Your search

For decades, participant carelessness has been considered a problem in collecting data using surveys. Although participant carelessness cannot be disputed to exist, the impact it has on data quality or the level of influence or bias it produces in results is questionable. The main purpose of this paper is to determine whether participant carelessness is a substantial problem that significantly influences or biases the results of statistical analyses. This is accomplished by analyzing established management relationships through a comparison of the full, careful, and careless samples to determine the impact participant carelessness has on data results regarding correlations, t-tests, and simple linear regressions. Four detection approaches were used to identify careless participants individually, in pairs, and in three method combinations. The second purpose of this paper is to use the resampled individual reliability (RIR) approach to detect careless participants and compare it to the individual reliability approach to determine whether the two approaches are fundamentally similar. Data were collected using Mechanical Turk (N = 678). Based on the findings, participant carelessness does not appear to be a substantial problem or demonstrate levels of bias in the results in this study. There are two significant differences between the full and careful samples with the t-tests and the regression comparisons of fit statistics demonstrate the careful samples to have a weak improvement over the full sample however, none indicate bias. The findings also suggest that the individual reliability and the RIR approaches are not entirely fundamentally similar. © ACPIL.

An interpretive qualitative approach insists on the plural and negotiated nature of the meanings that humans attach to their social realities. Thus, the qualitative researcher must navigate multiple and sometimes conflicting commitments to method, data, oneself, participants, and one’s reader. This can lead us to obscure the messiness of data analysis in final research reports and to downplay how methodological choices can make our participants ‘say things.’ In this article, we compare two interpretive methods, thematic and narrative analysis, including their shared epistemological and ontological premises, and offer a pedagogical demonstration of their application to the same data excerpt. However, our broader goal is to use the divergent results to critically examine how our choice of analytic method in interpretive research influences how we (researcher + method) ‘author’ data stories. Ultimately, researcher reflexivity must go beyond acknowledging how one’s position may influence the data analysis or the participant. © 2019, © 2019 Australian and New Zealand Communication Association.

With the growing access to technology in the medical domain, an increased volume of medical data is recorded. The size and complexity of these data make the process of analysis of meaningful discoveries of beneficial patterns more challenging. This problem has attracted numerous researchers around the world. Statistical methods have been employed to handle medical data for diagnosis purposes. Unfortunately, these methods were less capable of dealing with these massive and complex datasets. To solve this problem, we suggest a process to classify medical data which includes feature selection and classification using a number of supervised learning techniques. Binary Brain Storm Optimization (BBSO) is used for feature selection, which is a population search approach that simulates the process of electing the best idea (solution), among others. We simulated six different classifiers: Naive-Bayes, K-Nearest Neighbor, Support Vector Machine, Linear Discriminant Analysis, Decision Tree and Random Forest. Five datasets adopted from the UCI Machine Learning Repository, (Breast Cancer, Diabetes, Heart Disease, Chronic Kidney, and SPECT), are employed as a benchmark test data. The performance of BBSO is evaluated using accuracy on the datasets using the various classifiers. Experimental results show that the proposed approach improves the classification performance for better medical diagnosis. © 2019 ACM.

Keystroke dynamics has been used as a form of one-time user authentication and continuous verification especially when it comes to securing the cyberspace. In this paper, we present the idea of using keystroke dynamics as a form of second layer authentication in web applications. We showed that this method can authenticate a user with high accuracy and can be used as an alternate to CAPTCHA tests, security questions and image selections that are being used today. We have developed a working web-based platform in a browser environment that enforces the proposed second-layer security. We performed penetration test experiments by launching a total of 598,500 impostor and genuine authentication attempts and found the Equal Error Rate (EER) as 10.5%. © 2019 IEEE.

Agriculture ranks one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as scientific foundation for forming effective remediation strategies. However, the methods capable of accurately and efficiently calculating spatially explicit life cycle global warming and eutrophication impacts at a fine spatial scale over a geographic region are lacking. The objective of this study was to compare two regression models for estimating spatially explicit life cycle global warming and eutrophication, with corn production in the Midwest region as a demonstrating example. The results indicated that the gradient boosting regression tree model built with monthly weather features yielded higher predictive accuracy for life cycle global warming impact and life cycle EU. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required longer training time. Additionally, all machine learning models were million times faster than the traditional process-based model and were suitable for use in computationally-intensive applications like optimization and predication. © 2019 IEEE.

Post-discharge call programs are a simple and effective way to identify and intervene for patient care issues that may occur after discharge. Nurses play a key role in these programs and can lead quality improvement projects on their units to improve patient care during the transition from hospital to home. © 2019, Anthony J. Jannetti Publications, Inc.. All rights reserved.

