Block Model Research Articles

A sparse exponential family latent block model for co-clustering

A sparse exponential family latent block model for co-clustering

Digital transformation readiness in large manufacturing firms: a building block model proposition

PurposeThis study identified the key factors that can be used to assess manufacturers' readiness for DT.Design/methodology/approachNon-participating observations and semi-structured interviews were applied to five large manufacturers in Brazil and four in the UK, using content analysis.FindingsThe paper (1) identifies and discusses the aspects that companies need to consider for digital readiness; (2) presents a model developed in building blocks for DT readiness, which goes beyond the technical dimension; (3) argues that few firms fully understand DT’s meaning, opportunities and risks, and they should invest in strategic and socio-managerial preparation; (4) emphasizes government support as crucial to assist the business digital journey and (5) highlights that DT is a human transformation and technology users are the primary agents of this change.Research limitations/implicationsAlthough the literature proposes models regarding the levels of digital maturity, the present study investigates which organizational aspects need to be taken into account to be ready for digital change. The present study identified digital resources concerning the digital context (for instance, agile, collaborative, absorptive and data analytics capabilities) and provided an integrated view that encompasses also external aspects of the environment that need to be taken into consideration when performing the analysis, a contribution to strategic management literature in the sense that it goes beyond studies that usually explore only internal aspects of the firm that cannot properly explain how digitalization can be achieved by an organization.Practical implicationsTechnologies do not cause changes without people being able to extract and understand the value of the use and impact of these technologies. Business innovation demands engagement, attitudes and connections between people. If the factories do not mobilize and train internal and external collaborators to realize the value of digital technologies, the promised gains of DT will hardly be realized. Preparing people is one of the most challenging aspects of business digitalization. The proposed framework offers a multidimensional view of DT, providing insights into strategic aspects and the roles and mindset of employees in organizations on their digital journey, characterized by constant learning. DT is a work of change management, done with, for and by people. The article also helps companies identify critical factors to evaluate before operationalizing digital innovation practices.Social implicationsThis study also contributes to policymakers, such as government and industry consortia, in establishing relevant factors when policies are being drawn. Analyzing the factors for a sector, in general, might inform the investment priority in actions that might foster DT for that sector. Given the recurring turmoils in the global economic environment, broad policies need to be established by the government for business digitalization, which can boost the performance of the productive sector.Originality/valueThis study provides valuable insights consolidated into a DT readiness model. The article helps companies identify critical factors to evaluate before operationalizing digital practices. It compares the differences and commonalities among factories in an underdeveloped country versus an industrialized nation.

Statistical and computational efficiency for smooth tensor estimation with unknown permutations

We consider the problem of structured tensor denoising in the presence of unknown permutations. Such data problems arise commonly in recommendation systems, neuroimaging, community detection, and multiway comparison applications. Here, we develop a general family of smooth tensor models up to arbitrary index permutations; the model incorporates the popular tensor block models and Lipschitz hypergraphon models as special cases. We show that a constrained least-squares estimator in the block-wise polynomial family achieves the minimax error bound. A phase transition phenomenon is revealed with respect to the smoothness threshold needed for optimal recovery. In particular, we find that a polynomial of degree up to ( m − 2 ) ( m + 1 ) / 2 is sufficient for accurate recovery of order-m tensors, whereas higher degrees exhibit no further benefits. This phenomenon reveals the intrinsic distinction for smooth tensor estimation problems with and without unknown permutations. Furthermore, we provide an efficient polynomial-time Borda count algorithm that provably achieves the optimal rate under monotonicity assumptions. The efficacy of our procedure is demonstrated through both simulations and Chicago crime data analysis.

