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Paper spray-mass spectrometry (PS-MS) is a rapid, solvent-efficient, and high-throughput analytical method for analyzing complex samples. In this study, a PS-MS method was developed to obtain MS profiles of Aurantii Fructus Immaturus ( aka Zhishi in Chinese) in positive and negative ion modes. In combination with multivariate analyses, including principal component analysis and cluster analysis, the PS-MS profiles of 25 batches of Zhishi were discriminated in 25 batches of Citri Reticulatae Pericarpium Viride ( aka Qingpi in Chinese; an adulterant of Zhishi). Moreover, a rapid quantitative analysis of synephrine, a prescriptive quality control component of Zhishi listed in the Chinese Pharmacopoeia, was conducted with PS-MS using synephrine-d2 as an internal standard (IS). The linearity range was 1.68–16.8 μg/mL (R 2 = 0.9985), the limit of quantitation was 0.5 μg/mL. Relative standard deviations in the intra- and inter-day precision of the MS were 4.87 and 4.90%, respectively. Compared with HPLC results, there was no significant difference in the quantitation of synephrine. This study demonstrated that the PS-MS method is useful for the rapid discrimination and quality control of Zhishi samples. [ABSTRACT FROM AUTHOR]

In citation networks, the content relativity of papers is a precondition of engendering citations, which is hard to model by a topological graph. A geometric graph is proposed to predict some features of the citation networks with exponentially growing papers, which addresses the precondition by using coordinates of nodes to model the research contents of papers, and geometric distances between nodes to diversities of research contents between papers. Citations between modeled papers are drawn according to a geometric rule, which addresses the precondition as well as some other factors engendering citations, namely academic influences of papers, aging of those influences, and incomplete copying of references. Instead of cumulative advantage of degree, the model illustrates that the scale-free property of modeled networks arises from the inhomogeneous academic influences of modeled papers. The model can also reproduce some other statistical features of citation networks, e.g. in- and out-assortativities, which show the model provides a suitable tool to understand some aspects of citation networks by geometry. [ABSTRACT FROM AUTHOR]

The collection of SuperDARN ionospheric echo data to make ionospheric convection maps is of great significance for Space Weather research. The ionospheric echoes for SuperDARN are generally mixed with scatter from the ground or sea surface, thus the clustering analysis of SuperDARN backscatter echoes is important. In this paper, the first implementation of deep clustering based on the graph automatic encoder network is efficiently realized for classifying the SuperDARN data. In addition, the model is compared with the traditional algorithm and machine learning clustering algorithms. Application of the model to sample data reveals that the deep clustering algorithm can capture the structural characteristics of the echoes and improve the accuracy of echo clustering and classifying. [ABSTRACT FROM AUTHOR]

For freight and servicing movements entering a city, parking provides the access enabling service to customers. This will invariably occur on the street in parking spots or in modern buildings in off-street facilities. In light of changing urban planning priorities and as a result, declining on-street loading zone spaces, this paper explores the provision and challenges of off-street loading docks to support freight and servicing task activity in major urban centres. While it may not be fully appreciated, provisions to adequately accommodate a city's generated freight task is critical to urban planner's broader objectives. As a non-discretionary transport task, freight vehicles will continue to enter cities. If good off-street loading dock facilities are not provided, vehicles will seek out legitimate or illegal on-street parking, and urban planner's place making objectives are likely to be compromised. The paper first considers the planning approaches that govern the provision of loading docks. This paper focuses on Sydney but draws on comparisons primarily to the Borough of City of London. Based on recent observations in Sydney, it then considers various stakeholder perspectives towards loading dock provision and use. Modelling approaches to forecast better requirements that could assist to overcome the planning issues faced are then outlined. The paper concludes with actions taken to address the challenges. • Lack of tools for planning loading docks leads to conflict between developers and government. • Poor provision of loading docks in new developments can compromise urban planning objectives. • Regression and clustering analysis techniques allow the capacity of loading docks and their levels of service to be estimated. [ABSTRACT FROM AUTHOR]

The focus of this paper is to analyze the trends and locations of accidents in the Greater Melbourne Area (GMA) during a 15-year period (2006–2020). The places where accidents were most prevalent were discovered and the reasons which are contributing to the high accident rates in those areas are determined. Analyzing the patterns over time and variations in the frequency of accidents helped to identify areas that have improved or deteriorated in terms of road safety. A Tweedie model was developed to examine the intricate interaction between the accident frequency and its potential contributing factors such as socio-demographics, road transport infrastructure, and built environment. Ultimately, a clustering analysis was performed to elucidate the dispersion of road accident risk ratings among different local government areas (LGAs), offering useful insights into road safety initiatives and prioritization. • The study linked built environment to accidents with Twidee and Space-time Cube. • Accidents down overall (20 yrs), but hotspots move outward. • Melbourne suburbs form five risk clusters based on accident frequency. [ABSTRACT FROM AUTHOR]

This paper explores the classic beauty premium paradigm in the eSports Overwatch League. Using two-step probit selection models and unsupervised machine learning algorithms, we find that professional gamers rated as more attractive are more likely to receive a contract in the following year, controlling for productivity and other factors. However, there is no apparent lifetime earnings premium. Regression and cluster analyses reveal that it is not enough to be a good player – the inter-relatedness of productivity, attractiveness, and popularity on social media dictates employability. Looking at the league's wealthiest, most successful, and most appealing teams, we find that attractiveness on its own is a negative predictor of getting hired. • No earnings beauty premium paradigm in the eSports Overwatch League. • Performance always impacts employment. • In a two-step selection setup, attractive gamers have a higher chance of rehire. • Beauty is a negative predictor of securing a contract with the most successful team. • Unsupervised ML suggests that player types (by popularity and beauty) matter. [ABSTRACT FROM AUTHOR]

