Oklahoma State University System

Vaccine safety skeptics are often thought to be more likely to self-identify as Democrats (vs. Independents or Republicans). Recent studies, however, suggest that childhood vaccine misinformation is either more common among Republicans, or is uninfluenced by partisan identification (PID). Uncertainty about the partisan underpinnings of vaccine misinformation acceptance is important, as it could complicate efforts to pursue pro-vaccine health policies. I theorize that Republicans should be more likely to endorse anti-vaccine misinformation, as they tend to express more-negative views toward scientific experts. Across six demographically and nationally representative surveys, I find that—while few Americans think that “anti-vaxxers” are more likely to be Republicans than Democrats—Republican PID is significantly associated with the belief that childhood vaccines can cause autism. Consistent with theoretical expectations, effect is strongly mediated by anti-expert attitudes—an effect which supplemental panel analyses suggest is unlikely to be reverse causal.

Recent changes in the energy industry initiated by deregulation have accelerated the introduction of distributed generation at the subtransmission and distribution levels. In light of the well-known benefits as well as the various issues involved in DG incorporation, this paper proposes two new quadratic voltage profile improvement indices (VPII/sub 1/ and VPII/sub 2/). The primal-dual interior-point (PDIP) method has been employed to identify the optimal location and real and reactive power generation on the basis of the newly proposed indices. A simplified model of a 33-bus radial distribution system has been simulated in MATLAB to illustrate the use of the new indices.

The ubiquitous use of cameras in a home environment raises privacy concerns, which is one of the major barriers to the deployment of smart home systems for elderly and disabled care. Social robots are equipped with cameras that can witness embarrassing situations of their owners, such as nakedness during a morning bath. In this paper, our proposed solution is to let the robot detect such privacy-sensitive moments and then turn away to avoid observing the person. We proposed a method based on the Convolutional Neural Networks (CNN) to detect nakedness in a daily living scenario. In this method, two CNNs are used: one detects whether a person is present or not in the current scene, and the other detects privacy-sensitive situations. We implemented the method on the ASCC home service robot developed in our lab. The CNNs were trained with a database composed of more than 2900 pictures collected from the Internet and from our ASCC Smart Home. In our experiments, we considered three human poses: sitting, standing, and lying. If the robot detects a privacy-sensitive situation, it will turn around and verbally announce its intention, which ensures the person that he or she is not being watched. Our experiments validate the proposed privacy preserving method. We also conducted a user study and find that people prefer that the robot takes some actions to avoid directly watching them in privacy-sensitive situations.

The Radiocommunication Sector of the International Telecommunication Union (ITU-R) produces numerous global standards on the use and management of radiocommunication systems. Among these is Recommendation ITU-R P.676-11 (09/2016), which provides methods to estimate the attenuation by atmospheric gases for electromagnetic waves in the 1-1000 GHz frequency range. In this letter, we comment on the veracity of that recommendation in the 450-1000 GHz range. In particular, we compare ITU water vapor absorption estimates to the same data available from other sources: HITRAN and previously reported experimental measurements. We find that ITU estimates increasingly disagree with both sources as frequency and water vapor density increase, demanding a closer inspection as to the cause. This discrepancy is attributed to the method of inclusion of the summed contribution of resonance wings from absorption lines located in the >1 THz range, and the method to account for continuum absorption.

Early power systems were comprised of centrally dispatched conventional power generation facilities, delivering power to customers via transmission and distribution networks. With the changes in utility structures, development in technologies, increased attention on environmental concerns, increase in electricity demand coupled with attractive advantages offered by small to medium generation sources have led to increasing use of Distributed Generation (DG). However, high penetration of DG will have an adverse impact on the power system operation & protection. In this paper an extended review is done to identify the impact of fault current contribution from DG on the line protection performance. The disturbing factors for proper operation of distance protection and further the impact of fault currents on those disturbing factors are identified.

