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Abstract
Insurance rating territory design and accurate estimation of territory risk relativities are fundamental aspects of auto insurance rate regulation. It is crucial to develop methodologies that can facilitate the effective design of rating territories and their risk relativities estimate, as they directly impact the rate filing and the decision support of the rate change review process. This article proposes a Gaussian Mixture Regression model clustering approach for territory design. The proposed method incorporates a linear regression model, taking spatial location as model covariates, which helps estimate the cluster mean more accurately. Also, to further enhance the estimation of territory risk relativities, we impose sparsity through sparse matrix decomposition of the membership coefficient matrix obtained from the Gaussian Mixture Regression model. By transitioning from the current hard clustering method to a soft approach, our methodology could improve the evaluation of territory risk for rate-making purposes. Moreover, using non-negative sparse matrix approximation ensures that the estimation of risk relativities for basic rating units remains smooth, effectively eliminating data noise from the territory risk relativity estimate. Overall, our novel methodology aims to significantly enhance the accuracy and reliability of risk analysis in auto insurance. Furthermore, the proposed method exhibits potential for extension to various other domains that involve spatial clustering of data, thereby broadening its applicability and expanding its usefulness beyond auto insurance rate regulation.

Traditional electoral districting is mostly carried out by artificial division. It is not only time-consuming and labor-intensive, but it is also difficult to maintain the principles of fairness and consistency. Due to specific political interests, objectivity is usually distorted and controversial in a proxy-election. In order to reflect the spirit of democracy, this study uses computing technologies to automatically divide the constituency and use the concepts of &ldquo ; intelligent clustering&rdquo ; and &ldquo ; extreme arrangement&rdquo ; to conquer many shortcomings of traditional artificial division. In addition, various informational technologies are integrated to obtain the most feasible solutions within the maximum capabilities of the computing system, yet without sacrificing the global representation of the solutions. We take Changhua County, Taiwan as an example of complete electoral districting, and find better results relative to the official version, which obtained a smaller difference in the population of each constituency, more complete and symmetrical constituencies, and fewer regional controversies. Our results demonstrate that multidimensional algorithms using a geographic information system could solve many problems of block districting to make decisions based on different needs.

Natural Language Processing has become a common tool to extract relevant information from unstructured data. Messages in social media, customer reviews, and military messages are all very short and therefore harder to handle than longer texts. Document clustering is essential in gaining insight from these unlabeled texts and is typically performed after some preprocessing steps. Preprocessing often removes words. This can become risky in short texts, where the main message is made of only a few words. The effect of preprocessing and feature extraction on these short documents is therefore analyzed in this paper. Six different levels of text normalization are combined with four different feature extraction methods. These setting are all applied on K-means clustering and tested on three different datasets. Anticipated results can not be concluded, however other findings are insightful in terms of the connection between text cleaning and feature ...

The following article appeared in Journal of Chemical Physics 139.12 (2013): 124908 and may be found at http://scitation.aip.org/content/aip/journal/jcp/139/12/10.1063/1.4822038 ; This work reports on the phase behavior of hard spherical caps in the interval of particle shapes delimited by the hard platelet and hemispherical cap models. These very simple model colloidal particles display a remarkably complex phase behavior featuring a competition between isotropic-nematic phase separation and clustering as well as a sequence of structures, from roundish to lacy aggregates to no ordinary hexagonal columnar mesophases, all characterized by groups of particles tending to arrange on the same spherical surface. This behavior parallels that one of many molecular systems forming micelles but here it is purely entropy-driven ; This research is being supported by the Government of Spain via a Ramón y Cajal research fellowship

Vaccines are biological products containing antigens in the form of microorganisms or parts thereof or substances they produce which have been processed in such a way that they are safe, which when given to a person will cause active specific immunity against certain diseases. Vaccination is a process in the body so that a person becomes immune or protected from a disease. The large number of registrants who want to vaccinate against Covid-19 at the Kebun Lada Health Center has created a large pile of vaccination data that wants to vaccinate. These data are not only inputted directly on the government website, hard copy files are only stored in a file which is then stored in a folder. Seeing this situation, of course, from these data new information can be taken which is processed using data mining techniques to dig up useful information related to vaccination data. Data mining can help companies explore new knowledge by processing existing data with clustering methods and using the K-Means algorithm. Similar results were obtained in clusters 1 and 2, namely addresses originating from north binjai, while in cluster 3 there were similar results with cluster 1, namely in the category of vaccine recipients, namely the general public.

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. Although RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social networks among such populations because it does not typically collect the necessary data. Drawing on recent advances in computer science, the authors introduce a set of data collection instruments and RDS estimators for network clustering, an important topological property that has been linked to a network's potential for diffusion of information, disease, and health behaviors. The authors use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to respondent-driven samples with characteristics encountered in realistic field settings that depart from random walks. In particular, the authors explore the effects of multiple seeds, without replacement versus with replacement, branching chains, imperfect response rates, preferential recruitment, and misreporting of ties. The authors find that clustering coefficient estimators retain desirable properties in respondent-driven samples. This work takes an important step toward calculating network characteristics using nontraditional sampling methods, and it expands the potential of RDS to tell researchers more about hidden populations and the social factors driving disease prevalence.

