Block Model Research Articles

WRITING COMPARISON/CONTRAST PARAGRAPH WITH BLOCK STYLE

A comparison/contrast paragraph can be organized into two ways known point-by-point, and block model. The most of fifth semester students of English department at Nias Raya University, however, prefer to organize their writing with block model. The reasons for choosing the model were the purposes of this research. This research was carried out by using qualitative descriptive. The data were obtained through unstructured questionnaire. The result of this research showed that there are three reasons of the students in using block model in organizing the comparison/contrast paragraph; the first is easier to be organized; the next is good for the shorter paragraph; and the last is the similarities or differences between the compared subjects is understood easily. Hence, block organization for a comparison/contrast paragraph is the easiest way for the students to construct their paragraph to be interested writing rather than other style. Therefore, this information can be a consideration for the lecturer in teaching the type of the paragraph.

Identification of Membrane Fouling with Greywater Filtration by Porous Membranes: Combined Effect of Membrane Pore Size and Applied Pressure.

Membrane fouling caused by complex greywater synthesized by personal care products and detergents commercially available for household applications was investigated using dead-end microfiltration (MF) and analyzed systematically by a multistage Hermia blocking model as a first attempt. The highest flux decline was associated with the smallest pore size of the membrane (0.03 μm). This effectiveness was more pronounced at higher applied pressures to the membrane. A cake layer was formed on the membrane consisting mainly of silica particles present as ingredients in greywater. Although organic rejection was low by the porous MF membrane, the organic compound contributed to membrane fouling in the filtration stage. With a 0.03 μm pore size of the membrane, dominant fouling mechanisms were classified into three stages as applied pressure increased, such as complete pore blocking, intermediate pore blocking, and cake layer formation. Specifically, during the early stage of membrane filtration at 1.5 bar, membrane fouling was determined by complete pore blocking in the 0.10 μm pore size of the membrane. However, the later stage of membrane fouling was controlled mainly by intermediate pore blocking. Regardless of the applied pressure, pore constriction or standard blocking played an important role in the fouling rate with a 0.45 μm pore size of the membrane. Our results also support that complex formation can occur due to the concentration of organic and inorganic species present in simulated greywater. Thus, strategic approaches such as periodic, chemically enhanced backwashing need to be developed and tailored to remove both organic and inorganic fouling from MF membranes treating greywater.

Learning interpretable collective variables for spreading processes on networks.

Collective variables (CVs) are low-dimensional projections of high-dimensional system states. They are used to gain insights into complex emergent dynamical behaviors of processes on networks. The relation between CVs and network measures is not well understood and its derivation typically requires detailed knowledge of both the dynamical system and the network topology. In this Letter, we present a data-driven method for algorithmically learning and understanding CVs for binary-state spreading processes on networks of arbitrary topology. We demonstrate our method using four example networks: the stochastic block model, a ring-shaped graph, a random regular graph, and a scale-free network generated by the Albert-Barabási model. Our results deliver evidence for the existence of low-dimensional CVs even in cases that are not yet understood theoretically.

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

AbstractThis paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.

Learning Guarantee of SDP Relaxation on Directed Stochastic Block Models

Community detection in networks has been a focal point in various scientific domains, but the study of directed networks remains relatively under-explored despite their prevalence and importance in capturing real-world systems. This work addresses this research gap by focusing on Directed Stochastic Block Models (DSBMs), a natural extension of traditional Stochastic Block Models (SBMs) to directed graphs. The inherent complexity of directionality in DSBMs makes them challenging to analyze, requiring new mathematical frameworks and computational approaches. We introduce an augmented matrix to encapsulate the directional relationships within these networks, providing a nuanced perspective for further analysis. In this work, we prove the information-theoretical threshold for exact recovery in the DSBMs and propose an SDP relaxation that can achieve this threshold, thereby contributing to the theoretical understanding of community detection in the realm of directed graphs.