Water is a highly abundant nutrient in the human body and monitoring of its regulation is essential to keep the body hydrated. A number of critical health conditions including swelling of the brain and short/long term memory loss are associated with poor or excessive drinking habits. This can be prevented with the use of a real time hydration monitoring system. In this paper we presented AutoHydrate, a wearable hydration monitoring system which continuously monitors the drinking activities and daily fluid requirements of the user through automatic detection of drinking and body activities. The system is built using a throat microphone for collecting acoustic signals, a smartwatch for collecting body activity, an embedded computer for processing the signals and sending recommendation to a smartphone app in real time for an interactive information display. After different time, frequency and cepstral domain features are extracted from the signals, drinking activities are classified using Support Vector Machine (SVM) and body activity is classified using Gradient Boosting Decision Tree algorithm. The Dietary Reference Intake standard is followed for recommending the amount of fluid required using our detection. Based on our experimental results on 8 subjects, a Drinking detection accuracy of 91.5% and Body activity classification accuracy of 89.12% are obtained. Results show that our system is feasible for real time monitoring of body hydration.

The objective of this research is to build and demonstrate a design tool in a Virtual Reality (VR) environment. The goal of the tool is to streamline the assembly planning of complex systems. As a case study, we have generated a virtual reality environment for an assembly simulation of a zero-G treadmill for use on spacecraft. This study will help to understand the assembly of the VR model that might lead an assembler to optimize the design related costs. The VR environment has been created using Unity 3D along with Solidworks for creating models of Treadmill parts. A series of controlled user studies has been done to investigate the role of different visual cues (image and text) on user performance while performing manual assembly in an immersive VR setting (VIVE headset) and a non-immersive environment (desktop). To calculate the groundtruth, an optimized path sequences is computed using a genetic algorithm for a collision free layout.

Purpose The study aims to demonstrate how different arrangements and characteristics of institutions can generate or mitigate uncertainty thereby facilitating or hampering the possibilities of entrepreneurial action. Design/methodology/approach This is a conceptual paper that advances the theoretical understanding of the relationship between entrepreneurial uncertainty and the different institutional levels, their characteristics and their interplay. Findings Entrepreneurial uncertainty also comes from the institutional environment and this has direct impact on the propensity to take action. The characteristics of the different institutional levels, in specific, their quality, stability, alignment and the burden imposed by L2 impact in the emergence of entrepreneurial uncertainty. Research limitations/implications This is a conceptual paper that makes a number of theoretical suggestions which need to be further analyzed by empirical work. Practical implications The findings suggest that different institutional levels need to be dealt with differently by research studies and institutional agents, including policy makers. Among others, the findings also suggest that stability is key to entrepreneurship and that the benefits of high quality regulation can be undermined by its excessive burden, reducing entrepreneurial action and harming development. Social implications Institutional actors should provide stability and allow for the improvement of the environment overall. Specifically, policy makers should aim at good quality regulation that is valid across the board, that provides stability and gives room for improvement of the institutions. Policy makers should refrain from trying to foster specific industries; they should instead provide a leveled playing field without trying to direct the entrepreneurial efforts towards an industry or geographic region and without being overly demeaning. Originality/value This research breaks new ground. It unites ideas from entrepreneurship and institutions suggesting a novel, much more nuanced approach to their interplay. The results can be used by scholars in the fields of entrepreneurship, institutions and economic development. They also have the potential to help to educate policy makers in their quest to improve the context for entrepreneurs.

Hadoop has two components which are HDFS and MapReduce. HDFS is a distributed file system for storing data for users of Hadoop and MapReduce is the framework that executes jobs from users. Hadoop stores user data based on space utilization of data nodes on the cluster rather than the processing capability of the data nodes. Furthermore Hadoop runs in a heterogeneous environment as all data nodes may not be homogeneous. For these reasons, workload imbalances will occur when Hadoop runs resulting in poor performance. In this paper, we propose a dynamic algorithm to balance the workload between different racks on a Hadoop cluster based on information obtained from analyzing the log files of Hadoop. Moving tasks from the busiest rack to another rack improves the performance of Hadoop MapReduce by reducing the running time of jobs. Our simulations indicate that using our algorithm, we can decrease by more than 50% the remaining time of the tasks belonged to a job running on the busiest rack.

Probabilistic models for the power output of Wind Electric Conversion Systems (WECS) are considered. Wind speeds are modeled using Weibull distribution and probability density function for the power output of WECS is derived using the transformation theorem. Variable portion of the power output characteristic is modeled using different functions and the results are compared. Probability density functions for the combined power output of two, four and eight systems are presented and discussed.

