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Paper and pulp mills generate substantial quantities of cellulose-rich sludge materials that are disposed in landfills at a large scale. For sustainability purposes, sludge materials can be bioprocessed to produce renewable fuels and useful chemicals. The enzymatic hydrolysis of cellulose is the process bottleneck that affects the conversion economics directly by using zero-cost raw materials. In order to study and optimize the process, the characteristics of the sludge raw materials should be first evaluated. In this work, sludge samples were obtained from paper mills located at different locations in Wisconsin and Minnesota. Part of the sludge samples was washed (de-ashed) with hydrochloric acid while the other part remained unwashed. The samples were subjected to multiple spectroscopic analyses techniques to evaluate the morphological properties of cellulose fibers and to estimate the total structural carbohydrate content. The results showed that the de-ashing process changed some fiber characteristics and cellulose crystallinity structure in all sludge samples. Sludge sample A (obtained from Kraft pulp and recycled paper mill region) showed a high percentage of fiber, with crystalline cellulose, compared to the other two sludge samples suggesting that sludge A is a valuable source to make value-added products. Aspen Plus mass and energy calculations performed in view of the 'zero' cost and the reliable supply of sludge raw materials producing 2 mol H 2 /mol glucose. Moreover, the results showed that extracting crystalline cellulose from these sludge samples is more profitable than crystalline cellulose made from the other lignocellulosic feedstocks. The results reported here showed that the utilization of these sludge materials would be an economically attractive and promising alternative for the production of hydrogen. Image 1 • A new method for paper mill sludge (PMS) characterization as renewable feedstock. • The deashing step has a major effect on the bioconversion of the PMS. • Studying the crystalline cellulose of the PMS. • A relation between the characterization and potential sugar yield was established. [ABSTRACT FROM AUTHOR]

Introduction: Feature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy. Methods: In this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem. Results: The effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods. Conclusion: Experimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

This paper examines the views of three prominent Wisconsin progressives — Richard T. Ely, Tomas Sewall Adams, and John R. Commons — on taxes as social policy. In the 1890s, Wisconsin emerged as a national progressive leader, a 'laboratory of democracy' that produced the nation's first minimum wage, first unemployment insurance plan, the first civil service law, and the first state-level income tax. Yet, despite often bordering on the radical, Wisconsin economists were cautious about demands for income and wealth redistribution through the tax mechanism. Instead, they conceived of taxation as an instrument of social policy via three intersecting paths: (1) that the provision of government services could serve as a vehicle by which to achieve desirable socioeconomic outcomes, (2) that properly designed tax policy could improve morality, itself a worthy end, and (3) that inequality and distributional concerns should be understood as issues of power and property rather than of wealth. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method. Objective: The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages. Methodology: The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE–LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance. Results: ADE–LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE–LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE–LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE–LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method. Conclusion: Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment. [ABSTRACT FROM AUTHOR]

Increasing the number of science, technology, engineering, and mathematics (STEM) degrees is a national priority and one way to promote the socioeconomic mobility of students from low-income families. Prior research examining why students do not complete STEM majors often points to students' lack of academic preparation, preferences for non-STEM majors, or lack of information about the value of STEM. This paper uses a randomized experiment to investigate an alternative explanation, that some students lack the financial resources to succeed in demanding majors. In a control group of university students from low-income families, 18.6% of students had declared a STEM major by their third year of college. In a treatment group who were offered additional need-based grant aid upon entering college, 26.5% of students declared a STEM major. Among students who had graduated within six years after entering college, 12.2% of control group graduates had earned a STEM degree compared to 20.2% of treatment group students. Need-based grants thus appear to have the potential to increase the share of low-income students studying and earning degrees in STEM. [ABSTRACT FROM AUTHOR]

Social science and popular media have described political polarization as a threat to democracy and effective policy. Scholars connect right/left political divides to macro‐level social divisions, such as those between rural and urban residents, environmentalists and farmers, and pro‐versus anti‐government sentiments. While previous scholars have complicated these dichotomies, political polarization scholarship often seeks out evidence of polarization without considering these complications. In addition, we know little about how polarization is affecting community responses to social problems. This paper explores the rhetoric of political and social polarization as it appears in community responses to a particular social problem, the decline of small and mid‐sized dairy farms in southern Wisconsin. During interviews, farmers, municipal leaders, and community members indeed used polarizing rhetoric and identified polarization as a problem in their communities. We argue, though, that the connections between commonly bifurcated identities, including a common attachment to the land and relationships across the rural–urban continuum, are equally important. We conclude by encouraging policy responses meant to address the fallout of the loss of mid‐sized dairy farms to draw on these connections to avoid inadvertently reinforcing political divisions. [ABSTRACT FROM AUTHOR]

Introduction: Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants. Background: Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision. Methodology: This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier. Results: The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features. Conclusion: Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early. [ABSTRACT FROM AUTHOR]

Early stage of prediction and diagnosis is the only way to solve the challenges due to medical data. Machine learning tools and techniques help in prediction and diagnosis of different types of medical data. In this paper, Simplex Method based Social Spider Optimization (SMSSO) method is used which modifies the Social Spider Optimization (SSO) method. Different types of datasets were used to validate the SMSSO-NN technique. SMSSO-NN shows 99.36%, 94%, 95.78%, and 98% accuracy in Wisconsin Breast Cancer (WBC), Lung cancer, Diabetic, and Cervical cancer datasets, respectively that is better than other methods. The accuracy of the SMSSO-NN algorithm is associated with different classification techniques like Structured Singular Value (SSV), Local Linear Wavelet Neural Network-Firefly Algorithm (LLWNN-FA), and Rough Set Theory. [ABSTRACT FROM AUTHOR]

