Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interdisciplinary Examination of the Material Effects of Deceptive Sport Beverage Advertisements

Abstract Sport companies frequently make product claims in advertisements to influence consumer purchase decisions. Sport beverages, in particular, often tout health benefits and performance claims. Unfortunately, some sport beverage claims may be false or misleading, persuading consumers to erroneously purchase products due to incorrect information. The Federal Trade Commission (FTC) prohibits any deceptive advertising resulting in a material effect on the consumer. Consumer behaviorists, however, rarely consider the FTC’s guidelines in measuring consumer deception. Therefore, drawing upon consumer behavior theory and the FTC’s guidelines, this paper examines the material effects of deceptive sport beverage advertisements. The authors identify and define three types of materiality: cognitive, affective, and behavioral. Employing a within-subjects experimental design, two studies were conducted: print and Facebook. The results reveal both types of deceptive advertisements had positive and statistically significant effects on all three types of materiality. These findings substantiate the determination of a violation by the National Advertising Division (NAD) and support further investigation of deceptive sport beverage advertising and marketing practices by the FTC.
A Novel Approach Using Adaptive Neuro Fuzzy Based Droop Control Standalone Microgrid In Presences of Multiple Sources

In this paper, a novel Q/P droop control strategy for regulating the voltage and frequency in Standalone micro grid with multiple renewable sources like solar and wind is presented. The frequency and voltage control strategy is applied to a Standalone micro grid with high penetration of intermittent renewable generation system. Adaptive Neuro-Fuzzy logic Interface system (ANFIS) controller is used for frequency and voltage control for Renewable generation system. Battery energy storage system (BESS) is used to generate nominal system frequency instead of using the synchronous generator for frequency control strategy. A synchronous generator is used to maintain the state of charge (SOC) of the BESS, but it has limited capacity. For Voltage control strategy, we proposed reactive power/active power (Q/P) droop control to the conventional reactive power controller which provides voltage damping effect. The induced voltage fluctuations are reduced to get nominal output power. The proposed model is tested on different cases and results show that the proposed method is capable of compensating voltage and frequency variations occurring in the micro grid with minimal rated synchronous generator. ©2020. CBIORE-IJRED. All rights reserved
Alagille syndrome with moyamoya disease

We report a 5 year old male child who presented with a history of progressive jaundice since infancy and generalized pruritus. He was also found to have typical triangular facies, posterior embryotoxon on both eyes, peripheral pulmonary stenosis and paucity of bile ducts in liver biopsy. Magnetic resonance angiography of brain showed typical features of moyamoya disease. The child was diagnosed as a case of Alagille syndrome. This particular syndrome with feature of moyamoya disease has been rarely reported.
Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes

Abstract The Gaussian graphical model has been a popular tool for investigating the conditional dependency structure between random variables by estimating sparse precision matrices. The estimated precision matrices can be mapped into networks for visualization. However, the ability to investigate the conditional dependency structure when a two-level structure exists among the variables is still limited. Some variables are considered as higher-level variables while others are nested in these higher-level variables—the latter are called lower-level variables. For instance, genes are grouped into pathways for particular functions, so that pathways are the higher-level variables and genes within the pathways are the lower-level variables. Higher-level variables are not isolated; instead, they work together to accomplish certain tasks. Therefore, our main interest is to simultaneously explore conditional dependency structures among higher-level variables and among lower-level variables. Given two-level data from heterogeneous classes, we propose a method to jointly estimate the two-level Gaussian graphical models across multiple classes, so that common structures in terms of the two-level conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing higher-level variable factors within classes and then introducing common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantages of our approach using breast cancer patient data. Supplementary materials for this article are available online.
TILoc: Improving the Robustness and Accuracy for Fingerprint-Based Indoor Localization

In WLAN fingerprint-based indoor localization, signal noise in the measurement of received signal strength indicator (RSSI) often results in matching a set of disperse reference points (RPs), leading to unsatisfactory estimation and weak robustness. To mitigate the noise problem, we propose a novel indoor positioning strategy, torus intersection localization (TILoc), aiming to improve the robustness and accuracy of fingerprint-based indoor localization. In the online phase, we design a new type of online RSSI fingerprints by filtering out unstable access points (APs). We use part of robust APs to construct RP torus and take the RPs in the intersection of RP tori as the nearest RPs. For reducing sparse spikes noise, we apply robust principal component analysis (RPCA) to train offline and online fingerprints. In addition, we take the AP’s effect into consideration when we position a target. Our simulation and experiments show that the proposed algorithm outperforms other recent state-of-the-art algorithms in robustness and accuracy.
Relationships Between Women's Glass Ceiling Beliefs, Career Advancement Satisfaction, and Quit Intention

