Block Model Research Articles

Supporting the academic success of underrecognised higher education students through an immersive block model

ABSTRACT Immersive block models are an innovative approach to higher education (HE) that engage students over shorter periods of study than traditional semesters and aim to improve students’ academic success through a more focused and active learning experience. These models may be of particular benefit for HE students from cohorts that are historically underrecognised. We investigate this potential by comparing academic achievement (N = 30,108) outcomes for minoritised student groups in a six-week immersive block model and a traditional 13-week model at a public Australian university. Statistically significant improvements in academic success were observed for students identified as low socio-economic status, first-in-family, regional/remote, Indigenous, and registered with a disability, with improvements particularly pronounced for the latter two student groups. This case study indicates that immersive block models providing the conditions for more focused and active education can significantly enhance the academic achievement of underrecognised students. Immersive block models can contribute towards the important goal of developing more inclusive HE.

Bad Local Minima Exist in the Stochastic Block Model

AbstractWe study the disassortative stochastic block model with three communities, a well-studied model of graph partitioning and Bayesian inference for which detailed predictions based on the cavity method exist (Decelle et al. in Phys Rev E 84:066106, 2011). We provide strong evidence that for a part of the phase where efficient algorithms exist that approximately reconstruct the communities, inference based on maximum a posteriori (MAP) fails. In other words, we show that there exist modes of the posterior distribution that have a vanishing agreement with the ground truth. The proof is based on the analysis of a graph colouring algorithm from Achlioptas and Moore (J Comput Syst Sci 67:441–471, 2003).

Almost exact recovery in noisy semi-supervised learning

Abstract Graph-based semi-supervised learning methods combine the graph structure and labeled data to classify unlabeled data. In this work, we study the effect of a noisy oracle on classification. In particular, we derive the maximum a posteriori (MAP) estimator for clustering a degree corrected stochastic block model when a noisy oracle reveals a fraction of the labels. We then propose an algorithm derived from a continuous relaxation of the MAP, and we establish its consistency. Numerical experiments show that our approach achieves promising performance on synthetic and real data sets, even in the case of very noisy labeled data.

Investigation of hip flexibility training on dancesport optimization using machine learning video analysis

Dancesport, particularly the Paso Doble, requires high agility, coordination, and flexibility, especially in the hips. This study investigates the impact of an eight-week targeted Hip Flexibility Training (HFT) program on the performance of professional Paso Doble dancers. The need for this research stems from the lack of objective, data-driven evaluations in the field, where traditional methods rely heavily on subjective assessments. Previous studies have examined general flexibility in dance, but few have focused on the direct Biomechanical Effects (BF) and Physiological Effects (PE) of specific HFT on dancers. Further, such studies could not accurately measure hip joint movements and their coordination in order to achieve dance performance efficiency. The proposed study used motion-capturing devices to collect key movement data that impacts performance efficiency. The collected data is analyzed using the hybrid receptive field block (RFB) and residual network (ResNET) ML models to study the pre- and post-HFT results. Twelve highly trained dancers were assigned to have biomechanical and physiological metrics measured after and before the training. The data analysis has shown that there has been a significant increase in hip flexion from 65.4 ± 4.5° to 75.2 ± 3.7° (P < 0.05), hip extension from 25.3 ± 2.4° to 30.1 ± 2.1° (P < 0.05), and joint velocity from 1.18 ± 0.15 m/s to 1.32 ± 0.12 m/s (P < 0.05). Physiological metrics also showed improvements, such as a reduction in Oxygen Consumption (OC) from 2.02 ± 0.21 L/min to 1.85 ± 0.18 L/min (P < 0.05) and Energy Cost (EC) from 50.1 ± 7.2 kJ/min to 45.6 ± 6.4 kJ/min (P < 0.05).

A novel method: YOLO-CE and 3D point cloud-based feature extraction for welding seams of tower bases

Abstract Robotic automated welding of non-standard steel structures presents significant challenges, particularly for electric power tower bases. This study introduces a novel approach that integrates the You Only Look Once—Compact Invert Block and Efficient Local Attention (YOLO-CE) model, an enhanced version of YOLOV8 for 2D image segmentation, with 3D point cloud technology. The YOLO-CE model is used to accurately extract point cloud data from the target area, which is then processed using the MSAC algorithm for efficient plane segmentation. Weld lines are identified through plane equations, allowing for initial weld point cloud extraction. To further refine accuracy, an optimized evaluation equation is developed that accounts for both the distance between the weld point cloud and the fitted plane, and the angle between their normal vectors. This enables precise classification of the weld point cloud. From this classification, key weld feature points are identified, and their exact positions are determined by calculating the distances between these points and their intersections with three planes. The reliability of the proposed method was validated using a robot for precise measurements, with a total error margin of less than 1.5084 mm, demonstrating high accuracy and stability. Post-operation inspections confirmed that the welds were filled and free from defects, meeting all process requirements. The YOLO-CE model achieved a mIoU of 96.38% and a precision of 99.8%, highlighting its effectiveness. This method provides an efficient and precise solution for the automated welding of non-standard steel structural components and has promising application potential.

