the measured phase height versus the physical structure height could be related to phase noise from the environment and the measurement system itself. Nevertheless, the lateral and axial accuracies were adequate to investigate structures with dimensions close to the system’s diffraction limit. The measured values agree with the known sample information and provide a clear three-dimensional visualization of the sample.The LIPSS sample was investigated with our synthetic optical holographic microscope, showing the parallel periodic surface structures of the sample. A continuous-line scan along the X-axis while the controller’s Analog-Digital-Converter (ADC) recorded the photodetector (PD) signal with a frequency of The scanned holographic image was numerically reconstructed using the aforementioned algorithms, resulting in amplitude and phase images, as shown in By taking the red horizontal line at position de image a, the liThe measurements of the test target demonstrates that our synthetic optical holographic microscope was capable of imaging structures down to the confocally enhanced diffraction limit of the system with high lateral and axial accuracy. Its phase imaging capabilities were tested and shown for structures of a 120 nm height. Generally, phase structures in the range of the wavelength  can be investigated due to the The investigation of the presented LIPSS sample, performed with the SOH microscope, shows the system’s capability to three-dimensionally image laser-induced periodic surface structures. The lateral dimensions of both amplitude and phase agree well with the expectations from sample fabrication as well as with the comparison measurements. The heights measured with the holographic setup vary in respect to the heights measured with the AFM, although they were both in the same order of magnitude. Generally, holographic setups such as our SOH are sensitive to both height variations and changes in the complex refractive index in the studied materials. On the one hand, this can make the data interpretation more complex, but on the other hand, it can provide additional information about the changes in the chemical composition of the sample due to e.g., laser-induced chemical reactions or changes in the oxygen concentration. However, in our case, the difference between the holographically measured phase and the structure height measured with the AFM is most probably due to the different sample region with different LIPSS orientation caused by the limited availability of sample access and comparison AFM data. Furthermore, it is expected that the sample itself shows height variations, introduced during the fabrication process. The resolution of the system might also be a limiting factor for the quantitative height measurement, since the investigated LIPSS patterns with sizes in the range 830–1010 nm are close to the resolution of the imaging system with a resolution limit of 611 nm. Although the system is proven to be capable of imaging the given structures with a high lateral resolution as well as accurate phase sensitivity, the height detection of structures close to the resolution limit is also limited by the modulation depth. This boundary of the modulation transfer function could restrain the investigable heights over small lateral dimensions. However, this explains the height differences in the periodic patterns, but not in the mean depth of the overall structure valley.In addressing this issue, another phenomenon might play a significant role in variation in height. The processing of polycrystalline diamond substrates using laser pulses may create graphitic layers on the surface of the sample in the order of hundreds of nanometers, as has been reported in , as well65 nm in . It is n65 nm in  using SO65 nm in .Though the investigation of these issues that are outside the scope of this paper should be further analyzed, it can be emphasized that quantitative lateral measurement, as well as surface topography measurement, can be performed well with the synthetic optical holographic microscope. The reconstructed amplitude and phase images can be used to three-dimensionally image samples and derive the shape and periodicity of LIPSSs.In summary, it is here shown that three-dimensional investigation of laser-induced periodic surface structures on a polycrystalline diamond substrate can be performed with a synthetic optical holographic microscope with a diffraction-limited lateral resolution of


The development and use of intelligent drilling rigs make it available to obtain accurate lithology data of blast drilling. In order to make full use of drilling data to improve blasting efficiency, the following research was carried out. First, a database is established to manage and store the blast hole data recognized by the intelligent drill. Secondly, the blast hole lithology data is taken as a sample, and the inverse distance square method is used to interpolate the blasting range's solid elements to generate a three-dimensional solid model of the blasting rock mass. Afterward, the blasting range polygon and stope triangle grid are used successively in the solid model to obtain the cut 3D solid model of the blasting rock mass; finally, the blast hole charge is calculated based on the cut 3D solid model of the blasting rock. The C++ programming language is used to realize all the blast hole charge amount processes based on the three-dimensional solid model of the blasting rock mass. With the application example of No. 918 bench blasting of Shengli Open-pit Coal Mine in Xilinhot, Inner Mongolia, the blast hole charge amount in the blasting area is calculated and compared with the results of single hole rock property calculation, the results show that the blast hole charge calculated by three-dimensional rock mass model can be effectively reduced. Sahoo et al. established and proposed the traditional artificial neural network (ANN-GDM) based on gradient descent momentum training and the traditional genetic algorithm (GA) based on the genetic algorithm ANN artificial neural