Understanding consumers' purchase behaviors is fundamental to the success of the dairy industry. With its economic importance, the Chinese market is critical to dairy producers in most countries around the world. However, understanding consumers in this market is particularly challenging, as these consumers often have a different relationship with dairy products than consumers elsewhere in the world, given the country's historical dairy-related scandals. This special relationship can be characterized by what consumer behavior researchers call "high involvement," indicating that Chinese dairy consumers often attempt to reduce the level of risk associated with buying dairy products. Surprisingly, although this relationship affects several important aspects of purchase behavior, examination of the concept of involvement in the dairy sector has not been widely considered. Of note, there is no understanding of how Chinese consumers vary in their involvement levels and their implications on their dairy purchase behaviors. Nor have there been involvement-based insights provided on how dairy companies can position their marketing strategy to suit the needs of these consumers better. Thus, this study proposes a new approach to understanding Chinese consumer dairy decisions by introducing "consumer involvement" as a segmentation tool through which individuals' behaviors can be predicated according to their involvement profile. Based on an online survey administered in Shanghai using 1,073 dairy consumers, principal component analysis confirmed involvement with dairy is a multidimensional construct with the following 4 factors: pleasure value, symbolic value, risk importance, and risk probability. A 2-step cluster analysis identified 4 consumer clusters based on their involvement profile: face-concerned dairy lover, carefree dairy consumer, cautious dairy lover, and confused dairy consumer. According to a one-way ANOVA test and cross-tabulation with χ2 test, these consumer segments behave differently in relation to the extensiveness of decision making, cue utilization, trust of information sources, and consumption behavior. The outcomes in this paper further explain why efforts to restore consumer trust for dairy products do not work among some consumers, as individuals may exhibit diverse attitudes toward such information due to their heterogeneous involvement levels. The study also provides suggestions for market practitioners and organizations to develop effective target market strategies and policies according to different consumer clusters. [ABSTRACT FROM AUTHOR]

Trip-based models and activity-based models represent two extreme ends of the spectrum of travel demand models in data granularity requirement and ability to reflect the underlying motivation to travel. Modelling of representative freight activity-travel patterns (RFAPs) has the potential to serve as the bridge between these approaches. RFAP clusters represent homogeneous groups of establishments, where utility maximization models predict the probability that an establishment belongs to a particular cluster. However, it is still an open question how to define, interpret and model activity-travel patterns in the context of freight system. To answer this question, this study conducted a large-scale establishment-based freight survey (EBFS) in seven cities of India and resulted in a sample of 432 establishments and their 1613 shipment records. In the first part, this paper proposes a novel approach for identifying RFAPs based on the notion that "activities" that inspire trip-making for passenger is equivalent to "freight orders" in the case of establishments. The cluster analyses revealed the presence of three well separated main clusters and nine less separated nested clusters. Through interpretation and labelling of these RFAPs, freight travel market is categorized into useful segments. The results suggested that a priori industrial classification systems used in trip-based models are overly simplified representations of the complex structure of the travel patterns. In the last part, freight activity-travel pattern generation (FAPG) models are developed which predicts the probability that an establishment exhibits a particular RFAP. The FAPG models developed using these RFAPs could replace the traditional freight generation (FG) and freight trip generation (FTG) models due to its ability to convert the assigned activity-patterns to trips or tonnage. For example, FAPG model suggest that at an employment level of 120, there is a 56% probability that establishments will exhibit MDV-HFMH (medium duty vehicles - high frequency medium haul) pattern which, in turn, implies that FP = 1630 tons/year; shipment frequency, i.e., FTP = 8 trips/week; length of haul = 240.6 km and commercial vehicle type choice = MDV. Thus, FAPG models can present an enhanced representation of freight flows since both FG and FTG are jointly modeled in this approach. That is, the best features of both commodity-based modelling (i.e., ability to capture the fundamental mechanism that drives freight demand) and vehicle-based modelling (i.e., ability to capture freight traffic implications) are included in FAPG models. The study findings are expected to assist in identifying the variations in establishments' preferences so that it is possible to identify the type of transport supply improvements that the establishments will respond to accurately, and thus prioritize the infrastructure investments. Moreover, the discussions on these findings are expected to improve the behavioral and spatial foundations of traditional freight models. • Defines "freight orders" as logical equivalent of "household activities" that drive trip-making. • Activity based approach is used for modelling freight travel using EBFS data. • Identifies homogeneous segments of representative freight activity-travel patterns (RFAPs). • Propounds freight activity-travel pattern generation (FAPG) models based on RFAPs. • FAPG gives enhanced representations of freight flows (FG, FTG, vehicle type, haulage). [ABSTRACT FROM AUTHOR]

The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies. • The multi-scale fluctuation characteristics are analyzed for PV power forecast. • Identify dominant meteorological factors affecting PV power by path analysis. • The hour-level fluctuation is reduced by using path analysis-based clustering. • The phase space reconstruction is used to reduce the minute-level fluctuation. [ABSTRACT FROM AUTHOR]

This paper aims to assess container terminal (CT) efficiency with the effect of carbon neutrality. Among mechanisms to achieve carbon neutrality in CTs' operations, emission taxes are deemed the most preferred tool to internalize the external costs associated with greenhouse gas emissions by placing a financial burden on polluters. This paper first uses cluster analysis to determine the homogeneous decision-making units (DMUs), i.e., container terminals. A novel approach is proposed to determine the level of emission taxes that DMUs should be levied based on how much CO2 they discharge. Since emission taxes are an undesirable output from the operations of CT operators (CTOs), the Slacks-based Measure Data Envelopment Analysis (SBM-DEA) model is thus employed to incorporate the emission taxes into the research model and to determine the optimal emission taxes for CTOs. Finally, the CTOs in Vietnam (hereafter the VN-CTOs case) are empirically surveyed to verify the proposed model. The proposed research framework can contribute a methodological reference toward the relevant literature in achieving carbon neutrality. Further, the empirical results can provide practical information for the Vietnamese CTOs to improve their CT efficiency. • Selecting DMUs by the cluster analysis gratifies DMUs' homogeneity - one of the basic assumptions of the DEA theory. • Incorporating undesirable output in the evaluation framework of efficiency allows ranking DMUs' performance accurately. • Adopting SBM-DEA to incorporate undesirable output in evaluating efficiency is a methodological contribution to the relevant literature. • Empirical findings posit that some efficient CTOs took aggressive policies to alleviate pollutant emissions at port areas. [ABSTRACT FROM AUTHOR]

• Introducing a novel density measurement utilizing shared nearest neighbors, enhancing the assessment of data point density. • Developing an advanced K-means enhancement technique to dynamically determine cluster quantity and initial cluster center. • Integration of density-based and partition-based clustering methodologies, refining the K-means method's performance. • Achievement of peak performance in clustering outcomes via the proposed K-means modification. • Synergizing with various feature selection strategies for optimal clustering results proves its superior adaptability. User clustering is crucial for tapping the flexibility of the load side and realizing dynamic management of power loads in new power system. K-means method is widely used in clustering analysis due to its simplicity, high efficiency, and scalability, but it needs to specify the number of clusters in advance, and is sensitive to the initial clustering centers. The current initialization method does not take into account the neighborhood distribution of the data points, and the direct use of data that has undergone dimensionality reduction processing leads to inaccurate selection of the initial clustering centers. To address the above problems, a new K-means improvement method that takes into account the initialization problem and the adaptive determination of the number of clusters: K-means clustering method based on nearest-neighbor density matrix is proposed in this paper. The method improves the efficiency of nearest neighbor search by building a K-D tree, and enhances the performance of unsupervised classification by utilizing the adaptive selection strategy of the number of clusters and the initial clustering centers selection algorithm. The proposed method is applied to real datasets, and its effectiveness is assessed by calculating three clustering evaluation metrics of the clustering results in comparison with several existing initialization and clustering methods. The experimental results show that the method proposed in this paper has higher stability and better clustering performance than existing clustering methods. [ABSTRACT FROM AUTHOR]

