Block Model Research Articles

Bootstrap percolation on the stochastic block model

We analyze the bootstrap percolation process on the stochastic block model (SBM), a natural extension of the Erdős–Rényi random graph that incorporates the community structure observed in many real systems. In the SBM, nodes are partitioned into two subsets, which represent different communities, and pairs of nodes are independently connected with a probability that depends on the communities they belong to. Under mild assumptions on the system parameters, we prove the existence of a sharp phase transition for the final number of active nodes and characterize the sub-critical and the super-critical regimes in terms of the number of initially active nodes, which are selected uniformly at random in each community.

A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction

A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two-stage approach similar to the one proposed by Lai and Mckenzie (SIAM J Math Data Sci 2:368–395, 2020). However, under a weaker assumption and with less computational complexity, our algorithms are shown to be able to find a desired cluster with high probability. The “one cluster at a time" feature of our method distinguishes it from other global clustering methods. Numerical experiments are conducted on the synthetic data such as stochastic block model and real data such as MNIST, political blogs network, AT &T and YaleB human faces data sets to demonstrate the effectiveness and efficiency of our algorithms.

Pembuatan Blok Model Estimasi Sumberdaya Nikel Laterit Dengan Metode Inverse Distance di Wilayah Blok “X” Pada PT. Vale Indonesia Tbk

Block model is an attempt to produce estimates of mineral deposition wich is used as a reference for the mining process. The research objective to provide volume and tonnage estimation. The research method using conventional methods Inverse Dictance, needed the data among which the assay data, the data collar, geological data, and survey data. Research carried out by the number of drill point as much as 275 points and a drill spacing of 50 meters and 25 meters generate the amount of resources that have been calculated in the block model in the amount of resources that the limonite layer of 1,210,938 tonnes with the average levels of Ni: 0.8%, Co: 0.08%, Fe: 42.3%, SiO2: 7.8% and MgO: 1.21%. And for saprolite tonnage of 445,090 tonnes with the average levels of Ni: 1.91%,Co:0.18%, Fe: 32.78%, SiO2: 25% and MgO: 7.08%.

Creation of geomechanical block model of Severomuysky Tunnel area in Micromine Origin & Beyond

In 2016 Russian Railways made a decision to reconstruct the upper structure of the track and drainageand-transport adit in Severomuysky Tunnel in order to mitigate the impacts of adverse factors and to increase the tunnel capacity. As a rule, such work goes with the creation of a 3D model of subsoil in the area of reconstruction. The area of Severomuysky Tunnel features difficult geological and geotectonic conditions. The seismically active zones, subvertical tectonic delamination of granitoid rock mass in the Baikal Mega Dome, as well as the thermal and cold groundwater complicate operation and require special control activities to be undertaken. Creation of the geomechanical model is discussed, the modeling stages are described, the source data of the modeling are given, and the calculation procedure to find geomechanical determinants for the modeling is delineated. The modeling is performed in geological and mining information system Micromine Origin & Beyond. Visualization of distribution of quantitative data on rock mass quality and real-time updating of the model when new geomechanical observations arrive allow enhancing quality of project solutions, including designs of tunnel lining and new underground openings. With the block model at hand, it is possible to identify hazardous areas in rock mass and to predict rock mass damages in three and in two dimensions. The geological and mining information systems are usable both in solid mineral mining and in construction / reconstruction of various-purpose underground structures. At the comparatively low labor cost of modeling, specialists gain an offing to improve essentially the computational accuracy and to minimize the risks associated with construction and operation of objects in difficult geotechnical conditions.

Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images, which has been proved to be effective in improving the performance of person re-identification (Re-ID). However, most of the existing attention modules have the following two shortcomings: On the one hand, they mostly use global average pooling to generate context descriptors, without highlighting the guiding role of salient information on descriptor generation, resulting in insufficient ability of the final generated attention mask representation; On the other hand, the design of most attention modules is complicated, which greatly increases the computational cost of the model. To solve these problems, this paper proposes an attention module called self-supervised recalibration (SR) block, which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask. In particular, a special "Squeeze-Excitation" (SE) unit is designed in the SR block to further process the generated intermediate masks, both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels. Furthermore, we combine the most commonly used ResNet-50 to construct the instantiation model of the SR block, and verify its effectiveness on multiple Re-ID datasets, especially the mean Average Precision (mAP) on the Occluded-Duke dataset exceeds the state-of-the-art (SOTA) algorithm by 4.49%.

An Intensity Measure for the Rocking Fragility Analysis of Rigid Blocks Subjected to Floor Motions

A novel intensity measure (IM), dimensionless floor displacement, is presented for evaluating the seismic fragility of freestanding rigid blocks subjected to one-sine acceleration pulses in this paper. The rocking responses of rigid blocks are simulated using an equivalent single-degree-of-freedom (SDOF) model with a bespoke discrete damper to account for energy dissipation. The performance of various IMs is compared using simulation results for four different block models under different excitation conditions. In comparison to some well-known IMs, the proposed IM, determined by excitation magnitude and frequency as well as block geometry parameters, displays a considerably stronger correlation with the peak rotation of the rocking block. The comparative results show that effective IMs should consider not only the excitation characteristics but also the block geometric parameters. Finally, the fragility curve generated by the proposed IM performs best by significantly reducing the dispersion.

PlexusNet: A neural network architectural concept for medical image classification

State-of-the-art (SOTA) convolutional neural network models have been widely adapted in medical imaging and applied to address different clinical problems. However, the complexity and scale of such models may not be justified in medical imaging and subject to the available resource budget. Further increasing the number of representative feature maps for the classification task decreases the model explainability. The current data normalization practice is fixed prior to model development and discounting the specification of the data domain. Acknowledging these issues, the current work proposed a new scalable model family called PlexusNet; the block architecture and model scaling by the network's depth, width, and branch regulate PlexusNet's architecture. The efficient computation costs outlined the dimensions of PlexusNet scaling and design. PlexusNet includes a new learnable data normalization algorithm for better data generalization. We applied a simple yet effective neural architecture search to design PlexusNet tailored to five clinical classification problems that achieve a performance noninferior to the SOTA models ResNet-18 and EfficientNet B0/1. It also does so with lower parameter capacity and representative feature maps in ten-fold ranges than the smallest SOTA models with comparable performance. The visualization of representative features revealed distinguishable clusters associated with categories based on latent features generated by PlexusNet. The package and source code are at https://github.com/oeminaga/PlexusNet.git.

A Study on the Spatial Correlation Effects of Digital Economy Development in China from a Non-Linear Perspective

Accurate analysis of the spatial correlation effects, spatial aggregation patterns, and critical factors in the development of China’s digital economy is of great significance to the high-quality development of China’s economy. Based on the monthly data of “The Tencent Internet Plus” digital economy index for 31 provinces in China from 2018 to 2020, the non-linear Granger causality test and social network analysis were applied to reveal the spatial correlation effects of China’s digital economy. The quadratic assignment procedure (QAP) was used to empirically examine the factors influencing the formation of non-linear spatial association networks. The results show that the spatially linked relationships of the digital economy in 31 Chinese provinces exhibit a significant non-linear spatially correlated network structure. Block model analysis reveals that the development of the digital economy between the four major sectors is closely linked, and the national linkage effect is significant. The results of the secondary assignment procedure indicate that capital stock, information infrastructure, and geographical proximity have a significant positive impact on the formation of spatial linkages in the digital economy. In contrast, technological innovation has a significant negative impact.

