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The recently published Lethaia paper by Bergström et al. (https://doi.org/10.1111/let.12269) on the age of the Ordovician Winneshiek Shale (Iowa, USA), and the impact that formed the Decorah crater which hosts this rock unit, is an interesting scientific contribution, although there are a number of problems with the interpretations and data presentation that merit comment. Due mainly to a lack of adequate critical assessment of δ13C data and biostratigraphical control, we contend that the conclusions of Bergström et al. are poorly founded and should not be cursorily accepted and propagated in future scientific literature. [ABSTRACT FROM AUTHOR]

Visual communication design, as a type of artistic and three-dimensional design behavior, helps to spread visual behavior by designing it. The rapid development of new media technology has provided rich channels and vast space for visual communication design, and the elements and modes of visual communication design are constantly being updated, better promoting the development of visual communication technology. The Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities based on the cultivation of innovative and creative abilities is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the induced interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (I-IVIFHIHWA) operator is proposed based on the interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (IVIFHIHWA) operator and induced ordered weighted averaging (I-OWA) operator. Meanwhile, some ideal properties of I-IVIFHIHWA operator are studied. Then, the I-IVIFHIHWA operator is employed to cope with the MADM under IVIFSs. Finally, an example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities is employed to test the I-IVIFHIHWA operator. Thus, the main research aim of this paper is concluded as follows: [1] the I-IVIFHIHWA operator is constructed based on classical IOWA operator; [2] the I-IVIFHIHWA operator is put forward to cope with the MADM under IVIFSs; [3] an empirical example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities has been put forward to show the I-IVIFHIHWA operator. [ABSTRACT FROM AUTHOR]

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring. [ABSTRACT FROM AUTHOR]

Cross-efficiency (CE) evaluation is an extension of the data envelopment analysis approach that allows decision making units (DMUs) to assess their peers by means of their own appreciation weights. As a result, each DMU is presented with a vector of CE scores, which need to undergo an aggregation operation to yield the ultimate ranking score. The aggregation is commonly carried out through an appropriate aggregation operator. In this paper, we propose an induced ordered weighted averaging (IOWA) operator with dual preference setting (2-IOWA) as a new aggregation device. The 2-IOWA aggregation novelty resides in its twined order inducing variables, which are defined by exploiting exclusively the appreciative properties of the CE matrix. The first-order inducing variable is the voting rank order that characterizes the preference voting system embedded within the CE matrix. The corresponding IOWA-level 1 aggregation produces a composite vote for each DMU by employing as arguments the individual votes assigned to it. The second-order inducing variable is represented by these composite votes, which are adopted to induce a common order on the rows of the CE matrix as a part of the IOWA-level 2 aggregation. The 2-IOWA aggregation process is conducted with OWA weights that are generated through different minimax disparity models by using different optimism level values in order to corroborate the influence of subjectivity on the structure of the ranking patterns besides evaluating the robustness of the proposed methodological framework. [ABSTRACT FROM AUTHOR]

With the rapid development and application of internet technology, cross-border e-commerce (CBEC) has begun to popularize globally and play an important role in China's foreign trade. The Chinese government has successively introduced multiple policies and regulations to strongly support its rapid development. Compared to the booming trend of CBEC, the development of its supply chain is slightly lacking in momentum, which has formed a certain obstacle to the overall development of CBEC. The supply chain is the foundation of successful CBEC transactions, and the foundation of the supply chain is logistics. The primary task to improve the backwardness of supply chain development is to solve logistics problems. Therefore, while enjoying the dividends brought by the rapid development of CBEC, international logistics enterprises should continuously improve their logistics service capabilities, effectively evaluate their service quality, and then identify problems based on the evaluation results, analyze and improve them. The service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is a classical multiple attribute group decision making (MAGDM). The Spherical fuzzy sets (SFSs) provide more free space for DMs to portray uncertain information during the service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain. Therefore, this paper expands the partitioned Maclaurin symmetric mean (PPMSM) operator and IOWA operator to SFSs based on the power average (PA) technique and construct induced spherical fuzzy weighted power partitioned MSM (I-SFWPPMSM) technique. Subsequently, a novel MAGDM method is constructed based on I-SFWPPMSM technique and SFNWG technique under SFSs. Finally, a numerical example for service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is employed to verify the constructed method, and comparative analysis with some existing techniques to testy the validity and superiority of the I-SFWPPMSM technique. [ABSTRACT FROM AUTHOR]