The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate, and actionable framework and methodology to express the human element, namely user profiling, within this philosophy. To address this gap, this paper utilizes statistical methods such as factor analysis, cluster analysis, and regression analysis, building upon the concept of traditional user profiling. The aim is to integrate the three prominent approaches of goal-oriented, scenario-based, and data-driven user profiling, with the goal of complementing each other and designing a top-level user profiling framework for smart PSS. Furthermore, Industry 4.0 technologies and text mining techniques are employed to collect data on users' product and service usage. As these data contain real-time information about user needs, behaviors and goals at different time periods, they can be used to construct dynamic features of user profiling, and ultimately achieve the construction of dynamic user profiling for smart PSS. To validate the proposed user profiling framework and dynamic user features of smart PSS, this paper presents a case study focusing on the user group of maternal women. This case study promotes the in-depth exploration of smart PSS research in expressing the human element. [ABSTRACT FROM AUTHOR]

As one of the most popular machine learning methods, random forests have been successfully applied to different data analysis tasks such as classification, regression and cluster analysis. Recently, the random forest clustering method has received much attention due to its simplicity, accuracy and robustness. However, we cannot directly employ the random forest clustering algorithm to solve the discrete sequence clustering problem because of the lack of explicit features and "negative" sequences. In this paper, we propose a new random forest clustering algorithm for discrete sequences. The proposed method firstly injects a set of decoy sequences and then constructs the random forest in a supervised and adaptive manner by generating features on the fly. Experimental results on real data sets show that the proposed method can achieve better performance than those state-of-the-art discrete sequence clustering algorithms. • A discrete sequence clustering method based on random forest is proposed. • We solve the unsupervised sequence clustering problem in a "supervised" manner. • Experimental results demonstrate the feasibility and effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper presents photometric, astrometric, and kinematic analyses of the open clusters NGC 189, NGC 1758 and NGC 7762 based on CCD UBV photometric and Gaia Data Release 3 (DR3) data. According to membership analyses, we identified 32, 57 and 106 most probable member stars with membership probabilities P ⩾ 0.5 in NGC 189, NGC 1758 and NGC 7762, respectively. The color excesses and photometric metallicities of each cluster were determined separately using UBV two-color diagrams. The color excess E (B - V) is 0.590 ± 0.023 mag for NGC 189, 0.310 ± 0.022 mag for NGC 1758 and 0.640 ± 0.017 mag for NGC 7762. The photometric metallicity [Fe/H] is - 0.08 ± 0.03 dex for both NGC 189 and NGC 1758, and - 0.12 ± 0.02 dex for NGC 7762. Distance moduli and ages of the clusters were obtained by comparing PARSEC isochrones with the color-magnitude diagrams constructed from UBV and Gaia photometric data. During this process, we kept as constant color excess and metallicity for each cluster. The estimated isochrone distance is 1201 ± 53 pc for NGC 189, 902 ± 33 pc for NGC 1758 and 911 ± 31 pc for NGC 7762. These are compatible with the values obtained from trigonometric parallax. Ages of the clusters are 500 ± 50 Myr, 650 ± 50 Myr and 2000 ± 200 Myr for NGC 189, NGC 1758 and NGC 7762, respectively. Galactic orbit integration of the clusters showed that NGC 1758 completely orbits outside the solar circle, while NGC 189 and NGC 7762 enter the solar circle during their orbits. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of fault propagation in industrial facilities, where a fault in one process variable can lead to cascading faults in other variables. As a result, the propagation of alarms corresponding to these faulty variables occurs, leading operators to potentially receive an excessive number of alarm notifications that could significantly impact their decision-making capabilities. To address this issue, a systematic method is proposed to investigate potential fault propagation paths to provide decision support in response to alarm notifications, in order to minimize industrial process failures. The contributions of this paper are twofold. Firstly, it involves enhanced dependency analysis that captures dependent alarms and identifies both weak and strong dependencies among alarm variables using historical alarm and event (A&E) logs that are generated by industrial control systems. Secondly, it offers comprehensive visualization of fault propagation in response to single and multiple alarms, including extracting crucial timing information, identifying the shortest, longest, and critical paths, and determining effective operator actions. The proposed method is designed to enhance process operation and provide essential decision support for industrial operators. The effectiveness of the proposed approach is validated through a case study using real industrial data. [ABSTRACT FROM AUTHOR]

Nowadays, group decision making is an everyday occurrence in different scenarios, such as marketing or social networks. These social networks have facilitated communication between experts because they do not need to meet in person. However, communication between experts through the internet generates three problems: the management of large amounts of information, the fact that experts often provide their information using natural language, and the lack of analysis of experts' intentions. In this paper, we propose a novel large scale group decision making method to manage the information generated by a large number of experts, using the natural language processing approach, specifically sentiment analysis. This approach makes it possible to detect the degree of positivity and aggressiveness of each expert and thus proceed to a classification. Once the behaviours are detected, the experts are grouped according to them and, for each group, a weight and a unique preference relation is obtained. In addition, we propose an optimised consensus analysis process, in which it is not necessary to compare all experts with each other, but only groups of experts. • We created a classifier to detect aggressiveness and positivity. • Experts are clustered according to their aggressiveness and positivity. • The expert groups are used to create an LGDM system. • The LSGDM system determines a ranking of alternatives agreed upon by the experts. [ABSTRACT FROM AUTHOR]

Purpose: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few.Methods: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis.Results: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health.Conclusions: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health. [ABSTRACT FROM AUTHOR]

In many areas of science, like neuroscience, genomics and text mining, several important and challenging research questions imply the study of (subject) heterogeneity present in three-way data. In clinical neuroscience, for example, disclosing differences or heterogeneity between subjects in resting state networks (RSNs) underlying multi-subject fMRI data (i.e., time by voxel by subject three-way data) may advance the subtyping of psychiatric and mental diseases. Recently, the Clusterwise Independent Component Analysis (C-ICA) method was proposed that enables the disclosure of heterogeneity between subjects in RSNs that is present in multi-subject rs-fMRI data [1]. Up to now, however, no publicly available software exists that allows to fit C-ICA to empirical data at hand. The goal of this paper, therefore, is to present the CICA R package, which contains the necessary functions to estimate the C-ICA parameters and to interpret and visualize the analysis output. Further, the package also includes functions to select suitable initial values for the C-ICA model parameters and to determine the optimal number of clusters and components for a given empirical data set (i.e., model selection). The use of the main functions of the package is discussed and demonstrated with simulated data. Herewith, the necessary analytical choices that have to be made by the user (e.g., starting values) are explained and showed step by step. The rich functionality of the package is further illustrated by applying C-ICA to empirical rs-fMRI data from a group of Alzheimer patients and elderly control subjects and to multi-country stock market data. Finally, extensions regarding the C-ICA algorithm and procedures for model selection that could be implemented in future releases of the package are discussed. [ABSTRACT FROM AUTHOR]