Quantifying coal rib stability and support requirements using bonded block modeling and reliability analysis based on the random set theory

An apparent flaw in geomechanical analysis of rock structures is the application of single value parameters as input to numerical models. A known but often overlooked characteristic of the rock mass is its inherent strong property variability. In conventional reliability analysis of rock mechanics, subjective assumptions are often made on the probability density function of parameters because, in many cases, the results of geotechnical investigations are set-valued rather than being precise and point-valued. On the other hand, imprecise probability theories such as the Random Set Theory could be considered a possible attractive candidate to employ in these analyses. In this study, the Random Set Theory in combination with the Bonded Block Modeling (RS-BBM) approach is employed to demonstrate its practical applicability in determining coal rib stability and support requirements. Sensitivity analysis of the BBM input parameters is conducted. The uncertainties of the geomechanical parameters of the BBM contacts, which control model behavior, are considered in the reliability analysis model. The analyses were performed in UDEC, and the strength reduction method was used to evaluate rib stability. It is shown that RS-BBM is an efficient tool to determine the most likely bounds of coal rib performance. A method combining the RS-BBM approach with empirical methods is also described to illustrate its practical application.

Scheduling doesn't matter! A noninferiority study of block versus longitudinal scheduling for emergency medicine resident pediatric procedural training experience.

Emergency medicine (EM) residencies offer a wide variety of scheduling models for pediatric patient experience, including blocked weeks in pediatric emergency departments and longitudinal models with pediatric emergency pod/department shifts integrated within other clinical experiences. Concerns with autonomy, attending entrustment, and resident comfort imply that these different scheduling models may impact EM residents' pediatric procedure volumes. The purpose of this study is to quantitatively compare EM residents' pediatric procedure experience and volumes between block versus longitudinal scheduling models. We hypothesize noninferiority between the scheduling models. A retrospective review characterized the numbers and types of procedures performed by The Ohio State Emergency Medicine residents at the tertiary care pediatric hospital where residents' receive their pediatric emergency medicine clinical experience. Procedure numbers and variety were compared across six academic years: four with a block model, one reorganization year, and one integrated longitudinal year. 2552 procedures were performed by 266 resident academic years over the 6-year period. Overall, no statistically significant differences in the number of procedures performed per year or the variety of types of procedures performed per year were found when comparing the block and longitudinal models. Differences were seen in experience of PGY1 versus PGY3 residents between scheduling models and the overall experience and volumes of the PGY2 residents during the reorganization year. Our study quantitatively concluded that the longitudinal scheduling model is noninferior to the more traditional block scheduling model for emergency medicine residents' pediatric emergency medicine clinical experience when reviewing volumes and types of procedures performed in a pediatric emergency department. This suggests that procedural opportunities do not need to dictate scheduling models.

Denoising Method for Seismic Co-Band Noise Based on a U-Net Network Combined with a Residual Dense Block

To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic co-band denoising model Atrous Residual Dense Block U-Net (ARDU), which uses a U-shaped convolutional neural network (U-Net) as a basic framework and combines atrous convolution and the residual dense block (RDB). In the ARDU model, atrous convolution is connected with residual dense blocks to form the feature extraction unit of the model encoder. Among them, the residual dense blocks can deepen the network’s depth and enhance the feature extraction ability of the network on the premise of mitigating the gradient-vanishing and gradient-exploding problem. Atrous convolution can enlarge receptive fields, reduce waveform distortion, and protect effective signals without increasing network parameters. To test the denoising performance of the ARDU model, the Stanford Global Seismic dataset was used to construct a training set and a test set and the model was trained and tested on it. The experimental results of the ARDU model for different types of seismic co-band noise showed that this model can effectively remove seismic co-band noise, protect effective signals, improve the SNR of seismic signals, and enhance the quality of seismic signals. To further verify the denoising effect of the model, this model was compared with the wavelet threshold denoising U-Net model and the denoising residual dense block (DnRDB) model, and the results showed that the ARDU model has the best SNR, r (correlation coefficient), and root-mean-square error (RMSE) and the least distortion of the seismic signal waveform.