network (ANN-GA) method2. Santos et al. evaluated the advantages of methods based on Neural Network Classifier Ensembles—sets of neural networks working in a cooperative way to achieve a consensus decision- in the solution of the lithology recognition problem3. Shao et al. proposed that the BP neural network algorithm has the characteristics of high identification accuracy and fast identification speed, which is suitable for identifying geological research such as petrology, deposit prediction, rock and mineral identification, etc.4. Zhang et al. proposed a lithology identification method for the volcanic reservoir, which deals with the log data by combining the principal components analysis and the SOM neural network method to effectively improve neural network performance5.An intelligent drilling rig can accurately obtain the lithology data of the blasting area. The urgent problem that needs to be researched, developed, and solved is how to make full use of the lithology data of the blasting area, accurately calculate the volume of various rock masses, and innovate the calculation method of the blast hole charge to improve the blasting effect and reduce the blasting cost. Currently, many scholars have done much research on intelligent lithology recognition. Guohe Li et al. proposed a new lithology identification method, which uses multiple sampling points of geological data as input. From the comparison of the experiment, it can be seen that the SGAN-G algorithm can effectively use unlabeled data to achieve higher lithology prediction accuracy6. Song et al. created a semi-automatic 3D modeling and visualizing method for complex geological bodies by combining typical GIS systems with 3D modeling software such as ArcGIS and SketchUp7. Liu introduced the concept of “correlation distance” to analyze the correlations between geological borehole elevation values and calculated the correlation distances for each stratum elevation8. Jia et al. proposed an improved anisotropy-based, multiscale interpolation method applied to effectively model coal seam surface9. Che proposed a Three-Dimensional geological modeling of coal seams using the weighted Kriging method and multi-source data10. In order to bridge the currently existing gap between AutoCAD solid models and the grid modeling realm, Marschallinger established the Visual LISP program that converts AutoCAD solid models into voxel arrays11. However, due to the immature development of intelligent drilling rigs, no expert has been engaged in 3D solid modeling of blasting rock mass.Many scholars have conducted much research on 3D solid modeling. Sun presents a fast self-adaptive and improved inverse distance-weighted interpolation method depending on the borehole sampling data features12. Wang took Paishanlou gold mine construction in Liaoning as an example, carried out analysis and calculation of determining methods and practice data of various deep-hole blasting parameters combined with general engineering conditions such as resistance line of the undercarriage, pitch, space between rows, ultradeep value of drilling, and charge quantity of blast hole13. Adhikari et al. summarized the experience and methods for calculating the specific charge for open-pit blast design and found that the desired degree of fragmentation, distribution, transfer, and utilisation of explosive energy greatly influences the specific charge14. Shim estimated the rock factor from geologic data, and a sequential indicator simulation technique predicted its 3-D spatial distribution. The entire quarry in question was classified into three types of rock mass, and an optimum blasting pattern was proposed for each type based on the 3-D spatial distribution of the rock factor. It can be concluded that it is possible to design a blasting pattern to achieve a minimum production cost in large-scale quarrying operations by predicting rock fragmentation based on the 3-D spatial distribution of the rock factor15. At present, most of the commonly used methods for calculating blast hole charge are based on the lithology data or experience of exploration boreholes to obtain lithology distribution. Due to the high distribution density of exploration boreholes, the lithology distribution in the blasting area is inaccurate, which often causes excellent deviations to the blasting design. The way to calculate the blast hole charge based on the 3D solid model of the blasting rock mass is to make full use of the lithology data of the blasted rock mass to establish an accurate 3D solid model of the blasted rock mass and calculate the blast hole charge based on the accurate 3D solid model, improving the blasting effect and reducing the blasting cost.In order to reduce the cost of blasting while improving the blasting effect, many scholars and engineers have done plenty of research on the auxiliary calculation method of blast hole charge. Li accorded to the equivalent resistance theory of rock breaking for the columnar charge, the Livingston theory of blasting crater and the line-loaded density was applied to deduce the theoretic calculation formula of blast hole depth in the progressive spiral cutThis paper consists of four parts: the first part mainly introduces lithology identification and the establishment of blast hole database, the second part mainly introduces the calculation method of blast hole charge amount, and the third part mainly presents the calculation method based on the three-dimensional solid model of blasting rock mass. Finally, the calculation method of charge quantity is applied in the Xilinhot Shengli Open-pit Coal Mine in Inner Mongolia Autonomous Region, China, as an example.The mine rock blast hole data collected from the intelligent drilling rig is stored in files, and a database is established for storage and further management and application of blast hole data.The first seven lines of the data collected from the smart rig record the drilling number, rig status, rig number, boot time, longitude, latitude and elevation, respectively. Lines 8 to 16 are records of a