Profiling Standards to Improve Practical Interoperability

Abstract. Standard data models are key to enable a set of data integration functionalities, often characterised using the Findability, Accessibility, Interoperabilty and Reusability (FAIR) principles. However, standardisation is a process of trying to meet many requirements, and standard data models are inherently either very abstract or very comprehensive in the details. This results in several ambiguity pitfalls, inconsistent implementation of standard data models, which in turn hinders trust in the interoperability potential of standardised data, and complicates any integration processes. In practice profiling such standards is useful to overcome such issues to create more useful forms of standardised data for specific applications. However defining custom profiles typically requires a great deal of technical expertise in the underlying expression language of the standard. Maintaining access to this level of expertise is a challenge as profiles become outdated through the time and lose connection with the maintenance of the parent standard from which they originate. Therefore, in this paper, a scalable methodology is proposed, built on the OGC Building Blocks Model approach, that uses semantic modelling to support an easier composition of geospatial data models profiles which directly derive from available standards without losing the relevant dependencies that inform stakeholders which components are interoperable with other standards. The approach is tested within a digital building permit project (CHEK), in which data requirements derive from the semantics of city regulations and common geospatial standards (i.e., CityGML and INSPIRE) are used as reference.

Comparisons of experimentally and numerically determined statistics for predicting low-occurrence wind speeds around a 1:1:2 block model

Accurate prediction of low-occurrence wind speeds around urban structures is crucial for safe building design. Although Large-eddy simulation (LES) is commonly used as a high-fidelity model as compared with the Reynolds-Averaged Navier–Stokes (RANS) simulations, the present validation process relies on the comparison of fundamental statistics of the mean and standard deviations. The discrepancies in LESs and wind-tunnel experiments (WTEs) are unclear in terms of physical quantities characterizing the unsteadiness of the simulated turbulent flow such as probability density and power spectral densities, and low-occurrence winds speeds. Therefore, this study aims to evaluate the effectiveness of LES in predicting unsteady wind behavior around a 1:1:2 block model. The study identifies prominent differences to improve the accuracy of unsteady numerical simulations especially for the purpose of predicting low-occurrence wind speeds. Various advection schemes in LESs were investigated, including first-order upwind, second-order linear, and dynamic interpolation schemes. The results show significant discrepancies, particularly in higher-order statistics and low-occurrence wind speeds, with WTE consistently exhibiting higher energy levels across all frequencies. These findings highlight the need to refine advection schemes to enhance their predictive accuracy. LESs with minimal numerical errors from discretization schemes can substantially improve urban wind assessments and contribute to the design of safer structures.

(S)Tumbling Blocks: Making Sense of and Creating a Model for Mental Blocks in Sport

The yips are a complex phenomenon that leaves athletes unable to perform previously mastered athletic skills. Here, we coin the term athletic skills mental blocks (ASMBs) to collectively define the phenomenon, which has garnered several terms across scholarship and media outlets. Previous ASMBs literature is often post-positivist in nature, offers minimal insight into complex firsthand experiences, includes small sample sizes, and/or analyzes one or two sports. Through a pilot study of 12 tumbling athletes, and a primary study including 16 collegiate and professional athletes and coaches from baseball, golf, and tumbling, this study offers an in-depth understanding of ASMBs that highlights shared ASMB rationalizations across multiple sports. Guided by sensemaking theory, we use a rigorous thematic analysis to draw out themes within four aspects of the experience: phenomenon, perceived causes, strategies to overcome, and consequences. We highlight the role communication plays in ASMBs sensemaking through revealing the importance of internal dialogue and communication with coach, family, and teammates. We offer theoretical implications by illuminating how naming/labeling processes and identity are vital components to ASMB sensemaking and generating an ASMB sensemaking model that can be used in future research. Practically, this research serves sports organizations by expanding the vocabulary surrounding ASMBs and helps destigmatize the mental health complexities inherent in ASMBs across sports.