Review of machine learning-based Mineral Resource estimation

Mineral Resources estimation plays a crucial role in the profitability of the future of mining operations. The conventional geostatistical methods used for grade estimation require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering as well as clean validated data to build accurate block models. However, the geostatistical models are sensitive to changes in data and would have to be rebuilt on newly acquired data with different characteristics, which has proved to be a time-consuming process. Machine learning methods have in recent years been proposed as an alternative to the geostatistical methods to alleviate the problems these might suffer from in Mineral Resource estimation. In this paper, a systematic literature review of machine learning methods used in Mineral Resource estimation is presented. This has been conducted on such studies published during the period 1990 to 2019. The types, performances, and capabilities, of several machine learning methods have been evaluated and compared against each other, and against the conventional geostatistical methods. The results, based on 31 research studies, show that the machine learning-based methods have outperformed the conventional grade estimation modelling methods. The review also shows there is active research on applying machine learning to grade estimation from exploration through to exploitation. Further improvements can be expected if advanced machine learning techniques are to be used.

Very High Cycle Fatigue Damage of TC21 Titanium Alloy under High/Low Two-Step Stress Loading

Very high cycle fatigue (VHCF) tests were carried out under variable amplitude loading for TC21 titanium alloy. The first level of high amplitude loading was set as 950 MPa close to yield strength, and the second level of low amplitude loading was determined between 435 MPa and 500 MPa where fatigue cracks initiated at the specimen subsurface under constant amplitude. The results indicate that the high/low stress block significantly reduced the cumulative fatigue life of low stress amplitude, and the fatigue crack initiation site changed from the specimen subsurface under constant loading to the specimen surface under stress block. Based on continuum damage mechanics, the fatigue damage model of two-step stress block was established to estimate the fatigue damage process. The prediction of cumulative fatigue life generally agreed with the experimental data. The cumulative fatigue damage of the stress block was related to the stress amplitude and the cycle ratio, which determined the stress fatigue damage and its interaction damage. The surface crack initiation in the stress block accelerated fatigue damage of low stress amplitude, reducing the cumulative life.

An optimization-based rigid block modeling approach to seismic assessment of dry-joint masonry structures subjected to settlements

A rigid block modeling approach is presented for rocking dynamics and nonlinear static analysis of dry-joint masonry structures subjected to settlements and earthquake excitations. For the different types of analysis, a unified optimization-based formulation is adopted, which is equivalent to the system governing the static and dynamic structural response. Sequential solution procedures are used for time integration and for pushover analysis which take into account the effects of large displacements under the combined action of support movements and lateral loads. No-tension elastic contacts with finite shear strength are considered at block interfaces for time-history analysis and to obtain the elastic branch of pushover curves in nonlinear static analysis. A unilateral rigid contact behavior is also considered to obtain the descending post-peak branch of pushover curves corresponding to the activation of the rigid-body rocking motion, according to displacement-based assessment methods of failure mechanisms adopted in the standards. Comparisons with numerical models and experimental tests on a rocking block and on a buttressed arch are presented to show the accuracy of the developed approach. Simple tests on dry-joint tuff panels on the tilting table were also carried out to show the effects of imposed movements at support on the response to lateral loads. Finally, an application is presented to a full-scale triumphal arch subjected to the combined action of support movements and earthquake excitation to discuss, on the basis of the developed model, the effects of settlement-induced damage on seismic performance. The numerical analyses showed that the lateral force, the displacement capacity and the rocking response can be significantly affected by support movements, pointing out the relevance of the current building condition in the seismic safety assessment.

Efficiency evaluation for urban industrial metabolism through the methodologies of emergy analysis and dynamic network stochastic block model

Frequent economic activities in cities have exacerbated irreversible environmental degradation. It is urgent to balance the relationship between economic growth, resource investment and environmental protection. With the concept of urban metabolism, this study attempts to clarify the internal structure of urban system metabolism and the metabolic relationship between specific economic sectors. And then, it determines the metabolic level of current urban industrial development by means of metabolic efficiency evaluation, which can help more accurately reveal the sustainability of urban metabolic activities. Based on them, this study proposes a metabolic efficiency evaluation model combined with emergy analysis and slack-based measure dynamic network Data Envelopment Analysis (DEA). Through the multi-period and multi-stage network DEA model, the internal structure of urban industrial metabolism is opened. And considerring industrial waste as an undesirable carryover will be regarded as additional inputs in future period to reflect the impact on industrial metabolic efficiency. Using the proposed model, the industrial metabolic efficiency of 18 first-tier cities in China from 2016 to 2020 was analyzed. It is found that all cities are inefficient, which indicates that all evaluated cities need to make appropriate adjustments to improve their weak stages. In response to the specific problems of inefficiency in each city, an improvement framework is proposed to help each city better realize the sustainable development of the urban economy.