As the social work field increasingly recognizes economic abuse within intimate partner relationships, the field has developed financial empowerment programs to empower survivors for their financial future. Although research has demonstrated the effectiveness of financial literacy programs, there are barriers to their implementation in the field. Studies have explored, from the perspective of advocates, best practices in incorporating financial literacy into services; however, no studies have explored implementation approaches from the perspective of survivors. This study explores, from the perspective of 34 survivors, approaches for implementing financial literacy programming. Participants described their understanding of financial empowerment as being in charge of finances, having financial power, and not having to endure the struggle. To counter financial disempowerment, participants identified the need for financial confidence, knowledge, and tools. Participants shared their strategies for saving money, though many participants reported barriers to using banks as savings tools. Almost all participants stressed the importance of financial literacy services for survivors, especially around banking, credit, and debt. Finally, participants shared recommendations for job readiness and training programming. Findings have implications for domestic violence and broader social work organizations implementing financial empowerment services. Social workers can support financial empowerment efforts through program development and research efforts. © 2019 National Association of Social Workers.

The growth in using various smart wireless devices in the last few decades has given rise to indoor localization service (ILS). Indoor localization is defined as the process of locating a user location in an indoor environment. Indoor device localization has been widely studied due to its popular applications in public settlement planning, health care zones, disaster management, the implementation of location-based services (LBS) and the Internet of Things (IoT). The ILS problem can be formulated as a learning problem utilizing Wi-Fi technology. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in a various indoor location. Developing a classification model with high accuracy can be achieved using a machine learning approach. Artificial Neural Network is one of the most successful trends in machine learning. In this article, we provide our initial idea of using Cascaded Layered Recurrent Neural Network (L-RNN) for the classification of user localization in an indoor environment. Several neural network models were trained, with the best performance attainment is reported. The experimental results marked that the presented L-RNN model is highly accurate for indoor localization and can be utilized for many applications. © 2019 IEEE.

The model reactions CH3X + (NH—CH=O)M ➔ CH3—NH—NH═O or NH═CH—O—CH3 + MX (M = none, Li, Na, K, Ag, Cu; X = F, Cl, Br) are investigated to demonstrate the feasibility of Marcus theory and the hard and soft acids and bases (HSAB) principle in predicting the reactivity of ambident nucleophiles. The delocalization indices (DI) are defined in the framework of the quantum theory of atoms in molecules (QT-AIM), and are used as the scale of softness in the HSAB principle. To react with the ambident nucleophile NH═CH—O−, the carbocation H3C+ from CH3X (F, Cl, Br) is actually a borderline acid according to the DI values of the forming C…N and C…O bonds in the transition states (between 0.25 and 0.49), while the counter ions are divided into three groups according to the DI values of weak interactions involving M (M…X, M…N, and M…O): group I (M = none, and Me4N) basically show zero DI values; group II species (M = Li, Na, and K) have noticeable DI values but the magnitudes are usually less than 0.15; and group III species (M = Ag and Cu(I)) have significant DI values (0.30–0.61). On a relative basis, H3C+ is a soft acid with respect to group I and group II counter ions, and a hard acid with respect to group III counter ions. Therefore, N-regioselectivity is found in the presence of group I and group II counter ions (M = Me4N, Li, Na, K), while O-regioselectivity is observed in the presence of the group III counter ions (M = Ag, and Cu(I)). The hardness of atoms, groups, and molecules is also calculated with new functions that depend on ionization potential (I) and electron affinity (A) and use the atomic charges obtained from localization indices (LI), so that the regioselectivity is explained by the atomic hardness of reactive nitrogen atoms in the transition states according to the maximum hardness principle (MHP). The exact Marcus equation is derived from the simple harmonic potential energy parabola, so that the concepts of activation free energy, intrinsic activation barrier, and reaction energy are completely connected. The required intrinsic activation barriers can be either estimated from ab initio calculations on reactant, transition state, and product of the model reactions, or calculated from identity reactions. The counter ions stabilize the reactant through bridging N- and O-site of reactant of identity reactions, so that the intrinsic barriers for the salts are higher than those for free ambident anions, which is explained by the increased reorganization parameter Δr. The proper application of Marcus theory should quantitatively consider all three terms of Marcus equation, and reliably represent the results with potential energy parabolas for reactants and all products. For the model reactions, both Marcus theory and HSAB principle/MHP principle predict the N-regioselectivity when M = none, Me4N, Li, Na, K, and the O-regioselectivity when M = Ag and Cu(I). © 2019 Wiley Periodicals, Inc. © 2019 Wiley Periodicals, Inc.