The vibration behaviour of the vibrating rods is one of the key factors for the quality of concrete vibration that is essential for the long-term safety of concrete structures. Although many vibration operation regulations have been widely applied, evaluating method for concrete vibration samples is relatively rare. To fill this gap, this paper proposes a concrete vibration data acquisition system and attempts to perform a quality analysis of samples employing machine learning that amalgamates various parameters. The experimental results demonstrate that the data collection system to identify the vibration state achieves an accuracy of 93.75% and an accuracy of 90.3% in classifying vibration quality levels. The proposed method can classify and evaluate concrete quality levels, which strongly supports the visualization of concrete vibration process and the implementation of autonomous robot vibration. In the future, improving the reliability of the system and enhancing the accuracy of algorithms will be the focused. • A method for datafication conventional concrete vibration behaviour is proposed. • The concrete vibration data is sampled in order to classify with shallow learning. • The performance capabilities of the six algorithms in concrete samples were compared. • The identified framework proposed attains a stage recognition accuracy of 93.75%. • The proposed method strongly supports the implementation of autonomous vibration. [ABSTRACT FROM AUTHOR]

The concept of similarity measure is fundamental for evaluating the consistency between intuitionistic fuzzy sets (IFSs). Several researchers have developed various intuitionistic fuzzy similarity measures (IFSimMs) utilizing intuitionistic fuzzy distance measures (IFDisMs) or combinations of membership and non-membership degrees of IFSs. Nevertheless, there is still ample room for the improvement, as some of these measures do not satisfy the axiomatic properties of IFSimMs and produce unreasonable results. In this paper, we first establish a dual relationship between IFDisMs and IFSimMs based on fuzzy negations, enabling the construction of an infinite number of IFSimMs from a given IFDisM. We then propose two novel IFSimMs by directly manipulating the membership and non-membership degrees of IFSs and prove that they satisfy the axiomatic properties of strict IFSimMs (SIFSimMs). Furthermore, a comparative analysis reveals that the proposed SIFSimMs do not yield any unreasonable results in various scenarios. Finally, we apply these SIFSimMs to pattern recognition, medical diagnosis, and clustering analysis problems, demonstrating their superior performance compared to some existing measures. • Show the duality between intuitionistic fuzzy distance and similarity measures. • Identify the limitations of some existing intuitionistic fuzzy similarity measures. • The properties of strict intuitionistic fuzzy similarity measure are investigated. • Pattern recognition, medical diagnosis, and clustering analysis are discussed. • Superiority of the proposed approaches is explained. [ABSTRACT FROM AUTHOR]

• A multi-pulse friction compensation strategy is proposed. • The compensatory parameters are automatically tuned through Bayesian optimization. • Relationship between compensation parameters and joint acceleration discovered. • Online matching achieved between compensation parameters and robot configurations. • The strategy is verified by experiments on the Trimule robot. Friction nonlinearity near zero velocity causes substantial tracking errors during joint motion reversals. For hybrid robots, this phenomenon is further influenced by joint acceleration and robot configuration, unique characteristics of hybrid robots that can degrade the performance of traditional friction compensation methods. This paper presents a novel multi-pulse friction compensation strategy that can adapt to joint acceleration and configuration variations in hybrid robots. Bayesian Optimization is employed to automatically tune all compensation parameters. By analyzing experimental data, a potential relationship between compensation parameters and joint acceleration is explored, leading to a concise and effective method for estimating optimal parameters based on joint acceleration. In addition, the basic idea of cluster analysis is combined with a limited number of experiments to achieve online parameter-to-configuration matching. Experimental results on TriMule-200 hybrid robot demonstrate the outstanding performance of this strategy in suppressing tracking errors during velocity reversals, as well as its robustness to joint acceleration and robot configuration variations. [ABSTRACT FROM AUTHOR]

To reduce smoking uptake in adolescents, the medical students' network Education Against Tobacco (EAT) has developed a school-based intervention involving a face-aging mobile app (Smokerface). A two-arm cluster-randomized controlled trial was conducted, evaluating the 2016 EAT intervention, which employed the mobile app Smokerface and which was delivered by medical students. Schools were randomized to intervention or control group. Surveys were conducted at baseline (pre-intervention) and at 9, 16, and 24 months post-intervention via paper & pencil questionnaires. The primary outcome was the difference in within-group changes in smoking prevalence between intervention and control group at 24 months. Overall, 144 German secondary schools comprising 11,286 pupils participated in the baseline survey, of which 100 schools participated in the baseline and at least one of the follow-up surveys, yielding 7437 pupils in the analysis sample. After 24 months, smoking prevalence was numerically lower in the intervention group compared to control group (12.9 % vs. 14.3 %); however, between-group differences in change in smoking prevalence between baseline and 24-months follow-up (OR=0.83, 95 %-CI: 0.64–1.09) were not statistically significant (p = 0.176). Intention to start smoking among baseline non-smokers declined non-significantly in the intervention group (p = 0.064), and remained essentially unchanged in the control group, but between-group differences in changes at the 24-months follow-up (OR=0.88, 0.64–1.21) were not statistically significant (p = 0.417). While a trend towards beneficial effects of the intervention regarding smoking prevalence as well as intention to start smoking among baseline non-smokers was observed, our smoking prevention trial demonstrated no significant effect of the intervention. • A face-aging app intervention aimed to reduce adolescent smoking uptake. • Two-arm cluster-randomized controlled trial with 144 German secondary schools. • No significant difference in smoking prevalence change at 24 months (p = 0.176). • Subgroup analyses showed a tendency towards benefits among boys and grammar school pupils. • Intention to start smoking declined non-significantly in the intervention group (p = 0.064). [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor therapy beyond oncology has gained increasing attention. While a substantial number of publications have emerged in recent years, there has been a paucity of conducted bibliometric studies. Our objective is to systematically summarize and visually analyze the literature in the field of chimeric antigen receptors therapy beyond oncology and explore hotspots in this field. Web of Science Core Collection was selected as the data source, and the data was retrieved on December 23, 2022, according to the search strategy. CiteSpace 6.1.R6 and Vosviewer 1.6.18 were used to analyze publications and explore research hotspots and directions. A total of 338 publications written by 1832 authors from 516 institutions in 42 countries/regions were selected for the analysis. The number of publications is steadily increasing annually. The United States emerged as the primary contributor, and University of Pennsylvania was the leading institution. Frontiers in Immunology boasted the highest number of published papers. Kitchen SG, Riley JL, and Scott DW were the most productive researchers in this field. The keyword cluster analysis identified HIV, autoimmune diseases, transplant related diseases and COVID-19 as the primary focus areas within the realm of chimeric antigen receptor therapy beyond oncology. The advancement of chimeric antigen receptor therapy beyond oncology has witnessed rapid progress in recent years. We have explored the hotspots and research directions in this field. It is hoped that this study could provide references and directions for future clinical researches. [ABSTRACT FROM AUTHOR]

