Cluster Category Research Articles

A two-stage adaptive consensus reaching process with improved automatic strategy for multi-attribute large group emergency decision-making

The multi-attribute large group emergency decision-making (MALGEDM) problem has gained increasing attention and becomes an important topic in the field of decision-making. The existing consensus models, however, rarely consider the effective balance between experts’ willingness and time consumption. To address this problem, we design a two-stage adaptive consensus reaching process that combines two models of the minimum adjustment amount and the minimum iteration number in the MALGEDM context. The process can achieve consensus with an improved automatic strategy that considers experts’ willingness. Firstly, the self-organizing map algorithm is employed to classify large group heterogeneous experts into multiple subgroups for the dual decision of clustering and expert categories. Secondly, we classify different degrees of consensus into four scenarios (low, medium, medium-high, and high). On this basis, we have constructed a two-stage adaptive consensus model that incorporates the novel possibility degree. The minimum adjustment amount of the adaptive feedback process can be obtained by the optimization model in the first stage, which is used as a constraint for considering the experts’ willingness in the second stage. Finally, the practicality and timeliness of the proposed model is verified through an illustrative example of emergency decision-making. Moreover, comparative analyses are conducted to highlight the superiority of the proposed model.

Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm

Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability, they cannot utilize spatial information effectively and are highly sensitive to noise and outliers. In this study, we propose ACSpot, an autoencoder-based fuzzy clustering algorithm, as a solution to tackle these problems. Specifically, we employed a self-supervised autoencoder to reduce feature dimensionality, mitigate nonlinear noise, and learn high-quality representations. Additionally, a commonly used clustering method, Fuzzy c-means, is used to achieve improved clustering results. In particular, we utilize spatial neighbor information to optimize the clustering process and to fine-tune each spot to its associated cluster category using probabilistic and statistical methods. Result and Discussion: The comparative analysis on the 10x Visium human dorsolateral prefrontal cortex (DLPFC) dataset demonstrates that ACSpot outperforms other clustering algorithms. Subsequently, spatially variable genes were identified based on the clustering outcomes, revealing a striking similarity between their spatial distribution and the subcluster spatial distribution from the clustering results. Notably, these spatially variable genes include APP, PSEN1, APOE, SORL1, BIN1, and PICALM, all of which are well-known Alzheimer's disease-associated genes. Conclusion: In addition, we applied our model to explore some potential Alzheimer's disease correlated genes within the dataset and performed Gene Ontology (GO) enrichment and gene-pathway analyses for validation, illustrating the capability of our model to pinpoint genes linked to Alzheimer’s disease.

An optimized intelligent open-source MLaaS framework for user-friendly clustering and anomaly detection

As data grow exponentially, the demand for advanced intelligent solutions has become increasingly urgent. Unfortunately, not all businesses have the expertise to utilize machine learning algorithms effectively. To bridge this gap, the present paper introduces a cost-effective, user-friendly, dependable, adaptable, and scalable solution for visualizing, analyzing, processing, and extracting valuable insights from data. The proposed solution is an optimized open-source unsupervised machine learning as a service (MLaaS) framework that caters to both experts and non-experts in machine learning. The framework aims to assist companies and organizations in solving problems related to clustering and anomaly detection, even without prior experience or internal infrastructure. With a focus on several clustering and anomaly detection techniques, the proposed framework automates data processing while allowing user intervention. The proposed framework includes default algorithms for clustering and outlier detection. In the clustering category, it features three algorithms: k-means, hierarchical clustering, and DBScan clustering. For outlier detection, it includes local outlier factor, K-nearest neighbors, and Gaussian mixture model. Furthermore, the proposed solution is expandable; it may include additional algorithms. It is versatile and capable of handling diverse datasets by generating separate rapid artificial intelligence models for each dataset and facilitating their comparison rapidly. The proposed framework provides a solution through a representational state transfer application programming interface, enabling seamless integration with various systems. Real-world testing of the proposed framework on customer segmentation and fraud detection data demonstrates that it is reliable, efficient, cost-effective, and time-saving. With the innovative MLaaS framework, companies may harness the full potential of business analysis.