section of rock column. These data include drilling number, drilling depth, rotation speed, rotation pressure difference, pressurization pressure 1, pressurization pressure 2, drilling speed, wind pressure, and identified lithology. Subsequently, this record data will be circulated for each rock pillar until the blast hole is completed. The data listed in Table Use Microsoft's Access to build a blast hole database and establish three data tables in the database: blast hole table (hole), blast hole data table (data), and lithology table (rock) used to store data collected from intelligent drilling rigs. The relationship between these three data tables is shown in Fig. When extracting the blast hole data from the data files into the blast hole database, it is necessary to convert the blast hole coordinates expressed by the longitude and latitude of the blast hole into x, y coordinates. The obtained blast hole table, blast hole data table and lithology table are shown in Tables 18. In open-pit mining, blasting operation is one of the essential business processes of open-pit mining technology23, which is related to the production capacity and economic benefits of open-pit mining. Therefore, in the production blasting design, it is necessary to be as accurate and efficient as possible to achieve a better blasting effect to meet the requirements of high-efficiency production on site. Therefore, the calculation of the blast hole charge amount is fundamental, and the conventional formula for the calculation of the blasting design is as follows24:Q is the blast hole charge amount, kg; q is the powder factor, kg/m3; a is the spacing, m; w is the burden, m; h is the bench height, m.Nowadays, rock blasting has been widely used in civil, water conservancy, hydropower, transportation, and other construction engineering fieldsq is required to be very strict. This is very difficult for companies that do not have long-term production experience data as parameters. The improper value of the unit consumption of explosives q often results in inaccurate calculation of charge amount, which leads to insufficient charge or residual charge during blasting, affecting the efficiency of blasting.From the above calculation formula of blasting charge amount, to make the calculated total blasting charge amount close to the actual charge amount required for high-efficiency blasting, the selection of unit explosive consumption 32. The inverse distance square interpolation method has good universality, and it is available in the case of missing strata and extremely uneven borehole distribution, and the interpolation error is relatively small, so the inverse distance square interpolation method is adopted33. The inverse distance square method is a kind of interpolation method related to spatial distance. When calculating the value of interpolation points, according to the principle that the closer the distance is, the greater the weight value is, the linear weighting of several adjacent points is used to fit the value of estimated points36. The calculation formula is37:g—estimated value; gi—the ith sample; di—distance; p—power of distance, its value significantly affects the result of estimated value.Today, many scholars are devoting themselves to 3D solid modelingn is the total number of solid units. Taking the blast hole lithology distribution data as a sample, use the inverse square distance method to perform lithological interpolation on each solid unit in the solid unit set The blasting area is divided into many solid units according to the given solid unit's length, width, and height, obtaining the solid element set ith triangle, l is the total number of triangles of the blasting range polygon; the blasting area triangle set The triangle grid is generated via the blasting range polygon, and the blasting range triangle set 40 to get the triangle set Triangulate the step line of the stopeF is the total number of blast hole positions in the polygon of the blasting area, h-th divided triangle, H is the total number of triangles obtained by triangulating the blast hole position point and the polygon vertices of the blasting area.Triangulate the blast hole position point set If the three vertices of the triangle connecting the blast hole are all blast hole points, the center point of the triangle is considered as the vertex of the blast hole influence polygon;If only two of the three vertices of the triangle connecting the blast hole are blast hole points, then the midpoint of the two blast holes is taken as the vertex of the blast hole influence polygon;If only one of the three vertices of the triangle connecting the blast hole is a blast hole point, then the other two non-blast hole points of the triangle are taken as the vertices of the blast hole influence polygon.For each blast hole, from the blast hole triangle set J is taken as an example to explain the method of obtaining the influence polygon of the blast hole. The points connected to the blast hole J are A, B, C, D and E. Among them, A, B and C are blast hole points, while D and E are non-blast hole points. The triangles connected by these vertices are ∆ABJ, ∆JBC, ∆JCD, ∆JAE and ∆JDE. The three vertices of ∆ABJ and ∆JBC are all blast hole points, so take the center points G and H of ∆ABJ and ∆JBC as the vertices of the blast hole influence polygon; as only two vertices of ∆JCD are blast hole points, the midpoint I of the blast hole connecting line JC is used as the vertex of the blast hole influence polygon. Similarly, take the midpoint F of the two blast hole connecting line JA of ∆JAE as the vertex of the blast hole influence polygon; ∆JDE has only one vertex as the blast hole point, so take the other two non-blast hole points D and E of the triangle as the vertices of the blast hole influence polygon. The blast hole influence polygon's vertices are connected to form the influence polygon of blast hole J, and its vertex set is Connect these vertices to form a blast hole influence polygon. As shown in Fig. m-th blast hole is intersected with the 3D solid model