Urban proximity planning is foreseen as a solution to foster a "sustainable" city, including economic viability, environmental soundness and social inclusivity. This paper focuses on the inclusivity aspects by questioning the adoption of urban proximities: how far planning for urban proximities resonates or conflicts with current residential preferences and practices? Based on functional, social, and sensitive attributes, five latent residential preferences have been identified: spatial proximity, tranquility, elitism, social proximity, and conviviality. These aspirations form the basis for a longitudinal, spatial, and clustering analysis. The results of the analysis, conducted on 2200+ respondents in Geneva, reveal characteristics in residential preferences. Firstly, preferences are stable over time, but malleable to changing circumstances. Secondly, some preferences show clear spatial distribution patterns when regressed with residential location choice. Thirdly, the gap between preferences and actual residential practices varies across morphological attributes. As a main result, the clustering analysis shows that 43 % of the population aspire more to tranquility than to functional proximity; and those aspiring for proximities (32 %) are the youngest and wealthiest. This questions the relevance of models of urban proximities as an inclusive solution – raising issues of generational divide, territorial segregation, and injunctive and targeted planning. • 43 % of the population prefers tranquility over functional proximity. • Those aspiring for proximities (32 %) are the youngest and wealthiest. • The preferences for Spatial Proximity and Conviviality are oriented inward. • Elitism and Social Proximity are spread across the territory. • Residential preferences are stable over time. [ABSTRACT FROM AUTHOR]

The paper explores Coastal Energy-Based Industrial Clusters (EBICs) and their role in advancing energy efficiency and sustainability through collaborative innovation. Economic growth theory and energy sustainability have been introduced into industrial clusters to illustrate indicators that have a greater impact on the development of EBICs. This paper proposes an EBICs development model based on the Cobb-Douglas function, in which accounts for various factors that drive the progress of such clusters. The outcomes of the economic model also provide insights into how the interaction of various factors affects the economic growth of EBICs and their eventual dominance in the energy market of coastal regions, dependent on gradually investing in areas such as research and development (R&D). Different development strategies demonstrate that the final development of a cluster has low dependence on the cluster's initial advantages. The study also illustrates how clusters can gradually monopolize the energy market, even with initial disadvantages. Through quantitative analysis, it showcases the transformation process of developing advantageous clusters into sub-clusters. Next, an energy symbiosis framework for coastal is proposed, which places greater emphasis on the multi-energy complementary system and reduces production costs. Finally, this paper also sheds light on shaping energy strategies for public authorities who shape economic policies at various levels of aggregation and in diverse dimensions. • Multi-energy complementarity provides a new path for sustainable development of coastal energy. • Hardware investment is critical to the development of energy-based industry clusters. • Clusters with better natural advantages may become sub-clusters due to incorrect development strategies. • The essence of energy cluster development is the result of the interaction of multiple influencing factors. • The economic growth of energy clusters depends on the proportion of investment in software and hardware. [ABSTRACT FROM AUTHOR]

Incomplete multi-view clustering (IMVC) aims to partition samples into different groups for datasets with missing samples. The primary goal of IMVC is to effectively address the challenge posed by missing information in clustering analysis. Most existing IMVC methods focus on balanced incomplete multi-view data, assuming a uniform missing rate across all views. However, this assumption does not accurately reflect real-life scenarios. In reality, unbalanced incomplete multi-view data, characterized by varying missing rates among different views, is more prevalent. This presents significant challenges to the clustering process, as varying missing rates can lead to information imbalance. To address these challenges, this paper introduces a novel approach called projected cross-view learning for unbalanced incomplete multi-view clustering (PCL_UIMVC). Specifically, a reconstruction term is integrated, which leverages the information from the existing samples to facilitate the completion of the unbalanced incomplete multi-view data. Next, a projection matrix is incorporated into the model to harmonize feature dimensions across views, mitigating the impact of information imbalance. Then, a graph regularization term is integrated to preserve the geometric structure of the original data. Finally, an iterative algorithm is developed to solve the proposed model. Extensive experiments on eight standard datasets, featuring various rates of missing data, validate the superior clustering performance of the proposed method. • The paper introduces a projected cross-view learning method for unbalanced incomplete multi-view clustering. • Reconstruction terms of different dimensions are constructed to handle the unbalanced incomplete multi-view data by completing the missing samples. • A projection matrix is incorporated into the model to reduce the impact of information imbalance. • A graph regularization term is integrated to preserve the original latent spatial structure of the data. [ABSTRACT FROM AUTHOR]

Numerous studies have examined the utility of virtual reality (VR) technologies in the transportation and logistics sector. However, no systematic and comprehensive review of this research area has been performed. This need is addressed in this paper by reviewing publications on the development and applications of VR technologies in the four main categories of transportation and logistics: air, maritime, railway, and road. A clustering analysis of 200 papers on VR in transport and logistics published from 2010 to 2023 has been performed. It identifies six clusters of research: operator training in routine procedures, operator training in incident handling, simulation of carriers, design of traffic infrastructure and carriers, psychological studies on transportation and logistics operators or associated parties, and treatment of illness related to transportation and logistics. Based on the review and the clustering analysis, the paper proposes directions for future research on the development of VR and its applications in transportation and logistics. • A review of 200 peer-reviewed VR papers published from 2010 to 2023 is conducted. • The papers are from 4 main categories of logistics: air, marine, railway and road. • Six clusters of research are identified from a clustering analysis of the papers. • Directions for future VR research in transportation and logistics are proposed. [ABSTRACT FROM AUTHOR]