Purpose The purpose of this paper is to empirically test an existing conceptual model from Mak et al. (2012a, 2012b) to discern which factors have the most influence on food choices when travelers visit destinations with different options, i.e. local foods, other than those available in their home environments. Design/methodology/approach The quantitative study surveyed 330 travelers and used descriptive analyses of all the variables involved. A hierarchical linear regression was calculated to predict for the dependent variable of local cuisine consumption, based on the independent variables of culture and religion, socio-demographic factors, motivational factors, food trait personality and exposure effect/past experience. Findings Culture, motivational factors and food-related personality traits were consistently significant predictors of local food consumption. Research limitations/implications Limitations include using an English-only online questionnaire and self-reported bias. The impacting delimitation relates to data collection from US travelers and thus limiting generalizability findings. Practical implications The study explained factors involved in travelers’ decision to consume local foods at a destination. Government, tourism-related organizations, producers and service providers gain information to improve products, increase interest, create additional employment opportunities, increase tax revenues that assist local communities and increase consumption of local foods, products and services. Originality/value The limited availability of research on this topic prompted the interest of the researchers. Mak et al. (2012b) provide a conceptual model that was first tested empirically in this study. It presents a five factors impacting tourist food consumption at a destination. Local food consumption of tourists was tested using the aforementioned conceptual model.

This paper introduces a novel hierarchical decomposition approach for solving Multiagent Markov Decision Processes (MMDPs) by exploiting coupling relationships in the reward function. MMDP is a natural framework for solving stochastic multi-stage multiagent decision-making problems, such as optimizing mission performance of Unmanned Aerial Vehicles (UAVs) with stochastic health dynamics. However, computing the optimal solutions is often intractable because the state-action spaces scale exponentially with the number of agents. Approximate solution techniques do exist, but they typically rely on extensive domain knowledge. This paper presents the Hierarchically Decomposed MMDP (HD-MMDP) algorithm, which autonomously identifies different degrees of coupling in the reward function and decomposes the MMDP into a hierarchy of smaller MDPs that can be solved separately. Solutions to the smaller MDPs are embedded in an autonomously constructed tree structure to generate an approximate solution to the original problem. Simulation results show HD-MMDP obtains more cumulative reward than that of the existing algorithm for a ten-agent Persistent Search and Track (PST) mission, which is a cooperative multi-UAV mission with more than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> states, stochastic fuel consumption model, and health progression model.

Data is a valuable resource. Proper use of high-quality data can help people make better predictions, analyses and decisions. However, no matter how much effort we put into collecting a good dataset, errors will inevitably creep into the data, making it necessary for data cleaning. This becomes a concern particularly when large-scale heterogeneous data from multiple sources are integrated for other purposes. Data cleaning can be complicated, time-consuming, and expensive, but it is a necessary step in any data-related system since poor-quality data may not be suitable to achieve the intended purposes. The core of our data cleaning system is data association and repairing. Association aims to identify the same object and link with the most associated objects, and repairing is to make a database reliable by fixing errors in the data. For big data applications, we don't necessarily need to use all the data. In most situations, we only need a small subset of the most relevant data. So the goal of association is to convert big raw data into a small subset of the most relevant data that are most useful for a particular application. After we obtain a small amount of relevant data, we also need to further analyze the data to help people digest the data and turn the data into knowledge. We use a number of techniques to associate the data to get useful knowledge for data repairing. Our research shows that data association can effectively help with data repairing. To capture the interaction, we provide a uniform framework that unifies the association and repairing process seamlessly based on context patterns, usage patterns, metadata, and repairing rules.

This paper aims to develop a robotic platform which conducts cognitive orientation assessments for the elderly people. The robot asks questions based on time, space and memory recall, and analyzes the voice response from the user. With the help of the natural language processing package SyntaxNet, the robot evaluates answers given by the user and generates a score and a detailed report for the session conducted. The report is used to evaluate the progression of the cognitive impairment by caregivers or physicians. The whole system was developed and implemented on our ASCC Companion Robot. The system was tested on people from different ages and English accent with satisfying results.

Data is a valuable resource. The proper use of high-quality data can help make better predictions, analysis and decisions. Poor-quality data is detrimental to data analytics. Data from different sources may provide the same entities, but different identities. This becomes a concern particularly when large-scale heterogeneous data from multiple sources are integrated for other purposes. This paper aims to identify same or similar objects and link these associated objects together so that the data can be cleaned and combined efficiently. Our research harnesses both context and usage patterns of data items to determine relationships among objects. Our experimental results show that efficient linkage among multiple sources can be constructed using context and usage patterns.

