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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]

A pilot study was performed at the Fox River Fiber recovered paper processing company in DePere, Wisconsin, to determine the extent to which injection of oxygen and ozone could reduce the high chemical oxygen demand, COD, in the effluent and the effectiveness of the ozone/oxygen stream in suppressing production of hydrogen sulfide gas in downstream sewage lines. Adaptive Ozone Solutions, LLC, supplied the oxygen/ozone generation and injection system. Samples were analyzed both before and after oxygen/ozone injection. Hydrogen sulfide gas was continuously monitored at sewer stations downstream of Fox River Fiber. Results showed that with a very short contact time, effluent COD was reduced by over 15%. A simple kinetic model predicts that a contact time of fewer than 30 minutes could reduce COD by as much as 60%. In addition, downstream hydrogen sulfide gas production in the sewage mains was also better controlled, such that costly Bioxide applications could be reduced. [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]

The article presents information on how the change of ownership of the Oshkosh, Wisconsin-based newspaper "Oshkosh Northwestern," affected the quality of the paper. The "Oshkosh Northwestern," was one of the most successful newspapers in Wisconsin during the 1900s. In the fall of 1997 the two families that owned it brought in a team of consultants to upgrade the publication. The improvements set the stage for the paper to be sold three times in less than three years. The paper was first sold by its family owners to OgdenNewspapers Inc. on May 15, 1998. A little more than a month later in early July 1998 it was acquired by Thomson Corp. On July 21, 2000 the Northwestern became property of Gannett Co. Inc. The newspaper became demonstrably worse in the five years after its family owners sold it to a series of chains. With fewer reporters writing fewer local stories and generating less copy for the local section, circulation fell more than 4 percent from the time Gannett acquired the paper in 2000 through the end of 2002.

Examines the cultural implications of school assignments. Papers written in 1898 by senior class members of the Platteville Normal School in Wisconsin; Rhetorical demands of school writing assignments; Cultural work that the writing can accomplish.

Background and Aims: Projected changes in the atmospheric concentrations of CO and tropospheric O over the next 50 years are of significant concern due to the linkages in the cycling of carbon and water in forested ecosystems. Responses of tree roots to elevated CO (eCO) and O (eO) have been characterized primarily by studies of relatively shallow roots, yet deeper roots often play a disproportionately large role in water acquisition relative to their biomass. We undertook the present study to determine if there were significant root responses to eCO and eO below the maximum soil depths typically studied. Methods: In the current study, we characterized small root biomass and morphometric responses to eCO and eO at the Aspen-FACE Experiment in Rhinelander, Wisconsin down to a depth of one meter. Results: Elevated CO caused relatively undifferentiated growth stimulation. Elevated O stimulated root growth in the AA community at depth, while in the AB community there was a reduction in root growth in the shallow soil layer that was reversed in the deeper layers. Conclusions: Root responses below depths typically studied were qualitatively similar than those within shallower soils for eCO, but were sometimes compensatory for eO. [ABSTRACT FROM AUTHOR]

Background.Recent match trends by medical graduates have revealed a declining interest in general surgery. Our study evaluates the academic strength of recent graduates to determine the quality of those matching to general surgery residencies.Methods.All third-year students rotating through the 8-week surgical clerkship from July 1998–June 2000 (n = 291) were followed at the University of Wisconsin Medical School. Each student completed 4 weeks of general surgery and 4 weeks of surgical subspecialties. Match data provided residency choices and students were divided into general surgery (GS), surgical subspecialty (SS), and nonsurgical (NS) residencies. Student performance was based upon National Board of Medical Experiences (NBME) surgery exam, class rank and Alpha Omega Alpha (AOA) status.Results.Students at our institution scored at the national mean on the NBME exam. GS team, session, and timing of GS rotation had no relationship to exam score. Total number of operative cases observed was inversely related to exam performance (P = 0.02). Of students entering a GS career, most scored below the mean on the NBME exam, 46% graduated in the bottom two thirds of the class, and only 6% of AOA members entered a GS residency.Conclusion.Although many of the strongest medical students select surgical residencies, they choose to enter SS careers and not GS careers. [Copyright &y& Elsevier]