To make informed COVID-19 related decisions, individuals need information about their personal risks and how those risks may vary with specific demographic and health characteristics. The Fight COVID Milwaukee web-based risk assessment tool allows for assessment of COVID-19 mortality risk as a function of personal and neighborhood characteristics. The purpose of this study is to explore public understanding of this risk assessment tool and risk perception through community focus groups. Individuals were recruited from the general adult population in Milwaukee County, Wisconsin, USA, to participate in nine online focus groups where the risk assessment tool was presented for feedback. Three main themes were identified in the focus groups regarding the web-based risk assessment tool: some challenges in accessibility, variable ease of understanding, and personal usefulness but uncertain value for others. This paper explores how members of the community interpret individual risk assessments and life expectancy estimations, and how these vary with age, gender, race/ethnicity, socioeconomic status, and pre-existing comorbidities. [ABSTRACT FROM AUTHOR]

As agriculture becomes an increasingly data-driven sector, it is important to understand the technology use and barriers by the service providers who work closely with producers. This is especially important as these individuals can serve as drivers for technology adoption by farmers and ranchers. Although many surveys have looked at technology adoption by producers, little data on agricultural industry service providers exists. Surveys of Extension Professionals (EP) in Iowa, Michigan, North Dakota, Ohio, and Wisconsin, and several categories of Agricultural Service Providers (ASP) in Wisconsin were administered in 2018 to determine satisfaction with internet service and to learn more about information and technology usage and potential barriers to its adoption within the industry. In addition, follow-up informational telephone interviews were conducted with 13 Wisconsin EP. A paper survey was administered to ASP, which had a response rate of 35% (N=462). A similar online survey was administered to EP having an estimated 23% response rate (N=223). The frequency of transmission of data, information, and recommendations was high but specialized for both groups with more frequent use in crop production versus livestock (including dairy). The most commonly-cited barriers associated with the adoption of digital technologies by ASP were related to attracting and hiring well-qualified IT employees, training for both employees and clients, and data security. While data security was a commonly cited barrier, 16% of ASP worked for companies that did not have written policies on data privacy. ASP and EP consider their clients to be adept at using text messages, images, maps, and smartphones. They do not consider their clients to be skilled in their use of digitally accessed and used financial statements, spreadsheets, or laboratory analysis reports. Additional training should be better focused on these areas for ASP and EP which would allow them to be more effective in educating producers and to better realize the value associated with more thoughtful and widespread digital technology adoption. [ABSTRACT FROM AUTHOR]

In recent decades, breast cancer has become one of the leading causes of mortality among women. This disease is not preventable because of its unknown causes; however, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make more precise and reliable decisions. K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), Naive Bayes, AdaBoost (AB), linear discriminant analysis (LDA), random forest (RF), logistic regression (LR), and artificial neural network (ANN) are some of the methods employed. Sensitivity, accuracy, precision, mean absolute error F-score, root mean square error, Kappa, and relative absolute error calculated the performance of the methods. This paper applied a distinctive integration of evaluation measures and ML algorithms using the wrapper feature selection based on ICA (WFSIC) and BA (WFSB) separately. We compared two proposed approaches for the performance of the classifiers. Also, we compared our best diagnostic model with previous works reported in the literature survey. Experimentations were performed on the Wisconsin diagnostic breast cancer (WDBC) dataset. Results reveal that the proposed framework that uses the BA with an accuracy of 99.12% surpasses the framework using the ICA and most previous works. Additionally, the RF classifier in the approach of FS based on BA emerges as the best model and outperforms others regarding its criteria. Besides, the results illustrate the role of our techniques in reducing the dataset dimensions up to 90% and increasing the performance of diagnostic models by over 99%. Moreover, the result demonstrates that there are more critical features than the optimum dataset obtained by proposed FS approaches that have been selected by most ML models, including the standard error of area, concavity, smoothness, perimeter, the worst of texture, compactness, radius, symmetry, smoothness, concavity, and the mean of concave points, fractal dimension, compactness, concavity that can remarkably affect the efficiency of breast cancer prediction. This study illustrates the role of our approaches in enhancing treatment outcomes in healthcare operations. [ABSTRACT FROM AUTHOR]

The Wisconsin high-temperature superconductor axisymmetric mirror experiment (WHAM) will be a high-field platform for prototyping technologies, validating interchange stabilization techniques and benchmarking numerical code performance, enabling the next step up to reactor parameters. A detailed overview of the experimental apparatus and its various subsystems is presented. WHAM will use electron cyclotron heating to ionize and build a dense target plasma for neutral beam injection of fast ions, stabilized by edge-biased sheared flow. At 25 keV injection energies, charge exchange dominates over impact ionization and limits the effectiveness of neutral beam injection fuelling. This paper outlines an iterative technique for self-consistently predicting the neutral beam driven anisotropic ion distribution and its role in the finite beta equilibrium. Beginning with recent work by Egedal et al. (Nucl. Fusion , vol. 62, no. 12, 2022, p. 126053) on the WHAM geometry, we detail how the FIDASIM code is used to model the charge exchange sources and sinks in the distribution function, and both are combined with an anisotropic magnetohydrodynamic equilibrium solver method to self-consistently reach an equilibrium. We compare this with recent results using the CQL3D code adapted for the mirror geometry, which includes the high-harmonic fast wave heating of fast ions. [ABSTRACT FROM AUTHOR]