Relative pose estimation between fixed-wing unmanned aerial vehicles (UAVs) is treated using a stable and robust estimation scheme. The motivating application of this scheme is that of “handoff” of an object being tracked from one fixed-wing UAV to another in a team of UAVs, using onboard sensors in a GPS-denied environment. This estimation scheme uses optical measurements from cameras onboard a vehicle, to estimate both the relative pose and relative velocities of another vehicle or target object. It is obtained by applying the Lagrange-d'Alembert principle to a Lagrangian constructed from measurement residuals using only the optical measurements. This nonlinear pose estimation scheme is discretized for computer implementation using the discrete Lagrange-d'Alembert principle, with a discrete-time linear filter for obtaining relative velocity estimates from optical measurements. Computer simulations depict the stability and robustness of this estimator to noisy measurements and uncertainties in initial relative pose and velocities.

Arguments for term limits often focus on the need for politicianswhoare less motivated by career goals and more motivated by public policy goals. Yet little is known about whether term limits result in officeholders who are different from others. In this article, the authors examine whether the political motivations and ambitions of term-limited legislators differ from those of nontermlimited legislators. A survey of legislators in 15 states serves as the basis for analysis. The findings indicate that, as predicted by advocates, term-limited legislators are more likely to be motivated by issues. However, they are also more likely to possess progressive ambition, thereby countering arguments that limits attract fewer careerists. Policy goals and progressive ambition are not necessarily inconsistent. Legislators whose policy agendas have not been completed by the time their terms end maywant to pursue other offices to achieve their goals. The implications of these findings are explored in this article.

Abstract Emergent volunteer groups play a significant role during disasters. There is a rich literature on the role of volunteer groups in disasters and disaster volunteerism. However, the rapid proliferation of social media platforms in the last decade made a significant impact on human lives, and disaster volunteerism is no exception. This article argues that there is a need for understanding social media’s impact on disaster volunteerism. Using Harvey as a case, this article analyzes 74 Facebook groups that were created during the storm. The article compares the emergence and lifespan, structure, and function of online volunteer groups to those of volunteer groups before social media. Findings show important distinctions between online groups and those mentioned in the literature. First, online groups are easier to observe and analyze because of the digital traces they leave. Online groups emerge in different phases of disaster (response, early recovery) depending on people’s needs. Their structure can possess elements of hierarchy as opposed to structural characteristics of groups mentioned in the literature. Finally, online groups mostly function as information sharing hubs; however, they also carry out a wide variety of functions, some of which request special attention. The article makes suggestions for future research.

The effect of wheel slip in differential drive robots is investigated in this paper. We consider differential drive robots with two driven wheels and ball-type caster wheels that are used to provide balance and support to the mobile robot. The limiting values of traction forces for slip and no slip conditions are dependent on wheel-ground kinetic and static friction coefficients. The traction forces are used to determine the fraction of input torque that provides robot motion and this is used to calculate the actual position of the robot under slip conditions. The traction forces under no slip conditions are used to determine the limiting value of the wheel torque above which the wheel slips. This limiting torque value is used to set a saturation limit for the input torque to avoid slip. Simulations are conducted to evaluate the behavior of the robot during slip and no slip conditions. Experiments are conducted under similar slip and no slip conditions using a custom built differential drive mobile robot with one caster wheel to validate the simulations. Experiments are also conducted with the torque limiting strategy. Results from model simulations and experiments are presented and discussed.

The paper focuses on systems engineering challenges for biomanufacturing, as exemplified by the Integrated and Scalable Cyto-Technology (InSCyT) bio-manufacturing platform under the Defense Advanced Research Projects Agency (DARPA) Biologically-derived Medicines on Demand (Bio-MOD) program. One goal of the project is to apply modeling and simulation techniques to the design, control, and optimization of biomanufacturing operations. In this paper, we present models and control strategies for the unit operations within the InSCyT platform, including a perfusion bioreactor, several packed bed and membrane chromatography steps, and a conductivity- and pH-controlled buffer mixing unit.