It is well understood that most crashes are the result of human errors. Among human-related errors, distracted driving, particularly related to cellphones, has received significant attention. Conversely, the underlying factors associated with in-vehicle distractions that are non-cellphone use have not been fully explored. Thus, this paper uses data from driver distraction-related crashes to examine various in-vehicle distraction sources. A Bayesian Multinomial Logit (BMNL) model was developed using 5,078 distracted-driving related crashes from Iowa. Four in-vehicle distraction sources - cellphone use, non-cellphone electronic devices, passengers, and reaching in-vehicle fallen objects - were investigated to determine factors that increase their odds of occurrence. The results suggest that drivers under the influence of alcohol are more likely to be involved in crashes associated with the distraction from cellphones. Furthermore, older drivers are less likely to be involved with distracted driving due to passengers. As expected, the more people in the vehicle, the higher the likelihood a driver can be distracted by passengers. Moreover, the association of driver distraction and speed limit, time of the day, vehicle's age, among others, were evaluated. This study provides useful information for developing and implementing strategies that minimize distractions from all in-vehicle sources. [ABSTRACT FROM AUTHOR]

Presents an overview of a study on the impact of editors' view on the political role of online newspapers on local coverage of 2000 U.S. presidential campaigns in Iowa. Method used; Results of the study; Discussion.

Neural network methods are widely used in business problems for prediction, clustering, and risk management to improving customer satisfaction and business outcome. The ability of a neural network to learn complex nonlinear relationship is due to its architecture that uses weight parameters to transform input data within the hidden layers. Such methods perform well in many situations where the ordering of inputs is simple. However, for a complex reordering of a decision-maker, the process is not enough to get an optimal prediction result. Moreover, existing machine learning algorithms cannot reduce computational complexity by reducing data size without losing any information. This paper proposes an induced ordered weighted averaging (IOWA) operator for the artificial neural network IOWA-ANN. The operator reorders the data according to the order-inducing variable. The proposed sorting mechanism in the neural network can handle a complex nonlinear relationship of a dataset, which results in reduced computational complexities. The proposed approach deals with the complexity of the neuron, collects the data and allows a degree of customisation of the structure. The application further extended to IGOWA and Quasi-IOWA operators. We present a numerical example in a financial decision-making process to demonstrate the approach's effectiveness in handling complex situations. This paper opens a new research area for various complex nonlinear predictions where the dataset is big enough, such as cloud QoS and IoT sensors data. The approach can be used with different machine learning, neural networks or hybrid fuzzy neural methods with other extensions of the OWA operator. [ABSTRACT FROM AUTHOR]

While there is wide agreement that physical activity is an important component of a healthy lifestyle, it is unclear how many people adhere to public health recommendations on physical activity. The Physical Activity Guidelines (PAG), published by the CDC, provides guidelines to American adults, but it is difficult to assess compliance with these guidelines. The PAG further complicates adherence assessment by recommending activity to occur in at least 10 min bouts. To better understand the measurement capabilities of various instruments to quantify activity, and to propose an approach to evaluate activity relative to the PAG, researchers at Iowa State University administered the Physical Activity Measurement Survey (PAMS) to over 1000 participants in four different Iowa counties. In this paper, we develop a two-part Bayesian measurement error model and apply it to the PAMS data in order to assess compliance with the PAG in the Iowa adult population. The model accurately accounts for the 10 min bout requirement put forth in the PAG. The measurement error model corrects biased estimates and accounts for day-to-day variation in activity. The model is also applied to the nationally representative National Health and Nutrition Examination Survey. [ABSTRACT FROM AUTHOR]

With the rapid development of society, ideological and political education courses have occupied a very important position in various courses in major universities, playing a series of important functions and roles in student quality education, excellent quality cultivation, and other aspects. In the new era, the evaluation and assessment of ideological and political education quality is not only the primary factor to improve the teaching quality of ideological and political education courses in universities, but also an important means to promote the deepening reform of ideological and political education. However, there are many problems in the process of evaluating the quality of ideological and political education in colleges and universities at present, such as the deviation in understanding the importance of evaluation, the relatively single evaluation method, and the low quality of application of evaluation results. The teaching quality evaluation of ideological and political courses in universities is a classical multiple attribute group decision making (MAGDM). Spherical fuzzy sets (SFSs) provide more free space for decision makers (DMs) to express preference information during the teaching quality evaluation of ideological and political courses in universities. Therefore, this paper we first extend partitioned Maclaurin symmetric mean (PMSM) operator and IOWA operator to SFSs and develop induced spherical fuzzy weighted PMSM (I-SFWPMSM) operator. Subsequently, a new MAGDM method is established based on I-SFWPMSM operator and SFNWG operator under SFSs. Finally, a numerical example for teaching quality evaluation of ideological and political courses in universities is used to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven approach to estimate incident-induced delays (IIDs) using probe vehicle data while accounting for missing data. The proposed approach is applied to evaluate the effectiveness of a safety service patrol (SSP) program. Existing data-driven methods for IID estimation usually rely on complete data sets. The proposed approach employs a random forest-based classification model and an interpolation method to estimate IIDs when real-time data are completely or partially missing during the incident-impacted time period. It also identifies reference profiles from the closest spatial-temporal road segments to improve data availability. The case study shows that the SSP program in the Quad Cities area of Iowa reduces IIDs associated with various incidents by 15%–91%. This data-driven evaluation framework can be applied to other traffic incident management programs, allowing more accurate and objective evaluations of their effectiveness. [ABSTRACT FROM AUTHOR]