E of the rock mass in the blasting area after cutting, and the influencing entity set mth blast hole is obtained, where y-th influencing entity unit of the m-th blast hole, r is the total number of influencing entity units of the m-th blast hole.The intersection of each blast hole influence polygon and the three-dimensional solid model of rock mass in the cut blasting area is used to obtain the influence solid element of each blast hole. Suppose the influence polygon According to the unit explosive consumption m-th blast hole, Among them, Based on the lithology distribution data of the blasting area in Shengli open-pit coal mine, Xilinhot, Inner Mongolia Autonomous Region, China , and each cube entity is used as the element of the 3D solid model. The lithology data of the blast hole is taken as a sample. The inverse distance-square method is used to interpolate the lithology data of each cube element. Before interpolating, the interpolated holes should be determined according to the position of the elements, which can be divided into two steps: firstly, find all the blast holes in the range according to the searching range of 40 m; secondly, determine interpolated blast holes among blast holes obtained in the first step according to the distance from the element position from near to far and the size of the shielding angle. The inverse distance square method is used to finally complete the generation of the 3D solid model, including a total of 51,975 entities, as shown in Fig. Take the blasting range polygon to generate the blasting range grid Fig. a, and thTriangulation of the grid is operated on the bench line and measuring points of the stope to establish the triangular grid of the stope surface Fig. a. Cut thThere are 165 effective blast holes in this example. According to the method in 3.2, the data formed by the effective blast hole orifice position and blasting range polygon are triangulated to obtain the triangular grid of blasting range, with a total of 390 triangular surfaces, as shown in Fig. The intersection of the influence polygon of each blast hole and the 3D solid of rock mass in the blasting area is used to obtain the influence rock column solid of each blast hole, as shown in Fig. When calculating the explosive quantity required for each solid blasting, according to formula , the exp3, thus, the final charge amount of ZK2032 is 214.275 kg.Take the borehole ZK2032 as an example to calculate the charge amount of the borehole. The charge amount is calculated according to the volume of each entity unit and the unit explosive consumption required by different lithology. The volume and lithology of entity units of borehole ZK2032 are shown in Table By calculating the explosive quantity required by each affected entity unit in each hole's three-dimensional rock mass model, the explosive quantity required for each hole is obtained, as shown in Table Access database operation.The above establishment application example of a three-dimensional solid model of blasting rock mass is realized by Visual C++ 2012 in detail as follows:2.Establishment and visualization of 3D solid model of blasting rock mass.ADO mode is used to connect access database to realize the operation of blast hole database.The C++ programming is used to realize the lithologic interpolation of the cube primitive, and the blasting range polygon and stope surface triangle grid are used successively to cut the blasting rock mass into a three-dimensional solid model. AutoCAD 2016 is redeveloped based on ObjectARX 2016, and the AcDbSolid class is used to visualize the 3D solid model of blasting rock mass.With the use of the 3D solid model of blasting rock mass in calculating the charge quantity of the blast hole, the lithology of 3D solid can be adopted to calculate a more accurate charge quantity of the blast hole. In this way, the accuracy of charge quantity of blast hole and the blasting effect is improved.At present, the lithology of open-pit perforation blasting is mainly obtained based on exploration boreholes. Due to the extensive distribution density and long spacing of exploration boreholes, the lithology of blasting rock mass area will not be obtained accurately. Single-hole lithology is usually used to calculate the blast-hole charge.By comparing the blast hole charge calculated by single hole petrology with that calculated by the blast hole three-dimensional rock mass model, the results shown in Table Establish a blast hole database to store and manage intelligent lithology identification data;Take the blast hole lithology data as a sample, adopt the inverse square distance method to interpolate the solid elements within the blasting area, and then use the blasting range polygon and stope triangle grid to cut out the three-dimensional solid model of the blasting rock mass;According to the established three-dimensional solid model, through the calculation method of blast hole charge in this paper, the blast hole influence range and blast hole influence solid elements are obtained, and the blast hole charge is calculated. The charge result is compared with the charge calculated by single hole method, and the cost is reduced by 4.59%;Based on the rock recognition data of the intelligent drilling rig, the calculation and application of the blast hole charge volume based on the three-dimensional solid model of the blasting rock mass are realized, and the conclusions are as follows:All the processes of calculating the blast hole charge quantity based on the 3D solid model of the blasting rock mass are realized via C++ programming. The calculation of the actual mine blasting rock mass charge amount is achieved in the application example of Xilinhot Shengli Open-pit Coal Mine in Inner Mongolia, indicating that it is helpful for the enterprise in improving blasting efficiency and reducing blasting production costs. This paper only studies the calculation of blast-hole charge. In the next step, the three-dimensional solid model of blasting rock mass can be used to optimize the design of other blasting parameters.