We present the discovery from Transiting Exoplanet Survey Satellite (TESS) data of LTT 1445Ab. At a distance of 6.9 pc, it is the second nearest transiting exoplanet system found to date, and the closest one known for which the primary is an M dwarf. The host stellar system consists of three mid-to-late M dwarfs in a hierarchical configuration, which are blended in one TESS pixel. We use MEarth data and results from the Science Processing Operations Center data validation report to determine that the planet transits the primary star in the system. The planet has a radius of, an orbital period of days, and an equilibrium temperature of K. With radial velocities from the High Accuracy Radial Velocity Planet Searcher, we place a 3σ upper mass limit of 8.4 on the planet. LTT 1445Ab provides one of the best opportunities to date for the spectroscopic study of the atmosphere of a terrestrial world. We also present a detailed characterization of the host stellar system. We use high-resolution spectroscopy and imaging to rule out the presence of any other close stellar or brown dwarf companions. Nineteen years of photometric monitoring of A and BC indicate a moderate amount of variability, in agreement with that observed in the TESS light-curve data. We derive a preliminary astrometric orbit for the BC pair that reveals an edge-on and eccentric configuration. The presence of a transiting planet in this system hints that the entire system may be co-planar, implying that the system may have formed from the early fragmentation of an individual protostellar core. © 2019. The American Astronomical Society. All rights reserved..

This book examines the effects that political institutions, the legal system, and economic policies have had on the human rights record in the PRC since 1949. The authors first address the problems of assessing political liberties in a nation that emphasizes economic over civil rights and that has traditionally valued collective rights over individ. © 1988 by Taylor & Francis. All rights reserved.

Laser-induced fluorescence (LIF) technique was used to generate spectral signatures of endogenous fluorophores relevant to the tissue molecular composition changes in human brain glioma tumors. The goal is to study the changes of fluorescence emission spectra from endogenous fluorophores in human brain glioma of different grades, and to find new biomarkers for prognostic optical molecular pathological diagnosis. Two hundred and thirty-seven (237) native fluorescence spectra from 61 subjects were measured using LabRAM HR Evolution micro photoluminescence (PL) system for four grades of glioma tumors in ex-vivo. The differences of four grades of glioma tumors were identified by the characteristic fluorophores fingerprints under the excitation laser wavelength at UV 325nm. To our best knowledge, this is the first report for human brain study using this technique. The fluorescence peaks of biomarkers with major contribution were found, including tryptophan, collagen, elastin, reduced nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD) and phospholipids that play important roles in the cellular energy metabolism and glycolysis pathway. The ratios of peak intensities and the peak positions in fluorescence spectra of may be used to diagnose human brain diseases or to guide biopsy during surgical resection. © 2019 SPIE.

A VRR-LRR analyzer with handheld fiber‐optic probe is reported for the first time for diagnosis of brain GBM in vivo. The sensitivity for identification is 80% compared with histopathology examination. © OSA 2019. The Author(s).

This book explores new and leading edge marketing research approaches as successfully practiced by visionaries of academia and the research industry. Ideal as either a supplementary text for students or as a guidebook for practitioners, this book showcases the excitement of a field where discoveries abound and researchers are valued for solving weighty problems and minimizing risks. The authors offer rich new tools to measure and analyze consumer attitudes, combined with existing databases, online bulletin boards, social media, neuroscience, radio frequency identification (RFID) tags, behavioral economics, and more. The reader will profit from the numerous contemporary case studies that demonstrate the key role of marketing research in corporate decision-making.

Cosmopolitans are individuals with a distinctive kind of extended national and international orientation, a global vision, and sense of belonging to the world. These people are sophisticated and importantly engaged in the cultures outside of local geographical boundaries. But what do we know about them as consumers—their origins, values, media usage, and buyer behavior? This unique book details much about this group, and fills a knowledge gap that has long been overlooked largely because other related marketing areas have overshadowed and overlooked the notion of cosmopolitan consumers. Until this book, in fact, there has been no single authoritative source that directly and comprehensively covers the field of consumer cosmopolitanism. This book also includes original essays by an all-star cast of contributors, giving you an introduction to a powerful new approach to marketing, eclectically packed with novel ideas and insights that noticeably advance the marketing field and bring it more fully into the age of globalization.

The concept and framework of market sensing was introduced by George Day more than 20 years ago into the strategic marketing literature and especially the philosophy of the market-driven organization. Market sensing can be considered an expression of a company's capabilities to scan the external environment. It does this by using real time data and intelligence to understand business or uncertain changes, to meet the current and future needs of the market, increase customer value, and outperform competitors. Market sensing enables managers to resist complacency, as well as to exploit opportunities and to design appropriate competitive strategies in order to remain successful in today's uncertain, rapidly changing, and hypercompetitive market. The present volume, Market Sensing Today, is essential reading in the marketing discipline, given the rapidly escalating innovative developments in market sensing techniques. This book of essays by acknowledged experts in the field fills an important knowledge gap and provides a realistic basis for strategy. It is replete with real-life examples of market sensing that illustrate actionable ideas for immediate impact that will improve organizational learning and accelerate growth. This book of contemporary tested and comprehensive concepts and methods grounded in diverse and rich experience is intended to stimulate creativity and insightful approaches for educators offering courses in strategy as well as for practitioners involved in crucial strategic decision making.