Relationships Between Women’s Glass Ceiling Beliefs, Career Advancement Satisfaction, and Quit Intention by Michelle Roman MA, California State University, 2003 BA, Florida International University, 2000 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Organizational Psychology Walden University May 2017 Abstract Research on the glass ceiling shows that women may encounter obstacles in their pursuit of high-level management positions. The purpose of this quantitative study was to test the explanatory style theoretical framework by examining relationships between women’s glass ceiling beliefs, career advancement satisfaction, and quit intention and to determine whether satisfaction with career advancement opportunities mediated the relationshipResearch on the glass ceiling shows that women may encounter obstacles in their pursuit of high-level management positions. The purpose of this quantitative study was to test the explanatory style theoretical framework by examining relationships between women’s glass ceiling beliefs, career advancement satisfaction, and quit intention and to determine whether satisfaction with career advancement opportunities mediated the relationship between glass ceilings beliefs and quit intention. Data were collected from 179 working women in the public or private sector and women who exited the public or private sector job market within the past 5 years via Web-based surveys. Glass ceiling beliefs were assessed using the Career Pathways Survey (CPS), career advancement satisfaction was assessed using the Career Satisfaction Measure, and quit intention was assessed using the Intention to Quit Scale and data were analyzed using multiple regression and correlational statistical techniques. Findings indicated significant relationships between the principal variables. Results also showed that career advancement satisfaction had a significant mediating effect on denial, resilience, and acceptance glass ceiling beliefs and quit intention. Findings may be used to help women understand how their glass ceiling beliefs and career satisfaction drivers influence their reaction to workplace events and may be used by employers to implement proactive retention strategies.
HRCE: Detecting Food Security Events in Social Media

Analyzing food security events shared on social networks not only helps people deepen their understanding of food security events, but also helps managers cope with these events. In this paper, we propose a model that utilizes task-specific features and a deep learning model to detect food security events from tweets, called HRCE. Specifically, the proposed model leverages a hierarchical Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) that takes word embeddings as inputs, and combines contextual embeddings to identify food security events from social media. We collected a novel food security related dataset from Twitter, and manually annotated 2,418 tweets. We conducted experiments on this dataset and concluded that HRCE outperforms baseline methods in terms of precision, recall and F1-score.
Respiratory Health and Urinary Trace Metals among Artisanal Stone-Crushers: A Cross-Sectional Study in Lubumbashi, DR Congo

Background: Thousands of artisanal workers are exposed to mineral dusts from various origins in the African Copperbelt. We determined the prevalence of respiratory symptoms, pulmonary function, and urinary metals among artisanal stone-crushers in Lubumbashi. Methods: We conducted a cross-sectional study of 48 male artisanal stone-crushers and 50 male taxi-drivers using a standardized questionnaire and spirometry. Concentrations of trace metals were measured by Inductively Coupled - Plasma Mass Spectrometry (ICP-MS) in urine spot samples. Results: Urinary Co, Ni, As, and Se were higher in stone-crushers than in control participants. Wheezing was more prevalent (p = 0.021) among stone-crushers (23%) than among taxi-drivers (6%). In multiple logistic regression analysis, the job of a stone-crusher was associated to wheezing (adjusted Odds Ratio 4.45, 95% Confidence Interval 1.09–18.24). Stone-crushers had higher values (% predicted) than taxi-drivers for Forced Vital Capacity (105.4 ± 15.9 vs. 92.2 ± 17.8, p = 0.048), Forced Expiratory Volume in 1 Second (104.4 ± 13.7 vs. 88.0 ± 19.6, p = 0.052), and Maximum Expiratory Flow at 25% of the Forced Vital Capacity (79.0.1 ± 20.7 vs. 55.7 ± 30.1, p = 0.078). Conclusion: Stone-crushers were more heavily exposed to mineral dust and various trace elements than taxi-drivers, and they had a fourfold increased risk of reporting wheezing, but they did not have evidence of more respiratory impairment than taxi-drivers.
Mental Health Promotion Behaviors Associated With a 6-Month Follow-Up on Job-Related Mood Among Japanese Workers

Job stress and death from overwork (Karoshi) are serious health concerns in Japan. This observational study examined the relationship between baseline mental health promotion behaviors (MHPBs; physical activity, weekday cultural activities, enriching activities during vacations, interpersonal communication, relaxation, volunteering, interest and participation in new activities, and group affiliation) and condition of job-related mood (vigor, anger–irritability, fatigue, anxiety, and depression) at a 6-month follow-up to promote the resolution of mental health problems among Japanese employees of small- to medium-sized enterprises. Baseline and follow-up surveys were conducted with N = 635 participants. Participants reported their current engagement in MHPBs and current level of job-related mood as vigor (a positive mood state in the form of energy and enthusiasm for the job), anger–irritability, fatigue, anxiety, and depression. Structural equation modeling showed MHPBs to predict vigor, fatigue, and depression at both baseline and follow-up. Baseline MHPBs and job-related mood were carried over to a 6-month follow-up. Baseline job-related mood was found to mediate the relationship between baseline MHPBs and follow-up job-related mood. These findings suggest that engagement in MHPBs has contributed to improving job-related mood, especially vigor in Japanese workers, and may be applicable in other international settings.
The assessment of shoulder instability