In order to explore the internal connection between information sharing and investment performance in the venture capital network community, this study took environmental-governance start-ups as the research object and used the 2009–2020 environmental-social enterprise start-up venture capital investment events as a data sample. The successful exit rate of the venture capital portfolio and the successful listing rate of investment ventures were used as the measurement criteria. Combined with regression analysis, the relationship between information sharing and investment performance in the venture capital network community was analyzed in detail. Research shows that there are differences between the ways of information sharing in the venture capital network communities. In the regression results, all coefficients are less than 0.01. There is a positive correlation between information sharing and investment performance in the venture capital network community. With the increase in enterprise characteristic variables, the degree of enterprise risk information sharing is getting higher and higher. This ultimately leads to more and more frequent corporate investment performance and a higher possibility of acquisition. Among them, the degree of information sharing in the venture capital network community is relatively high, and venture capital companies that are supported by corporate venture capital institutions will benefit even more from listed capital. Not only was the analysis of the relationship between finance and investment in the venture capital network community pointed out in this research, but also the investment development of entrepreneurial enterprises was also provided with feasible suggestions. Venture capital is a form of capital with high risk and high income. Venture capital mostly uses private equity funds to gain market recognition for related companies. Venture capital is often utilized by small- and medium-sized enterprises with high growth and high risk. These companies are short of funds for early development, but the technology and capabilities of these companies can often bring huge benefits to the capital market . This isInformation sharing refers to the use of information systems and related communication tools among different departments, industries, and enterprises in order to transfer social information, integrate social resources, save social costs, and create more value . VentureOn this basis, this study expects to clarify the relationship between information sharing and investment performance in the venture capital network community through the analysis of relevant enterprise data. This study takes environmental-social governance start-ups as the research object, puts forward reasonable assumptions with the help of relevant references, uses the network analysis to analyze in the dimension of network community research, and implements the relevant venture capital network model. The effectiveness of the model proposed here is further verified by data tests and empirical analysis. The network community is the sum of the social network analyses that are formed between different social subjects, which contain the content of correlation matrix and graph theory. Through the weight between different communities, the network community relationship of related enterprises is finally built. This study adopts the method of network community research, which involves the analysis of the relationship among social subjects, subjects, and actors in the network, and includes the resources and information network that is built between the subjects of the network community. Although society has a certain degree of stability, other people in the online community will also be subject to corresponding restrictions. This will reduce the communication of people in the online community and further reduce the exchange of information ,12.Degree centrality , eigenvei is removed from the network community.In (1), munities , this stSince the degree centrality is only a simple analysis index, and relevant scholars have proposed the eigenvector centrality index, this index analyzes the importance of other venture capital institutions that are connected with venture capital institutions in the network. By cooperating with other venture capital institutions that have a higher centrality, the centrality of the feature vectors of the institutions will also increase accordingly. The eigenvector centrality of venture capital institutions can be expressed as:j and k, and j and k through institution i. If venture capital needs to ensure a certain degree of real time, then it requires better network accessibility, which requires only a short time to obtain effective network information data and will involve the connection distance. The specific calculation is as follows:i and j in the network. If venture capital i and j are connected at the shortest distance through two “intermediary” institutions, the shortest step number between i and j is 3. If venture capital institutions i and j relate to each other due to joint investment behavior, the shortest step number of connecting institutions is 1.Many scholars have studied the relationship between the network community of venture capital institutions and the investment portfolio. Among them, Abdulla et al. 2021) implemented a structural equation model to explain the complex relationship between the social network and corporate performance by introducing the intermediary roles of trust, sales ability, and pricing ability. Then, using the empirical data of 380 SMEs in Indonesia, they found that the use of social media in the management process did not affect the growing corporate performance. Trusted social networks enable enterprises to obtain pricing and sales capabilities, which have a positive impact on corporate performance . Tatiana21 implemHypothesis 1 (H1).The network community of venture capital institutions is conducive to improving the successful exit rate of portfolio.Hypothesis 2 (H2).Venture capital institutions’ network community is conducive to the successful listing or acquisition.Many scholars have also reported the