A phenomenological exploration of experience of Syrian dentists with online Course “Traumatic dental injuries”

BackgroundSeveral learning modalities have been implemented to improve learning about Traumatic Dental Injuries (TDIs) worldwide. Free online courses about TDIs might be an effective and convenient approach for equipping Syrian dentists with essential competencies during the Syrian crisis. Therefore, this qualitative study with a descriptive phenomenological method was undertaken to explore the lived experience of Syrian dentists enrolled in an online course about TDIs and investigate areas requiring improvement. MethodsEthical approval was obtained from the Faculty of Dentistry, Damascus University, and the University of Dundee. Educational contents of the TDI course were developed. About 10 dentists who completed the TDI course, were interviewed. The interviews were recorded, transcribed, and analyzed to identify emerging themes. Inductive thematic analysis was performed to extract all data. ResultsAbout 10 clustered categories were first developed and this has led to the emergence of 3 themes that represent the lived experience including usefulness, challenges, and recommendations. Participants were so motivated and keen to take advantage of the course despite the personal, technical problems, and crisis-related challenges. TDIs course was effective for general and specialists despite the challenges they experienced. Factors that lead to effective TDIs courses as reported by participants were flexibility in time and location, interactivity with colleagues, other commitments, quantity and quality of content, easiness, and variety of virtual environment tools. Conversely, negative attitudes were linked to factors like lack of interest, unfamiliarity with the learning environment and tools, late participation, lack of confidence, anxiety about independent learning and insufficient interactivity and engagement tools. ConclusionSyrian dentists can benefit from online courses if constraints and various learning needs are addressed during the design and delivery of online courses. Future work is still required to identify other effective instructional modalities that equip Syrian dentists to overcome challenges and enhance their learning.

Luminescence properties of endohedrally doped group-IV clusters.

Endohedrally doped clusters form a large category of cage clusters, with unique structures, diverse elemental compositions, and highly tunable electronic structures and physisochemical properties. They have been widely achieved in laboratory and may serve as functional building blocks for assembling new supermolecular structures and devices. In this paper, for the first time, we disclosed the luminescence properties of endohedrally doped group-IV clusters by time-dependent density functional theory calculations. A total of 64 cage clusters have been explored in terms of stability, emission wavelength, and the energy difference between the first excited singlet and triplet states. The key geometric and electronic factors governing the photophysical properties of these cage clusters were unveiled, to provide crucial insights for crafting atomically precise nanoclusters for optical and optoelectronic applications.

Ecological and climatic transferability of airborne lidar-driven aboveground biomass models in Piñon-Juniper woodlands

ABSTRACT Piñon-juniper (PJ) woodlands are an expansive and dynamic dryland ecosystem in the US that encompass a wide range of spatial, temporal, and ecological diversity. The dynamism of PJ woodland extent and abundance over space and time is attributable to variability in species compositions, stand structures, climatic conditions, and disturbance patterns. PJ aboveground biomass (AGB) quantification is important for understanding its role in the global carbon cycle, for tracking climate change impacts, and for informing forest management practices. Quantifying AGB in PJ woodlands is challenging due to complex vegetation structure. Although airborne laser scanning (ALS) has proven effective in estimating PJ AGB in local scale studies, these efforts only capture a fraction of the overall PJ range leaving open an important question as to whether a broad-scale, ecosystem-wide effort to map PJ woodland AGB could be successful. This research seeks to determine whether AGB predictive models built in ecologically distinct portions of PJ’s range can be accurately applied to prediction of AGB in other portions of the range. Using a large database of 497 field plots compiled from eight sources and a random forest modeling framework, transferability between predictive models was evaluated by grouping field reference plots into two clustering categories based on PJ characteristics: one category generated with environmental variables (climate and topography) and the other using species compositions. For both clustering categories, models trained on one cluster were used to predict AGB in every other cluster within that category. Relatively distinct clusters, such as those characterized by notably higher temperatures and precipitation totals or dominated by a unique species, had high transferability when models were trained on the distinct cluster and applied to other clusters, but low transferability when other clusters were tested on the distinct cluster. High training transferability for distinct clusters is likely due to the inability of random forests to extrapolate beyond the range of training data, while the high testing transferability is likely due to ecological reasons. Therefore, it is important to capture the distinct environments and species compositions within the range of PJ woodlands when training a range-wide model to map PJ AGB. With the eventual goal of a range-wide PJ AGB map, we developed a preliminary range-wide model of PJ woodland AGB using our entire field reference dataset. The bootstrapped accuracy assessment of the range wide model (median R2 = 0.52; median rRMSE = 0.49) suggests promise for future comprehensive PJ AGB maps. However, high AGB values in the range-wide model were under-predicted, suggesting that gathering additional field plots in high AGB PJ woodlands has the potential to enhance the accuracy of the predictive model across the range of PJ AGB.

