In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.

Leafy spurge (Euphorbia esula L.) and purple loosestrife (Lythrum salicaria L.) are invasive weeds that displace native vegetation. Herbicides are often applied to these weeds during flowering, so it would be ideal to identify them early in the season, possibly by the leaves. This paper evaluates the spectral separability of the inflorescences and leaves of these plants from surrounding vegetation. Leafy spurge, purple loosestrife, and surrounding vegetation were collected from sites in southeastern North Dakota and subjected to spectral analysis. Partial least-squares discriminant analysis (PLS-DA) was used to separate the spectral signatures of these weeds in the visible and near-infrared wavelengths. Using PLS-DA, the weeds were discriminated from their surroundings with R2 values of 0.86 to 0.92. Analysis of the data indicated that the bands contributing the most to each model were in the red and red-edge spectral regions. Identifying these weeds by the leaves allows them to be mapped earlier in the season, providing more time for herbicide application planning. The spectral signatures identified in this proof of concept study are the first step before using ultra–high resolution aerial imagery to classify and identify leafy spurge and purple loosestrife.

We study Tate motives with integral coefficients through the lens of tensor triangular geometry. For some base fields, including $\overline{\mathbb{Q}}$ and $\overline{\mathbb{F}_{p}}$ , we arrive at a complete description of the tensor triangular spectrum and a classification of the thick tensor ideals.

This communication assesses the use of a portable near infrared (NIR) instrument to measure quantitative (fatty acid profile) properties and qualitative (‘Premium’ and ‘Non-premium’) categories of individual Iberian pork carcasses at the slaughterhouse. Acorn-fed Iberian pigs have more unsaturated fats than pigs fed conventional compound feed. Recent advances in miniaturisation have led to a number of handheld NIR devices being developed, allowing processing decisions to be made earlier, significantly reducing time and costs. The most common methods used for assessing quality and authenticity of Iberian hams are analysis of the fatty acid composition of subcutaneous fat using gas chromatography and DNA analysis. In this study, NIR calibrations for fatty acids and classification as premium or non-premium ham, based on carcass fat measured in situ, were developed using a portable NIR spectrometer. The accuracy of the quantitative equations was evaluated through the standard error of cross validation or standard error of prediction of 0.84 for palmitic acid (C16:0), 0.94 for stearic acid (C18:0), 1.47 for oleic acid (C18:1) and 0.58 for linoleic acid (C18:2). Qualitative calibrations provided acceptable results, with up to 98% of samples (n = 234) correctly classified with probabilities ⩾0.9. Results indicated a portable NIR instrument has the potential to be used to measure quality and authenticity of Iberian pork carcasses.

This article contributes to an ongoing critical examination of feasting by developing a classification scheme that emphasizes the variable contexts in which feasts have occurred. Many recent archaeological and ethnographic accounts have focused on the political and economic roles feasts play in creating power and status differences among participants, while others have highlighted how they build community and increase solidarity within a group. My scheme reconceptualizes the term by giving two independent variables—group size and level of sociopolitical competition—equal roles in determining whether a given eating event is a feast; in turn, my dual-dimensional model facilitates more sophisticated interpretations of archaeological remains. After outlining its utility for describing and comparing eating events, this article evaluates the evidence for feasting at a precontact Native American mound site in the Lower Mississippi Valley. Botanical, faunal, and ceramic analyses of materials from the Feltus mounds (AD 750–1100) reveal fairly typical food-related assemblages, whereas the sheer amount of material, speed with which it was deposited, and size of individual specimens are exceptional. The resulting interpretation emphasizes feasting's role in creating and maintaining group solidarity at Feltus and advances understanding of noncompetitive outcomes of feasting behavior in the precontact American Southeast.

Let $f:M\rightarrow M$ be a dynamically coherent partially hyperbolic diffeomorphism whose center foliation has all its leaves compact. We prove that if the unstable bundle of $f$ is one-dimensional, then the volume of center leaves must be bounded in $M$ .

