Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing E-contact to Reduce Intergroup Anxiety and Increase Positive Attitudes Towards Individuals Who Identify as Bisexual

ABSTRACT Bisexual individuals are invisible sexual minorities, who face prejudice and “double discrimination” from both heterosexual and sexual minority communities. Despite this, little empirical research has examined bisexual prejudice reduction. To address this, the current study tested an E-contact intervention – a highly structured, text-based, online interaction tool – to reduce bisexual prejudice. Heterosexual participants (N = 170) were randomly allocated to an intergroup (E-contact) or intragroup (control) interaction, with either a male or female interaction partner. Findings indicated that intergroup E-contact resulted in less intergroup anxiety compared to an intragroup interaction, and that this effect was more pronounced when the bisexual interaction partner was male for heterosexual men, but not for heterosexual women. Although E-contact had no direct effect on tolerance toward bisexual individuals or the perceived stability of bisexuality as a sexual orientation, findings revealed that reduced intergroup anxiety mediated the effect of E-contact on these outcome variables. This result was found in the intergroup dynamic of heterosexual men interacting with bisexual men, providing an important experimental demonstration of the potential for harnessing an E-contact intervention to reduce affective prejudice toward bisexual individuals.
Interocular Phase Disparity Tuning of Binocular Contrast Summation Depends on Carrier Spatial Frequency and Orientation

SIGNIFICANCE Binocular summation is a sensitive metric of binocular integration. As such, characterization of the mechanisms underlying binocular summation is a key step in translating and applying this knowledge to abnormal binocular systems afflicted with strabismus and amblyopia. PURPOSE Computational models of binocular summation have advocated the operation of mechanisms sensitive to the interocular phase disparity of first-order carrier gratings. This study investigated if such generalization depended on carrier spatial frequency and orientation. METHODS Monocular and binocular contrast detection thresholds were measured in nine observers with normal binocular vision. Stimuli comprised Gabor targets presented with one of three spatial frequencies (1, 3, and 9 cycles per degree [cpd]), two orientations (horizontal and vertical), and five interocular phase disparities (0, ±π/2, ±π radians). Horizontal and vertical fixation disparities were measured for each binocular threshold condition. Binocular summation ratios were computed by dividing the mean monocular detection threshold by the respective binocular detection threshold. RESULTS Binocular summation ratio varied significantly with interocular phase disparity for the 1- and 3-cpd horizontal and vertical gratings. Phase dependency was reduced with the 9-cpd horizontal grating and absent for the 9-cpd vertical grating, even though binocular summation ratio exceeded predictions of probability summation. Computational modeling that incorporated the variability of fixation disparity into a vector summation model predicted a reduction in peak binocular summation ratio with increasing carrier spatial frequency but did not account for the reduction of phase sensitivity noted with the 9-cpd stimulus. CONCLUSIONS Binocular summation magnitude is less dependent on interocular phase disparities for carrier spatial frequencies that exceed 3 cpd, especially with vertical gratings. Although vergence variability due to fixation disparities contributes to the overall reduction in binocular summation magnitude with increasing carrier spatial frequency, it does not provide a complete account for the lack of interocular phase disparity tuning noted with high grating spatial frequencies.
Participation in cost-offset community-supported agriculture by low-income households in the USA is associated with community characteristics and operational practices