Estimasi Sumberdaya Bauksit Menggunakan Metode Ordinary Kriging Pada PT. Wahana Petra Sejahtera Kabupaten Bengkayang Provinsi Kalimantan Barat

PT. Wahana Petra Sejahtera, which operates in the bauxite mining sector, requires resource calculations as a basis for carrying out its mining activities. This research aims to determine the levels and distribution of Al2O3, SiO2, and Fe2O3 and estimate bauxite resources using the ordinary kriging method. This research uses secondary data in coordinates, elevation, test pit depth, and slope data. Data processing uses software in database creation, statistical analysis, block and composite modeling, variogram analysis, resource estimation, and model validation. The calculation results for the Jirak block show that the Al2O3 waste content is 1,488,000 tons, low-grade 4,532,000 tons, medium-grade 1,824,000 tons, and high-grade 32,000 tons. For SiO2 the average content is 13.54% and the average Fe2O3 content is 13.50%. Meanwhile, in the Sindu block the Al2O3 waste content is 64,000 tons, medium-grade 1,188,000 tons, and high-grade 3,056,000 tons. Meanwhile, the average SiO2 content is 9.13% and the average Fe2O3 content is 12.06%. The total resources in the Jirak block are 7,696,000 tons with the amount meeting the COG of 800 tons. Meanwhile, in the Sindu block, the total resources are 5,008,000 tons with an amount that meets the COG of 1,280,000 tons.

Partial recovery and weak consistency in the non-uniform hypergraph stochastic block model

Abstract We consider the community detection problem in sparse random hypergraphs under the non-uniform hypergraph stochastic block model (HSBM), a general model of random networks with community structure and higher-order interactions. When the random hypergraph has bounded expected degrees, we provide a spectral algorithm that outputs a partition with at least a $\gamma$ fraction of the vertices classified correctly, where $\gamma \in (0.5,1)$ depends on the signal-to-noise ratio (SNR) of the model. When the SNR grows slowly as the number of vertices goes to infinity, our algorithm achieves weak consistency, which improves the previous results in Ghoshdastidar and Dukkipati ((2017) Ann. Stat.45(1) 289–315.) for non-uniform HSBMs. Our spectral algorithm consists of three major steps: (1) Hyperedge selection: select hyperedges of certain sizes to provide the maximal signal-to-noise ratio for the induced sub-hypergraph; (2) Spectral partition: construct a regularised adjacency matrix and obtain an approximate partition based on singular vectors; (3) Correction and merging: incorporate the hyperedge information from adjacency tensors to upgrade the error rate guarantee. The theoretical analysis of our algorithm relies on the concentration and regularisation of the adjacency matrix for sparse non-uniform random hypergraphs, which can be of independent interest.

Investigation of Dynamic Characteristics of Holonomic Robot with Mecanum wheels Based on 3D Parametric Simulation Model

The paper analyzes dynamic characteristics of a four-wheeled mobile robot driven by omnidirectional mecanum wheels. To obtain information about dynamic characteristics of the robot, a computer model was developed in the MATLAB Simulink block modeling software environment. The constructed simulation model allowed to research the behavior of a holonomic mobile object with mecanum wheels in start-braking modes, based not only on its mathematical model, but also ta-king into account the weight and size parameters of the robot, introduced into the model through the integration of a three-dimensional digital prototype of the object. The investigation of the omnidirectional robot dynamics was considered within modeling the acceleration, uniform movement and deceleration of an object on a plane along a rectilinear trajectory. As a result, the dependences of kinematic, dynamic and mechanical characteristics were obtained, such as the dependence of angular velocity and torque of each wheel on time; the dependence of linear velocity of the robot's center of mass on time; the dependence of distance traveled by the robot on time; positioning errors while processing a given movement; spatial visualization of speed fluctuations. With the help of Mechanical Explorer tool, using a digital clone of the robot integrated into the model, an animation of the object’s movement along the trajectory was obtained. This approach, based on the integration of mathematical block and three-dimensional parametric models of the object with the possibility of visualization and animation of the results, allows to most fully investigate the dynamics and kinematics of nonlinear mechatronic systems. The obtained information of positioning errors and speed fluctuations in three coordinates allowed to conclude that there were random fluctuations during the robot’s movement, but their presence did not have a noticeable effect on the accuracy of the specified trajectory.