This article describes five elementary statistics projects involving the Covid-19 data made available to the public in csv files by the Centers for Disease Control and Prevention. The first project examined data available at the beginning of the covid surge in New York City in spring, 2020, and used the correlation coefficient to estimate the total number of deaths that could be expected as the spike ran its course. The second project is an easy one on the concept of excess deaths and on the mechanics of extracting parts of a data file that answer relevant questions. The data is from a spike in deaths in the particularly bad flu surge in the winter of 2017–2018. The third and fourth projects ask the student to fit a logistic growth curve to observed cumulative numbers of deaths in a spike, like the Covid spikes in New York City and Wisconsin and the nationwide 2017–2018 flu spike. The method is a simple linear regression with transformed variables. The fifth project involves hypothesis testing and judging when a Poisson model might be useful. The paper also documents difficulties and adaptations of the sort familiar to all teachers who have taught during the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Breast cancer is a cancer disease that seriously threatens women's health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model. [ABSTRACT FROM AUTHOR]

Belief theory involving the use of Shafer's model and source combination rules has found wide use for the development and improvement of classification models. Availability of theoretical constructs for making the best use of available information including the ability to deal with ignorance makes it an attractive option. Representation of ignorance means that we can deal with hard to classify samples by deferring the decision rather than taking an erroneous one. This makes it especially useful in the biomedical field where the cost of making an error is high. In this paper, we develop a belief theory-based classification model and apply it to the Wisconsin breast cancer database. The proposed method is compared with an existing belief theory-based classifier, the conventional support vector machine classifier and other recent approaches using different performance parameters such as error rate, false-positive rate, false negative rate, and confusion matrix. The proposed model is found to outperform the approaches under consideration and is also able to deal with data samples having missing feature values effectively. [ABSTRACT FROM AUTHOR]

The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel family of multivariate mixed Poisson-Generalized Inverse Gaussian INAR(1), MMPGIG-INAR(1), regression models for modelling time series of overdispersed count response variables in a versatile manner. The statistical properties associated with the proposed family of models are discussed and we derive the joint distribution of innovations across all the sequences. Finally, for illustrative purposes different members of the MMPGIG-INAR(1) class are fitted to Local Government Property Insurance Fund data from the state of Wisconsin via maximum likelihood estimation. [ABSTRACT FROM AUTHOR]

This paper explores the context of a Kindergarten team in a suburban P-3 school in Wisconsin developing literacy coaching support. Facing recent neoliberal accountability reforms that have greatly expanded teacher competition, dismantled teachers' unions, and added the role of the coach to the school, "confidence" is an issue that the coach and teachers struggle with as they seek to improve minority student achievement. Using a case study design and feminist sociological framework highlighting emotional labour, the affective economy, and the psychosocial possibilities of the work of Pierre Bourdieu , this research examines professional capital as a form of educators' experience. Ultimately, we see that the landscape of power and emotions is complex as it divides "good" and "bad" White and middle-class teachers. Possibilities for extending the uses of feminist sociological theory in early childhood literacy are discussed. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature. [ABSTRACT FROM AUTHOR]

Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning (ML) systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems. [ABSTRACT FROM AUTHOR]

The dominant wavelength and hue angle can be used to quantify the color of lake water. Understanding the water color is important because the color relates to the water quality and its related public perceptions. In this paper, we compared the accuracy levels of two methods in calculating dominant wavelength and hue angle values using simulated satellite data calculated from in situ reflectance hyperspectra for 325 lakes and rivers in Minnesota and Wisconsin. The methods developed by van der Woerd and Wernand in 2015 and Wang et al. in 2015 were applied to simulated sensor data from the Sentinel-2, Sentinel-3, and Landsat 8 satellites. Both methods performed comparably when a correction algorithm could be applied, but the correction method did not work well for the Wang method at hue angles < 75°, equivalent to levels of colored dissolved organic matter (CDOM, a440) > ~2 m−1 or chlorophyll > ~10 mg m−3. The Sentinel-3 spectral bands produced the most accurate results for the van der Woerd and Wernand method, while the Landsat 8 sensor produced the most accurate values for the Wang method. The distinct differences in the shapes of the reflectance hyperspectra were related to the dominant optical water quality constituents in the water bodies, and relationships were found between the dominant wavelength and four water quality parameters, namely the Secchi depth, CDOM, chlorophyll, and Forel–Ule color index. [ABSTRACT FROM AUTHOR]