Geographic Information Systems (GIS) are available as stand-alone desktop applications as well as web platforms for vector- and raster-based geospatial data processing and visualization. While each approach offers certain advantages, limitations exist that motivate the development of hybrid systems that will increase the productivity of users for performing interactive data analytics using multidimensional gridded data. Web-based applications are platform-independent, however, require the internet to communicate with servers for data management and processing which raises issues for performance, data integrity, handling, and transfer of massive multidimensional raster data. On the other hand, stand-alone desktop applications can usually function without relying on the internet, however, they are platform-dependent, making distribution and maintenance of these systems difficult. This paper presents RasterJS, a hybrid client-side web library for geospatial data processing that is built on the Progressive Web Application (PWA) architecture to operate seamlessly in both Online and Offline modes. A packaged version of this system is also presented with the help of Web Bundles API for offline access and distribution. RasterJS entails the use of latest web technologies that are supported by modern web browsers, including Service Workers API, Cache API, IndexedDB API, Notifications API, Push API, and Web Workers API, in order to bring geospatial analytics capabilities to large-scale raster data for client-side processing. Each of these technologies acts as a component in the RasterJS to collectively provide a similar experience to users in both Online and Offline modes in terms of performing geospatial analysis activities such as flow direction calculation with hydro-conditioning, raindrop flow tracking, and watershed delineation. A large-scale case study is included in the study for watershed analysis to demonstrate the capabilities and limitations of the library. The framework further presents the potential to be utilized for other use cases that rely on raster processing, including land use, agriculture, soil erosion, transportation, and population studies. [ABSTRACT FROM AUTHOR]

While sense of place (SOP) has been used in amenity landscapes to understand pro-environmental behavior, in working landscapes, SOP has not been a valid or reliable predictor for explaining conservation behavior. In this paper, we advance theory on SOP in working landscapes by assessing the relationship between several new and modified sense of place measures and farmer adoption of cover crops in Iowa. We used data from a 2018 survey of Iowa farmers and a Bayesian logistic regression, finding that physical dependence and economic dependence are distinct dimensions of SOP in working landscapes and the addition of a measure beyond SOP of who farmers feel responsible to when making land management decisions provides insights on how social groups are influential in farmers' decision-making. Our results suggest the SOP conceptual framework has the potential to help explain conservation behavior in working landscapes, and identifies opportunities for further reconceptualization and testing. [ABSTRACT FROM AUTHOR]

School bullying is a pervasive social problem that has been linked to severe mental health consequences for students. Though 50 states have adopted anti-bullying policies, research into the effectiveness of these policies has suggested that many such policies are not effective in reducing rates of bullying in schools. This paper aims to identify strategies for policy makers and social workers to reduce bullying in schools. This paper focuses on the Iowa Safe Schools Law as a case study for better understanding the strengths and limitations of statewide anti-bullying laws. Consistent with general evaluations of statewide anti-bullying laws, comparisons of rates of bullying before and after the passage of the Iowa Safe Schools Law reveal no reductions in rates of bullying. Through an examination of the extant academic and popular literature, this analysis identifies several critiques of the policy, namely that such policies infringe upon freedom of expression and that such policies focus on individual behavior modification rather than on changing underlying social norms that contribute to hostile environments. This paper presents several recommendations for advancing the prevention of bullying. Future research should identify underlying factors that render these policies ineffective and common factors of policies that have been found to be more effective. Bullying prevention policies should include funding allocated to these efforts and components that have been empirically linked to reductions in bullying. Social workers hold a key role in advocating for funding for this policy and offering institutional and individual-level interventions to reduce bullying. [ABSTRACT FROM AUTHOR]

Many small and rural places are shrinking. Interactive dashboards are the most common use cases for data visualization and context for exploratory data tools. In our paper, we will use Iowa data to explore the specific scope of how dashboards are used in small and rural area to empower novice analysts to make data-driven decisions. Our framework will suggest a number of research directions to better support small and rural places from shrinking using an interactive dashboard design, implementation and use for the every day analyst. [ABSTRACT FROM AUTHOR]

Heave motion of ships is a complex nonlinear dynamic process and cannot be accurately forecasted using a single prediction model. In this paper, an effective combined forecasting method is proposed to perform ship's heave motion prediction. The proposed method combines back propagation neural network (BPNN), autoregressive model (AR) and extreme learning machine (ELM) through an induced ordered weighted averaging (IOWA) operator. The prediction accuracy is selected as the induced variable and the prediction results are sorted according to prediction accuracy and IOWA operator assigns larger weights to the position with the smallest prediction error. The optimal weights are determined by maximizing the B‐mode relational degree. Experimental results demonstrate its effectiveness of the proposed method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