Comparative mathematical and data-driven models for simulating the performance of forward osmosis membrane under different draw solutions

Mathematical models have frequently been used to simulate system performance, including forward osmosis (FO)-related processes. More recently, Industry 4.0 has increased in importance, and artificial intelligence (AI) technology has proven to be effective at assisting decision-making across a range of industries, including membrane processes. This study, for the first time, proposes a comparison of the accuracy of predictions for water permeability under various types of draw solutions using conventional models and AI-based models. The results showed that the AI-based models, including artificial neural networks (NN) (2 layers; 10 neurons) and adaptive neuro-fuzzy inference system (NFIS) (with grid partition; membership functions = 5) performed significantly better than the conventional mathematical intermediate blocking (CIB) model, with the coefficient values (R) of 0.974, 0.994, and 0.963, respectively. The corresponding errors of these simulations were 0.04, 0.70, and 0.97 LMH, indicating that the AI technique was better suited than the mathematical CIB model for simulating FO-related system performance. Additionally, using AI to forecast system performance is timely (i.e., considerably quicker than developing a sophisticated mathematical model) and cost-effective (i.e., reducing the expense for analysis of environmental factors for CIB model construction). The authors also successfully constructed an application for end-users for better and simpler experiences with modeling and simulation, even for those with varying knowledge in the field.

Effect of Substrate Type on Survival and Growth of Sweet Potato Vitroplants Under Acclimatization

The lack of healthy planting materials is one of the major constraints of sweet potato cultivation in Benin. One of the solutions to this problem is the use of vegetal biotechnology through micropropagation. At the end of the production of vitroplants, a crucial phase consists in acclimatizing them before their transfer to the real environment. The type of substrate used plays an important role in the success of acclimatization. This study aims at evaluating the effect of the type of substrates on the survival and growth of sweet potato vitroplants in acclimatization of the two accessions (Mèché and Bombo vôvô). For this, three substrates were assessed. These are Potting soil alone, Potting soil + Sawdust, and potting soil + Charcoal powder in proportion 2:1. These substrates were first sterilized separately in an oven at 150°C for 2 hours. The experiment was conducted in the greenhouse of Central Laboratory of Vegetal Biotechnology and Plant Improvement of the Faculty of Sciences and Techniques of the University of Abomey-Calavi following a model of random complete blocks in split plot design. The results show that the type of substrate influences the success of acclimatization in terms of survival rate, the gain of size and the number of neoformed leaves (p= 0.0033; 0.0019 and 0.0001). The Potting soil + Charcoal powder substrate influenced these parameters better than the other two with 95%, 3.13 cm and 3.165 respectively for survival rate, size gain and the number of neoformed leaves. It was followed by the Potting soil + Sawdust substrate. Acclimatized plants have been successfully introduced into the field, proving that this technique is fruitful in the increase of the production of healthy cuttings and make them available to farmers. The protocol used in this study can be applied in the acclimatization of sweet potato vitroplants.