Identification methodology for MIMO Hammerstein nonlinear model with process noise

In this paper, we present a methodology for identifying the multi-input–multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented comprises neural fuzzy models (NFMs) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, that is, the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function-based least squares method in the presence of measurable input–output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input–output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a nonlinear process, and it is known from the research results that compared with existing identification techniques, the methodology utilized achieved higher identification accuracy.

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

ABSTRACT Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM’s reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

Strain Partitioning in the Southeastern Tibetan Plateau From Kinematic Modeling of High‐Resolution Sentinel‐1 InSAR and GNSS

AbstractFault slip rates estimated from geodetic data are being integrated into seismic hazard models. The standard approach requires modeling velocities and relative (micro‐)plate motions, which is challenging for fault‐based models. We present a new approach to directly invert strain rates to solve for slip rates and distributed strain simultaneously. We generate velocity and strain rate fields over the southeastern Tibetan Plateau, utilizing Sentinel‐1 Interferometric Synthetic Aperture Radar data spanning 2014–2023. We derive slip rates using block modeling and by inverting strain rates. Our results show a partitioning between localized strain on faults and distributed deformation. The direct inversion of strain rates matches the geodetic data best when incorporating distributed moment sources, accounting for a similar proportion to on‐fault sources. The direct strain methodology also aligns best with the independent geological slip rates, especially near fault tips. As high‐resolution strain rate fields become increasingly available, we recommend direct inversion as the preferred practice.

An urban agricultural block construction method based on low-carbon concepts: A case study of the Beijing-Tianjin area in China

With the ongoing progress of urbanization, various issues such as resource scarcity, climate change, and carbon emissions overload have gradually emerged. Urban agriculture (UA) is recognized as a pivotal strategy to promote sustainable urban development. In this context, the study proposes a spatial scale hierarchy, encompassing “single building - building cluster - urban block," to formulate spatial strategies for the theoretical model of urban agricultural blocks. The theoretical model integrates production spaces, production methods, and spatial systems based on relevant design principles and the needs of residents. Nankai District's Qingfeng Road East Block in Tianjin is used as the site for applying and simulating the theoretical model. The results of the study show that UA in a 500m × 500m block can ideally eliminate the need for external food inputs by 100% for starchy vegetables, legumes and fruits, and meat/poultry, while achieving a 254.73% self-sufficiency rate for fungi. Additionally, it can reduce energy inputs by 28.33%, water inputs by 38.13%, and completely eliminate waste output for 8754 people. Carbon emission analysis indicates that the urban agricultural block can reduce approximately 2250.01t CO2 emissions from buildings and waste, with greater carbon-neutral potential in transportation and agriculture, forestry, and other land uses (AFOLU). This is significant for tackling various urban challenges and enhancing future development.

Using optimization approach to design dental implant in three types of bone quality - A finite element analysis

Background/PurposeThe use of finite element (FE) analysis in implant biomechanics offers many advantages over other approaches in simulating the complexity of clinical situations. The aim of this study was to perform an optimization analysis of dental implants with different thread designs in three types of bone quality. Materials and methodsThe three-dimensional FE model of a mandibular bone block with a screw-shaped dental implant and superstructure was simulated. In the optimization analysis, the design variables included the thread pitch and the thread depth of the implant. The objective was to minimize the displacement of the implant to the target value. Three FE models with different bone qualities (D2: better bone quality; D3: ordinary bone quality; D4: poor bone quality) were created. ResultsThe FE results showed that the displacement of the implant and the stress of the cortical bone increased, while the Young's modulus of the cancellous bone decreased. In the D2 bone, changing the thread pitch and thread depth had little effect on cortical stress and implant displacement. However, in D3 and D4 bone, increasing thread depth reduced cortical stress by 40 % and implant displacement by at least 9 %. ConclusionAdjusted thread depth for D3 and D4 bone would reduce crestal bone stress and increase implant stability, but only a little alteration on crestal bone stress and implant stability for D2 bone.