At present, with the continuous changes in the market situation and the continuous improvement of the supply chain network structure, the competition in all walks of life has become more and more intense, which has risen from simple enterprise competition to competition in the entire supply chain. In the construction industry, the structure of the construction supply chain is more complex and diverse, and it is more necessary to select high-quality suppliers for sincere cooperation. This requires construction companies to establish a complete supply chain management system, select high-quality suppliers to achieve win-win cooperation and improve their competitiveness. Therefore, construction enterprises need to comprehensively consider various factors, build a reasonable and feasible evaluation index system according to their own demand for materials, and use appropriate evaluation methods to select material suppliers with specific advantages, so as to ensure the entire construction supply chain of the project. of smooth operation. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further propose the induced interval-valued intuitionistic fuzzy Hamacher ordered weighted average (I-IVIFHOWA) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHOWA operator to deal with the multiple attribute decision making (MADM) problems under IVIFSs. Finally, an example for selecting the building material suppliers is used to test this new approach. [ABSTRACT FROM AUTHOR]

Streambank erosion is a major source of sediment to rivers and it is important to find locations where severely eroding streambanks are occurring in watersheds. In this study, a new approach to mapping eroding streambanks at a regional scale was conducted using high‐resolution Light Detection and Ranging (LiDAR) elevation data. By calibrating field mapping studies to LiDAR derivative data, the extent of severely eroding streambanks in third‐ to sixth‐order streams and rivers found in Iowa was estimated. Study results suggest that over 35,200 km of streambanks may be severely eroding, or approximately 41% of all third‐ to sixth‐order streambanks. The extent of streambank erosion was not uniform across the state with more erosion occurring in hillier western and southern Iowa compared to flatter and more recently glaciated northern Iowa. Streambank erosion was related to greater bank heights and was more prevalent (as a percent of the total stream length) in larger fifth‐ and sixth‐order rivers. Overall, despite limitations in the study related to the collection and extrapolation of the LiDAR dataset, study results provide new evidence for the magnitude of the streambank erosion problem in Iowa and likely apply to similarly glaciated regions of the United States Midwest. Research Impact Statement: The purpose of the Research Impact Statement (RIS) is to highlight one major insight or finding of broad interest to researchers, policy makers, practitioners, and the general public and should emphasize your paper's practical or policy significance. This information may be used to promote the paper on social media sites (e.g., Twitter, LinkedIn) and therefore, to the extent possible, should be jargon free. The RIS is limited to 200 characters including spaces and punctuation. [ABSTRACT FROM AUTHOR]

With the rapid development of air transportation, the complexity, importance, and severity of civil aviation safety have gradually become prominent. It is essential to use various data to analyze and predict the level of aviation safety. This paper used a combined prediction model based on Induced Ordered Weighted Averaging (IOWA) operator to forecast the civil aviation incident rate. We compiled and calculated civil aviation incident data and total flight hours from 2008 to 2019 in China and took the civil aviation incident rate (incident numbers per ten thousand flight hours) as the prediction object. First, this paper used the nonlinear regression model, Grey Verhulst model, and Holt-Winters exponential smoothing model to forecast the civil aviation incident rate individually. Then, it used the smallest sum of squared errors as the principle to use a combined prediction model based on the IOWA operator. It can be seen from the experimental results that the prediction accuracy of the combined model is better than single models. Finally, this paper forecasted the civil aviation incident rate in 2020 and 2021. The results showed that the predicted rates are 0.524 and 0.551. Most notably the incident rate will increase significantly compared with 2019. [ABSTRACT FROM AUTHOR]

Recent interventions in geography regarding the Anthropocene have demonstrated how Western logics of order and containment have produced massive geologic transformations. This paper focuses on odor as a sense that, when engaged critically, disrupts those logics by exposing the porousness of the body to other bodies and spaces. Visceral reactions to smell produce affective responses in the body which are informed by circulating political discourses. Thus, this paper explores how research focused on odor can reveal the complicated dynamics through which bodies are enrolled into subject formation and become a terrain of political struggle. Research on the everyday experience of hog manure in a rural Iowa watershed forms the case study through which these questions are raised. Since the transition to concentrated livestock agriculture at the end of the twentieth century, the disposal of animal waste has caused serious concern. This waste is often collected and later spread on fields across the state, producing foul odors and potential toxins. Political discourses that maintain this system claim waste is ordered and properly maintained, generating positive affective responses to foul odors and thereby maintaining support for industrialized agriculture. [ABSTRACT FROM AUTHOR]

The catastrophic derecho that occurred on 10 August 2020 across the midwestern United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth-orbit passive-microwave imager and GOES-16 satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and normalized difference vegetation index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies. [ABSTRACT FROM AUTHOR]