Strain Partitioning, Interseismic Coupling, and Shallow Creep Along the Ganzi‐Yushu Fault From Sentinel‐1 InSAR Data

AbstractThe Ganzi‐Yushu fault (GYF) is one of the most seismically active fault systems in eastern Tibet, having experienced five M > 7.0 earthquakes over the past 300 years. Here, we use Sentinel‐1 InSAR data spanning from 2014 to 2023 to derive the interseismic velocity fields along the GYF. We calculate the strain rate fields for the entire fault system, which reveal localized strain accumulation along the GYF as well as along two secondary faults within the Bayan Har block. The inversion results obtained from the elastic block model indicate left‐lateral strike slip rates of 4.0–6.5 mm/yr along the GYF and five locked segments distributed along strike. Furthermore, we identify two shallow creeping segments on the InSAR velocity maps. Based on the locations of the creeping sections and their temporal decay characteristics, we infer that the shallow creep along the GYF is afterslip of the 2010 Yushu earthquake.

Granular characterisation of coal spoil dump using unmanned aerial vehicle data to enhance stability analysis

Open pit mining operations generate significant spoil dumps that need to be characterised for stability to identify potentially unstable slopes. However, the current subjective practice for spoil characterisation often involves tedious and risky field work. To this end, this study demonstrated the use of periodically acquired unmanned aerial vehicle (UAV)-based images over a coal mine spoil dump in New South Wales, Australia. A granular approach that captures the variability of each truck offload pile on a dump was adopted through morphology-based segmentation and ensemble algorithm-based classification which consolidates predictions from multiple classifiers. Overall accuracy of over 90% in the material characterisation based on the classification framework was achieved. The two-dimensional classification outcome was then transformed into three-dimensional (3D) block models using a point-based interpolation approach for stability analysis. The factor of safety derived from the granular approach offered improved assessment of failure risk compared to the conventional approaches, which treat the entire dump as a uniform category. This rapid classification and assessment method proposed in this study will help reduce the uncertainty associated with the variability of spoil dumps in slope stability assessments, thereby enhancing the safety and efficiency of mining operations.

Network community detection via neural embeddings

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.

Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels

Two-sample network hypothesis testing is an important inference task with applications across diverse fields such as medicine, neuroscience, and sociology. Many of these testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known. This assumption is often untrue, and the power of the subsequent test can degrade when there are misaligned/label-shuffled vertices across networks. This power loss due to shuffling is theoretically explored in the context of random dot product and stochastic block model networks for a pair of hypothesis tests based on Frobenius norm differences between estimated edge probability matrices or between adjacency matrices. The loss in testing power is further reinforced by numerous simulations and experiments, both in the stochastic block model and in the random dot product graph model, where the power loss across multiple recently proposed tests in the literature is considered. Lastly, the impact that shuffling can have in real-data testing is demonstrated in a pair of examples from neuroscience and from social network analysis.

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.

Unraveling the impact of salinity on biofouling on ultrafiltration membranes: A spectroscopic and microscopic view

Biofouling significantly hampers the performance and longevity of ultrafiltration (UF) membranes employed in saline wastewater treatment. This research addresses a crucial knowledge gap by meticulously investigating the influence of salinity variations (0–10 g/L NaCl) on biofouling characteristics, utilizing advanced spectroscopic and microscopic techniques. Combined blocking models elucidated the evolution of fouling mechanisms under varying salinity conditions. Higher salinity levels promoted cake layer formation, potentially circumventing initial fouling stages observed under lower salinity conditions. Quantification of confocal laser scanning microscopy images revealed a denser, thinner, and less heterogeneous cake layer at high salinity, composed of proteins, nucleic acids, and α-polysaccharides. The relatively high abundance of polysaccharides may contribute to maintaining osmotic pressure and bacterial cell viability. Moreover, the integration of the confocal Raman mapping technique and non-negative matrix factorization analysis was innovatively applied to characterize the biofouling layer, identifying the important role of carotenoids within the layer. Carotenoids were found to be more abundant in the upper regions of the biofouling layer formed at high salinity, potentially scavenging the reactive oxygen species within bacterial cells. In conclusion, this investigation offers a comprehensive understanding of the salinity-dependent alterations in the chemical composition and spatial structure of the membrane fouling layer. The findings may facilitate the development of targeted mitigation strategies to combat membrane biofouling in saline wastewater treatment applications.

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.