relationship between the degree of information sharing and the Initial Public Offering (IPO) of an enterprise. Among them, Hu et al. (2020), using the dynamic game model of real options, studied the relationship between pricing constraints and IPO timing complexity in the stock market, and found that IPO timing was a complex dynamic game in the stock market. The model showed that IPO pricing constraints reduced the exercise value of real options at the IPO timing, thus limiting the independent timing of enterprises and promoting early listing. The IPO price limit had a great influence on high-quality enterprises such as science and technology enterprises . Wu et aHypothesis 3a (H3a).The degree of information sharing of venture capital institutions is positively correlated with the first day earnings of IPO.Hypothesis 3b (H3b).The degree of information sharing of venture capital institutions is negatively correlated with the first day return of the IPO of start-ups.https://data.cvsource.com.cn/cn/login (accessed on 12 June 2015)). The database contains all venture capital transactions from 1989 to 2020, mainly involving investment institutions, investment information, investment amounts, transaction information, innovation and entrepreneurship information, and the overall evaluation level of the industry. Risk investment transaction events are accompanied by detailed data content [https://us.gtadata.com/ (accessed on 12 June 2015)) database [https://data.cvsource.com.cn/cn/login (accessed on 12 June 2015).). The research objects in Research data mainly comes from the CV database ; since many innovative enterprises will be liquidated due to a lack of experience and related management problems, such venture capital often needs higher investment returns. Therefore, many scholars have made the relevant measurements. Enrica et al. (2019) studied the relationship between external and internal factors and SPM adoption by using new data sets from 1864 emerging social enterprises around the world. The results showed that the adoption of venture capital performance in social enterprises was related to the successful exit rate of the portfolio . Sun et The main variables of the sample interval are exit, network centrality, other characteristics of venture capital institutions, entrepreneurial enterprise characteristics, venture capital market environment, and listed enterprise characteristics. The descriptive statistical results are shown in Panel A shows that the average successful exit rate of venture capital from 2000 to 2011 is 36.15%, over which time the IPO exit rate is 11.68% and M & A exit rate is 24.47%. In The impact of the venture capital social network on successful exit rate is shown in After controlling the market investment environment and other characteristics of venture capital institutions, the regression coefficients of the social network centrality index of venture capital institutions are significantly positive, so Hypothesis 1 is valid. The social network is beneficial to improve the value-added service ability of venture capital institutions, so it can play a positive role in the successful exit of entrepreneurial enterprises in the portfolio. Pandher (2021) pointed out that the exit of start-ups is related to the investment portfolio. On this basis, the social network has a positive effect on the value-added service capabilities of venture capital institutions . The impAccording to the results of In The results show that the Nofexit and Experience coefficients are significantly positive, indicating that the reputation of venture capital institutions helps to improve the network centrality. Compared with venture capital institutions with a Chinese background, venture capital institutions with foreign capital and Chinese-foreign joint ventures have richer investment experience and more professional investment skills. Thus, their social network centralities are significantly higher. The persistence test of venture capital institutions is shown in In The robustness test of the portfolio’s successful exit rate is shown in The robustness test is to test the robustness of the successful exit rate of the portfolio. Among them, In At present, in terms of the background of venture capital institutions, joint investment and launch environment, factors for the successful exit of Chinese venture capital institutions are being discussed. Taking the social network of venture capital institutions as a research perspective, this study systematically analyzes the impact of venture capital institutions’ social networks on investment performance through empirical research on the venture capital transactions of venture capital institutions that were headquartered in mainland China from 2009 to 2020. After controlling the influence of factors such as the characteristics of venture capital institutions and the market investment environment, the social network of venture capital institutions has a significant positive impact on the success of the investment portfolio. After controlling the characteristics of the invested ventures, there is also a significant positive relationship between the social network of venture capital institutions and the successful listing or merger of ventures. From the two aspects of the portfolio investment performance of venture capital institutions and the successful performance of start-up enterprises, the positive role of the social network of venture capital institutions in China’s venture capital field is confirmed. Secondly, the research on the first-day earnings of start-ups found that venture capital institutions with higher network centers are more likely to be recognized by the market. Therefore, the listing of venture companies that are participating in the investment can better attract investors to subscribe in the secondary market and push up the return on the first day of listing. Through the research on the factors that influence the social network position of the venture capital institution, the positive influence relationship between the historical investment behavior of the venture capital institution and the centrality of the social network