In the dynamic ensemble selection algorithm, the region of competence of the test sample was composed of fixed samples, which would affect the classifier selection. Therefore, the paper proposed the DES-DCR-CIER algorithm based on dynamic region of competence strategy. Firstly, this algorithm used the heterogeneous classifier to generate the base classifier pool to deal with the problem that the difference between the homogeneous ensemble classifiers was little and the number of heterogeneous ensemble classifiers was small. Next, it applied three steps including the mutual K nearest neighbor with adaptive distance algorithm, approaching the distance center of the sample set and removing the class edge samples to determine the dynamic region of competence of the test sample and it used overall complementarity index to select a set of classifiers. Finally, the classifiers were synthesized by the ER rule to get integration. Experiments on the diagnosis data of breast mass from 8 sonographers in a tertiary hospital in Hefei, Anhui Province and American Wisconsin Breast Cancer Diagnostic data set showed that the diagnostic model based on the DES-DCR-CIER algorithm had better accuracy. [ABSTRACT FROM AUTHOR]

Dairying among Wisconsin's Amish and Old Order Mennonite farmers expanded through 2017, and by early 2018 Amish farms contained 12.9 percent of the state's total dairy herds of cows, goats, and sheep. Old Order Mennonite herds comprised 5.0 percent. Faced with a variety of challenges, Wisconsin lost 198 Amish herds between April 2018 and July 2020, yet its number of Old Order Mennonite herds grew. Together, these groups operated over half of the dairy farms within 59 of Wisconsin's towns in 2020. The paper examines the spatial patterns of expansion and contraction of Old Order dairying between 2012 and 2020, noting and explaining differences between the Amish and Mennonite farmers, as well as among the three conferences of Old Order Mennonites. Losses among the Amish were the greatest for can milk producers, yet 19 percent of Wisconsin's Amish dairy herds were on certified organic farms in 2020. [ABSTRACT FROM AUTHOR]

In short‐term nonlife (e.g., car and homeowner) insurance, policies are renewed yearly. Insurance companies typically keep track of each policyholder's claims per year, resulting in longitudinal data. Efficient modeling of time dependence in longitudinal claim data will improve the prediction of future claims needed for routine actuarial practice, such as ratemaking. Insurance claim data usually follow a two‐part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long‐tailed distribution. This two‐part data structure leads to difficulties in applying established models for longitudinal data. In this paper, we propose a two‐part D‐vine copula model to study longitudinal mixed claim data. We build two stationary D‐vine copulas. One is used to model the time dependence in binary outcomes resulting from whether or not a claim has occurred. The other studies the dependence in the claim size given occurrence. Under the proposed model, the prediction of the probability of making claims and the quantiles of severity given occurrence is straightforward. We use our approach to investigate a dataset from the Local Government Property Insurance Fund in the state of Wisconsin. [ABSTRACT FROM AUTHOR]

Drawing on an ethnography of Wisconsin-based descendants of Belgian immigrants, this article explores social dynamics relating to ways lay users of genomic ancestry testing (GAT) understand genomic ancestry and how their understanding raises questions concerning the construction of their identities. The study focuses on a group that presents interesting features for which the existing literature is sparse (i.e. a rather secure Belgian ancestry/present identity, the absence of a clear ascription to the category "Belgium/Belgian" from the tests, and a biological connectedness in the Belgian community). GAT is approached as a socio-cultural object contextualized at a local level. The analysis involves specifying the discursive practices by which individuals entangle with this knowledge, following a two-pronged perspective derived from the nature of the information received by users, which is both individuating/deindividuating and deterministic/probabilistic. The paper, therefore, provides new insights into the manners in which social actors trigger their responsibility in response to genomic ancestry, and this questions the users' sense of identity. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers among worldwide, and its detection is recognized as a significant public health problem in today's society. Extensive studies have been conducted to classify patients into malignant or benign groups, but given the importance of the problems, efforts are still ongoing. This paper aims are to parameters tuning of Multi- Layer Perceptron (MLP) neural network for the breast cancer detection. This work presents an MLP-based homogeneous ensemble approach for classifying breast cancer samples. Basically, ensemble learning is used to improve the classification process. This technique is a method of combining different basic classifiers from which a new classifier is derived. In this regard, several optimization algorithms including GA, PSO, and ODMA have been used to determine which algorithm provides the most suitable parameters for MLP. These parameters include effective features, number of hidden layers, number of nodes in layers, and weight values. The proposed algorithm is applied to three datasets of the Wisconsin Breast Cancer Database (i.e., WBCD, WDBC, and WPBC) and then comparison is made between different algorithms to achieve the highest accuracy. Experiments show that the proposed classifier has promising results in breast cancer detection than other state-of-the-art classifiers with 98.79% in the WBCD. Data analysis and its results can confirm the superiority of ensemble classifiers over state-of-the- art methods for breast cancer detection. [ABSTRACT FROM AUTHOR]

This paper presents a case study of sentencing policy in the adjoining, and demographically and culturally similar, states of Minnesota and Wisconsin. Using administrative data, together with historical and legal analyses of sentencing policy in each state, it (1) argues that the destabilization of the penal order represented a critical juncture during which commission‐based, presumptive sentencing guidelines were an option in both states; (2) analyzes how the decision to adopt this guidelines system in Minnesota and the failure to do so in Wisconsin initiated path‐dependent processes, making it difficult to reverse the initial policy choice; and (3) assesses how these distinct policy paths led to different penal outcomes. A concluding section discusses how analyses of path dependency shed light on the state variation in sentencing policy and provide a framework for assessing the possibilities of, and obstacles to, penal reform. [ABSTRACT FROM AUTHOR]