Abstract Objective: Subsidised or cost-offset community-supported agriculture (CO-CSA) connects farms directly to low-income households and can improve fruit and vegetable intake. This analysis identifies factors associated with participation in CO-CSA. Design: Farm Fresh Foods for Healthy Kids (F3HK) provided a half-price, summer CO-CSA plus healthy eating classes to low-income households with children. Community characteristics (population, socio-demographics and health statistics) and CO-CSA operational practices (share sizes, pick up sites, payment options and produce selection) are described and associations with participation levels are examined. Setting: Ten communities in New York (NY), North Carolina (NC), Vermont and Washington states in USA. Participants: Caregiver–child dyads enrolled in spring 2016 or 2017. Results: Residents of micropolitan communities had more education and less poverty than in small towns. The one rural location (NC2) had the fewest college graduates (10 %) and most poverty (23 %) and poor health statistics. Most F3HK participants were white, except in NC where 45·2 % were African American. CO-CSA participation varied significantly across communities from 33 % (NC2) to 89 % (NY1) of weeks picked up. Most CO-CSA farms offered multiple share sizes (69·2 %) and participation was higher than when not offered (76·8 % v. 57·7 % of weeks); whereas 53·8 % offered a community pick up location, and participation in these communities was lower than elsewhere (64·7 % v. 78·2 % of weeks). Conclusion: CO-CSA programmes should consider offering a choice of share sizes and innovate to address potential barriers such as rural location and limited education and income among residents. Future research is needed to better understand barriers to participation, particularly among participants utilising community pick up locations.
Association of social determinants, multimorbidity, and functional status with mortality after hospitalization for pneumonia in South Korea

Previous research found that functional status and unfavorable social determinants of health were associated with readmissions after pneumonia. Given the substantial disease burden of pneumonia in older adults, understanding how different degrees of social, functional, and multimorbidity factors affect prognosis would be crucial to deliver patient-centered care for optimizing recovery. This study aimed to (1) identify unique clusters of older adults with pneumonia that share social, functional, and multimorbidity factors and (2) examine their associations with 6-month mortality and functional status. We hypothesized that the presence of social support would mitigate the risk of 6-month mortality.
Association between poor sleep quality and emotional eating in US Latinx adults and the mediating role of negative emotions

ABSTRACT Introduction Poor sleep quality is associated with negative emotions and may modulate emotional eating (EE). However, this has not been studied among US Latinx adults, a group experiencing sleep disparities. Objective To examine the association between sleep quality and EE in Latinx adults and explore the mediating role of negative emotions. Methods This cross-sectional analysis used data from the Latino Health and Well-being Study. Sleep quality was measured with the Pittsburgh Sleep Quality Index. EE was measured with the EE subscale of the Three Factor Eating Questionnaire R18-V2 (categorized as no EE, low EE, and high EE). Negative emotions were measured via a composite score that included depression, anxiety, and perceived stress. Poisson regression models with robust variance errors estimated prevalence ratios (PR) and 95% confidence intervals (CI). Mediation was evaluated with the Karlsson-Holm-Breen method. Results More individuals with poor (vs. good) sleep quality experienced high EE (39.1% vs. 17.9%). Individuals with poor (vs. good) sleep quality were more likely to experience high EE vs. no EE (total effect = 1.74; 95% CI = 1.34, 2.26). Controlling for negative emotions, the effect of poor sleep on high EE was reduced to 1.23 (95% CI = 0.92, 1.65), leaving an indirect effect of 1.41 (95% CI = 1.25, 1.60); 62.6% of the effect was explained by negative emotions. Conclusion Poor sleep quality was associated with high EE in US Latinx adults and negative emotions partially mediated this relationship. Longitudinal studies are needed. Interventions and clinical programs should concomitantly address sleep quality and negative emotions to help prevent dysfunctional eating behaviors.
Event-Triggered Distributed Average Tracking Control for Lipschitz-Type Nonlinear Multiagent Systems

This article investigates the event-triggered distributed average tracking (ETDAT) control problems for the Lipschitz-type nonlinear multiagent systems with bounded time-varying reference signals. By using the state-dependent gain design approach and event-triggered mechanism, two types of ETDAT algorithms called: 1) static and 2) adaptive-gain ETDAT algorithms are developed. It is the first time to introduce the event-triggered strategy into DAT control algorithms and investigate the ETDAT problem for multiagent systems with Lipschitz nonlinearities, which is more practical in real physical systems and can better meet the needs of practical engineering applications. Besides, the adaptive-gain ETDAT algorithms do not need any global information of the network topology and are fully distributed. Finally, a simulation example of the Watts–Strogatz small-world network is presented to illustrate the effectiveness of the adaptive-gain ETDAT algorithms.
Computational aero-acoustic modelling of external rear-view mirrors on a mid-sized Sedan