is confirmed. Venture capital institutions with a richer investment experience and outstanding investment performance are more likely to actively invite or passively be invited to become joint investment targets.In order to effectively analyze the problems of venture capital and reduce the problems that result in difficult decision making due to the non-disclosure of information, the related theories of venture capital were used, taking venture capital-related online communities as the research object and using corporate venture capital data from 2009 to 2020 as a model. The centrality of the online community was used as a basic indicator, which extends a variety of calculation methods. According to the summary of the literature, relevant research hypotheses and their corresponding analysis models were constructed. Finally, the effectiveness of the proposed model was further verified through empirical data. Among them, there was a positive correlation between information sharing and investment performance in venture capital network communities. When the value of enterprise characteristic variables continues to increase, the degree of enterprise risk information sharing becomes higher and higher, which causes a higher possibility of the enterprise investment performance to accelerate acquisition. The higher the degree of sharing in the online community, the higher the capital benefit that will be determined. This has important reference value for promoting the research of venture capital-related fields and provides a direction for the development of enterprise performance in the field of venture capital. Meanwhile, this study provides actionable points for the listing of joint venture capital companies, namely, adjusting the social network centrality of venture capital institutions, and it has certain practical significance. Although, the relationship between corporate information sharing and investment performance was resolved. The disadvantages are firstly, only selected environmental-social enterprise start-ups were analyzed. The sample was unitary and may have had certain limitations for other related companies. Therefore, the sample needs to be further expanded. Secondly, it was confirmed that information sharing in online communities is crucial to investment performance; there is a positive correlation between them. However, this conclusion cannot effectively identify the influence channels of online communities. After that, the possible mediating effect between the two needs to be analyzed. Meanwhile, the sample needs to be continuously expanded, and more in-depth mechanisms need to be further analyzed. The research content will be mainly applied in the field of venture capital development.


It is difficult to improve image resolution in hardware due to the limitations of technology and too high costs, but most application fields need high resolution images, so super-resolution technology has been produced. This paper mainly uses information redundancy to realize multi-frame super-resolution. In recent years, many researchers have proposed a variety of multi-frame super-resolution methods, but it is very difficult to preserve the image edge and texture details and remove the influence of noise effectively in practical applications. In this paper, a minimum variance method is proposed to select the low resolution images with appropriate quality quickly for super-resolution. The half-quadratic function is used as the loss function to minimize the observation error between the estimated high resolution image and low-resolution images. The function parameter is determined adaptively according to observation errors of each low-resolution image. The combination of a local structure tensor and Bilateral Total Variation (BTV) as image prior knowledge preserves the details of the image and suppresses the noise simultaneously. The experimental results on synthetic data and real data show that our proposed method can better preserve the details of the image than the existing methods. Super Resolution (SR) refers to the process of reconstructing a High Resolution (HR) image from a single Low Resolution (LR) image or multiple low resolution images by the software method without modifying the hardware environment. SR is often used in satellite remote sensing , medicalThe first MFSR was proposed  in 1984,Because SR itself is an ill-posed problem, regularization techniques are widely used to solve the minimization problem. The methods of regularization mainly contain the fidelity term and the regularization term. The purpose of the fidelity term is to minimize the observation errors between the reconstructed HR image and the input LR images. The purpose of the regularization term is to make the reconstructed HR image reach a robust state.The widely-used fidelity terms in the SR are the L1 norm and the L2 norm. Both the L1 norm and the L2 norm have their advantages and drawbacks. The L2 norm can produce lower variance than the L1 norm. However, the L2 norm is sensitive to outliers. M-estimators are explored in MFSR later. For example, the Huber function  is propoImage prior knowledge is used to regularize the image reconstruction. Tikhonov regularization  is one oIn recent years, many researchers have proposed a large number of MFSR methods, but MFSR is an ill-posed problem and it is very difficult to reconstruct a satisfactory HR image because the result of MFSR is affected by motion estimation, image registration, unknown blur, noise, and so forth. Even if they are studied separately, each affecting factor is extremely challenging.We propose a fast and effective method for selecting appropriate LR images, which provide a relatively good input for the reconstruction;A novel fidelity term is proposed. We adopt the half-quadratic function as the error norm adaptively. All parameters are automatically adjusted according to observation errors;We propose a local structure tensor weighted BTV regularization term. The novel image prior can better simultaneously preserve image details and suppress noise by assigning a certain weight to each pixel according to the local structure tensor