As agriculture becomes an increasingly data-driven sector, it is important to understand the technology use and barriers by the service providers who work closely with producers. This is especially important as these individuals can serve as drivers for technology adoption by farmers and ranchers. Although many surveys have looked at technology adoption by producers, little data on agricultural industry service providers exists. Surveys of Extension Professionals (EP) in Iowa, Michigan, North Dakota, Ohio, and Wisconsin, and several categories of Agricultural Service Providers (ASP) in Wisconsin were administered in 2018 to determine satisfaction with internet service and to learn more about information and technology usage and potential barriers to its adoption within the industry. In addition, follow-up informational telephone interviews were conducted with 13 Wisconsin EP. A paper survey was administered to ASP, which had a response rate of 35% (N=462). A similar online survey was administered to EP having an estimated 23% response rate (N=223). The frequency of transmission of data, information, and recommendations was high but specialized for both groups with more frequent use in crop production versus livestock (including dairy). The most commonly-cited barriers associated with the adoption of digital technologies by ASP were related to attracting and hiring well-qualified IT employees, training for both employees and clients, and data security. While data security was a commonly cited barrier, 16% of ASP worked for companies that did not have written policies on data privacy. ASP and EP consider their clients to be adept at using text messages, images, maps, and smartphones. They do not consider their clients to be skilled in their use of digitally accessed and used financial statements, spreadsheets, or laboratory analysis reports. Additional training should be better focused on these areas for ASP and EP which would allow them to be more effective in educating producers and to better realize the value associated with more thoughtful and widespread digital technology adoption. [ABSTRACT FROM AUTHOR]

This paper describes development of a nitrate decision support tool for groundwater wells (GW-NDST) that combines nitrate leaching and groundwater lag-times to compute well concentrations. The GW-NDST uses output from support models that simulate leached nitrate, groundwater age distributions, and nitrate reduction rates. The support models are linked through convolution to simulate nitrate transport to wells. Spatially distributed parameters were adjusted through calibration to 34,255 nitrate sample targets. Prediction uncertainty is illustrated via Monte Carlo realizations informed during calibration. Over 78% of target concentrations were within the simulated range of results from 450 realizations. An example forecasting scenario illustrates that a range of feasible outcomes exist and should be considered when interpreting forecasts for decision making. Uncertainty in forecasting is unavoidable; the intent of characterizing uncertainty in the GW-NDST is to facilitate decision making by increasing insight into the response of nitrate contamination to physical and chemical processes. • This Decision Support Tool assists resource managers improve drinking water quality. • The flexible Reduced-Complexity framework matched nitrate concentrations in wells. • An Iterative Ensemble Smoother (IES) generated 450 posterior parameter realizations. • IES realizations were informed through Monte Carlo methods during calibration. • Six leaching reduction scenarios use uncertainty in nitrate concentration forecasts. [ABSTRACT FROM AUTHOR]

Background: The ACTION trial evaluated the effect of a modified version of the Respecting Choices´ advance care planning programme in patients with advanced cancer in six European countries. For this purpose, an advance directive acceptable for all six ACTION countries to be used for documenting the wishes and preferences of patients and as a communication tool between patients, their caregivers and healthcare staff, was needed. Aim: To describe the development of a multinational cancer specific advance directive, the ´My Preferences form´, which was first based on the 2005 Wisconsin 'Physician Orders of Life Sustaining Treatment´ Form, to be used within the ACTION trial. Methods: Framework analysis of all textual data produced by members of the international project team during the development of the ACTION advance directives (e.g. drafts, emails, meeting minutes...). Setting/participants: ACTION consortium members (N = 28) with input from clinicians from participating hospitals (N = 13) and ´facilitators´ (N = 8) who were going to deliver the intervention. Results: Ten versions of the ACTION advance directive, the ´My Preferences form´, were developed and circulated within the ACTION consortium. Extensive modifications took place; removal, addition, modification of themes and modification of clinical to lay terminology. The result was a thematically comprehensive advance directive to be used as a communication tool across the six European countries within the ACTION trial. Conclusion: This article shows the complex task of developing an advance directive suitable for cancer patients from six European countries; a process which required the resolution of several cross cultural differences in law, ethics, philosophy and practice. Our hope is that this paper can contribute to a deeper conceptual understanding of advance directives, their role in supporting decision making among patients approaching the end of life and be an inspiration to others wishing to develop a disease-specific advance directive or a standardised multinational advance directive. [ABSTRACT FROM AUTHOR]

The Wisconsin Green Party, a state affiliate of the US Green Party, is a third party in a two-party system. The US electoral system is not kind to third parties; 1949 was the last time a third party was represented in the US Congress, in the 2016 presidential election, just 1% of voters in Wisconsin voted for the Green Party candidate. Ethnographic fieldwork combined with in-depth interviews for this study finds that the policies and practices of the party may be inhibiting its efforts to grow support and improve its electoral standing. This paper details how the party operates in a narrow window of antiparty sentiment, with the emphasis on the intersection of their four core policy pillars, and party practices of 'being the message' serving to deter their two likeliest sources of new support; the logic of constituency representation given primacy over electoral competition. These issues, though currently acting as constraints on the party, potentially however also afford long-term opportunity for the party. [ABSTRACT FROM AUTHOR]

Purpose: Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest‐abdomen‐pelvis and abdomen‐pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. Acquisition and validation methods: The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest‐abdomen‐pelvis or abdomen‐pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. Data format and usage notes: The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric‐CT‐SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. Potential applications: This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient‐specific organ dose estimation. [ABSTRACT FROM AUTHOR]