The aggregation of several predictors in time series forecasting has been used intensely in the last decade in order to construct a better resulting model. Some of the most used alternatives are the ones related to the Induced Ordered Weighted Averaging (IOWA), in which the prediction values are ordered using a secondary vector, often related to the accuracy of the prediction model in the last prediction. Although the time series study has been historically a subject related to statistics and stochastic processes, the random behaviour of the aggregation process is typically not considered. In addition, extensions of aggregation functions with a weaker notion of monotonicity, pre-aggregation functions, are appearing as better alternative for some topics such us classification. In this paper, a pre-aggregation extension of the IOWA operator, the Induced Ordered Linear Fusion (IOLF), is defined as a way to aggregate time series model predictions and its behaviour is studied from a probabilistic point of view. The IOLF operator over random vectors is defined, its properties studied and the relation between some averaging aggregation functions established. The expressions of the optimal weights according to statistical criteria are derived. The advantages and consequences of the use of the IOLF operator are studied, and its behaviour is compared to the usual procedures. Numerical results illustrate its performance on a practical example. • A generalization of the Induced Ordered Weighted Averaging operator is considered. • Predictions of different time series prediction models are fused. • Probability results state the necessity of negative weights. [ABSTRACT FROM AUTHOR]

By authorizing outdated terms for North American Indigenous peoples, the Library of Congress Subject Headings (LCSH) vocabulary deprioritizes or ignores the preferred names of the peoples being described. As a result, cataloging and metadata professionals constrained by LCSH often must apply names imposed during colonization. For example, in many library catalogs, works about people of the Meskwaki Nation in Iowa are labeled with “Fox Indians--Iowa” and “Sauk Indians--Iowa,” and Ioway peoples are described as “Iowa Indians.” As part of a diversity, equity, and inclusion (DEI) initiative at Iowa State University Library, a working group in the Metadata Services department undertook a project to build, publish, and use a controlled vocabulary of preferred terms for Indigenous communities with ties to land that is now part of the state of Iowa. This paper describes the working group’s research, outreach efforts, published vocabulary, and process for adding the preferred subject headings to library metadata. [ABSTRACT FROM AUTHOR]

Purpose: Iowa is one of 42 states with a direct access dental hygiene workforce model. Public health supervision (PHS) in Iowa allows dental hygienists (DH) to provide services in community settings without a prior examination from a dentist. The purpose of this study was to assess the current PHS workforce in Iowa and add to the body of evidence on direct access DH care. Methods: A 40-item mixed-mode survey was administered to all DH working under PHS in Iowa (n=126). Consent letters were mailed with directions to an online survey. Follow-up letters were sent to non-responders with an enclosed paper copy of the survey. Univariate analyses were performed to analyze the data. Results: The response rate was 52% (n=62), with 69% (n=42) of participants currently providing services under PHS. The most common employer categories were local public health agencies (59%), community health centers (CHCs) (20%), and nonprofit clinics (10%). The most common types of services provided under PHS were dental screenings (95%), fluoride varnish (91%), and sealants (50%). The majority of supervising dentists worked in private practice (61%) and CHCs (27%). Most supervising dentists (71%) accepted some referrals; however, a majority of PHS participants (71%) reported that it was somewhat or very difficult to find dentists to accept patient referrals. Conclusions: Most PHS DHs were employed by government agencies, however the majority of supervising dentists worked in private settings. Although most supervising dentists accepted at least some patient referrals, PHS DHs still experienced a high degree of difficulty referring patients for care. [ABSTRACT FROM AUTHOR]

This paper studies intuitionistic fuzzy (IF) decision making problems using an integrated approach. Firstly, an IF induced generalized ordered weighted averaging distance (IFIGOWAD) operator is introduced, that covers numerous IF aggregations distance operators. Then, two more generalized operators named IF quasi induced OWAD (Quasi-IFIOWAD) and IF induced generalized hybrid average distance (IFIGHAD) operator are presented. Furthermore, a new method using above operators is introduced to IF decision making problem and online trade systems. Finally, two real cases are present to show the effect of the method. [ABSTRACT FROM AUTHOR]

This paper studies intuitionistic fuzzy (IF) decision making problems using an integrated approach. Firstly, an IF induced generalized ordered weighted averaging distance (IFIGOWAD) operator is introduced, that covers numerous IF aggregations distance operators. Then, two more generalized operators named IF quasi induced OWAD (Quasi-IFIOWAD) and IF induced generalized hybrid average distance (IFIGHAD) operator are presented. Furthermore, a new method using above operators is introduced to IF decision making problem and online trade systems. Finally, two real cases are present to show the effect of the method. [ABSTRACT FROM AUTHOR]

