Cohesive Structures Research Articles

A Comparison of Medical Education Policies in Japan and Singapore with a Focus on Governance: Implications for Korea

Among Asian nations, Japan, Singapore, and South Korea exemplify countries with high standards of medical quality. This review explores the differences in medical education policies between Japan and Singapore, particularly concerning governance, and discusses the implications for South Korea’s medical education policies. Relevant documents were analyzed by referencing scholarly articles and data from governmental and expert organizations in each country. In Japan, advances in medical education policies include initiatives such as the regional quota system and the core curriculum model, which emphasize stakeholder engagement and transparency. However, challenges persist due to limited stakeholder participation, necessitating a transition toward a more equitable governance paradigm. Singapore’s model features robust public-private partnerships with minimal direct governmental intervention, emphasizing innovation and community integration, as seen in the Healthier SG project. These case studies demonstrate effective governance involving significant stakeholder collaboration and strategic financial investments. Conversely, South Korea’s medical education policies face challenges from a predominantly government-centric approach, with an absence of cohesive governance structures and inadequate involvement from essential professional stakeholders. This situation has led to policy inconsistencies and a deficit of strategic direction, exacerbated by insufficient financial support for educational infrastructure and program development. The experiences of Japan and Singapore indicate that it would be beneficial for South Korea to adopt integrated governance frameworks that prioritize transparency and collaboration. Furthermore, increasing financial investment in medical education could mitigate existing deficiencies and improve the quality and effectiveness of its healthcare education system.

What is behind water user satisfaction with irrigation organizations´ performance? An empirical analysis under different water scarcity conditions

The level of satisfaction of individuals within a community sharing a common pool resource is essential to strengthening collective actions. In the case of irrigation water, which is becoming a critical resource due to its declining availability in many regions worldwide, the satisfaction level in Water User Associations (WUAs) is not guaranteed and also varies across individuals within these organizations. This study analyzed the satisfaction of users in WUAs and identified organization- and user-level characteristics associated with it. Using a sample of 393 users of 20 WUAs from the Mediterranean portion of Chile, we performed confirmatory factor analysis, correlational analyses, and multilevel regressions. A composite indicator of user satisfaction showed significant correlations with the positive evaluation of the WUA staff performance, the effectiveness of monitoring and enforcement, and the confidence that other users do not withdraw more water than they are allocated. We also compared users’ satisfaction levels from WUAs facing higher and lower water scarcity problems and found that user satisfaction is significantly higher in the latter case. The results of the multilevel econometric models show that in WUAs under higher water scarcity, user satisfaction is affected by several structural characteristics (water withdrawal mechanism and location along the canal) and users' human and social capital variables (experience, education, and participation in organizations); in contrast, in WUAs with a low level of scarcity, few covariates were significant while unobserved characteristics became more relevant (34 % of total variance explanation). Our study contributes to the existing literature on user satisfaction in community resource management, which could be useful to promote effective and cohesive governance structures as, ultimately, users determine cooperation rates and willingness to participate in WUA actions.

Multilateral collaborative governance of Arctic shipping safety: Examining the impact of the Arctic Council via collaborative network analysis

Global climate change has dramatically reduced Arctic sea ice, opening new potential shipping routes, which has garnered international attention. This study explores the role of the Arctic Council in the collaborative governance network focused on Arctic shipping safety. For this purpose, a collection of 49 collaborative events initiated by international organisations and the Arctic Council was used as the data sample. These events are divided into two groups, one containing all the events and the other excluding those initiated by the Arctic Council. As a result, two collaborative networks are developed and evaluated based on network density, average path length, network transitivity, centrality indices (degree, closeness, betweenness), and clustering analysis. The results demonstrate that the Arctic Council significantly enhances the network density and transitivity of the international collaboration network, and reshapes core-periphery dynamics and cohesive subgroup structures. For instance, under the influence of the Arctic Council, Canada, France, and Japan surpass the United Kingdom in centrality (degree, closeness, betweenness). Results of the clustering coefficients reveal the Arctic Council's impact on connections among states, leading to shifts in collaboration patterns. Additionally, subgroup cohesion and density coefficients confirm the Arctic Council's role in promoting collaboration among states and restructuring governance networks.