Ever-rising fuel costs necessitate design of fuel-efficient vehicles. Consequently, modern vehicle manufacturers are focused on designing low aerodynamic drag vehicles which would in-turn reduce the fuel consumption. This study analyses the contribution of external rear-view mirrors to the total drag force and the overall sound pressure level at the A, B and C pillars, while optimising the external rear-view mirror design accordingly. Solid Works renditions of external rear-view mirror models mounted on a reference luxury sedan were analysed using a commercially available computational fluid dynamic package ANSYS FLUENT. A different approach was followed to carry out the empirical flow visualisation and predict sound pressure levels. The aerodynamic characterisation of the vehicle was done utilising the widely used shear stress transport turbulence model, while the analysis of wind noise and the contributing vortices employed a large eddy simulation. This approach significantly reduced computational time without compromising on accuracy.
ScholarWorks@UTEP Refined Moderation Analysis with Binary Outcomes Refined Moderation Analysis with Binary Outcomes REFINED MODERATION ANALYSIS WITH BINARY OUTCOMES Proposed Method (Reﬁned Moderation Analysis with Binary Outcomes) An Illustrative Example using the Wart dataset

With the growing interest in personalized or precision medicine, it is indispensable that moderation analysis which is primarily related to the study of diﬀerential treatment ef-fects among patients with diﬀerent characteristics, also serves as the bedrock for precision medicine is taken more seriously. Concerning moderation analysis with binary outcomes, we start with an interesting observation, which shows that heterogeneous treatment eﬀects could be equivalently estimated via a role exchange between the outcome and the treatment variable. The result holds for both experimental data and observational data, yet with an important diﬀerence in interpretation. Two estimators of moderating eﬀects corresponding to two GLM models can be obtained. We combine the two models into a single model and employ the GEE approach to simultaneously obtain parameter estimates associated with the moderating eﬀects (interaction terms), on which basis of reﬁned inference can be made. The improved eﬃciency is helpful in addressing the lack-of-power problem that is common in the search for moderators. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial concerning wart treatment. Essentially, the new revelation about the ‘role swapping’ technique can be useful by oﬀer-ing more ﬂexible and computational ease in scenarios where direct modeling of interactions encounters diﬃculties and becomes inconvenient, owing to modeling complexity, numerical diﬃculty, or unavailability of implementation.
A Survey of Agile Transition Models

Nowadays, since business environment is highly dynamic, software necessities are continuously being improved in order to meet the needs of modern industrialized world. Therefore, IT organizations seek for a quick way of software delivery and for adapting to the necessary technological changes. From this ideal viewpoint, traditional plan-driven developments lag behind to overcome these conflicts. The purpose of this chapter is to present the existing models and frameworks which guide organizations to adopt agile methods. This may help organizations to follow professionals' suggestions during their migration from traditional systems to agile development.
The First Amendment, Religious Freedom, and Public Schools in the South

Abstract This study was designed to explore preservice elementary teacher understanding of The First Amendment and religious freedom in public schools in the Southeastern United States. Participants of the study were 160 preservice elementary teachers enrolled in the teacher preparation programs of 3 universities across 2 states located in a region commonly referred to as the “Bible Belt. ” Data was collected via an electronic survey containing questions and scenarios requiring short responses, and field notes were recorded during class discussions of the topic. Results indicated that preservice elementary teachers possessed some basic understanding of The First Amendment and that public educators are acting government agents of the state. Yet, they demonstrated a dearth in understanding of their professional duties in accordance with this law, specifically with regards to the Establishment Clause.
Deep learning offering resilience from trending cyber-attacks, a review