Recent Block Kinematics and Fault Slip Rates in the Pamir, Central Asia, From an Integrated GNSS Velocity Field

AbstractContemporary kinematic characteristics of the Pamir plateau are characterized by (a) significant NS‐shortening across the northern front, (b) EW‐extension within the Pamir and (c) gravitationally driven westward mass‐outflux of the western Pamir into the Tajik basin. The 2015 Mw 7.2 Sarez and 2023 Mw 6.8 Murghab strike‐slip earthquakes highlight the crucial role of shear deformation in the Pamir's interior, but the detailed secular kinematics of the remote Plateau are still elusive. Here, we employ elastic block models to fit GNSS velocities compiled from our own reprocessed velocity solution and from previous studies to determine slip rates along major active faults within and bounding the Pamir plateau. Our favored model separates the Pamir's interior into three micro‐blocks along the NNE‐trending sinistral Sarez‐Karakul fault system and the SSE‐trending Kongur Shan extensional system. The model confirms significant crustal shortening along the Pamir's northern boundary and strike‐slip motion along its western boundary, namely the Pamir thrust system and the Darvaz fault, respectively. In contrast, strike‐slip motion on the eastern boundary is minor. In the interior of the Plateau, we find an upper limit of sinistral shear of 8.8 ± 0.3–9.5 ± 0.2 mm/yr along the Sarez‐Karakul fault system and dextral shear of 6.3 ± 0.3 mm/yr along the WNW‐trending Muji fault. Our model also confirms recent observations of an active, SW‐continuation of the Sarez‐Karakul fault system. In the eastern Pamir, crustal extension mainly occurs across the northern segment of the Kongur Shan extensional system. The recent large strike‐slip earthquakes on the Pamir plateau suggest that shear motion on the plateau is accommodated on a much wider range than only by the Sarez‐Karakul fault system.

Integration of stability factors A, B and C on the Mathews Stability Graph into a mining-level optimization algorithm

Purpose. This study aims to enhance the optimization approach by integrating stability analysis using Mathews Stability Graph into stope mining-level optimization algorithm. Methods. The programming language is employed to integrate the Mathews Stability Graph into the stope mining-level optimization algorithm at the preliminary optimization stage, providing dimensional constraints based on rock conditions. Algorithm validation is conducted using three scenarios reflecting rock conditions in the block model: fixed stope dimensions with a maxi-mum stope size, fixed stope dimensions with a minimum stope size, and variable stope dimensions based on the proposed algorithm. Additionally, to validate the stability of the stope in the optimization algorithm, the stability of each stope wall is confirmed by back plotting on a stability graph. Findings. The algorithm manages to create a stope design that complies both with geotechnical and economic aspects, based on the data provided in the synthetic block model. Originality. Optimal stope design is often determined by the stope’s economic parameter, whereas geotechnical variables, easily available in the block model, are neglected. The proposed algorithm aims to include stability analysis using the Mathews Stability Graph into the stope mining-level optimization algorithm. Practical implications. The method was successfully tested using data from a block model simulating the conditions of a real ore body in Indonesia. In addition, the method may be used by mine planners during the early stage of feasibility assessment.

К вопросу о теоретических основах государственного регулирования экономики

The article considers some theoretical aspects of the fundamentals of the development of approaches to the implementation of state regulation within the framework of various economic directions and concepts. The main features inherent in various approaches to the implementation of state regulation are highlighted. The author's vision of the economic and theoretical component of the term "State regulation" has been formed. Using the block model, the mechanism of interaction of the components of state regulation of the economy is presented. An economic and practical description of the functioning of the elements of such a mechanism and the consequences of its impact on the economy of the country and the market has been carried out. Based on the conducted research, a promising industry has been identified for practical testing of the research results.