All eyes on the signal? - Mapping cohesive discourse structures with eye-tracking data of explanation videos

In this paper, we consider the issue of how the fine-grained multimodal design of educational explanation videos, such as those widely available on YouTube and other platforms, may be made accessible to empirical studies of reception and effectiveness. This is necessary because previous research has often led to conflicting conclusions concerning the roles of particular design elements. We argue that this may largely be due to insufficient characterizations of multimodal design itself. To achieve tighter control of this potential source of variation, we present a multimodal descriptive annotation framework drawing on multimodal (cohesive) film discourse analysis. This framework is seen as a critical first step toward being able to highlight just those differences in design that have functional consequences. For such consequences to accrue, however, viewers need to attend differently to corresponding design differences. The goal of the current paper, therefore, is to use eye-tracking techniques to explore the extent to which discourse structures revealed by our analytic framework relate to recipients' attention allocation. We hypothesize that any potentially emerging anomalies in regards to discourse organization, such as instances of unsuccessful cohesion signaling, may have correlations in the behavioral data. We report our current state of development for performing this kind of multimodal cohesion analysis and some of the unresolved challenges raised when considering how such analyses may be related to performance data.

Family purpose: an empirical investigation of collective purpose

ABSTRACT Individual purpose is associated with positive outcomes. Collective purpose, or an enduring intention shared among members of a group for how they seek to contribute to the world beyond their group, may be similarly beneficial. Collective purposes stand to benefit the groups that pursue them as well as the communities that groups find purpose in serving. Despite this, limited research has explored collective purposes. As an instance of collective purpose, the present study explored family purpose. Interviews were conducted with members (N = 87) of 25 families. Findings suggest family purposes exist, they take varied forms based on the target of family members’ shared commitments, and several factors support their pursuit, including shared moral and/or civic virtues, cohesive family structures, family purpose champions, foundational religious beliefs, humble leaders, and shared family identities. Implications of these findings, both for the study of family purpose in particular and collective purposes more generally, are addressed.

네트워크 텍스트분석을 통한 치매대상자 인간중심돌봄 연구 경향 분석

The purpose of this study is to explore research trends in person-centered care for people with dementia.To this end, in this study, the papers searched for 'dementia', 'human-centered', 'person-centered', 'patient-centered', and 'care' were selected as research subjects among the papers contained in the domestic academic research information journal. Fifty-two academic papers suitable for this research topic were analyzed, and network analysis was performed on the research subject. As a result of the study, 'long-term care institutions for the elderly', 'behavioral psychological symptoms', 'nursing caregivers', and 'the elderly' were derived as the core main subjects in the human-centered care research related to dementia subjects, and the results of the connection centrality index related to the core main subjects were also high in the order of 'long-term care institutions for the elderly', 'behavioral psychological symptoms', 'nursing care workers', and 'the elderly', similar to the results of the frequency analysis. As a result of grouping the subjects with high conceptual relationships, five cohesive structures (Humanity Tude, adult guardianship system for decision-making of the elderly with dementia, communication skills for the care of dementia patients, ethics, moral anguish, social welfare for the elderly with dementia, working environment of dementia care workers in long-term care facilities for the elderly, and emotional labor) were derived. Based on the results of this study, it is thought that in order to provide human-centered care for dementia patients admitted to long-term care institutions for the elderly, it is necessary to provide educational interventions including communication skills, ethical awareness, and coping with behavioral and psychological symptoms of dementia patients for nursing care workers, who are the main caregivers.

