•Entropy TOPSIS and Theil index show differences in industrial green development.•Use hierarchical clustering analysis to evaluate industrial green competitiveness.•Industrial green development is ...highest in Pearl River Delta (PRD).•PRD is highest in industrial green growth & environmental pressure competitiveness.•Beijing-Tianjin-Hebei has the highest balanced industrial green development.
Urban agglomerations are important regional economic units. Industrial green development and green competitiveness are of great significance to the sustainable development of urban agglomerations. This study, for the first time, uses the Entropy TOPSIS model and Theil index to reveal differences between industrial green development in China’s three major urban agglomerations. Factor and hierarchical clustering analysis is also used to evaluate and compare industrial green competitiveness in these agglomerations. Four important findings arise from the analysis. First, industrial green development is highest in Pearl River Delta, followed by Beijing-Tianjin-Hebei, and lowest in Yangtze River Delta. Beijing, Shanghai and Shenzhen have the highest industrial green development level. Second, the variation in green development is smallest between Beijing-Tianjin-Hebei and Yangtze River Delta. Cities in the Beijing-Tianjin-Hebei have more balanced industrial green development than the other two urban agglomerations. Third, industrial green competitiveness is highest in Pearl River Delta. It is superior to the Yangtze River Delta and Beijing-Tianjin-Hebei in industrial green growth competitiveness and resource and environmental pressure competitiveness. Finally, after classifying 45 major cities of urban agglomerations into four echelons by industrial green competitiveness, we find that only Beijing and Shenzhen are in the highest echelon. Shanghai and Guangzhou are in the second echelon, and Hefei, Tianjin, Hangzhou, Suzhou and Zhuhai are in the third echelon. These findings have important policy implications and we offer recommendations based on the findings.
Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current ...methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics.
We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere.
We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores.
These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The demand for “online meetings” and “collaborative office work” keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing ...service has attracted extensive attention. Existing meeting summarizing models overlook the utilization of multi-modal information and the information offsetting during summarizing. In this paper, we develop a knowledge-enhanced multi-modal summarizing framework. Firstly, we construct a three-layer multi-modal meeting knowledge graph, including basic, knowledge, and multi-modal layer, to integrate meeting information thoroughly. Then, we raise a topic-based hierarchical clustering approach, which considers information entropy and difference simultaneously, to capture the semantic evolution of meetings. Next, we devise a multi-modal enhanced encoding strategy, including a sentence-level cross-modal encoder, a joint loss function, and a knowledge graph embedding module, to learn the meeting and topic-level presentations. Finally, when generating summaries, we design a topic-enhanced decoding strategy for the Transformer decoder which mitigates semantic offsetting with the aid of topic information. Extensive experiments show that our proposed work consistently outperforms state-of-the-art solutions on the Chinese meeting dataset, where the ROUGE-1, ROUGE-2, and ROUGE-L are 49.98%, 21.03%, and 32.03% respectively.
Education is one of the pillars of human societies, such that achieving better indicators in this area is a common goal for different federate entities. In this context, identifying patterns on the ...results of such indicators, evaluated for different entities, as well as grouping them based on their similarities, can lead to a better understanding of the educational scenario of a population. This knowledge, moreover, might subsidize the formulation of public policies and allow the decision-making by the responsible managers. In the present work, we present an illustrative example of the application of spatial and non-spatial clustering algorithms in the analysis of data from six important indicators of basic education (middle and high school) evaluated for the municipalities of the state of Paraná, Brazil. Clusters provided by each method were evaluated according to their spatial distributions and educational features. The different clustering algorithms produced clusters with different levels of spatial contiguity and homogeneity regarding the educational indicators, reflecting the importance of choosing the appropriate clustering technique based on the research objectives.
The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. Two ...algorithms are found in the literature and software, both announcing that they implement the Ward clustering method. When applied to the same distance matrix, they produce different results. One algorithm preserves Ward’s criterion, the other does not. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Ward’s hierarchical clustering method.
The purpose of this study was to investigate the influence of selected saccharides (glucose, sucrose, and maltodextrin) on the foam characteristic of egg white liquid. The rheological measurements ...suggested that increasing sugar concentrations in egg white led to an increase in both apparent viscosity and viscoelasticity. Moreover, the addition of saccharides shortened the relaxation time of hydrogen proton, indicating the presence of hydration effect between sugar and water. Samples with 3% saccharides increased the surface hydrophobicity and reduced the surface tension of the compound egg white solution, leading to the increase of foam capacity and stability. The best foam performance of compound egg white solution was achieved when the 3% maltodextrin was present in egg white, along with the smallest and most homogeneous bubbles. The classification of mixture conditions by the principal component analysis (PCA) and cluster analysis (HCA) revealed that the maltodextrin-egg white system had good scores both at 0.6% and 3% adding ratios, indicating maltodextrin is a suitable additive for the modification of egg white foam properties.