We have developed a 12-item questionnaire for completion by patients presenting with shoulder instability. A prospective study of 92 patients was undertaken involving two assessments, approximately six months apart, performed in an outpatient department. Each patient completed the new questionnaire and the SF36 form. An orthopaedic surgeon completed the Constant shoulder score and the Rowe assessment. The new questionnaire and the Rowe clinical score each achieved a large standardised effect size (≥0.8) and compared favourably with relevant items on the SF36. By contrast, the Constant score barely registered any effect, confirming that it may be relatively insensitive to changes in clinical status for this particular condition. The questionnaire provides a measurement of outcome for shoulder instability which is short, practical, reliable, valid and sensitive to changes of clinical importance.
Nursing Students’ Satisfaction with Their Clinical Learning Environments

Clinical placement is a vital part of nursing students’ education. The clinical learning environments consider the first place of professional practice for nurses and student opinion contribute to its improvement. The aim of the current study was to assess nursing student's level of satisfaction with their clinical learning environment. A descriptive, cross sectional research design was used. A convenient sample of 400 nursing students enrolled from Faculty of Nursing. This study was conducted at the Faculty of Nursing, Menoufia University. Clinical Learning Environment Inventory (CLEI) used to measure nursing student's satisfaction with their clinical learning environments. The mean score of the total CLEI scale was 118.90 (54.86) which indicate a medium level of satisfaction. The satisfaction subscale perceived by the nursing students as the most important domain 23.68(9.98) of clinical learning environment. While, the least important domain perceived by nursing students was individualization subscale 14.42 (7.87). Nursing students have a medium level of satisfaction with their clinical learning environments. Assessment of nursing student's level of satisfaction plays an important role in the education process, thus it is necessary to assess the learning environment from different perceptions. Collaboration between the nursing educational institutions and health care agencies is essential to ensure the availability of effective clinical learning environment, which meet the needs of undergraduate nursing students.
Design Strategy for Self-Healing Epoxy Coatings

Self-healing strategies including intrinsic and extrinsic self-healing are commonly used for polymeric materials to restore their appearance and properties upon damage. Unlike intrinsic self-healing tactics where recovery is based on reversible chemical or physical bonds, extrinsic self-healing approaches rely on a secondary phase to acquire the self-healing functionality. Understanding the impacts of the secondary phase on both healing performance and matrix properties is important for rational system design. In this work, self-healing coating systems were prepared by blending a bio-based epoxy from diglycidyl ether of diphenolate esters (DGEDP) with thermoplastic polyurethane (TPU) prepolymers. Such systems exhibit polymerization induced phase separation morphology that controls coating mechanical and healing properties. Structure–property analysis indicates that the degree of phase separation is controlled by tuning the TPU prepolymer molecular weight. Increasing the TPU prepolymer molecular weight results in a highly phase separated morphology that is preferable for mechanical performances but undesirable for healing functionality. In this case, diffusion of TPU prepolymers during healing is restricted by the epoxy network rigidity and chain entanglement. Low molecular weight TPU prepolymers tend to phase mix with the epoxy matrix during curing, resulting in the formation of a flexible epoxy network that benefits TPU flow while decreasing Tg and mechanical properties. This work describes a rational strategy to develop self-healing coatings with controlled morphology to extend their functions and tailor their properties for specific applications.
Hypercholesterolemia Accelerates Both the Initiation and Progression of Angiotensin II-induced Abdominal Aortic Aneurysms