Potential of Pinhão husk (Araucaria angustifolia) as a structural reinforcement agent in the properties of edible films of Pinhão flour and gelatin

AbstractIn this study, the use of Pinhão husk as a source of reinforcement material for development of edible films, where the Pinhão seed flour and bovine gelatin were used as matrices for the films. Mechanical properties, water vapor permeability, solubility and opacity, microstructure, and thermal degradation characterized the films produced. The films presented homogeneous and cohesive structures. Pinhão husk content positively affected film properties by increasing tensile strength (TS) and decreasing water vapor permeability (WVP), with Pinhão flour film formulations (5.0% Pinhão flour, 1.2% glycerol, and 0.4% Pinhão husk) and gelatin (5.0% gelatin, 2.0% glycerol, and 0.4% Pinhão husk) those that presented the best results (5.06 MPa for TS and 0.14 g.mm/kPa.h.m2 for WVP) and (3.88 MPa for TS and 0.28 g.mm/kPa.h.m2 for WVP), respectively The thermal degradation study revealed that the films are stable at temperatures below 150°C, losing only free water and volatile compounds of low molecular weight. Pinhão husk can reinforce films, making them suitable as biodegradable and edible packaging materials for eco‐friendly food products. This contributes to the circular economy, preserves biodiversity, and reduces plastic waste, offering promising sustainable packaging solutions.

Multiresolution Local Spectral Attributed Community Search

Community search has become especially important in graph analysis task, which aims to identify latent members of a particular community from a few given nodes. Most of the existing efforts in community search focus on exploring the community structure with a single scale in which the given nodes are located. Despite promising results, the following two insights are often neglected. First, node attributes provide rich and highly related auxiliary information apart from network interactions for characterizing the node properties. Attributes may indicate the community assignment of a node with very few links, which would be difficult to determine from the network structure alone. Second, the multiresolution community affords latent information to depict the hierarchical relation of the network and ensure that one of them is closest to the real one. It is essential for users to understand the underlying structure of the network and explore the community with strong structure and attribute cohesiveness at disparate scales. These aspects motivate us to develop a new community search framework called Multiresolution Local Spectral Attributed Community Search (MLSACS). Specifically, inspired by the local modularity, graph wavelets, and scaling functions, we propose a new Multiresolution Local modularity (MLQ) based on a reconstructed node attribute graph. Furthermore, to detect local communities with cohesive structures and attributes at different scales, a sparse indicator vector is developed based on MLQ by solving a linear programming problem. Extensive experimental results on both synthetic and real-world attributed graphs have demonstrated the detected communities are meaningful and the scale can be changed reasonably.

Dispersion of low concentration of particulate or fibrillated protein fillers into plant protein melts and their impact on the elasticity and tenderness gap of plant-based foods

The development of plant-based foods has emerged as a promising strategy to decrease meat consumption. Using predominantly globular plant proteins challenges the crafting of cohesive, viscoelastic, and anisotropic structures similar to animal meat. In this study, we investigated the effect of incorporating low concentrations (<1%, w/w) of the polar gelatin or the non-polar zein on the mechanical properties of high moisture meat analogue prototypes. These proteins were studied in both particulate (bulk) and nano-fibrillated forms. Building on our previous work, soy protein (68% protein) and mung bean protein (81% protein) concentrates were chosen based on their distinct protein purity and high propensity to rearrange into new ordered secondary structural elements upon extrusion. Gelatin and zein were incorporated into the extrusion process via aqueous dispersion in particulate form or previously converted into nanofibers (210–440 nm diameter) using food-grade electrospinning. The incorporation of both fillers (either particulate or fibrillate) decreased the elasticity and Warner-Bratzler Shear Force (WBSF, known to be inversely correlated to meat tenderness) of mung bean extrudates. However, the addition of 0.1% fibrillated gelatin or particulate/fibrillated zein increased up to 44% or 22% the WBSF of soy extrudates, respectively. In all cases, the incorporation of fillers caused dramatic concentration-dependent changes in the secondary structures (FTIR) of soy proteins and the water mobility (LF-NMR) of extrudates. This work highlights the potential of multifunctional fillers and the combination of top-down (extrusion) and bottom-up (fibrillation) approaches to reduce the tenderness gap between animal meat and structured foods made with less-refined plant-protein fractions.