Aggregation operators play an essential role in the aggregation of various individual input arguments in group decision-making (GDM). In this paper, we have proposed a family of IOWA operators with reliability measurement to aggregate uncertain decision information represented by interval numbers in GDM problems. In particular, we introduce the reliability-induced uncertain OWA (R-IUOWA) operator and the clusters' reliability-induced uncertain OWA (CR-IUOWA) operator. This type of operators uses the reliability measurement representing the opinion consensus of individuals as the associated order-inducing variable and considers the decision-makers' preference in the calculation of the position weights. Thus, the aggregation results have a higher consensus level. The R-IUOWA and CR-IUOWA operators have three primary properties such as commutativity, idempotency and boundness. The generalized formulas and some special cases of the two operators are outlined. Finally, the proposed operators are applied to a GDM problem regarding the selection of an investment company. The validity of the two operators is illustrated by comparing the aggregation results with that of other operators. [ABSTRACT FROM AUTHOR]

This paper reviews and compares theories of fuzzy sets and soft sets from the perspective of transformation, and we prove that every fuzzy set on a universe U can be considered as a soft set, and show that any soft set can be regarded as even a fuzzy set. This paper presents two mapping methods to implement the transformation, namely, the methods of the binary-coded genetic algorithm (BCGA) and the ordered weighted averaging (OWA) operators. In practical applications, it can be used to establish the membership function of fuzzy sets, and it can also be applied to pattern recognition, decision-making, etc. In general, it provides a new perspective to observe the relationship between soft sets and fuzzy sets, and it can be regarded as a general strategy to establish the membership function of fuzzy sets. Further, it reveals that the transformation method is similar to the process of building neurons, which opens a door to machine learning for soft set theory. [ABSTRACT FROM AUTHOR]

Introduction: Driver retirement and determination of fitness-to-drive are important aspects of reducing the risk of motor-vehicle collision for an older driver. A lack of information about the review process may lead to poor evaluation of drivers or an increased testing burden to referred drivers. Methods: This paper evaluates the license review process for the state of Iowa. We evaluated data from January 2014 to January 2018 and described the source of referral, testing process, and ultimate license disposition. Cox proportional hazards for competing risk were used to determine the risk of having a change in restrictions on the license and the risk of license denial. Results: 20,742 individuals were followed through the medical referral process. The most common source of referrals was licensing officials (39.7%). Drivers referred by licensing officials were less likely to be denied their license when compared to drivers from other sources (HR = 0.92 95%CI: 0.87–0.98); however, licensing official referrals were more likely to result in license restrictions compared to other sources (HR = 1.91, 95%CI: 1.82–2.00). Drivers referred by either law enforcement or a physician were more likely to ultimately have their license denied. Conclusions: Physician and law enforcement referred the drivers most likely to have their license denied. A smaller proportion of drivers were referred by physicians and law enforcement compared to licensing officials. Practical Applications: Licensing agencies should work with physicians and law enforcement to identify drivers who may need a review of their license. Comprehensive tracking of all medical referrals for a driver's license review is important for individual states to understand the burden of their driver referral process and for identifying referral sources with a high proportion of referrals with no licensing change for targeted outreach and education. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about the prevalence of multiple jobholding practices among dental hygienists or the factors contributing to these employment patterns. The purpose of this exploratory study was to examine predictors of multiple jobholding practices among dental hygienists in the state of Iowa. Methods: A mailed paper survey was sent to all licensed dental hygienists (n=2080) in Iowa in May 2018. The dependent variable was whether hygienists worked more than one job in dental hygiene. Key independent variables included individual, family, and practice-related factors. Descriptive, bivariate, and binary logistic regression analyses were completed. Results: A total of 1215 dental hygienists participated in the survey, for a response rate of 58%. Among respondents, 12.2% worked more than one job overall, with 10.7% working 2 jobs and 1.5% working three or more. Respondents who had at least a bachelor's degree, did not have children in the household, were not married, had worked more years at their primary job, and worked more hours per week, were more likely to hold multiple jobs after adjusting for other factors. Conclusions: Consistent with national estimates, there was a high multiple jobholding rate among dental hygienists in Iowa. Multiple individual, family, and practice characteristics were found to be related to multiple jobholding, with the strongest predictors being the hygienist's highest level of education and the number of hours worked at the primary job. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to design energy consumption modeling systems to predict the energy consumption value due to today's energy use in cities increasing. A new Modified Fish Migration Optimization Algorithm-based Numerical Moment Matching (MFMOA-NMM) technique is applied as an initial indeterminacy evaluation method to forecast the electrical power use in large-scale datasets of single-family residential homes with the application of main characteristics. To develop energy modeling, the DesignBuilder is used. The energy performance of the homes is assessed by combining these techniques. With the application of the information from the energy audit and data of the assessor (8368 single-family homes), four effective parameters including attic insulation R-value, the efficiency of air conditioners (SEER), type of the window, and the area of homes are inputted into the model improvement. Waterloo in Iowa is selected as the case study. The results showed that the yearly electrical power use expected is equal to 10,195 kWh which is within 5% of the experimental electrical power use. Also, the monthly expected electrical power use Average Bias Error is equal to 2.4% and the Coefficient of Variation with the Root Average Square Error is equal to 6.7%. This technique can be applied to create a small group of typical buildings to show the energy performance of a bigger group of buildings. • Energy consumption modeling to predict energy consumption for single-family homes in Waterloo. • Energy performance optimization by a Modified Fish Migration Optimization Algorithm. • Combining the introduced new algorithm with Numerical Moment Matching technique. [ABSTRACT FROM AUTHOR]