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]

The article presents titles and abstracts of papers published in the January 1920 issue of the journal "Annals of the Association of American Geographers." The 'Southern Indiana,' includes the unglaciated portion of the state, the areas of Pre-wisconsin drift on each side of the unglaciated tract, and the margin of the area of Wisconsin drift border has been largely determined by geologic structure. In the title 'The Geographic Factors in the Distribution of Animals, with particular reference to the distribution of the land Reptiles of the Davis Mountains Region, Texas,' the distribution of the land reptiles of the Davis Mountains Region, Texas, shows that the zonal distribution is less than would be expected on the life-zone theory and that there is evidence that other geographic factors than climate are to be recognized.

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]

This article presents abstracts of papers which will be presented at the twenty-fourth Annual Meeting of the Society for Psychophysiological Research (SPR) to be held at the Marc Plaza Hotel in Milwaukee, Wisconsin, from October 18-21, 1984. One of the central arguments of the paper on orienting response (OR) and information processing is that there are two frameworks within which questions concerning the relationship between the OR and information processing have been posed. The first involves questions about the role that the OR and its habituation might play when organisms attend, learn and memorize.

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]

The article presents abstracts of papers presented at the eighteenth annual meeting of The Society for Psychophysiological Research, which was held at The Concourse Hotel in downtown Madison, Wisconsin, from September 15-18, 1978. The Co-Chairmen of the program committee were Rafael Klorman and Ted Weerts. The bulk of the research reports were given and discussed informally at two science fairs held during the meeting. An abstract titled "Measuring HR variability: Two studies suggesting the successive difference mean square statistic and the IBI scale as the most appropriate analysis technique" was presented by researcher R.J. Heslegrave.

Presents abstracts of geography-related papers, as of March 1926. "The Status of Zoogeography in North America," by Chas. C. Adams; "Settlement and Economic Development of the San Juan Region of Colorado," by Wallace W. Atwood; "Culture and Landscape at Madison, Wisconsin," by V.C. Finch.

Reports on the development of enzymatic de-inking process for paper recycling by researchers at the University of Wisconsin-Madison. Use of a chemical-scrubbing enzyme to clean paper so that the paper fiber can be reused to produce high-quality paper; Testing of the process.

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]

This study examined the independent and interactive effects of elevated carbon dioxide (CO) and ozone (O) on the foliar quality of two deciduous trees species and the performance of two outbreak herbivore species. Trembling aspen ( Populus tremuloides) and paper birch ( Betula papyrifera) were grown at the Aspen FACE research site in northern Wisconsin, USA, under four combinations of ambient and elevated CO and O. We measured the effects of elevated CO and O on aspen and birch phytochemistry and on gypsy moth ( Lymantria dispar) and forest tent caterpillar ( Malacosoma disstria) performance. Elevated CO nominally affected foliar quality for both tree species. Elevated O negatively affected aspen foliar quality, but only marginally influenced birch foliar quality. Elevated CO slightly improved herbivore performance, while elevated O decreased herbivore performance, and both responses were stronger on aspen than birch. Interestingly, elevated CO largely offset decreased herbivore performance under elevated O. Nitrogen, lignin, and C:N were identified as having strong influences on herbivore performance when larvae were fed aspen, but no significant relationships were observed for insects fed birch. Our results support the notion that herbivore performance can be affected by atmospheric change through altered foliar quality, but how herbivores will respond will depend on interactions among CO, O, and tree species. An emergent finding from this study is that tree age and longevity of exposure to pollutants may influence the effects of elevated CO and O on plant-herbivore interactions, highlighting the need to continue long-term atmospheric change research. [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]

Metso Paper will rebuild Stora Enso North America''s paper machine at the Biron mill in Wisconsin, USA under a €23 million deal. [Copyright &y& Elsevier]