Insights into professional learning for intensive Block Model: Lessons from a participatory evaluation for capacity building

We share lessons gained through supporting an institution-wide curriculum innovation via a post-graduate professional learning program. At the inception of the innovation, an intensive Block Model (BM) was unfamiliar to both the institution and its professional learning facilitators. The Graduate Certificate of Tertiary Education was re-modelled with BM as the heart of professional learning so academics would encounter BM as students. The program modelled BM principles, reinforced by meta-conversations to provide students with a reflective, immersive experience. Through a participatory evaluation, professional learning facilitators’ individual reflections were distilled to generate collaborative insights into academics’ capacity building for BM. Their lessons inform strategies to cultivate institution-wide capability-building including their own professional growth. Lessons shape the study recommendations. Recommendations originate from effectively engaging time-poor, diverse cohorts. (1) In recognition of the ease with which students can fall behind, embedding strategies to manage their time and stress helps to maintain a realistic study pace. (2) Authentic assessments provide students with useful products for their teaching. (3) Peer-feedback and examples of students’ work exposes them to how their colleagues present their work and illustrates good BM practices. (4) Modelling BM principles must be reinforced by meta-conversations to provide students with a reflective, immersive experience of the pedagogical principals. We observed that well planned efficiencies for students often provide consequent efficiencies for staff. These insights are captured in a model for scalable institutional-based professional learning practice. Capability growth flourishes at the intersection of action, reflection and evaluation. Professional collegial conversations are the catalyst for developing context-relevant professional learning.

Belonging in remote higher education classrooms: The dynamic interaction of intensive modes of learning and arts-based pedagogies

In this paper, the authors explore the conditions that support belonging in remote VU Block Model® teaching. They examine the role of arts-based, embodied pedagogy in promoting engagement in learning, connection between students, and between students and teachers, and in an environment in which vulnerability and risk-taking in learning is valued. A discussion of belonging in higher education and the practice of embodied learning is followed by the reflections of seven participants. These participants were students in a remotely taught, arts-based higher education block unit, which had been mindfully adapted to retain the embodied nature of delivery during the COVID-19 pandemic. After students’ participation in focus group interviews, the lead author constructed found poetry from their comments and reflections. This found poetry forms the dataset through which the questions of belonging are explored. The researchers found that when explored through the lens of the Community of Inquiry Framework, embodied and arts-based practices provided opportunities for students to develop a sense of belonging, deepen understanding of lived experiences, and realise higher education and career goals. This study elevates the voices of students, providing opportunities for higher education teachers to consider the importance of belonging for student success in remote, intensive, and on-campus modes of delivery.

The success, satisfaction and experiences of international students in an immersive block model

Despite growing interest in immersive block models in higher education, very little is known about the experiences of international students in these non-traditional forms of learning. To enable an initial view of how international students perceive and perform in an immersive block model, we used an exploratory mixed methods approach to examine the academic success, satisfaction, and experiences of international students in a 6-week immersive block model at a regional public Australian university. Inferential statistical tests were used to explore the success rates and unit and teaching satisfaction of onshore and offshore international students in the immersive block model and in the traditional trimester model. Overall, the immersive block model made a significant positive difference to the academic success of international students, both onshore and offshore. However, a decline in satisfaction was observed among science and engineering students, contrasting with an increase in satisfaction among business and arts students. Data collected through semi-guided interviews with 10 students from this latter group indicate several key benefits and challenges associated with immersive block learning. Students reported heightened focus and motivation, supportive teaching, and a healthy study-work-life balance. Challenges included not knowing what to expect, forming social connections with classmates, and the fast turnover between assessments. These findings indicate that it is important for institutions to prepare international students well for the pace and time management demands of studying in an immersive block model and to encourage the formation of social connections. Assessment timing, volume, and scaffolding should also be key considerations in immersive block model curriculum design.