Tracer experiments conducted in the laboratory on undisturbed core samples (<7.3‐cm‐diameter) have been a standard method for estimating hydraulic and transport properties of fractured till since the 1980s. This study assesses the relationship between visible fractures on the top and bottom of core samples and the resulting hydraulic and mass transport properties of the core. We hypothesized that more visible fractures would indicate the presence of a well‐connected fracture network, leading to greater hydraulic conductivity (K) values and earlier chemical breakthrough times. To test this hypothesis, water flow and bromide (Br‐) tracer experiments were performed on 10, 16‐cm diameter, 16‐cm‐tall samples of fractured Dows Formation till from central Iowa. Visually identifiable fractures were present on the top and bottom of every sample. Results indicate that the visual identification of fractures does not predict a connected fracture network, as some samples produced breakthrough curves showing rapid first arrival times and shapes characteristic of solute transport in a fractured medium, while others appeared similar to an unfractured medium. No correlation was found between the number of visible fractures and K (Pearson's r = 0.25), or Br‐ first arrival time (r = −0.33), but a strong negative correlation between K and first arrival time (r = −0.92). Results indicate that the sample volume was not large enough to reliably contain a connected fracture network. Thus, testing large volumes of till at the field scale coupled with fracture‐flow modeling likely represents the best approach for estimating hydraulic and mass transport properties for fractured till. Article impact statement: This paper assesses whether the visible presence of fractures on the ends of till columns are indicative of fracture flow within the column. [ABSTRACT FROM AUTHOR]

This paper explores the performance of the analysis‐and‐assimilation configuration of the National Water Model (NWM) v1.0 in Iowa. The NWM assimilates streamflow observations from the United States Geological Survey (USGS), which increases the performance but also limits the available data for model evaluation. In this study, Iowa Flood Center Bridge Sensors (IFCBS) data provided an independent nonassimilated dataset for evaluation analyses. The authors compared NWM outputs for the period between May 2016 and April 2017, with two datasets: USGS streamflow and velocity observations; Stage and streamflow data from IFCBS. The distribution of Spearman rank correlation (rs), Nash–Sutcliffe efficiency (E), and Kling–Gupta efficiency (KGE) provided quantification of model performance. We found the performance was linked with the spatial scale of the basins. Analysis at USGS gauges showed the strongest performance in large (>10,000 km2) basins (rs = 0.9, E = 0.9, KGE = 0.8), with some decrease at small (<1,000 km2) basins (rs = 0.6, E = −0.25, KGE = −0.2). Analysis with independent IFCBS observations was used to report performance at large basins (rs = 0.6, KGE = 0.1) and small basins (rs = 0.2, KGE = −0.4). Data assimilation improves simulations at downstream basins. We found differences in the characterization of the model and observed data flow velocity distributions. The authors recommend checking the connection of USGS gauges and NHDPlus reaches for selected locations where performance is weak. Research Impact Statement: Streamflow and velocity estimates from the National Water Model (NWM) are evaluated using an independent dataset. NWM predictions are better at larger watersheds and downstream basins in Iowa. [ABSTRACT FROM AUTHOR]

During early 2019, a series of events set the stage for devastating floods in eastern Nebraska, western Iowa, and southeastern South Dakota. When the floodwaters hit, dams and levees failed, cutting off towns while destroying roads, bridges, and rail lines, further exacerbating the crisis. Lives were lost and thousands of cattle were stranded. Estimates indicate that the cost of the flooding has topped $3 billion as of August 2019, with this number expected to rise. After a warm and wet start to winter, eastern Nebraska, western Iowa, and southeastern South Dakota endured anomalously low temperatures and record-breaking snowfall. By March 2019, rivers were frozen, frost depths were 60-90 cm, and the water equivalent of the snowpack was 30-100 mm. With these conditions in place, a record-breaking surface cyclone rapidly developed in Colorado and moved eastward, producing heavy rain toward the east and blizzard conditions toward the west. In areas of eastern Nebraska, western Iowa, and southeastern South Dakota, rapid melting of the snowpack due to this rain-on-snow event quickly led to excessive runoff that overwhelmed rivers and streams. These conditions brought the region to a standstill. In this paper, we provide an analysis of the antecedent conditions in eastern Nebraska, western Iowa, and southeastern South Dakota and the development of the surface cyclone that triggered the historic flooding, along with a look into the forecast and communication of flood impacts prior to the flood. The study used multiple datasets, including in situ observations and reanalysis data. Understanding the events that led to the flooding could aid in future forecasting efforts. [ABSTRACT FROM AUTHOR]