Maximal D-Truss Search in Dynamic Directed Graphs

Community search (CS) aims at personalized subgraph discovery which is the key to understanding the organisation of many real-world networks. CS in undirected networks has attracted significant attention from researchers, including many solutions for various cohesive subgraph structures and for different levels of dynamism with edge insertions and deletions, while they are much less considered for directed graphs. In this paper, we propose incremental solutions of CS based on the D-truss in dynamic directed graphs, where the D-truss is a cohesive subgraph structure defined based on two types of triangles in directed graphs. We first analyze the theoretical boundedness of D-truss given edge insertions and deletions, then we present basic single-update algorithms. To improve the efficiency, we propose an order-based D-Index, associated batch-update algorithms and a fully-dynamic query algorithm. Our extensive experiments on real-world graphs show that our proposed solution achieves a significant speedup compared to the SOTA solution, the scalability over updates is also verified.

A fast approximate method for k-edge connected component detection in graphs with high accuracy

Finding k-edge connected components (k-ECCs) is widely used to investigate the major cohesive structures within a graph. Traditional methods utilizing global minimum cut to split the graph iteratively are too slow to deal with graphs of a large size. Recently, approximate algorithms based on a decomposition framework have been proposed to speed up the process. However, these algorithms produce low-quality results in graphs containing vertex pairs that are k-connected but do not belong to a k-ECC. In this paper, we propose a decomposition framework that utilizes a local edge connectivity check method to obtain high-quality results in a shorter period. The proposed method utilizes an improved maximum flow algorithm to detect k-ECCs from local k-cores by merging more vertices in each iteration than existing methods. Moreover, a pruning strategy is applied in the decomposition framework to reduce unnecessary branches in the decomposition tree. Our experimental results on both synthetic and real graphs show that our proposed algorithm is faster than the state-of-the-art algorithms in most datasets and achieves effective results among the approximate algorithms with a 96% accuracy on our experimental datasets.

Community stressors and coping mechanisms in accessing the health system during a double crisis: a qualitative case study from Yangon Region, Myanmar

BackgroundDue to the COVID-19 pandemic and political crisis, Myanmar’s health system has suspended routine services while struggling to respond to the pandemic. Many people who need continuous care, like pregnant women and people with chronic illnesses, have been facing challenges in seeking and receiving essential health services. This study explored community health seeking practices and coping mechanisms, including their views on health system stressors.MethodsThis was a cross-sectional qualitative study based on 12 in-depth-interviews with pregnant people and persons with pre-existing chronic health conditions in Yangon. Sampling was purposive, convenience-based and snowball sampling was also used. The 3-delays framework was used to understand how persons were interacting with and accessing health care services; community and health system stressors and coping mechanisms in relation to COVID-19 were also identified.ResultsFindings showed that Yangon region was the hardest hit with both the pandemic and political crisis and its health system was greatly affected. People were unable to access essential health services on time. The health facilities were not available to see patients, with serious shortages in human resources, medicines and equipment, resulting in interruption of essential routine services. The prices of medicines and consultation fees, and transportation costs, increased during this period. Limited options for accessing care were available due to travel restrictions and curfews. It became challenging to receive quality care because of unavailable public facilities and private hospitals being costly.Despite these challenges, the Myanmar people and health system have shown resilience. Cohesive and organized family support structures and extended and deep social networks played an important role in accessing health care. At times of emergency, people relied on community-based social organizations for transportation and accessing essential medicines. The health system also showed resilience through establishing new service provision options, such as teleconsultations, mobile clinics, and sharing medical advice through social media.ConclusionsThis is the first study in Myanmar to explore peoples’ perceptions on COVID-19, the health system and their healthcare experiences during political crisis. Although there is no easy way to cope with this dual hardship, the people and the health system, even in a fragile and shock-prone setting like Myanmar, stayed resilient by developing alternative pathways for seeking and providing health services.