information of the image.In order to simultaneously preserve the image edge and texture details and remove the influence of noise, we propose a new robust MFSR method. In this paper, there are three major contributions to improving the quality of the final reconstructed HR image, as follows:The rest of this paper is organized as follows. kth LR image, kth LR image, the size is kth LR image, the size is kth LR image, the size is In practical applications, noise, blur, motion and other factors will affect the final SR. Therefore, the observation model of MFSR can be expressed as followsWe can simplify Equation (1) by combining atrix Wk , and EquThe observation error The basic framework of MFSR can be expressed asBTV is calculated simply and is easy to implement, so it is often selected in the regularization term. The formula of BTV can be expressed asWe first select appropriate LR images from a set of LR frames as the input for MFSR. The overall SR framework of this paper is to employ a coarse to fine strategy, enlarging LR images to the target image size gradually in order to adapt to the large scale factor. The half-quadratic function as the loss function is used to control residuals between the unknown HR image and LR images for the fidelity term. For the regularization term, we combine the local structure tensor information of the image and the image sparsity as the image prior knowledge to regularize the estimated HR image.Many existing MFSR algorithms input all LR images instead of selecting input LR images. Some LR images not only do not improve the quality of the reconstructed HR image, but also consume more time and increase the computational complexity. In order to improve the practical applications of MFSR, it is necessary to select LR images with appropriate quality. In this paper, we can remove some low quality LR images quickly to SR.The number of images required for SR is at least twice the square of the magnification factor under the ideal environment according to . The numkth LR image Calculate the variance of all LR images. The variance formula of the Calculate the average of the variance of all LR images, which can be expressed as:Calculate the difference between variance per LR image and the average of the variance which can be defined as:Sort Although there are many quality evaluation methods ,25,26 foThis paper uses the optical flow algorithm to register selected LR images.Our proposed algorithm is based on the maximum a posteriori (MAP) estimation and Bayesian theory, the expression of the estimated HR image can be written as follows:Since Let us suppose that each LR image The Half-Quadratic (HQ) function is first proposed in  as a potThe L1 norm, L2 norm, half-quadratic function, Huber function, Lorentzian function and Tukey function are shown in kth LR image, ith pixel of the kth LR image.The fidelity term of our robust MFSR by the half-quadratic function is:kth LR image The observation error of the kth LR image on (MAD)  to estimDue to the BTV regularization term, we cannot distinguish the edge information of the image well. We propose combining the local structure tensor information of the image with the image sparsity as image prior knowledge in order to simultaneously better preserve the edge information of the image and suppress the noise.The proposed regularization term can be expressed as:The value of Because the local structure tensor In order to describe the local structure information of the image well, we construct a local structure tensor weight matrix As the image prior weight, In this paper, the formula of the reconstructed HR image can be written asThe regularization parameter r et al.  to deterThere are many optimization algorithms for solving the minimization problem for MFSR. We employ Scaled Conjugate Gradient (SCG)  to solveWe used synthetic data and real data to test our proposed MFSR algorithm. We implemented our method on a laptop computer with Intel(R) Core(TM) i7-8650U CPU and 16 GB RAM, . Our algorithm was compared to the following MFSR algorithms: L2 + Tikhonov , L2 + BTOur proposed method was first measured on synthetic data quantitatively, because the ground truth HR images were available. We used peak-signal-to-noise ratio (PSNR), structural similarity (SSIM) and Information fidelity criterion (IFC) to assess image quality in this paper. We used common HR images to generate synthetic LR images by using the Set 14 dataset , shown iWe first selected 25 appropriate LR images by using our method mentioned in Except for synthetic data, we tested our proposed algorithm on real data which came from the Multi-Dimensional Signal Processing Research Group (MDSP) . In pracThe experimental results on synthetic data and real data show that our proposed method has a better effect compared with current methods; especially for preserving edge information, our method has better performance.MFSR is a very challenging problem. In this paper, we propose a new robust MFSR algorithm to preserve richer image detail information while suppressing noise. We first select the LR images with appropriate quality simply and quickly instead of all LR images as the input of MFSR, which reduces the computational cost of MFSR and makes it more practical. For the fidelity term, we analyze and use the half-quadratic function as the loss function. We adjust the function parameter and confidence weights adaptively according to the observation errors. We gradually reduce the observation errors of the LR images and the estimated HR image through iteration. We better suppress the noise and preserve the image edge details simultaneously using the local structure tensor weighted BTV regularization term. The combination of the local structure tensor and the sparsity of the image, as the prior knowledge of the image, can reflect the structural characteristics of the natural images.Our proposed method is a non-blind reconstruction, which assumes that the point spread function is known. The blur kernel is often unknown in practical applications. Our next task is to study the unknown blur in MFSR. One direction of our future work is to extend our method to blind MFSR.