During the Covid-19 pandemic world has witnessed the rise of cyber-attacks, especially during the Lockdown time course announced by the countries throughout the world, when almost every aspect of life changed the routine from offline to online. Protecting and securing information resources during pandemics has been a top priority for the modern computing world, with databases, banking, E-commerce and mailing services, etc. being the eye-catching credentials to the attackers. Apart from cryptography, machine learning and deep learning can offer an enormous amount of help in testing, training, and extracting negligible information from the data sets. Deep learning and machine learning have many methods and models in the account to detect and classify the different versions of cyber-attacks occasionally, from the datasets. Some of the most common deep learning methods inspired by the neural networks are Recurrent Neural Networks, Convolutional Neural Networks, Deep Belief Networks, Deep Boltzman Networks, Autoencoders, and Stacked Auto-encoders. Also counting machine learning algorithms into the account, there is a vast variety of algorithms that are meant to perform classification and regression. The survey will provide some of the most important deep learning and machine learning architectures used for Cyber-security and can offer protective services against cyber-attacks. The paper is a survey about various categories of cyber-attacks with a timeline of different attacks that took place in India and some of the other countries in the world. The final section of the report is about what deep learning methods can offer for developing and improving the security policies and examining vulnerabilities of an information system.
Design and Development of Controller Based Automatic Ground Cleaning Robot

Today's households are growing more sophisticated and automated. Individuals benefit from home mechanization since it provides them with additional options. The rationale for planning and executing Automatic Floor Cleaning Robot Autonomous sand manual using Phone Application. Rather than using a manual cleaning cycle, programmed floor cleaning robots are meant to make the cleaning cycle easier. The primary goal of this assignment is to plan and execute an Automatic Floor Cleaning Robot using Arduino UNO, Bluetooth module, Power semiconductors, Motor ShieldL293D, Water pump, Servomotor, and to achieve the goal of this enterprise. This project will include few client-friendly initiatives. This paper coordinates the advancement of a cleaner programmable platform. Currently, a significant emphasis is placed on the field of mechanical technology in order to reduce human endeavours. The primary goal of this research work is to develop a cleaner that is totally programmed to do dry and wet cleaning as well as UV cleansing. Cleaners with only a few uses are now competing on the market. The proposed model uses Arduino because of its low cost and ease of use. The proposed cleaning robot will be a component for creating a convenient lifestyle by resolving challenges in conventional floor cleaning strategies.
Retinal Malady Classification Using AI: A novel ViT-SVM combination architecture

Macular Holes, Central serous retinopathy and Diabetic Retinopathy are some of the most widespread maladies of the eyes responsible for either partial or complete vision loss, thus making it clear that early detection of the mentioned defects is detrimental for the well-being of the patient. This study intends to introduce the application of Vision Transformer and Support Vector Machine based hybrid architecture (ViT-SVM) and analyse its performance to classify the Optical Coherence Topography (OCT) Scans with the intention to automate the early detection of these retinal defects.
Chromatic dispersion measurement of EDFA using optical sampling oscilloscope

Summary form only given. In this report, we directly measured relative group delay time of a highly Er-doped EDFA (Er-doped fibre amplifier). (MFD: 6.9 /spl mu/m, Er/sup 3+/ density: 1760 ppm, Al density: 13800 ppm, EDF length: 20 m) under 1480-nm pumping for the first time, to our knowledge, using an optical sampling oscilloscope and an optical short-pulse generator. We also derived its dispersion.
A Multilayered Adaptive Recurrent Incremental Network Model for Heterogeneity-Aware Prediction of Derived Remote Sensing Image Time Series