Eudaimonic Leadership: The Why and How of building a company that nudges towards being the best version of oneself

In a day and age where remote or hybrid work is becoming the norm, we need to find better ways of creating cohesive human structures built upon empathy, trust, and excellence. To do that, we need to be able to generate the appropriate working ecosystems that are capable of fostering eudaimonic growth in all the hierarchical levels of the organisation. In this paper we give way to the toolkit that has allowed BeWay to grow from four individuals in the beginning of 2021 up to over 70 professionals by May 2023, becoming the global leader of Applied Behavioral Science in the private sector.

Reinforcement learning based coarse-to-fine search for the maximum [formula omitted]-plex problem

The maximum k-plex problem is a computationally challenging problem, which emerged from graph-theoretic social network studies in the context of community detection. As a relaxation of the traditional maximum clique problem, intended to identify cohesive structures within a graph that are not necessarily fully connected, the aim of this problem is to find the largest set of vertices in a graph in which all vertices are not adjacent to at most k vertices within this set. This paper introduces an effective hybrid local search algorithm for solving the maximum k-plex problem that combines a stochastic local search method and a reinforcement learning strategy. The proposed approach includes a number of distinguishing features, including: a new coarse-to-fine strategy to balance the search process, a distance-and-quality reward for actions, and a parameter control mechanism based on reinforcement learning. We conduct a computational analysis of the key components of the proposed algorithm, assessing their impact on the overall performance. Extensive experiments for the maximum k-plex problem (k=2,3,4,5) on 80 benchmark instances from the second DIMACS Challenge demonstrate that the proposed approach competes favourably with the state-of-the-art algorithms from the literature.

Familiarity-Based Collaborative Team Recognition in Academic Social Networks

Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose faMiliarity-based cOllaborative Team recOgnition (MOTO) algorithm to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then, MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale dataset. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by the MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution.

Optimizing Business Strategies: A Structural Mechanics Lens on Management

In the dynamic landscape of modern business, the integration of a structural mechanics lens into management practices emerges as a transformative approach. This article explores the application of principles from structural mechanics to optimize business strategies, foster resilience, and navigate the complexities of the contemporary corporate environment. The structural mechanics lens involves viewing organizations as dynamic structures, where equilibrium, resilience, and efficiency become focal points. Through this lens, strategic decision-making is guided by achieving a harmonious equilibrium, organizational resilience is fortified akin to structural integrity, and operational efficiency is inspired by the mechanics of workflow optimization. Strategic alignment and cross-functional collaboration are emphasized to build cohesive business structures, while educational evolution aims to nurture structural thinkers. The conclusion highlights the importance of applying structural wisdom to build stable and adaptable business foundations in a rapidly changing business landscape.

How students reason about matter flows and accumulations in complex biological phenomena: An emerging learning progression for mass balance

AbstractIn recent years, there has been a strong push to transform STEM education at K‐12 and collegiate levels to help students learn to think like scientists. One aspect of this transformation involves redesigning instruction and curricula around fundamental scientific ideas that serve as conceptual scaffolds students can use to build cohesive knowledge structures. In this study, we investigated how students use mass balance reasoning as a conceptual scaffold to gain a deeper understanding of how matter moves through biological systems. Our aim was to lay the groundwork for a mass balance learning progression in physiology. We drew on a general models framework from biology and a covariational reasoning framework from math education to interpret students' mass balance ideas. We used a constant comparative method to identify students' reasoning patterns from 73 interviews conducted with undergraduate biology students. We helped validate the reasoning patterns identified with &gt;8000 written responses collected from students at multiple institutions. From our analyses, we identified two related progress variables that describe key elements of students' performances: the first describes how students identify and use matter flows in biology phenomena; the second characterizes how students use net rate‐of‐change to predict how matter accumulates in, or disperses from, a compartment. We also present a case study of how we used our emerging mass balance learning progression to inform instructional practices to support students' mass balance reasoning. Our progress variables describe one way students engage in three dimensional learning by showing how student performances associated with the practice of mathematical thinking reveal their understanding of the core concept of matter flows as governed by the crosscutting concept of matter conservation. Though our work is situated in physiology, it extends previous work in climate change education and is applicable to other scientific fields, such as physics, engineering, and geochemistry.