The Wisconsin Idea is a widely acclaimed late nineteenth-century vision for Wisconsin higher education that promotes democratic engagement, the search for truth, and developing knowledge of importance to Wisconsin citizens. While the Wisconsin Idea is often celebrated as an enduring feature of public higher education in Wisconsin, this paper points to a myriad of cultural, economic, and political forces that have reshaped its meaning. Among these forces, fragmenting purposes of higher education, globalization, and changes in state political culture have edited understandings of the Wisconsin Idea in ways that have eliminated its moral and democratic purposes. These shifts that occurred throughout the twentieth century and early twenty-first mirror changes in the broader American higher education landscape. Ultimately, such changes have resulted in divided lives among students, faculty, staff, and administrators that seek wholeness, connection, and deep civic purposes in their work. This paper retraces the Wisconsin Idea's history and concludes with a discussion about ways in which its early notions might be revived to inspire new thinking about higher education's role in society. [ABSTRACT FROM AUTHOR]

The Wisconsin Green Party, a state affiliate of the US Green Party, is a third party in a two-party system. The US electoral system is not kind to third parties; 1949 was the last time a third party was represented in the US Congress, in the 2016 presidential election, just 1% of voters in Wisconsin voted for the Green Party candidate. Ethnographic fieldwork combined with in-depth interviews for this study finds that the policies and practices of the party may be inhibiting its efforts to grow support and improve its electoral standing. This paper details how the party operates in a narrow window of antiparty sentiment, with the emphasis on the intersection of their four core policy pillars, and party practices of 'being the message' serving to deter their two likeliest sources of new support; the logic of constituency representation given primacy over electoral competition. These issues, though currently acting as constraints on the party, potentially however also afford long-term opportunity for the party. [ABSTRACT FROM AUTHOR]

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that ϕ (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method. [ABSTRACT FROM AUTHOR]

In this paper, we obtain an optimal reinsurance contract explicitly in the form of excess-of-loss with a limit when the risk measure is Range-Valueat-Risk. Then we study optimal reinsurance with model uncertainty, where the uncertainty set contains a greatest element in the sense of stochastic order. Furthermore, we study the Stackelberg game in reinsurance with model uncertainty. In order to illustrate how our findings can be applied in practice to determine the optimal reinsurance contract, we perform an empirical study using the Wisconsin Local Government Property Insurance Fund dataset. Tweedie distributions are fit to the building and contents loss amounts from the property fund, and used to determine the optimal loading factor, and reinsurance contract corresponding to the loading factor. From the analysis, we discover that some policies have more recoveries from the optimal reinsurance contract than the premium paid, while others have smaller recoveries than the premium. [ABSTRACT FROM AUTHOR]

Cancer is the principle wellspring of death around the globe with 2.09 million cases so far in 2018 [1]. Around 627000 deaths accounting to 6.6% are caused because of female breast cancer and it ranks five amongst the list of top causes for deaths, the prime reason being prognosis being favorable in developed countries. The timely empathy of breast cancer further makes the process of prognosis better hence improving the rates of survival, because this will indorse on time treatment which is given clinically to patients. When the classification is done in an accurate way for malignant and benign tumours, it stops the suffering of patients with excessive ailments. The best possible recognizable proof of breast cancer disease and the process of characterizing into benign and malignant groups is that the main concern of a ton of investigation and research. When thrown light on its particular advantages in significant alternatives recognition from the datasets of entangled breast cancer, the generally perceived option is Machine Learning, because of the philosophy of determination in breast cancer to arrange pattern and forecast modelling. This paper will in general, survey machine learning and assessment of this particular paper, WBCD: Wisconsin Breast Cancer Database has been used as the benchmark dataset. [ABSTRACT FROM AUTHOR]

This paper describes how in‐depth interviews and content analysis of water‐related policies and plans were used to assess good governance principles (transparency, effectiveness, equity, accountability, and appropriate scale) for Lake Wausau in central Wisconsin. The purpose of the research was to support and inform development of a lake management plan. One of the key findings was that the existing system of water governance lacked transparency. In addition, responsibility for and benefits from potential improved lake conditions were distributed unevenly and inequitably among stakeholders. Local and county plans were vague and lacked strong language (e.g., "should" vs. "must" comply) to indicate which actions were required. Both barriers to and opportunities for creating a more effective system were identified. This paper offers suggestions for improving the governance system, discusses the limits of local watershed planning for overcoming watershed management issues, and provides suggestions for anyone wishing to undertake governance analyses to support water resources management. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the consumer impacts of Wisconsin's "phosphorus rule", which created the most stringent statewide water quality and effluent standards for phosphorus in the country. We examine how compliance with the rule affects real billing rates at sewer utilities in Wisconsin, providing the first empirical estimates of water pollution regulation on utility billing rates. We find that compliance with the phosphorous rule increases the average real sewer utility bill in our sample by 7–12%. Our results imply that sewer utilities pass through approximately $65.8 million to residential sewer rate payers annually, which is roughly ⅔ of the total sewer utility compliance costs of the rule. State government subsidies cover an additional ¼ of the sewer utility compliance costs. [ABSTRACT FROM AUTHOR]