Recurrent segmentation meets block models in temporal networks

A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, which leads to having timestamped edges. Many real-world temporal networks have a recurrent or possibly cyclic behaviour. In this paper, our main interest is to model recurrent activity in such temporal networks. As a starting point we use stochastic block model, a popular choice for modelling static networks, where nodes are split into R groups. We extend the block model to temporal networks by modelling the edges with a Poisson process. We make the parameters of the process dependent on time by segmenting the time line into K segments. We require that only H≤K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H \\le K$$\\end{document} different set of parameters can be used. If H<K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H < K$$\\end{document}, then several, not necessarily consecutive, segments must share their parameters, modelling repeating behaviour. We propose two variants where a group membership of a node is fixed over the course of entire time line and group memberships are allowed to vary from segment to segment. We prove that searching for optimal groups and segmentation in both variants is NP-hard. Consequently, we split the problem into 3 subproblems where we optimize groups, model parameters, and segmentation in turn while keeping the remaining structures fixed. We propose an iterative algorithm that requires OKHm+Rn+R2H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O} \\left( KHm + Rn + R^2\\,H\\right)$$\\end{document} time per iteration, where n and m are the number of nodes and edges in the network. We demonstrate experimentally that the number of required iterations is typically low, the algorithm is able to discover the ground truth from synthetic datasets, and show that certain real-world networks exhibit recurrent behaviour as the likelihood does not deteriorate when H is lowered.

Retracted: Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

Retracted: Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

Consistent community detection in inter-layer dependent multi-layer networks

Community detection in multi-layer networks, which aims at finding groups of nodes with similar connective patterns among all layers, has attracted tremendous interests in multi-layer network analysis. Most existing methods are extended from those for single-layer networks, which assume that different layers are independent. In this paper, we propose a novel community detection method in multi-layer networks with inter-layer dependence, which integrates the stochastic block model (SBM) and the Ising model. The community structure is modeled by the SBM model and the inter-layer dependence is incorporated via the Ising model. An efficient alternative updating algorithm is developed to tackle the resultant optimization task. Moreover, the asymptotic consistencies of the proposed method in terms of both parameter estimation and community detection are established, which are supported by extensive simulated examples and a real example on a multi-layer malaria parasite gene network.

Achieving success in post-graduate studies: A qualitative exploration of staff experiences transitioning from traditional to block mode delivery in an Australian university

ABSTRACT Responding to student demand for flexibility in the delivery of classes as well as the potential barriers and enabling factors supporting student success, universities have introduced distinctive educational models, including replacing the standard 12-week, sequential delivery of units of study with 4- or 8-week blocks of one or two units at a time (Block Model). Most Block Model delivery and evaluation has been at the undergraduate level. The aim of this study was to investigate, via individual interviews, university staff experiences of teaching the Block Model at the postgraduate level. Staff affirmed that the Block Model resulted in high levels of engagement in teaching and learning; effective learning management systems; and up-to-date, engaging, and problem-based module design. However, staff also recognised the timetabling of units for students who were employed or had family commitments, and the cognitive burden carried by time-poor staff.

Reducing apical voids for intra-canal sealer injection using a modified passive deflation needle.

The aim of the study was to evaluate the ability of a modified passive deflation needle to reduce the apical voids during the intra-canal iRoot SP sealer injection. A modified passive deflation injection needle was designed. Forty 20°-curved and twenty S-shaped single-canal resin block models were allocated into six groups. Each group was mechanically prepared to #25/04 or #25/06 file; then the root canals were injected with iRoot SP sealer using a modified needle or a normal plastic needle. Radiographs were taken to measure the length of apical void in each specimen. Similarly, twenty single-canal extracted premolars were collected and randomly divided into four groups. Each group was prepared to #25/04 or #25/06 file; then the root canals were injected with iRoot SP sealer using a modified needle or a normal plastic needle. Roots were then scanned using micro-computed tomography (micro-CT), and the volume of voids in root canals was analyzed and compared among groups. Statistical analysis demonstrated that the length and the volumetric percentage of voids were much lower in the modified needle group in both resin block root canal models and extract teeth than the normal plastic needle group (P < 0.05). The modified passive deflation needle can efficiently reduce apical voids during the intra-canal iRoot SP sealer injection.