Researchers and practitioners in education and school psychology regularly use ranked data to drive student- and systems-level decision-making. These types of data can be derived from assessments of individual preferences among researchers and practitioners, assessments of preferences among stakeholders including parents and children, and rankings of students on academic and social-emotional competency. However, the analysis of ranked data in education and psychology has typically been limited to simple approaches such as the examination of mean ranks assigned to items. This paper unifies a collection of classical methodologies, as well as proposes new techniques, for analyzing ranked data used across disciplines and applies the methods to data generated in school psychological research. The scope of the paper is to serve as a roadmap for researchers in education and school psychology who seek to more fully leverage information contained in ranked data. These methodologies include descriptive analyses, visualizations, tests of uniformity, cluster analyses, and predictive models. We demonstrate these techniques on the survey data of Fefer, DeMagistris, and Shuttleton (2016) and illustrate how using a broader set of tools can yield improved insights by researchers and practitioners. [ABSTRACT FROM AUTHOR]

• This paper considers unobserved or latent features when modeling injury severity. • Latent class analysis is used to segment crash data to reduce heterogeneity. • Partial proportional odds models are developed to explore bicyclist injury severity. • Some variables affect bicyclist injury severity differently between various clusters. • Policy-related recommendations and advices on traffic management are provided. Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results. [ABSTRACT FROM AUTHOR]

Ensuring a high level of customer satisfaction with public transport is a key objective for transit agencies wishing to maintain and/or increase service uptake. Importance – Performance Analysis (IPA) can be used to identify gaps in the performance of public transport service attributes relative to their importance. However, this approach is often used without adequate consideration of its validity or reliability. Using a case study of bus services in Tehran, Iran, this paper contributes to the literature by improving the validity and reliability of IPA through: incorporating Exploratory Factor Analysis (EFA) to identify key components of bus service attributes, proposing a method for prioritising attributes, introducing confidence intervals around each attribute to aid in the interpretation of results, and dividing the sample using Cluster Analysis (CA) to mitigate the effects of heterogeneity on the results. The performance and importance of 15 different bus service attributes was assessed through a survey of 390 bus passengers conducted at six high-traffic bus terminals in Tehran. EFA results showed that the bus attributes could be represented by four key components: convenience, comfort, reliability and security. CA results classified the sample into three clusters based on socio-demographic and bus travel characteristics. IPA results indicated that transit agencies and policy-makers need to give greater attention to improving comfort related attributes of bus services in Tehran such as ventilation, cleanliness of buses, and providing facilities for elderly/disabled people. The enhanced approach to IPA developed in this paper can be applied to other jursidictions where agencies are seeking to identify areas to improve public transport customer satisfaction. [ABSTRACT FROM AUTHOR]

• The presented studies provide insights into categories of drivers choosing different interaction strategies when using advanced driver assistance systems. • Extreme driver categories with interaction strategies indicative for both misuse and disuse were observable. • Results indicate that some drivers had difficulties to adapt their level of trust and reliance strategy to changing driving environments. • Results underline the need of tutoring systems that are tailored for different driver categories. When using advanced driver assistance systems (ADAS) drivers need to calibrate their level of trust and interaction strategy to changes in the driving context and possible consequent reduction of system reliability (e.g. in harsh weather conditions). By investigating and identifying categories of drivers who choose inadequate interaction strategies, it is possible to address unsafe usage with e.g. tutoring lessons tailored to the respective driver category. This paper presents two studies investigating categories of drivers who apply different interaction strategies when using ADAS. Study I was designed as an exploratory field study with 37 participants interacting with a SAE level 2 system. For the exploratory study, it was important to observe and understand the interaction strategies in a driving context which entails the real complexity of the driving task. The experimental set-up of study II (simulator study), however, allowed to clearly interpret the interaction strategies as either calibrated or un-calibrated by varying the situational risk. Participants (N = 33) were driving in a situation where the system was either working reliably (low-risk condition) or in a situation where the system displayed repeatedly errors under harsh weather conditions (high-risk condition). Cluster analyses with the variables trust, monitoring behavior towards the system and usage behavior were performed to analyze potential categories of drivers. Extreme driver categories with interaction strategies indicative for both misuse and disuse were observed in both studies. In study I, drivers were categorized as either highly trusting attentive , moderately trusting attentive , moderately inattentive , inattentive or skeptical. In study II, drivers were categorized as either un-calibrated , calibrated , inconsistent or skeptical. Taken together, results underline the need of tutoring systems that are tailored for different driver categories. [ABSTRACT FROM AUTHOR]

• Unsupervised clustering algorithms can be used for crop type identification on remote images. • Results of performance of clustering algorithms and distance measures are analysed. • 26 datasets built with all the combination of 5 crops and more than 45000 parcels. • Best performances are obtained for Spectral clustering method. • Manhattan and Euclidean are in general the best distance measures. With the current challenges in population growth and scarceness of food, new technologies are emerging. Remote sensing in general and satellite imagery more specifically are part of these technologies which can help provide accurate monitoring and classification of cultivars. Part of the increase in the use of these technologies has to do with the ongoing increment on the spatial–temporal resolution together with the free availability of some of these services. Typically time series are used as a pre-processing technique and combined with supervised learning techniques in order to build models for crop type identification in remote images. However, these models suffer from the lack of labelled data sets needed to train them. Unsupervised classification can overcome this limitation but has been less frequently used in this research field. This paper proposes to test and analyse the performance of several unsupervised clustering algorithms towards crop type identification on remote images. In this manner combinations of clustering algorithms and distance measures, a key element in the behaviour of these algorithms, are studied using an experimental design with more than twenty datasets built from the combinations of five crops and more than 45000 parcels. Results highlight better clustering methods and distance measures to create accurate and novel crop mapping models for remote sensing images. [ABSTRACT FROM AUTHOR]