Background: In this paper we describe the use of the Consolidated Framework for Implementation Research (CFIR) to study implementation of a web-based, point-of-care, EHR-linked clinical decision support (CDS) tool designed to identify and provide care recommendations for adults with prediabetes (Pre-D CDS).Methods: As part of a large NIH-funded clinic-randomized trial, we identified a convenience sample of interview participants from 22 primary care clinics in Minnesota, North Dakota, and Wisconsin that were randomly allocated to receive or not receive a web-based EHR-integrated prediabetes CDS intervention. Participants included 11 clinicians, 6 rooming staff, and 7 nurse or clinic managers recruited by study staff to participate in telephone interviews conducted by an expert in qualitative methods. Interviews were recorded and transcribed, and data analysis was conducted using a constructivist version of grounded theory.Results: Implementing a prediabetes CDS tool into primary care clinics was useful and well received. The intervention was integrated with clinic workflows, supported primary care clinicians in clearly communicating prediabetes risk and management options with patients, and in identifying actionable care opportunities. The main barriers to CDS use were time and competing priorities. Finally, while the implementation process worked well, opportunities remain in engaging the care team more broadly in CDS use.Conclusions: The use of CDS tools for engaging patients and providers in care improvement opportunities for prediabetes is a promising and potentially effective strategy in primary care settings. A workflow that incorporates the whole care team in the use of such tools may optimize the implementation of CDS tools like these in primary care settings. Trial registration Name of the registry: Clinicaltrial.gov.Trial Registration Number: NCT02759055. Date of registration: 05/03/2016. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT02759055 Prospectively registered. [ABSTRACT FROM AUTHOR]

Public geospatial data (geodata) is created at all levels of government, including federal, state, and local (county and municipal). Local governments, in particular, are critical sources of geodata because they produce foundational datasets, such as parcels, road centerlines, address points, land use, and elevation. These datasets are sought after by other public agencies for aggregation into state and national frameworks, by researchers for analysis, and by cartographers to serve as base map layers. Despite the importance of this data, policies about whether it is free and open to the public vary from place to place. As a result, some regions offer hundreds of free and open datasets to the public, while their neighbors may have zero, preferring to restrict them due to privacy, economic, or legal concerns. Minnesota relies on an approach that allows counties to choose for themselves if their geodata is free and open. By contrast, its neighboring state of Wisconsin has passed legislation requiring that specific foundational geospatial datasets created by counties must be freely available to the public. This paper compares the implications and outcomes of these diverging data cultures. [ABSTRACT FROM AUTHOR]

Transportation agencies have been introducing responsible and sustainable winter maintenance practices to alleviate impacts on the environment, human health, vehicles, infrastructure, and reduce costs. Salt brine implementation is an alternative to reduce the amount of salt used in winter maintenance. Laboratory tests have provided evidence for the benefits and effectiveness of salt brine. However, laboratory findings may not reflect complex dynamics in the field. Thus, field data were collected from four counties in Wisconsin to evaluate pairs of study and control roadway segments and quantify material usage, performance, and cost. Data collection consisted of route, equipment, and storm data. The focus of the analysis was to compare total salt and cost per lane-kilometer, time to reach bare/wet conditions, and travel speed. Statistical tests were used to compare observations from study and control routes. Storm data analysis included 70 winter storms. The results of this research showed an overall 34% reduction in salt use and 5.1 fewer hours to reach bare/wet conditions with the study group using mostly salt brine compared to the control group using solid salt. Results suggest that implementation of salt brine in winter maintenance significantly reduces the amount of salt released to the environment and associated costs while maintaining a similar or better level of service. This paper contributes to the existing literature by using field data at study and control routes under the same winter weather conditions to evaluate the impact of salt brine on salt usage, cost, and level of service. [ABSTRACT FROM AUTHOR]

Arsenic in private drinking water wells is a significant problem across much of eastern Wisconsin, USA. The release mechanism and stratigraphic distribution of sulfide and iron (hydr)oxide sources of arsenic in bedrock aquifers are well understood for northeastern Wisconsin. However, recent geologic mapping has identified numerous small bedrock folds to the south, and the impact of these geologic structures on local groundwater flow and well contamination has been little studied. This paper examines the hydrologic and structural effects of the Beaver Dam anticline, southeast Wisconsin, on arsenic in groundwater in the region. Multivariate logistic regression shows wells near the Beaver Dam anticline are statistically more likely to detect arsenic in groundwater compared to wells farther away. Structural and hydrologic changes related to folding are interpreted to be the cause. Core drilled near the fold axis is heavily fractured, and many fractures are filled with sulfides. Elevated hydraulic conductivity estimates are also recorded near the fold axis, which may reflect a higher concentration of vertical fractures. These structural and hydrologic changes may have led to systematic changes in the distribution and concentration of arsenic-bearing mineral hosts, resulting in the observed detection pattern. For areas with similar underlying geology, this approach may improve prediction of arsenic risk down to the local level. [ABSTRACT FROM AUTHOR]

Through the lens of critical rural scholarship, this methodological paper offers reflections on the theoretical similarities between the qualitative research practice of place anonymization and the neoliberal project of place erasure. Using empirical data from a year-long ethnographic study of a remote rural community in Northern Wisconsin, I raise methodological tensions related to the choices we make as qualitative researchers related to masking or obscuring the names of rural places. Applying a critical geographic framework, I offer critiques of place anonymization as a protective measure for rural participants and decontextualization as a goal for broader theoretical application of the research findings. I then reflect on place masking as a form of methodological erasure that echoes the neoliberal erasure of rural places. In conclusion, I offer implications and suggestions for rural qualitative scholars concerned with spatial inequalities and place erasure. [ABSTRACT FROM AUTHOR]