An Exploratory Study of Undergraduate Nursing Students' Experiences of Group Work in Block Model Teaching.

The study's aim was to examine students' perceptions of group work taught in a block model in the first unit of an undergraduate nursing program. The study used a qualitative descriptive design with open-ended questions. Data from 27 students were analyzed using thematic analysis. Three main themes emerged from the data: 1) unmet expectations, 2) cognitive and metacognitive strategies (subtheme, reflection), and 3) engagement. The benefits and challenges associated with group work using the block model are highlighted, along with implications for education, practice, and further research.

Penanaman Nilai-nilai Pendidikan Karakter Melalui Kegiatan Kepramukaan di Madrasah Tsanawiyah

Character education can be widely described as a school-based process to promote personal development in youth, through the development of virtue and moral values. This paper describes how scoutmasters of MTS Tribakti Al-Iklas 2 Anak Tuha Central Lampung have infused the character education values on scout activities at this school. This study uses qualitative methods, with a case study approach. The data were collected through observation, interview, and documentation. The collected data were then analysed descriptively by using three ways namely data reduction, data presentation, and conclusion or verification. The results show that the infusing of characer education through actualization model scouting activities in collaboration with teachers as well as block models that have been programmed at the beginning of the school year, such as camps and nature exploration. The characters that can be formed are patience, cooperation, responsibility, social care, courage, confidence, perseverance, creative, religious, patriotism, environmental awareness, independence, discipline and hard work. Terefore, it needs a collaboration scoutmasters of with parents student to to succeed the scouting program. For future research, it is recommended to to probe parent's role in supporting students' scouting activities.

Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms

We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation–maximization (EM) and variational EM (VEM) algorithms, which are based on the genetic approach and allow us to accurately explore the parameter space, reducing the chance to be trapped into one of the multiple local maxima of the log-likelihood function. Their performance is examined through an extensive Monte Carlo simulation study where they are employed to estimate latent class, hidden Markov, and stochastic block models and compared with the standard EM and VEM algorithms. We observe a significant increase in the chance to reach global maximum of the target function and a high accuracy of the estimated parameters for each model. Applications focused on the analysis of cross-sectional, longitudinal, and network data are proposed to illustrate and compare the algorithms.

Microvascular network based on the Hilbert curve for nutrient transport in thick tissue.

To address the uneven nutrient distribution within three-dimensional (3D) tissue models and organoids currently used in medical research, this study introduces a microvascular network based on the Hilbert curve. Our aim was to develop innovative solutions for enhancing nutrient supply in thick tissue models in vitro. By using 3D bioprinting, we engineered microvascular networks of varying Hilbert orders and validated their efficacy in enhancing nutrient uniformity through numerical simulations and experiments. These networks facilitated broader and more uniform nutrient distribution throughout the thick tissue models, particularly the 2° Hilbert microvascular structure, which occupies less space and significantly reduces regions of cellular death. Furthermore, we explored the potential of assembling larger tissue constructs using the 2° Hilbert microvascular network, showcasing its applicability in constructing large-scale biological models. The findings suggest that the 2° Hilbert microvascular structure is particularly effective in ensuring adequate nutrient delivery, thus enhancing the viability and functionality of large-volume tissue models. These innovations hold significant promise for advancing the fields of tissue engineering and regenerative medicine by improving nutrient delivery to in vitro thick tissue block models. This provides a robust foundation for future in vitro research and clinical applications, potentially leading to more effective treatments and interventions in the medical field. The development of these microvascular networks represents a crucial step forward in overcoming the limitations of current 3D tissue models and organoids, paving the way for more sophisticated and reliable biomedical research tools.