Objective This study determined whether hypercholesterolemia would contribute to both the initiation and progression of angiotensin (Ang)II-induced abdominal aortic aneurysms (AAAs) in mice. Methods and Results To determine whether hypercholesterolemia accelerates the initiation of AAAs, male low-density lipoprotein (LDL) receptor -/- mice were either fed one week of Western diet prior to starting AngII infusion or initiated Western diet one week after starting AngII infusion. During the first week of AngII infusion, mice fed normal diet had less luminal expansion of the suprarenal aorta compared to those initiated Western diet after the first week of AngII infusion. The two groups achieved comparable luminal dilation on week 2 through week 6 of AngII infusion as monitored by ultrasound. To determine whether hypercholesterolemia contributed to the progression of established AAAs, male LDL receptor -/- mice were fed Western diet and infused with AngII for 4 weeks. Mice with established AAAs were then stratified into two groups based on luminal diameters measured by ultrasound. While AngII infusion was continued for another 8 weeks in both groups, mice in one group were continuously fed Western diet, but diet in the other group was switched to normal laboratory diet. In the latter group, plasma cholesterol concentrations were reduced rapidly to approximately 500 mg/dl within one week after the diet was switched from Western diet to normal laboratory diet. Luminal expansion progressed constantly in mice continuously fed Western diet, whereas no continuous expansion was detected in mice that were switched to normal laboratory diet. Conclusions Hypercholesterolemia accelerates both the initiation of AAAs and progression of established AAAs in AngII-infused male LDL receptor -/- mice. Clinical Relevance Hypercholesterolemia is modestly associated with AAAs in observational or retrospective clinical studies. It is not feasible to study whether hypercholesterolemia contributes to the initiation of AAAs or progression of established AAAs in human. This study using AngII-induced AAA mouse model provides solid evidence that hypercholesterolemia contributes to both the initiation and progression of AAAs, supporting that statin therapy at any stage of AAA development may be beneficial to hypercholesterolemic patients with AAAs.
Algorithms, applications and systems towards interpretable pattern mining from multi-aspect data

How do humans move around in the urban space and how do they differ when the city undergoes terrorist attacks? How do users behave in Massive Open Online courses~(MOOCs) and how do they differ if some of them achieve certificates while some of them not? What areas in the court elite players, such as Stephen Curry, LeBron James, like to make their shots in the course of the game? How can we uncover the hidden habits that govern our online purchases? Are there unspoken agendas in how different states pass legislation of certain kinds? At the heart of these seemingly unconnected puzzles is this same mystery of multi-aspect mining, i.g. , how can we mine and interpret the hidden pattern from a dataset that simultaneously reveals the associations, or changes of the associations, among various aspects of the data (e.g., a shot could be described with three aspects, player, time of the game, and area in the court)? Solving this problem could open gates to a deep understanding of underlying mechanisms for many real-world phenomena. While much of the research in multi-aspect mining contribute broad scope of innovations in the mining part, interpretation of patterns from the perspective of users (or domain experts) is often overlooked. Questions like what do they require for patterns, how good are the patterns, or how to read them, have barely been addressed. Without efficient and effective ways of involving users in the process of multi-aspect mining, the results are likely to lead to something difficult for them to comprehend. This dissertation proposes the M^3 framework, which consists of multiplex pattern discovery, multifaceted pattern evaluation, and multipurpose pattern presentation, to tackle the challenges of multi-aspect pattern discovery. Based on this framework, we develop algorithms, applications, and analytic systems to enable interpretable pattern discovery from multi-aspect data. Following the concept of meaningful multiplex pattern discovery, we propose PairFac to close the gap between human information needs and naive mining optimization. We demonstrate its effectiveness in the context of impact discovery in the aftermath of urban disasters. We develop iDisc to target the crossing of multiplex pattern discovery with multifaceted pattern evaluation. iDisc meets the specific information need in understanding multi-level, contrastive behavior patterns. As an example, we use iDisc to predict student performance outcomes in Massive Open Online Courses given users' latent behaviors. FacIt is an interactive visual analytic system that sits at the intersection of all three components and enables for interpretable, fine-tunable, and scrutinizable pattern discovery from multi-aspect data. We demonstrate each work's significance and implications in its respective problem context. As a whole, this series of studies is an effort to instantiate the M^3 framework and push the field of multi-aspect mining towards a more human-centric process in real-world applications.
Synthetic Minority Oversampling Technique pada Averaged One Dependence Estimators untuk Klasifikasi Credit Scoring

Bank or financial institution is a business entity whose activities are collecting funds from the public in the form of deposits and channeling them to the public in the form of credit and or other forms. In credit financing problems often occur and one of the problems faced in credit assessment is imbalance class data sets or dataset class imbalances. This problem can be overcome by resampling method, namely by using Oversampling, undersampling and hybrids that combine the two sampling approaches. This research proposes the method of applying SMOTE or Synthetic Minority Oversampling Technique on Averaged One Dependence estimators (AODE) to improve the performance of the accuracy of the credit rating classification on German Credit Creditetsets. The results of this experimental study on the GermanCredit dataset with the classification method without the Resampling process on 13 classifiers produce an average performance value of 70%. The results of the classification with classification techniques that apply the SMOTE method on the AODE algorithm can increase the accuracy performance by 5.5% with an accuracy value of 0.817 or 81.69%. While the classification technique that applies the SpreadSubSample + AODE method decreased by 0.041 or 4.1% but still higher than the accuracy value of other methods with an accuracy value of 0.723 or 72.33%. The researcher concludes that by applying the Resampling technique with the SMOTE method on the AODE algorithm can increase the value of accuracy performance effectively on the imbalance class used for credit scoring or credit rating on GermanCredit datasets.