Objectives: Assessment, Feedback, Incentives, and eXchange (AFIX) is a quality improvement model used to increase childhood and adolescent immunization rates in the United States. We evaluated implementation of a similar quality improvement model to boost adult immunization rates. Methods: During November 2016 through May 2017, adult immunization outreach specialists conducted 124 in-person visits to clinics in Wisconsin that immunize adults, submit immunization information to the Wisconsin Immunization Registry (WIR), and agreed to participate in adult AFIX. Outreach specialists ran immunization assessment reports using the WIR and showed a paper copy of the report during the visit. Health care providers were encouraged to implement at least 1 of 18 strategies (eg, reminder-and-recall intervention, giving adult immunization resources to patients) to increase adult immunization rates. Outreach specialists conducted follow-up with health care providers at 3, 6, and 9-18 months after the initial visit to encourage strategy implementation. We compared AFIX sites with control clinics on practice type, geographic location, and clinic size. Results: Clinics that participated in adult AFIX had a significantly larger increase in median adult immunization rates for completion of the human papillomavirus vaccine series at the 9- to 18-month follow-up than control clinics did (10.4% vs 7.7%; P =.02). The median immunization rate for 13-valent pneumococcal conjugate vaccine/23-valent pneumococcal polysaccharide vaccine completed in series was higher, but not significantly so, among adult AFIX clinics than among control clinics (12.6% vs 10.7%; P =.18). Conclusions: Adult AFIX resulted in increased awareness about adult immunization recommendations and may be a useful tool for increasing adult immunization rates. [ABSTRACT FROM AUTHOR]

Purpose: Oral cancer risks have been shown to be modified by improving public awareness and reducing barriers to preventive care. The purpose for this study was to assess oral cancer knowledge and awareness and provide oral cancer screenings and education to a population of rural farmers in Wisconsin. Methods: Attendees 18 years of age and older at a rural farming exposition in Wisconsin were invited to complete a 12-item oral cancer awareness paper survey and to receive a visual and tactile head and neck examination/ oral cancer screening. Completing both the survey and the screening were optional. Participants also received educational materials on oral cancer. Individuals with abnormal lesions were provided with dental referrals. Results: A total of 236 attendees consented to participate either the survey or oral cancer screening (n=236). Most (72%) reported seeing a dentist in the past six months regardless of insurance status. In spite of having had recent dental encounters, only 28% of women and 46% of men were able to identify at least one risk factor associated with oral cancer. Among participants consenting to the oral cancer screening (n=194), 17% (n=33) presented with oral lesions requiring additional assessment and were recommended for follow-up care. Conclusions: Knowledge and awareness of oral cancer risk factors, signs and symptoms was low among the participants in this rural population despite high rates of dental care access. Oral cancer screenings and education provided in varied settings could improve oral cancer knowledge and awareness and early detection of malignant oral lesions in rural communities. [ABSTRACT FROM AUTHOR]

Learning the customers' experience and behavior creates competitive advantages for any company over its rivals. The insurance industry is an essential sector in any developed economy and a better understanding of customers' risk profile is critical to decision making in all aspects of insurance operations. In this paper, we explore the idea of using copula-based dependence models to learn the hidden risk of policyholders in property insurance. Specifically, we build a novel copula model to accommodate the dependence over time and over space among spatially clustered property risks. To tackle the computational challenge caused by the discreteness feature of large-scale insurance data, we propose an efficient multilevel composite likelihood approach for parameter estimation. Provided that latent risk induces correlation, the proposed customer learning method offers improved predictive analytics by allowing insurers to borrow strength from related risks in predicting new risks and also helps reveal the relative importance of the multiple sources of unobserved heterogeneity in updating policyholders' risk profile. In the empirical study, we examine the loss cost of a portfolio of entities insured by a government property insurance program in Wisconsin. We find both significant temporal and spatial association among property risks. However, their effects on the predictive distribution of loss cost are different for the new and renewal policyholders. The two sources of dependence are complements for the former and substitutes for the latter. These findings are shown to have substantial managerial implications in key insurance operations such as experience rating, capital allocation, and reinsurance arrangement. [ABSTRACT FROM AUTHOR]

Previous surveys of people's attitudes toward automated vehicles (AVs) and transit integration have primarily taken place in large urban areas. AV-transit integration also has a great potential in small urban areas. This paper is based on a survey of people's attitudes towards AV-transit integration carried out in two small urban areas in the US State of Wisconsin. A total of 266 finished responses were analyzed using text mining, factor analysis, and regression analysis. Results show that respondents know about AVs and driving assistance technologies and welcome AV-transit integration but are unsure about its potential impacts. Technology-savvy respondents were more positive but had more concerns about AV-transit integration than others. Respondents who enjoyed driving were not necessarily against transit, as they were more positive about AV-transit integration and were more willing to use automated buses than those who did not enjoy driving as much. Transit users were more positive toward AV-transit integration than non-transit users. [ABSTRACT FROM AUTHOR]