The temporal rich club phenomenon

Identifying the hidden organizational principles and relevant structures of complex networks is fundamental to understand their properties. To this end, uncovering the structures involving the prominent nodes in a network is an effective approach. In temporal networks, the simultaneity of connections is crucial for temporally stable structures to arise. Here, we propose a measure to quantitatively investigate the tendency of well-connected nodes to form simultaneous and stable structures in a temporal network. We refer to this tendency as the temporal rich club phenomenon, characterized by a coefficient defined as the maximal value of the density of links between nodes with a minimal required degree, which remain stable for a certain duration. We illustrate the use of this concept by analysing diverse data sets and their temporal properties, from the role of cohesive structures in relation to processes unfolding on top of the network to the study of specific moments of interest in the evolution of the network. Uncovering structures in temporal networks requires different tools than in their static counterparts. A metric now quantifies whether the nodes with a large number of connections also tend to stay simultaneously connected for longer times.

Query driven-graph neural networks for community search

Given one or more query vertices, Community Search (CS) aims to find densely intra-connected and loosely inter-connected structures containing query vertices. Attributed Community Search (ACS), a related problem, is more challenging since it finds communities with both cohesive structures and homogeneous vertex attributes. However, most methods for the CS task rely on inflexible pre-defined structures and studies for ACS treat each attribute independently. Moreover, the most popular ACS strategies decompose ACS into two separate sub-problems, i.e., the CS task and subsequent attribute filtering task. However, in real-world graphs, the community structure and the vertex attributes are closely correlated to each other. This correlation is vital for the ACS problem. In this vein, we argue that the separation strategy cannot fully capture the correlation between structure and attributes simultaneously and it would compromise the final performance. In this paper, we propose Graph Neural Network (GNN) models for both CS and ACS problems, i.e., Query Driven-GNN (QD-GNN) and Attributed Query Driven-GNN (AQD-GNN). In QD-GNN, we combine the local query-dependent structure and global graph embedding. In order to extend QD-GNN to handle attributes, we model vertex attributes as a bipartite graph and capture the relation between attributes by constructing GNNs on this bipartite graph. With a Feature Fusion operator, AQD-GNN processes the structure and attribute simultaneously and predicts communities according to each attributed query. Experiments on real-world graphs with ground-truth communities demonstrate that the proposed models outperform existing CS and ACS algorithms in terms of both efficiency and effectiveness. More recently, an interactive setting for CS is proposed that allows users to adjust the predicted communities. We further verify our approaches under the interactive setting and extend to the attributed context. Our method achieves 2.37% and 6.29% improvements in F1-score than the state-of-the-art model without attributes and with attributes respectively.

Towards a carbon neutral economy: How government should respond to market failures and market absence

The transition towards a carbon-neutral economy is a fundamental change, it involves not only transforming the energy sector but also radical reforms across the whole economy. Managing fundamental and wholesale change across such a large economy is a massive coordination challenge requiring the simultaneous deployment of a collection of instruments and institutional change. This paper looks into the key challenges in building a carbon-neutral economy and discusses how governments and markets should work together in addressing these challenges. Due to significant failures in key markets relevant to tackling carbon emissions and to the absence of crucial markets, this paper argues that governments must play an active role in formulating and implementing effective environmental policies, regulations and design. This paper discusses major market failures and market absence, leading to suggestions on policy measures that governments should take to overcome these challenges, enabling markets to give better signals in directing resource allocation and guiding the low-carbon transition. Governments must act to facilitate a transition that enables equity in opportunities and outcomes across regions and individuals. Implementing these strategies and policies requires cohesive government structures, led from the most senior levels, to foster the necessary investment, innovation and change needed.