BLOCK MODEL OPTIMIZATION AND RESOURCE ESTIMATION OF THE ANGOURAN MINE BY TRANSFERRING THE EXPLORATORY DATA FROM THE LOCAL COORDINATE SYSTEM TO THE UTM

Resource estimation is one of the most important steps in the mining process. Precise resource estimation has a significant influence on the optimization of subsequent mining steps, i.e. mine planning and scheduling. The previous resource estimation in the Angouran Mine was conducted based on the provided information in the local coordinate system which causes considerable errors in estimations. Therefore, an attempt is made in this research to optimize the block model of the Angouran Mine and resource estimation based on the information in the UTM global coordinate system. For this purpose, exploratory data is firstly transferred from the local coordinate system to the UTM environment. Then, block model optimization is conducted using indicator kriging (IK) in which the waste blocks are removed and the block model was optimized. Finally, resource estimation is performed using the inverse distance weighting (IDW) and simple kriging (SK) methods. After variogram analyses in different directions, it was found that the mine deposit is anisotropic. Also, validation results showed that the acquired correlation coefficient in the carbonate and sulfide sections for IDW, SK and IK is 0.86, 0.87 and 0.92, and 0.88, 0.87 and 0.92, respectively. Finally, the obtained grades and tonnages are compared with the actual data of the exploratory boreholes, mined blocks and previous resource estimation in the mine. Comparative results showed that the obtained grades and tonnages from both previous and new models are over-estimated and higher than the actual values. The minimum errors of grade estimation equal 46% and 23.1% for previous and new resource estimations (before and after the waste removal), respectively. Also, the mining errors of tonnage estimation are 50.29% and 28.37% for previous and new models, respectively. This field comparison proved that transferring the exploratory data to the UTM system, utilization of the IK to remove the waste blocks and applying the SK for resource estimation lead to the optimization of the block model and a reduction in the estimation error compared to the previous estimations for the mine.

MICROMINE CALCULATIONS OF ORE RESERVES IN THE BOROV DOL PORPHYRY COPPER DEPOSIT, REPUBLIC OF NORTH MACEDONIA

The calculations of ore reserves in the Borov Dol deposit were mainly carried out with the support of the AutoCAD software package, and those results received official confirmation in the Republic of North Macedonia. The latest calculation using MICROMINE provides a wider range of use of source data from detailed geological surveys and rapid correlation between metals and associated elements in the deposit. The MICROMINE procedure for the calculation of ore reserves in the Borov Dol deposit consists of several stages starting from: creation of new project and related data base (Excel and Micromine), Micromine database validation process, building and creating wireframe, creation of the particular block model as well as calculation of ore reserves by MICROMINE software package. The obtained results are highly correlative and compatible with the same obtained through the method of vertical parallel profiles calculated using the AutoCAD software package (difference 1.21%).

Управление качеством углей по критерию их опасности с использованием цифровых моделей полезного ископаемого на Кутинском буроугольном месторождении

The purpose of the research is to substantiate the need for preliminary planning with the use of block models in the extraction of TPI as an important element of designing an economically and environmentally efficient enterprise for the development of a deposit of solid minerals. The object of the study is the Kuta brown coal deposit, which has areas of coal with increased concentrations of natural radionuclides. The method of planning the quality of coals, taking into account the hazard criterion and products of their processing with the use of Micromine Alastri software (software) is considered. The objectives of the study are to test the author’s hypothesis regarding the use of BM in the process of predicting the quality of the extracted PI according to the obtained characteristics. Expected results of the scientific study: it is expected that the specific activity in the samples of dust and slag will be recorded several times; when conducting field studies on the content of carbon in coal seams and coal dust with the inclusion of rock, it is necessary to establish the regularity of increasing the value of Aeff in coal dust with the inclusion of rock particles relative to coal samples. The results of the interpolation of the contents of the ORE into the blocks of the digital block model of the coal seam I of the Kuta brown coal deposit are presented. In conclusion, the author highlights the methodology for determining the parameters of mined coal and coal products, taking into account the hazard criterion, which has the following logic: burning of an experimental batch of coal; analysis of the correlation regularity between the average content of OER in coal by block and the average content of OER in coal combustion products, determination of the dependence of the oxygen content in coals and combustion products; assignment by reverse interpolation of the obtained forecast values to each three-dimensional block of the block model included in the mining blocks for each horizon and their transfer to the maps of horizon-by-horizon mining planning. Thus, obtaining a safety map of the extracted PI with the predicted values of the content of N in the combustion products.