Estimasi Cadangan Batugamping Menggunakan Block Model Berdasarkan Metode Interpolasi IDW pada IUP OP 231 Karangkemojing

The increasing development of infrastructure development has an impact on the increase in cement demand. Cement is used as the main raw material in making concrete and other construction materials. Therefore, limestone mining as a raw material for cement is also increasing. One of the areas that has prospects for limestone mining is IUP OP 231 Karangkemojing which is still in the exploration stage. The purpose of this study is to determine the geological and geomorphological conditions of the study area, determine the amount of limestone reserves, and determine the estimated mine life based on production targets. The methods used in this research are field observation and laboratory analysis including petrographic analysis and X-Ray Fluorescence (XRF) analysis. Furthermore, the calculation of limestone reserves was carried out using block modeling and the Inverse Distance Weighting (IDW) interpolation method processed using Surpac software. Based on field geological mapping, the research area is included in the wavy hilly and steep hilly structural landforms, which consist of two lithological units, namely sandstone and claystone intermixture units and limestone units. The results of the calculation of limestone reserves in the study area were obtained at 61,444,362 tons with an estimated mine life of about 24 years and 6 months.

RCYOLO: an innovative surface defect detection model for magnetic blocks integrating RevCol and Yolov8s

ABSTRACT The surface defect detection of magnetic blocks faces challenges such as low accuracy, high missed detection rates, and weak anti-interference capabilities. Additionally, existing models are often large in size and require significant computational resources. To address these issues, this article integrated the Yolov8 framework and designed the RCYOLO model to improve detection performance and resource utilization. Firstly, we used RevCol as the backbone, and explored its optimal number of sub-networks to reduce model parameters and complexity, as well as improve model robustness. Secondly, we removed some deep structures from Yolov8s to simplify the model, making it easier to deploy. Afterwards, we developed a more efficient feature extraction module, C2f_SA, and replaced all existing modules with it to compensate for accuracy loss caused by simplification and improve overall detection performance. Finally, we optimized reg_max to explore the best minimum value for detecting defects in magnetic blocks to reduce unnecessary computational burden. Experimental results show that compared to Yolov8s, although RCYOLO’s mAP0.5 was reduced by 0.2%, it significantly reduced the model parameters, FLOPs, and model size by 51.64%, 30.31%, and 50.22%, respectively, achieving a better balance between accuracy and resources. Moreover, RCYOLO demonstrated good detection performance compared to other improved models.

Scheduling as a Constraint Satisfaction Problem (Using the Example of Open-Pit Minе Production Scheduling Problem)

The research described in the work is aimed at developing methods for Scheduling. The fundamental disadvantage of the existing methods of Mixed-Integer Linear Programming in application to the problems under consideration is the fact that they are too demanding on the amount of RAM. The difficulty of applying local search procedures to such high-dimensional problems is to develop an effective way to find an acceptable initial approximation and determine the neighboring state transition function, which would allow achieving the optimum fast enough. In the Operations Research Theory, adding additional conditions to a problem can lead to a fundamental change in the problem-solving scheme. The methods proposed in the study are implemented within the framework of the Constraint Programming Paradigm which makes it possible to represent the subject domain dependencies saving RAM, as well as to provide the ability to step-by-step take into account heterogeneous problem conditions without essentially changing the scheme of finding solutions. A significant part of the research deals with methods of logical inference on constraints to reduce the search space and speed up the computational process. The approach to scheduling is illustrated by the Open-Pit Mine Production Scheduling Problem, which was first proposed to be solved as a Constraint Satisfaction Problem. In order to find the first feasible solution, a «greedy» search method is proposed, the result of which can be improved using the developed hybrid method. Both methods rely on original procedures of inference on constraints. The proposed approach has proven its efficiency for block models with sizes of tens and hundreds of thousands of blocks.