In the field of psychopathology there is still a lack of consensus on how mental disorders, such as depression, should be classified and explained. Many of our current classifications suffer from disorder heterogeneity and are conceptually vague. While some researchers have argued that mental disorders are better explained from a biological perspective, others have made the case for pluralistic and integrative explanations. Using depression as an extended example, we explore the challenges in classifying and explaining psychopathology. We begin by evaluating the current approaches to classification, including frameworks for what we consider a mental disorder. This is followed by a detailed summary of current explanatory perspectives in psychiatry. The relationship between classification and explanation presents unique theoretical challenges in understanding mental disorders. We suggest that by adjusting our focus from understanding syndromes to clinical phenomena we can advance our understanding of mental disorders.

In order to ensure low-altitude safety, a tracking and recognition method of unmanned aerial vehicle (UAV) and bird targets based on traditional surveillance radar data is proposed. First, several motion models for UAV and flying bird targets are established. Second, the target trajectories are filtered and smoothed with multiple motion models. Third, by calculating the time-domain variance of the model occurrence probability, the model conversion probability of the target is estimated, and then the target type is identified and classified. The effectiveness and robustness of the algorithm is demonstrated by several groups of Monte Carlo simulation experiments, including setting different recognition steps, different model transformation probability, filtering and smoothing algorithm comparison. The algorithm is also successfully applied on the ground-truth radar data collected by the low-altitude surveillance radar at airport and coastal environments, where the targets of UAVs and flying birds could be tracked and recognised.

The phenomenon of buying-shopping disorder (BSD) was described over 100 years ago. Definitions of BSD refer to extreme preoccupation with shopping and buying, to impulses to purchase that are experienced as irresistible, and to recurrent maladaptive buying excesses that lead to distress and impairments. Efforts to stop BSD episodes are unsuccessful, despite the awareness of repeated break-downs in self-regulation, experiences of post-purchase guilt and regret, comorbid psychiatric disorders, reduced quality of life, familial discord, work impairment, financial problems, and other negative consequences. A recent meta-analysis indicated an estimated point prevalence of BSD of 5%. In this narrative review, the authors offer a perspective to consider BSD as a mental health condition and to classify this disorder as a behavioral addiction, based on both research data and on long-standing clinical experience.

This article proposes a classification of the different national health care systems based on the way the network of health care providers is organised. To this end, we present two rivalling models: on the one hand, the integrated model and, on the other, the separated model. These two models are defined based on five dimensions: (1) integration of insurer and provider; (2) integration of primary and secondary care; (3) presence of gatekeeping mechanisms; (4) patient’s freedom of choice; and (5) solo or group practice of general practitioners. Each of these dimensions is applied to the health care systems of 24 OECD countries. If we combine the five dimensions, we can arrange the 24 national cases along a continuum that has the integrated model and the separated model at the two opposite poles. Portugal, Spain, New Zealand, the UK, Denmark, Ireland and Israel are to be considered highly integrated, while Italy, Norway, Australia, Greece and Sweden have moderately integrated provision systems. At the opposite end, Austria, Belgium, France, Germany, the Republic of Korea, Japan, Switzerland and Turkey have highly separated provision systems. Canada, The Netherlands and the United States can be categorised as moderately separated.

It has been suggested that a dimensional model of personality pathology should be adopted for the development and refinement of personality disorder classification. In this article, the advantages and challenges of moving toward a dimensional model are briefly reviewed. However, it is suggested that although categories and dimensions are valuable frameworks for personality pathology diagnosis, an expansion beyond categories and dimensions is needed to improve the shortcoming seen in current diagnostic systems. Ideas and examples are offered for how this might occur.

Instead of thinking about “national identity,” scholars of nationalism would do well to study nationalism as a process of classification, treating national conflict as disputes over nationalized categories. Disputes over national classification take various forms: patriots argue over which category applies, which categories exist, and which categories have which status. Techniques for imposing classificatory categories also deserve attention. The contributors to this themed issue of Nationalities Papers illustrate the power of analysis based on classification.

The classificatory efforts that accompanied the modernization of the Habsburg state inadvertently helped establish, promote, and perpetuate national categories of identification, often contrary to the intentions of the Habsburg bureaucracy. The state did not create nations, but its classification of languages made available some ethnolinguistic identity categories that nationalists used to make political claims. The institutionalization of these categories also made them more relevant, especially as nationalist movements simultaneously worked toward the same goal. Yet identification with a nation did not follow an algorithmic logic, in the beginning of the twentieth century, sometimes earlier, various nationalisms could undoubtedly mobilize large numbers of people in Austria-Hungary, but people still had agency and nationness remained contingent and situational.