This paper applies clustering analysis to the Colombian armed conflict. Indeed, when applied to a FARC terrorist act database, this statistical procedure finds a natural clustering of the different FARC units according to the different types of terrorist acts they commit and identifies the military hard core of the FARC. The facts revealed in this paper should be useful not only for future military strategies, but also to determine a better priorization and geographical allocation of the scarce military resources. ; Este artículo aplica la técnica conocida como análisis de grupos al conflicto armado colombiano. De hecho, cuando se aplica a una base de datos de actos terroristas de las FARC, este procedimiento estadístico encuentra una agrupación natural de las diferentes unidades de las FARC de acuerdo a los diferentes tipos de hechos terroristas que ellos cometieron y también permite identificar el núcleo militar de las FARC. Los hechos revelados en este artículo deberían ser útiles no sólo para la estrategia militar, sino también para hacer una mejor priorización y asignación geográfica de los escasos recursos militares.

AbstractThis paper illustrates the role of international production networks (IPNs) and industrial clusters (ICs) in the upgrading process with a view to gain a better understanding of their upgrading experiences. This can shed some light on the ongoing debate regarding the relative importance of IPNs and ICs and their implications for prudential industrial policy. The automotive and hard disk drive (HDD) industries in Thailand are chosen as case studies because their outstanding export performance in the world market in the past two decades suggests their success in industrial upgrading. Nonetheless, these two industries differ in their modes of networking. In the former, industrial clustering has been observed and has reached a level where the local content of a locally manufactured vehicle is approaching 100 percent. For the latter, industrial clustering has naturally occurred and reached a certain level, while IPNs still play a crucial role. This result suggests the possibility of coexistence between IPNs and ICs. The observed industrial clustering in the HDD industry in the later stage also shows that ICs would be a developmental outcome rather than a precondition of technological upgrading. The choice between IPNs and ICs should be a private sector decision, driven by the economic fundamentals.The public sector should focus on strengthening the supply‐side capabilities of local firms as well as creating an investment climate to further promote upgrading activities.

This paper illustrates the upgrading experiences of the automotive and hard disk drive (HDD) industries in Thailand, chosen because of their outstanding export performance in the developing world. An understanding of their upgrading experiences can shed some light on the ongoing debate regarding the relative importance of international production networks (IPNs) and industrial clusters (ICs) and their implications for prudential industrial policy. The impact of the recent global recession is also discussed in this paper. There is evidence of industrial upgrading in both the automotive and HDD industries. Yet one primary policy challenge still remains, that is, the limited role of indigenous suppliers in the multinational enterprise (MNE) production networks. This limited role is, to a certain extent, related to the overall incentive structure. Where these two industries differ is in their mode of networking, that is, whether they are part of an IPN or an IC. In the case of the automotive industry, industrial clustering has been observed and has reached a level where the local content of a locally manufactured vehicle is approaching 100%. In the case of the HDD industry, industrial clustering has naturally occurred and reached a certain level. Even though the current global economic crisis has severely affected each industry's production and exports, the hollow out scenario is unlikely to apply to either. In other words, Thailand should remain a base of production and exports for MNEs, a situation which points to the need for continual industrial upgrading. Three policy-related conclusions are drawn in this paper. Firstly, the limited linkages between MNE affiliates and indigenous suppliers point to the need for a comprehensive study probing the potentially distorting effect of the cascading tariff structure - a key theme of tariff policy for the past three decades. Despite consecutive governments' efforts since the mid-1990s to neutralize the tariff structure, it is clear that much remains to be done. Secondly, the choice between an IPN and an IC is a purely private sector decision, driven by the nature of the particular industry. There is also the possibility of coexistence between IPNs and ICs. Industrial clustering can be a developmental outcome rather than a pre-condition of technological upgrading. Finally, to promote industrial upgrading process, the government should emphasize policies that strengthen the supply-side capabilities of local firms and create an investment climate that encourages further upgrading activities.