This paper reports original evidence regarding the potential role of seed transmission of Xanthomonas vasicola pv. vasculorum in the epidemiology of bacterial leaf streak (BLS) in maize. We evaluated the occurrence of the pathogen on seeds from diseased fields and its subsequent transmission to seedlings. In 2016 and 2017, X. vasicola pv. vasculorum was detected by TaqMan PCR from 22 of 41 maize seed lots harvested from naturally infected fields in Colorado, Nebraska, and Iowa. However, many of the PCR-positive samples did not yield culturable X. vasicola pv. vasculorum colonies. The highest levels of seed contamination were detected in dent maize and popcorn from NE and CO. Seed transmission was evaluated in greenhouse grow-outs from eight seed lots, totaling more than 14,000 plants. Putative seed transmission events from naturally contaminated seed lots, estimated from PCR results, occurred at a frequency between 0.1 and 0.5% in 10-seedling pooled samples and at a frequency of 2.7% from individual plant assays. However, no seedling symptoms were observed during these assays and live X. vasicola pv. vasculorum colonies were not recovered from PCR-positive seedlings. In contrast, seed transmission was readily demonstrated from artificially contaminated seed lots, including typical symptoms and recovery of live bacteria. Seed transmission consistently occurred from seeds soaked in bacterial suspensions with concentrations of 106 CFU/ml, suggesting that a threshold population of the bacterium is necessary for the development of BLS symptoms and recovery of live bacteria. The low bacterial populations on naturally contaminated seeds apparently were not sufficient to result in diseased seedlings. [ABSTRACT FROM AUTHOR]

This paper analyses the statistical properties of the threshold model of collective actions. We establish the conditions of the threshold model under which the sequence of decisions is exchangeable. Then, if the probability of participation is a continuous and strictly increasing function of the willingness to participate, then the unique urn representation of the threshold model is the Pólya urn. In such a case, the sequence of participation rates converges almost surely to a random variable Z that has a beta distribution. Also, we show the critical mass and self-sustaining participation rates are fixed points of the distribution function. Further, we discuss the non-exchangeable model of collective actions, and we define the feedback leadership condition. We apply these results to characterise the process of the adoption of hybrid corn in Iowa, and the diffusion of the Internet for different levels of income. [ABSTRACT FROM AUTHOR]

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. [ABSTRACT FROM AUTHOR]

Current estimations of sediment transport at the watershed scale are limited by the difficulty of accurately simulating the sediment transfer along the main stem. The typical approach to simulating watershed sediment transport involves the adoption of hydrologic sediment routing schemes that do not fully capture the contribution and timing of side tributaries, and the inclusion of a simplified channel geometry that does not include its hydraulic feedback. In this paper, we present the results of a coupled hydrologic-hydraulic model of sediment transport applied to a small watershed of Iowa. The model was developed to simulate both the hydrologic network and non-equilibrium sediment transport that occur during a flood. The model results highlight the importance of including side tributaries in order to capture a realistic duration of shear stress that ultimately affects sediment transport. Comparisons with bank erosion measurements indicate that the presented approach is also promising to estimate sediment sources along the main stem. [ABSTRACT FROM AUTHOR]

Trust is crucial for purchasing decisions in social commerce. However, inexperienced users may not have a direct trust relation to experienced users in practice. Besides, users tend to give their trust degrees to others with linguistic labels rather than crisp values. To evaluate the trust degree for inexperienced users to experienced ones, we propose an interval type‐2 fuzzy trust evaluation model in this paper. Interval type‐2 fuzzy linguistic labels are used to represent trust degree among users. An interval type‐2 fuzzy Algebraic t‐norm is addressed to compute propagative trust degrees. Considering the effect of trust path length, the induced ordered weighted averaging (IOWA) operator is extended to aggregate the interval type‐2 fuzzy trust degrees obtained from multiple paths. In addition, the final interval type‐2 fuzzy trust degree is transferred into the corresponding linguistic label to help users make decisions more naturally. Finally, a case study in social commerce and a related comparison are given to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper reports on the development and implementation of a process of peer evaluation of teaching to assess librarian instruction in a high-enrollment online information literacy course for undergraduates. This paper also traces a shift within libraries from peer coaching to peer evaluation models. One common model for peer evaluation, using pre- and post-observation meetings between instructor and evaluator, as well as a formal summative report, has been adapted to focus attention on key aspects of online teaching. The paper also discusses the need for evaluating librarians' online teaching performance, as distinct from online course design. [ABSTRACT FROM AUTHOR]