Modelling the probability of roof fall using digital tools

Information on rock mass characteristics, encompassing the overall quality of the rock masses and various geological parameters, is crucial in mining operations. This data guides the selection of mining techniques and the design of roof control methods to minimize the risk of flaking. However, the lithology of roof rocks is often so variable that predicting their behaviour, especially when disturbed by mining activities, is challenging. The study begins with a detailed review of existing literature on roof fall mechanisms and predictive modelling techniques. It was described the dataset used, which includes geological, environmental, and operational parameters collected from various mining sites. This article outlines a methodology for conducting detailed studies of the lithology of coal seam roof rocks in roadways and longwalls as mining progresses. According to the research, forecasting the extent of roof rock fall during the planning stage of mining operations can inform the implementation of appropriate countermeasures to minimize this phenomenon before exploitation begins, thereby significantly reducing ongoing mining costs. Block modelling and stratigraphic (mesh) modelling were employed to predict the volume of rockfall that could contaminate the ore in the analysed mining plot. In the future, this proposed methodology could be used to analyse the progression of roof falls, estimate the mass of falling roof rocks, and calibrate a system for continuous measurement of ore quality in mine workings.

Características estruturais e fatores de influência da rede espacial do comércio agrícola: evidências de 15 países do RCEP

ABSTRACT: Based on national agricultural trade panel data of Regional Comprehensive Economic Partnership (RCEP) countries from 2002 to 2020, the structural characteristics of the RCEP national agricultural trade spatial network are reconstructed by the social network analysis method in this paper. Specifically, the characteristics consist of overall characteristics, individual characteristics, core-periphery structure analysis, and block models. Moreover, the influence factors on the agricultural trade network are also examined by the QAP regression model. The conclusions are summarized as follows. Firstly, the agricultural trade in RCEP countries has significant spatial correlation, showing favorable stability and accessibility. Secondly, Australia, China, Thailand, and Vietnam are the central actor’s leading position in the correlation network. While Cambodia, Laos, Myanmar, and other countries are in the marginal position. Thirdly, the network can be divided into four blocks, including the net benefit block, the two-way spillover block, the broker block, and the net spillover block. The spillover effect between the blocks is transitive. And finally, geographical distance, economic and social conditions, resource endowment, and language proximity index significantly impact the RCEP agricultural trade network structure.

A porcine large animal model of radiofrequency ablation-induced left bundle branch block.

Electrocardiographic (ECG) features of left bundle branch (LBB) block (LBBB) can be observed in up to 20%-30% of patients suffering from heart failure with reduced ejection fraction. However, predicting which LBBB patients will benefit from cardiac resynchronization therapy (CRT) or conduction system pacing remains challenging. This study aimed to establish a translational model of LBBB to enhance our understanding of its pathophysiology and improve therapeutic approaches. Fourteen male pigs underwent radiofrequency catheter ablation of the proximal LBB under fluoroscopy and ECG guidance. Comprehensive clinical assessments (12-lead ECG, bloodsampling, echocardiography, electroanatomical mapping) were conducted before LBBB induction, after 7, and 21days. Three pigs received CRT pacemakers 7days after LBB ablation to assess resynchronization feasibility. Following proximal LBB ablation, ECGs displayed characteristic LBBB features, including QRS widening, slurring in left lateral leads, and QRS axis changes. QRS duration increased from 64.2 ± 4.2ms to 86.6 ± 12.1ms, and R wave peak time in V6 extended from 21.3 ± 3.6ms to 45.7 ± 12.6ms. Echocardiography confirmed cardiac electromechanical dyssynchrony, with septal flash appearance, prolonged septal-to-posterior-wall motion delay, and extended ventricular electromechanical delays. Electroanatomical mapping revealed a left ventricular breakthrough site shift and significantly prolonged left ventricular activation times. RF-induced LBBB persisted for 3weeks. CRT reduced QRS duration to 75.9 ± 8.6ms, demonstrating successful resynchronization. This porcine model accurately replicates the electrical and electromechanical characteristics of LBBB observed in patients. It provides a practical, cost-effective, and reproducible platform to investigate molecular and translational aspects of cardiac electromechanical dyssynchrony in a controlled and clinically relevant setting.