This paper addresses reduced order homogenization of composites with strength difference (SD) effects in elastoplasticity coupled to damage, while containing several well-known plasticity criteria as special cases. We extend two approaches for this purpose: 1. nonuniform transformation field analysis (NTFA by Michel and Suquet, 2003) and 2. a recent variant called cluster-based NTFA (CNTFA by Ri et al., 2021), and conduct a comparative study on them. For the NTFA approach, a space–time decomposition is done separately for volumetric and deviatoric inelastic strain fields. A coupled model is derived for the present case to govern the evolution of resulting reduced variables. For the CNTFA approach, a clustering analysis is additionally performed for a spatial decomposition of the micro-domain. Unlike the NTFA, the online analysis is formulated as a unified minimization problem, which does not require a major adaptation for the present case. For both approaches, localization rules are deduced from the superposition principle and then homogenized to obtain the effective responses. FE-based implementation is presented in detail for both approaches. Numerical results show that both approaches provide a striking acceleration rate against conventional FE computations. The CNTFA predictions are more accurate than the NTFA ones by involving clustered microscopic fields in the online analysis, thus resulting into a slightly increased memory requirement. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have gradually become the mainstream way to solve complex optimization problems with their streamlined structure and robust performance. Innovative achievements have increasingly emerged with the development and widespread application of metaheuristic algorithms. In order to provide a better overview of relevant work in this field and clarify its prospects, this paper analyzed 1676 literature on the application of metaheuristic algorithms from 1994 to 2023, based on the Web of Science database. Firstly, utilizing a large amount of literature data, the overall trend of the application field development of metaheuristic algorithms, core authors, and mainstream journals was analyzed from the perspective of bibliometrics. Secondly, combined with the visualization software CiteSpace, major cooperation maps in the application field of metaheuristic algorithms were drawn, and the correlation relationships between authors, countries, and institutions in the area were obtained. Thirdly, co-word and cluster analyses were used to explore the research hotspots of applying metaheuristic algorithms, and six prevalent research topics in this field were obtained. Finally, through the co-cited literature cluster map and time-zone map, vital citing literature in the metaheuristic algorithms' application field and the evolution of time-based development frontiers were summarized, and three development stages of this field were obtained. The overall review of the application of metaheuristic algorithms can provide assistance for researchers interested in this field. [ABSTRACT FROM AUTHOR]

Personalized document-level sentiment analysis (PDSA) plays an important role in many real-world applications. So far, various deep learning models for PDSA have been proposed. However, there has been no systematic survey in this area. To address this issue, in this paper, we overview the existing methods of PDSA, and propose a novel two-dimensional PDSA taxonomy. Specifically, in the dimension of attribute usage type , PDSA works are divided into two groups: (1) user-based and (2) user and product-based models. In the dimension of attribute processing method , PDSA works are divided into two groups: (1) feature-based and (2) relation-based models. To fill in the research blank indicated by the taxonomy, we further propose an architecture named User Correlation Mining (UCM) for PDSA. Specifically, UCM contains two components, namely Similar User Cluster Module (SUCM) and Triple Attributes BERT Model (TABM). SUCM is responsible for user clustering, and TABM aims to classify the user's sentiment based on the information of users, products, user clusters and user reviews. To evaluate the performances of the existing works as well as UCM, we conduct extensive experiments on three real-world datasets. The experiment results show that our proposed architecture UCM outperforms the other state-of-the-art methods. • To the best of our knowledge, this is the first survey of PDSA. • We propose a PDSA taxonomy, and summarize the ideas of existing works for each group. • We propose a model for PDSA based on user correlation to fill the existing vacancies. [ABSTRACT FROM AUTHOR]

• Evaluation of the injury severity of government-registered accidents on the highway network in the Netherlands. • A hybrid approach integrating two-step cluster analysis and latent class ordered regression model with covariates aims to explore the unobserved heterogeneous effects of risk factors. • Road lighting, time of accident, road surface, weather, and season are identified as covariates influencing class membership prediction. • High speed, alcohol involvement, frontal collision points, and older drivers increase fatal-severe injury risk. • Heterogeneity effects exist in the two classes regarding temporal characteristics, the number and type of vehicle involved, as well as driver gender. Highway crashes are responsible for a significant number of severe and fatal injuries drawing considerable attention from transportation authorities and safety researchers. This paper aims to investigate the unobserved heterogeneous effects of various risk factors, such as pre-crash circumstances, environmental and road conditions, vehicle-involved information, and driver attributes on injury severities. Our methodology uses a hybrid approach that combines two-step cluster analysis and latent class ordered regression model with covariates. The proposed approach extends traditional latent class model by elucidating potential relationships among predictors, covariates, and outcomes. A cross-sectional crash data covering a period of over five years (2011–2016) was obtained via the Dutch crash registration database for modeling injury severity outcomes. The results reveal substantial and statistically significant differences in injury severity between two latent classes. Moreover, we identify road lighting, time of crash, road surface conditions, weather, and season as covariates influencing class membership prediction. Factors such as high speed, alcohol involvement, frontal collision points, and older driver demographics increase the probability of serious injury and facility across all cases analyzed. Additionally, we observe notable heterogeneity effects between the two classes regarding temporal characteristics, the number and type of vehicles involved, as well as driver gender. Our findings provide specific and valuable insights into injury severity outcomes, which can inform the formulation of targeted safety countermeasures and regulatory strategies for traffic policies and relevant agencies. [ABSTRACT FROM AUTHOR]

The recent destructive hurricane seasons and concerns related to the future influence of climate change have increased the relevance of coastal storm hazards and, in particular, of storm surge hazard estimation when discussing the resilience of coastal communities. This hazard is generally represented as surge inundation probabilities over a large number of individual locations in the geographic domain of interest, and is typically assessed utilizing an ensemble of storm scenarios (i.e., storm events) that are representative of the regional climatology. This paper investigates the storm ensemble selection within this setting, with the objective of identifying a small number of storm scenarios that are consistent with some chosen hazard descriptions over a large geographic region. Beyond the storm events, the occurrence rates (i.e., weights) are also updated. Following past works, a two-stage optimization is adopted for the storm selection. The inner-loop identifies the occurrence rates for a given storm subset, formulating the problem as a linear programming optimization for the sum of absolute deviation for the predicted hazard. The outer-loop searches for the best subset with the desired number of storms, adopting a genetic algorithm integer optimization for minimizing the aforementioned deviation. This work extends this implementation to extended coastal regions, with many locations of interest. In this case, it is computationally intractable to consider all the locations in the domain within the linear programming formulation, and for this reason, a subset of representative locations is chosen through cluster analysis. The hazard description for only these locations is used in the storm ensemble selection. For clustering, different strategies using correlation between locations based on geospatial information, surge response, or a combination of both are examined. Additionally, the correlation in the hazard description is better integrated into the storm selection by establishing a modification of the objective function adopted for the outer optimization loop. Applications to different North Atlantic coastal domains are presented as case studies. • Hazard-consistent probabilistic scenario optimization (HPSO) for extended geographical domains is examined. • Dimensionality reduction is introduced to accommodate computational tractability. • A representative subset of locations is carefully chosen for guiding the storm ensemble selection. • Modifications are also proposed to better integrate the spatial correlation of the hazard description. • The correlation across the locations for the hazard maps is introduced to achieve this objective. [ABSTRACT FROM AUTHOR]