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance.

Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.

Ptolemy diagrams and torsion pairs in m-cluster categories of type D

In this paper, we give a complete classification of torsion pairs in m-cluster categories of type D when m is odd, denoted by C D n m , via a bijection to combinatorial objects called Ptolemy diagrams of type D. As applications, we classify m-rigid subcategories of C D n m , which gives Jacquet-Malo’s classification of m-cluster tilting subcategories of C D n m . When m = 1, we generalizes the work of Holm, Jørgensen and Rubey [ 14 ] for the classification of torsion pairs in cluster categories of type Dn . Communicated by Steffen Oppermann

Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering

Semi-supervised clustering aims to guide the clustering by utilizing auxiliary information about the class labels. Among the semi-supervised clustering categories, the constraint-based approach uses the available pairwise constraints in some steps of the clustering procedure, usually by adding new terms to the objective function. Considering this category, Semi-supervised FCM (SSFCM) is a semi-supervised version of the fuzzy c-means algorithm, which takes advantage of fuzzy logic and auxiliary class distribution knowledge. Despite the performance enhancement caused by incorporating this extra knowledge in the clustering process, semi-supervised fuzzy approaches still suffer from some problems. All the data attributes in the feature space are assumed to have equal importance in the cluster formation, while some features may be more informative than others. Thus the feature importance issue is not addressed in the semi-supervised category. This paper proposes a novel Semi-Supervised Fuzzy c-means approach, which is designed based on Feature-Weight, and Cluster-Weight learning, named SSFCM-FWCW. Inspired by the SSFCM, a fuzzy objective function is presented, which is composed of (1) a semi-supervised term representing the external class knowledge; (2) a feature weighting; and (3) a cluster weighting. Both feature weights and cluster weights are determined adaptively during the clustering. Considering these two techniques leads to insensitivity to the initial center selection, insensitivity to noise, and consequently helps to form an optimal clustering structure. Experimental comparisons are carried out on several benchmark datasets to evaluate the proposed approach's performance, and promising results are achieved. The Matlab implementation source code of the proposed method is publicly accessible at https://github.com/Amin-Golzari-Oskouei/SSFCM-FWCW.

Leclerc's conjecture on a cluster structure for type A Richardson varieties

Leclerc [32] constructed a conjectural cluster structure on Richardson varieties in simply laced types using cluster categories. We show that in type A, his conjectural cluster structure is in fact a cluster structure. We do this by comparing Leclerc's construction with another cluster structure on type A Richardson varieties due to Ingermanson [27]. Ingermanson's construction uses the combinatorics of wiring diagrams and the Deodhar stratification. Though the two cluster structures are defined very differently, we show that the quivers coincide and clusters are related by the twist map for Richardson varieties, recently defined by Galashin–Lam [21].

Mutation of torsion pairs in discrete cluster categories of Dynkin type A∞

Mutation of torsion pairs is a generalization of mutation of cluster tilting subcategories. Torsion pairs and cluster tilting subcategories in Igusa-Todorov cluster categories of type A∞ have been classified by Gratz-Holm-Jørgensen in [12]. In this paper we give a geometric realization of mutation of torsion pairs in discrete cluster categories of Dynkin type A∞ by rotating some combinatorial models in S1. This result generalizes a work of Zhou-Zhu for cluster category of type A∞[27].

The Grothendieck groups of m-cluster categories of type Dn

In this paper, we study the Grothendieck groups of m-cluster categories of type Dn , denoted by C D n m , when m is odd, via the m-cluster tilting subcategories of C D n m . When m = 1, we generalize the work of Barot, Kussin and Lenzing for studying the Grothendieck groups of cluster categories of type Dn .