Catastrophic forgetting of previously acquired knowledge is a major setback suffered by the neural networks (NNs) when these are trained on tasks in sequential fashion. The NN variants, including the deep network models, as commonly used in remote sensing data prediction are also not free from this limitation. The issue becomes more prominent when the prediction is performed over data collected from spatial zones with a large degree of subregional variations or heterogeneity. In order to tackle this problem, in this article, we propose a multilayered adaptive recurrent incremental network (MARINE) model, offering a spatial heterogeneity-aware self-adaptation scheme that resolves the catastrophic forgetting issue in an autonomous manner. Typically, the proposed MARINE is equipped with an intrinsic mechanism of clustering the spatial subregions as per their heterogeneity levels and auto-constructing the recurrent network layers in an ensemble fashion so that the knowledge acquired about one group of heterogeneity level does not overwrite that acquired about other groups. With respect to spatio-temporal prediction of normalized difference vegetation index (NDVI) time series, as derived from MODIS Terra satellite remote sensing imagery, we demonstrate that our proposed MARINE achieves competitive results when compared with the state-of-the-arts. More significantly, in the presence of higher degree of spatial heterogeneity, MARINE outperforms others by better avoiding the catastrophic forgetting issue.
Internet of Things (IoT) based Comprehensive testing of 5G network for Specific Absorption Rate (SAR)

Our daily lives are filled with a wide variety of electromagnetic waves . To ensure that radio products are safe to use, they must undergo RF exposure evaluation. RF exposure evaluation, EMF measurements, or SAR testing can be used to conduct this evaluation. Time-consuming SAR measurement methods are used by the SAR standard. Mobile wireless device compliance test lead times are incompatible with industry needs and are becoming a significant pain as 5G communications proliferate. When it comes to peak spatialaverage SAR evaluation, this study proposes a new technology aimed to solve this challenge and provide instantaneous and accurate peak SAR evaluation.
Coupled fluid–solid interaction under shock wave loading

This paper considers the treatment of fluid–solid interaction problems under shock wave loading, where the solid experiences large bulk Lagrangian displacements. This work addresses the issues associated with using a level set as a generalized interface for fluid–solid coupling where the fluid–solid interface is embedded in an unstructured fluid grid. We outline the formulation used for the edge‐based unstructured‐grid Euler solver. The identification of the fluid–solid interface on the unstructured fluid mesh uses a super‐sampled ℒ︁2 projection technique, which in conjunction with a Lagrangian interface position, permits fast identification of the interface and the concomitant imposition of boundary conditions. The use of a narrow‐band approach for the identification of the wetted interface is presented with the details of the construction of interface conditions. A series of two and three‐dimensional shock‐body computations are presented to demonstrate the validity of the current approach on problems with static and dynamic interfaces, including projectile/shock interaction simulations. Copyright © 2010 John Wiley & Sons, Ltd.
Modeling Disease Progression Flexibly with Nonlinear Disease Structure via Multi-task Learning

Alzheimer’s Disease (AD) is the most common dementia characterized by loss of brain function. Multi-tasking learning methods have been widely used to predict cognitive performance and select important imaging biomarkers in AD research. The temporal smoothness assumption, prevalent for modeling AD progression, means the difference between cognitive scores at two consecutive time points is relatively small. However, it’s not appropriate due to the presence of sample disturbance and the effectiveness of drug therapy. In addition, many multi-task learning methods select discriminative feature subset from MRI features, assuming that correlations between tasks are consistent, which ignores the complex intrinsic correlation structure of tasks. In this paper, we present a multi-task learning framework which utilizes generalized fused Lasso and generalized group Lasso (GFGGL for abbreviation) to model the disease progression with the complex intrinsic nonlinear structures of disease. The proposed framework is more flexible to utilize the inherent nonlinear relation of AD than existing methods for the reason of we represent the intrinsic structure as three correlation matrices which are functions of super parameters. The framework involves (1) two nonlinear structures of disease progression and (2) one nonlinear structure among tasks. An efficient optimization method is designed for the difficult optimization problem due to the presence of three nonsmooth penalties. Extensive experimental results using dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of the proposed method.