Metastable ternary ceramic alloys with clustering tendencies are candidates for hard coating applications. In this work, mixing thermodynamics and structural parameters of ceramic Ti1-xAlxB2 alloys are investigated with theoretical first-principles based techniques. Lattice dynamics and temperature dependent phase stability are explored. The effect of lattice vibrations on the total free energy is investigated and found to not significantly affect phase stability at temperatures below 1200K. The isostructural phase diagram is derived using both cluster expansion-based Monte Carlo simulations and a mean field approach. The phase diagram shows a miscibility gap that does not close at temperatures below the melting or decomposition temperatures of the constituent binaries TiB2 and AlB2. The lattice mismatch between phases in the system is small regardless of their composition even at elevated temperatures. These findings support the prospect of age hardening due to coherent isostructural decomposition, such as spinodal decomposition, in coatings of Ti(1-x)AlxB(2) as diffusion is activated at elevated temperature. (c) 2020 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). ; Funding Agencies|Knut and Alice Wallenberg (KAW) FoundationKnut & Alice Wallenberg Foundation [KAW 2015.0043]; Swedish Research Council (VR)Swedish Research Council [2014-6336, 2019-05403]; Marie Sklodowska Curie Actions [INCA 600398]; Knut and Alice Wallenberg Foundation (Wallenberg Scholar Grant) [KAW-2018.0194]; Swedish Foundation for Strategic Research through the Future Research Leaders 6 program [FFL 15-0290]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [2009 00971]; Thailand Toray Science Foundation (TTSF); Ratchadaphiseksomphot Endowment Fund, Chulalongkorn University and Grants for Research [CU-GR_62_66_23_26]

Includes bibliographical references (leaves 90-93). ; Globalization has transformed the world into a big village in which the rich are becoming richer and the poor getting poorer. In the commercial world the trend is for big business to buy out the smaller companies and consequently get bigger. Yet it is arguable that small businesses have assisted in providing much needed services to small communities that occupy informal settlements and exist on or below the poverty datum line. The South African government has amongst its main objectives the alleviation of poverty and the improvement of life in previously disadvantaged communities. The government has allowed the micro-enterprises and small businesses in the informal sector to thrive and in this sector are Spaza shops that supply a wide range of grocery commodities to informal settlements. This paper is about an application framework of soft and hard operational research (OR) techniques used to address the performance of micro-enterprises with Spaza shops in Western Cape as a specific case study. The techniques include Strategic Options Development and Analysis (SODA) using Causal mapping and Soft System Methodology (SSM). These were chosen because of their suitability to understand performance problems faced by Spaza shops owners and find ways of improving the current situation by modelling the intervention of stakeholders. The improvement of Spaza shop businesses is a matter for all stakeholders. Causal mapping, helped to identify and structure the multiple conflicting aspects of Spaza shops business. Soft System Methodology made it possible to conceptualize the intervention model based on the rich picture and root definitions for relevant world-views and see what changes are culturally feasible and systematically desirable. Computer simulations were used to help design and test performance measurement indicators for the Spaza shops so as to enable decision-makers to choose the optimal strategy. Statistical analysis came into account to enable us to capture the seasonality and bring up clustering patterns.

The Index of Economic Freedom is an annual index and ranking created by The Heritage Foundation and The Wall Street Journal in 1995 to measure the degree of economic freedom in the world's nations. According to the web site of Heritage Foundation, Economic freedom is defined as below: In an economically free society, individuals are free to work, produce, consume, and invest in any way they please. In economically free societies, governments allow labor, capital, and goods to move freely, and refrain from coercion or constraint of liberty beyond the extent necessary to protect and maintain liberty itself. Cluster analysis is a method for clustering a data set into groups of similar objects. It is an approach to unsupervised learning and also one of the major techniques in pattern recognition. Hard clustering methods allow each point of the data set to exactly one cluster. In fuzzy clustering, fuzzy techniques are used to cluster the data and with these techniques an object can be classified in more than one cluster. The advantage of fuzzy clustering over classical clustering methods is that it provides more detailed information on the data. In this study, European Countries has been classified with the help of Economic Freedom Data published by Heritage Foundation. Countries are classified according to a five-year period (2013 to 2017) with the help of fuzzy clustering analysis. So that european countries are divided into classes according to their economic freedoms. Also, countries with varying degrees of five-year period have been identified too.

International audience ; assess the implementation and effectiveness of various interventions in order to identify possible success or failure factors. Methods: Comprehensive Systematic review was performed. After three steps data searching (in PubMed, SCOPUS, Cochrane, PsycINFO, Google Scholar) and critical appraisal, 63 studies out of 739 were fully retrieved. Results: Implementation aspects as intervention types (single or combined targeted risk factors), type of audience (students, parents, school staff, communities), settings (home, school or outdoor), type of organization (face to face, groups, online), professional or peer-led, communication type (written materials as brochures, posters or videoconferencing) or effectiveness measures were extracted and synthesized. Important features of context such as funding, European or government programs were used for clustering the studies. Conclusions: Health literacy interventions in adolescents need the involvement of education staff and parents participation. Health literacy for adolescents has to take into account cultural context, language, psychological features of each age; either digital or non-digital interventions cannot elude face to face communication between adolescents and their parents, academics or peers. Key messages: Health literacy school-based interventions are effective but the content for adolescents audience should rely on guidelines provided by interdisciplinary teams of experts. Health Literacy interventions in adolescents should be age-appropriate and do-not-harm precautions must always be taken in order to avoid prejudices, body-image or self-esteem concerns.