Research on long term power load grey combination forecasting based on fuzzy support vector machine

Aiming at the problem of low accuracy of single model in power load forecasting, a method based on fuzzy support vector machine and grey combination forecasting is proposed. The power load data is analyzed and prepared by means of data repair, preprocessing and fuzzy C-means clustering, which is combined with fuzzy support vector machine and grey prediction. The clustering results are taken as the input of the grey prediction model, and the preliminary prediction results are obtained through parameter modification. Then, the results are taken as the input of support vector machine, and the long-term power load prediction is carried out by combining the two models. The experimental results show that the proposed method has excellent performance in predicting long-term power load accurately, and the prediction accuracy is significantly improved compared with the traditional single forecasting model. The application of this method to long-term power load forecasting below 4000 KWH proves its high prediction accuracy and reliability once again. When the clustering category of power load fuzzy clustering is set to 30, the best data clustering effect can be obtained, and the prediction accuracy is further improved.

Driving forces behind the adoption of IEQ principles in building design: evidence from Nigerian quantity surveying firms

PurposeThe main objective of this study is to evaluate the driving forces behind the adoption of indoor environmental quality (IEQ) principles in building designs from the perspectives of Nigerian quantity surveying firms.Design/methodology/approachA quantitative approach was used which involved administering a well-structured questionnaire to a sample of 114 quantity surveyors. The collected data were analyzed using various statistical methods, including frequencies, percentages, mean item scores, Kruskal–Wallis test and exploratory factor analysis.FindingsThe top five ranked drivers were climate change mitigation, conservation of natural resources, reduction of waste and pollution, use of sustainable building materials and development of new materials and building systems. Based on the factor analysis, the study identified five clusters of drivers: (1) health and well-being drivers (2) economic drivers (3) environmental drivers (4) innovation and technology drivers and (5) regulatory drivers.Practical implicationsThe findings from this study suggest that to effectively integrate IEQ principles, quantity surveying firms should consider developing comprehensive guidelines and checklists that align with the identified drivers and clustered categories. These resources can serve as practical tools for project teams, facilitating a structured and holistic approach to the incorporation of IEQ factors throughout the project lifecycle.Originality/valueThe study’s identification of the top drivers and the subsequent clustering of these drivers into five distinct categories contributes to the existing body of knowledge on IEQ. This approach provides a structured framework for comprehensively understanding the factors influencing IEQ adoption, offering a valuable tool for researchers, policymakers and industry practitioners.

Cluster pattern analysis of environmental stressors and quantifying their impact on all-cause mortality in Belgium

Environmental stress represents an important burden on health and leads to a considerable number of diseases, hospitalisations, and excess mortality. Our study encompasses a representative sample size drawn from the Belgian population in 2016 (n = 11.26 million, with a focus on n = 11.15 million individuals). The analysis is conducted at the geographical level of statistical sectors, comprising a total of n = 19,794 sectors, with a subset of n = 18,681 sectors considered in the investigation. We integrated multiple parameters at the finest spatial level and constructed three categories of environmental stress through clustering: air pollution, noise stress and stress related to specific land-use types. We observed identifiable patterns in the spatial distribution of stressors within each cluster category. We assessed the relationship between age-standardized all-cause mortality rates (ASMR) and environmental stressors. Our research found that especially very high air pollution values in areas where traffic is the dominant local component of air pollution (ASMR + 14,8%, 95% CI: 10,4 – 19,4%) and presence of industrial land (ASMR + 14,7%, 95% CI: 9,4 – 20,2%) in the neighbourhood are associated with an increased ASMR. Cumulative exposure to multiple sources of unfavourable environmental stress (simultaneously high air pollution, high noise, presence of industrial land or proximity of primary/secondary roads and lack of green space) is associated with an increase in ASMR (ASMR + 26,9%, 95% CI: 17,1 – 36,5%).

Health care utilization after enrollment in an outpatient structured clinical program for children with medical complexity

Abstract Objectives Although children with medical complexity (CMC) with high health resource utilization use outpatient structured clinical programs (SCP) to optimize their health, little is known about variation in trends of their health service use shortly after enrollment. We measured these trends and assessed the utility of patient characteristics to predict them. Methods Retrospective analysis of 506 CMC newly enrolled in an outpatient, academic SCP. We measured outpatient and inpatient health service use for the first 6 months following enrollment. Using cluster analysis, we categorized CMC with similar trends by health service. We assessed patient demographic (e.g., age) and clinical [type and number of complex chronic conditions (CCC)] characteristics with the cluster categories. Results Most (90.3%) CMC enrolled had ≥1 CCCs; 53.8% had ≥3 CCCs. For all CMC, outpatient specialty visits, phone calls, and hospitalizations (not involving ICU) decreased significantly over the first 6 months after enrollment. For example, the hospitalization rate decreased from 9.7% to 4.5% in the 1st and 6th months, respectively, (P < 0.001). Cluster analysis revealed four categories of hospitalization trends, 6 months after enrollment: 72.9% of CMC had no hospital use; 16.0% had increased then decreased use; 8.3% had decreased use; and 2.8% had increased use. No patient and clinical characteristics clearly distinguished which hospitalization trend CMC experienced. Conclusions Although the overall population of CMC experienced decreased outpatient and inpatient health services utilization over time, this decrease was not uniformly experienced. Fundamental patient demographic and clinical characteristics did not predict health service trends.