• Large groups need to seek decision representatives to participate in consensus negotiations. • Local and global influence can be estimated based on centrality metrics of social networks. • Representatives codetermined by strong and weak ties lead to faster consensus formation. In the era of social media, the issue of large-scale group decision-making (LSGDM) is becoming increasingly prominent. The complexity of large group interactions increases rapidly with the expansion of the group size. Current research has often used cluster analysis to reduce the dimensionality of LSGDM, but the decision agents following dimensionality reduction are not clearly defined, which hinders the practical application of LSGDM methods. This paper studies the LSGDM through the context of community representatives which are determined by combining strong and weak ties. The participation of such representatives in subsequent decision-making can reduce negotiation costs and efficiently form a consensus from both global and local perspectives. To identify these community representatives, this study improves the traditional Laplacian centrality, calculates the local and global centrality of individuals, and defines metrics for measuring representatives. The opinion dynamics model is used to verify the effectiveness of the LSGDM model with community representatives. The karate club network is used to illustrate the application of the proposed LSGDM model. Simulation and comparative analysis results show that the proposed model takes less time to reach consensus than traditional ones. Furthermore, there is no complete linear relationship between the representative size and the time taken to reach group consensus. [ABSTRACT FROM AUTHOR]

Bauxite residue (red mud) has high alkalinity and salinity, which seriously restricts its reuse. It is crucial to understand the salt distribution pattern and alkalinity characteristics in the bauxite residue disposal area (BRDA), which can provide a theoretical basis for realizing the salinity and alkalinity regulation and reuse of bauxite residue. In this paper, samples from different locations and depths of the BRDA were used for the determination of soluble salts content, pH, and electrical conductivity (EC), and the correlation between the indicators was analyzed. The results show that bauxite residue has a high content of soluble salts, and the spatial distribution of soluble salts in the BRDA is relatively complex, with moderate variability, but does not show an obvious regularity. The major cations are Na+, K+ and the major anions are CO 3 2−, HCO 3 −. Through cluster analysis, the soluble ions in bauxite residue can be divided into two main groups, one is Na+, CO 3 2−, HCO 3 −, and the other is Ca2+, Mg2+, K+, Cl− and SO 4 2−. PH has a significant positive correlation with Na+, K+, CO 3 2− and HCO 3 −, and a negative correlation with Ca2+. And its alkalinity depends to a large extent on carbonate and bicarbonate, and the Na+ content can be predicted from EC, thus reflecting the concentration of alkaline residues in the bauxite residue. [Display omitted] • The distribution of soluble salt ions varies widely within the BRDA. • The main ions in bauxite residue are Na+, CO 3 2− and HCO 3 −. • The alkalinity is largely dependent on the concentrations of CO 3 2− and HCO 3 −. • An equation for predicting alkaline residues from EC was proposed. [ABSTRACT FROM AUTHOR]

The Robust Perron Cluster Analysis (PCCA+) has become a popular spectral clustering algorithm for coarse-graining transition matrices of nearly decomposable Markov chains with transition states. Originally developed for reversible Markov chains, the algorithm only worked for transition matrices with real eigenvalues. In this paper, we therefore extend the theoretical framework of PCCA+ to Markov chains with a complex eigen-decomposition. We show that by replacing a complex conjugate pair of eigenvectors by their real and imaginary components, a real representation of the same subspace is obtained, which is suitable for the cluster analysis. We show that our approach leads to the same results as the generalized PCCA+ (GPCCA), which replaces the complex eigen-decomposition by a conceptually more difficult real Schur decomposition. We apply the method on non-reversible Markov chains, including circular chains, and demonstrate its efficiency compared to GPCCA. The experiments are performed in the Matlab programming language and codes are provided. • Our method can cluster stochastic matrices with complex eigenvalues. • It is equivalent to other methods that use the real Schur decomposition. • We provide the first MATLAB implementation of this clustering method. [ABSTRACT FROM AUTHOR]

Cluster analysis is one of the most important research issues in data mining and machine learning. To date, numerous clustering algorithms have been proposed to tackle the fixed-length vector data. In many real applications, we need to detect clusters from a set of discrete sequences in which each sequence is an ordered list of items. Due to the sequential and discrete nature, the discrete sequence clustering problem is more challenging and most of existing vector data clustering algorithms cannot be directly employed. In this paper, we present a stochastic algorithm for clustering discrete sequences. Our method first quickly generates a set of random partitions over the sequential data set and then merges these random clustering results via weighted graph construction and partition. We perform extensive empirical comparisons on real data sets to show that our method is comparable to those state-of-the-art clustering algorithms with respect to both accuracy and efficiency. • A randomized algorithm is proposed to solve the discrete sequence clustering issue. • Our algorithm provides high success rate for sequence linkage in clusters. • Experimental results demonstrate the feasibility and effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper presents the neutrosophic genetic algorithm (NGA) to address the research gap in the application of neutrosophic theory in conjunction with genetic algorithms. NGA introduces three distinct solution spaces—truth, falsity, and indeterminacy—enabling it to entirely encompass neutrosophic solution spaces in the operational process. Fine-tuning in the true solution space (TSS), adaptive regeneration in the false solution space (FSS), and modified crossover and mutation operations in the indeterminate solution space (ISS) enhance NGA ability to navigate away from local optima while reducing computational complexity. Evaluation against several prior algorithms based on the CEC2017 test suites demonstrates the superior performance of NGA, achieving the highest overall score of 92.11% in various problems and conditions. Sensitivity analysis of NGA parameters provides significant insights into algorithm performance variations, emphasizing the substantial impact of these parameters on the NGA's performance. The application of NGA to optimize the K-means method for clustering analysis of rock discontinuity sets showcases its efficiency and potential for practical applications in related fields, highlighting its advantages over other methods. This research establishes NGA as an innovative and efficient approach to address imprecision, incompleteness, and uncertainty in practical data scenarios, with significant implications for future development and applications. [ABSTRACT FROM AUTHOR]

Polysaccharides have a wide range of applications due to their excellent antioxidant activity. However, the low purity and unclear structure of polysaccharides have led some researchers to be skeptical about the antioxidant activity of polysaccharides. The current reports on the structure-activity relationship of polysaccharides are sporadic, so there is an urgent need to systematically summarize the antioxidant effects of polysaccharides with clear structures and the relationships between the structures to provide a scientific basis for the development and application of polysaccharides. This paper will systematically elucidate the structure-activity relationship of antioxidant polysaccharides, including the molecular weight, monosaccharide composition, glycosidic linkage, degree of branching, advanced conformation and chemical modification. For the first time, the antioxidant activity of polysaccharides is related to their chemical structure through histogram and radar map, and further studies using principal component analysis and cluster analysis. We critically discussed how the source, chemical structure and chemically modified groups of polysaccharides significantly contribute to their antioxidant activity and summarized the current research status and shortcomings of the structure-activity relationship of antioxidant polysaccharides. This review provides a theoretical basis and new perspective for further research on the structure-activity relationship of antioxidant polysaccharides and the development of natural antioxidants. [ABSTRACT FROM AUTHOR]