Display omitted
•Saccharides improved both the foam capacity and stability of egg white.•Maltodextrin is a suitable additive for modification of egg white foam properties.•Saccharides improved rheological properties of egg white by changing water sate.•Quantitative associations between foam and physicochemical indexes were studied.
Group decision-making (GDM) in large-group social network environment (LGSNE) has attracted considerable attention in the field of decision science. Social relationships exist among decision-makers, ...and individual decisions are often influenced by others they are connected with. Opinions among large-scale decision-makers can easily be controversial and conflicting. Reaching consensus is necessary, but it requires the adjustment of some individual opinions. Due to differences in self-interest and perception, some decision-makers are noncooperative with regard to adjusting their opinions to promote consensus. This may delay consensus convergence and ultimately affect decision quality. This study proposes a two-dimensional consensus convergence model considering noncooperative behaviors. We first describe the characteristics of GDM problems in LGSNE. Two measurement attributes – trust relationship and opinion similarity – are identified as important factors throughout the decision-making process. Then, we propose a hierarchical clustering method based on the trust–similarity measure. A weight-determining method for clusters is presented that considers the internal and external features of a cluster. Based on these, a two-dimensional consensus convergence process is designed to reduce opinion differences and manage noncooperative behaviors. Finally, a numerical experiment is used to illustrate the feasibility and efficacy of the proposed model, and comparative analysis reveals its features and advantages.
•The characteristics and components of GDM problems in LGSNE are analyzed.•Opinion similarity and trust relationship are important for GDM events in LGSNE.•A trust–similarity measure-based hierarchical clustering method is presented.•A two-dimensional consensus convergence model is developed.•A weight-determining method for clusters considering multiple factors is proposed.
•Congestion effects on driver behavior in post-congestion driving were examined.•Congestion negatively affected driver behavior on the post-congestion roads.•More aggressive driving patterns were ...observed in post-congestion driving.•Drivers became less focused on the dashboard area in post-congestion driving.•Findings here highlight the importance of attending to safe driving after congestion.
Traffic congestion is more likely to lead to aggressive driving behavior that is associated with increased crash risks. Previous studies mainly focus on driving behavior during congestion when studying congestion effects. However, the negative effects of congestion on driving behavior may also affect drivers’ post-congestion driving. To fill this research gap, this study examined the influence of traffic congestion on driver behavior on the post-congestion roads (i.e., the roads travelled right after congestion). Twenty-five subjects participated in a driving simulation study. They were asked to complete two trials corresponding to post-congestion and non-congestion conditions, respectively. Driver behavior quantified by driving performance measures, eye movement measures, and electroencephalogram (EEG) measures was compared between the two conditions. Ten features were selected from the measures with statistical significance. The selected features were integrated to characterize drivers’ response patterns using a hierarchical clustering method. The results showed that driver behavior in post-congestion situations became more aggressive, more focused in the forward area but less focused in the dashboard area, and was associated with lower power of the β-band in the temporal brain region. The clustering results showed more aggressive and lack-of-aware response patterns while driving in post-congestion situations. This study revealed that traffic congestion negatively affected driver behavior on the post-congestion roads. Practical implications for driving safety education was discussed based on the findings from the present study.
Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification ...of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies.
We have developed a novel agglomerative clustering method that we call pcaReduce to generate a cell state hierarchy where each cluster branch is associated with a principal component of variation that can be used to differentiate two cell states. Using two real single cell datasets, we compared our approach to other commonly used statistical techniques, such as K-means and hierarchical clustering. We found that pcaReduce was able to give more consistent clustering structures when compared to broad and detailed cell type labels.
Our novel integration of principal components analysis and hierarchical clustering establishes a connection between the representation of the expression data and the number of cell types that can be discovered. In doing so we found that pcaReduce performs better than either technique in isolation in terms of characterising putative cell states. Our methodology is complimentary to other single cell clustering techniques and adds to a growing palette of single cell bioinformatics tools for profiling heterogeneous cell populations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a ...challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHC-AA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data. KEYWORDS IoT Sensor Data, Semantics, Automatic Annotation, Incremental Hierarchical Clustering, Healthcare, Agent, SPARQL.
PDF