A Typology of Micropolitan Areas in the United States

In 2003, the U.S. Census Bureau created a new census designation known as micropolitan statistical areas to better differentiate between metropolitan and nonmetropolitan areas. This article uses hierarchical clustering and discriminate analysis to create a typology of micropolitan areas using U.S. census data on demographic, housing, and economic characteristics. The regional concentration ratio (RCR) is then used to determine whether a cluster type is over- or underrepresented at the census division. The analysis revealed a three-cluster solution with: (1) a middle- class cluster overrepresented in the East North Central and West North Central divisions; (2) a poor, minority cluster overrepresented in the South Atlantic, East South Central, West South Central, and Mountain divisions; and (3) a high human capital cluster overrepresented in the New England, Mountain, and Pacific divisions. Of micropolitan areas, 59.8 percent were considered middle-class; 28.1 percent were poor, minority; and 12.1 percent were high human capital. The nine-cluster solution further refined cluster categories.

Presentations of Braid Groups of Type A Arising from (m+2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(m+2)$$\\end{document}-angulations of Regular Polygons

Coloured quiver mutation, introduced by Buan, A.B., Thomas, H (Adv. Math. 222(3), 971–995 2009), gives a combinatorial interpretation of tilting in higher cluster categories. In type A work of Baur, K., Marsh, B. (Trans. Am. Math. Soc. 360(11), 5789-5803 2008) shows that m-coloured quivers and m-coloured quiver mutations have a nice geometrical description, given in terms of (m+2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(m+2)$$\\end{document}-angulations of a regular polygon, and rotations of an m-diagonal. In this paper, using such correspondence, we describe presentations of braid groups of type A arising from coloured quivers of mutation type A.

Automated Pricing and Replenishment Decision-Making for Vegetable Products Based on Statistical Optimization Models

This article uses a statistical optimization model to address pricing and replenishment decision-making issues for supermarkets. Firstly, it visually presents the distribution patterns of sales volumes for different vegetable types and individual items. Secondly, it analyzes the sales volume distribution patterns of six categories of individual vegetables through K-means clustering. Then, it calculates the correlation between different vegetable categories and individual items by using Pearson’s correlation coefficient. Finally, it predicts the sales volume of each category for the next seven days using a time series forecasting model and determines the pricing strategy by using a cost markup method. The following conclusions are drawn: (1) Kale has the highest sales volume and significant seasonal variations; peppers and edible mushrooms have relatively large sales volumes and obvious seasonal variations. (2) The sales order of the six clustered categories is 3>5>2>4>6>1; the seasonal differences of the 2nd, 3rd, and 4th categories are significant. (3) There is a strong correlation between cauliflower and kale, a relatively strong correlation between peppers and kale, a weak correlation between eggplant and the other five vegetables, and a strong correlation between edible mushrooms and aquatic root vegetables. In summary, this article uses the aforementioned models to visualize the distribution patterns of vegetable sales volumes, determine correlations using methods such as Pearson’s correlation coefficient and time series forecasting, predict the sales volume of each category for the next seven days, and determine pricing strategies. This will help vegetable supermarkets make pricing and replenishment decisions for different vegetable categories without knowing specific items or purchase prices.

INDECOMPOSABLE MODULES IN THE GRASSMANNIAN CLUSTER CATEGORY CM(B5,10)

In this paper, we study indecomposable rank 2 modules in the Grassmannian cluster category CM(B5,10). This is the smallest wild case containing modules whose profile layers are 5-interlacing. We construct all rank 2 indecomposable modules with a specific natural filtration, classify them up to isomorphism, and parameterize all infinite families of non-isomorphic rank 2 modules