Miracle-dz/UniFAR-FTUs huggingface dataset:
Dispositional Active Coping Predicts Patterns of Adolescents’ Cortisol Responsivity in the Context of School-related Stressors

ABSTRACT Objective The goal of this study was to examine the direct and conditional effects of active coping and prior exposure to school-related stressors on cortisol reactivity and recovery in response to an academically salient, social stress task. Method Participants included N= 758 adolescents (50% male; M age = 12.03 years, SD = .49) enrolled in the 7th grade in Title 1 middle schools. Adolescents were predominantly ethnic minorities (62% Hispanic, 12% non-Hispanic White, 11% non-Hispanic Black, 7% Native American, and 8% “other”). Youth completed self-reported assessments of their dispositional use of active coping strategies, prior exposure to school hassles, pubertal status, medication use, and relevant demographic information. In addition, youth engaged in an academically salient group public speaking task adapted for adolescents and provided salivary cortisol sample pre-task, immediately post-task, 15-, and 30-minutes post-task. Results Results from piecewise latent growth curve modeling revealed that active coping independently predicted lower cortisol reactivity to the stress task. Furthermore, active coping was associated with slower cortisol recovery when adolescents reported not having experienced any school hassles in the past three months and faster recovery when having experienced several school hassles in the past three months. Results from multinomial logistic regressions revealed that greater use of active coping strategies was less likely to predict a hyper-reactive pattern of cortisol responding compared to other patterns. Conclusion Findings provide support for active coping as a way to promote adaptive physiological responding to school-related stressors among ethnically diverse youth residing in low-income communities.
Educational digital data analytics: Five research areas and Four Philosophical Divides

The growth of access to digital data in education has led to a massive increase in research utilizing digital data in education over the last several decades. Despite this growth, however, researchers remain unsure of the impact that learning analytics and other research areas are having on the educational landscape, or even how to measure this impact. This theoretical piece situates learning analytics within the larger research area of educational digital data analytics, summarizes different research within this larger research area, and offers a conceptual framework to situate different research within the educational digital data analytics research area that includes four philosophical divides. Suggestions are made for how this understanding of a larger research area within which learning analytics is a subset can be used to evaluate the quality and impact of research.
Conversion of Synthesis Gas to Light Olefins: Impact of Hydrogenation Activity of Methanol Synthesis Catalyst on the Hybrid Process Selectivity over Cr–Zn and Cu–Zn with SAPO-34

Direct conversion of syngas to hydrocarbons occurs over hybrid catalyst mixtures containing methanol synthesis and microporous acid components. In particular, both copper and zinc oxide-based as well as chromium- and zinc-based catalysts are active for methanol synthesis and can be used in the hybrid catalyst process. The choice of methanol synthesis catalyst alters product selectivity and distribution. In particular, reaction products of the Cu–Zn/SAPO-34 system include only saturated hydrocarbons, while the Cr–Zn/SAPO-34 catalyst enables light olefin production directly from syngas. Hydrogenation properties of the methanol synthesis catalyst influence the C3/C2 yield ratios in the hydrocarbon products. We analyze the observed differences of selectivity with respect to olefin hydrogenation activities of the methanol synthesis components and their interaction with SAPO-34 for methanol-to-olefins conversion. A simplified kinetic model for the hybrid system is proposed to describe the observed selectivity p.
Wild Bee Pollinators Foraging in Peanut and Cotton Adjacent to Native Wildflower Strips

Abstract Wild bees are major contributors to pollination of economically important crops. However, widespread habitat conversion to agriculture and pesticide exposure are associated with declines in wild bee abundance and biodiversity. A growing number of studies have investigated the incorporation of a variety of flower species in agroecosystems to augment resource and habitat availability to wild bees and pollination of nearby crops. Here we investigated if wildflower strips containing Gaillardia pulchella Foug. (Asteraceae) in 2018 and G. pulchella, Rudbeckia hirta L. (Asteraceae), and Monarda citriodora Cerv. ex Lag. (Lamiaceae) in 2019 could promote pollinator abundance and pollination of nearby peanut and cotton. We used bee bowls in crop fields to capture bees and identified the pollen on the bees. We captured a total of 291 bees in peanut and 89 bees in cotton that were comprised of 2 families (Apidae and Halictidae) with 10 species represented from these families. The species in peanut were comprised mostly of Melissodes communis Cresson and Melissodes bimaculatus Lepeletier (Apidae). The bee species in cotton was comprised of mostly Lasioglossum reticulatum Robertson (Halictidae), M. communis, and M. bimaculatus. At peak abundance in peanut, 48% of bees bore both G. pulchella and peanut pollen. At peak abundance in cotton, 37% of bees bore 1 or more wildflower strip pollen and cotton pollen. Throughout the season, 62% of the bees captured in cotton had unidentified pollen from surrounding sources. These results indicate that the wildflower buffers had provided pollinators and bee foragers to these crops in early season. By studying bee foraging between crop fields and wildflower strips based on identification of pollen grain on bee bodies, we showed the potential to increase pollination in crop fields through the provision of floral resources throughout the growing season.
Hierarchical Scheme for Vehicle Make and Model Recognition

A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.
Managing patients in the community after spinal injury

Spinal cord injury (SCI) involves damage to any level of the spinal cord and can result in significant disability. It is principally caused by trauma and is treated with long-term rehabilitation, the majority of which takes place in the community. Currently, no comprehensive guidelines exist on the long-term management of SCI in the community. This article, therefore, aims to amalgamate the current literature and provide a guide of how to manage SCI in the community, along with a brief overview of its epidemiology and aetiology.
Comparative Phytochemical and Pharmacological Properties of Commonly Available Tropical Green Seaweeds

ABSTRACT This study analyzed the ethyl acetate-methanol extracts of tropical green seaweeds for phytochemical indicators and antioxidant activities. Compositional analysis of the organic extracts of green seaweeds Ulva lactuca (formerly Ulva fasciata), Halimeda macroloba, Chaetomorpha linum and Chaetomorpha antennina identified the presence of phytoconstituents, such as total phenols, terpenoids, saponins and flavanoids. U. lactuca exhibited higher levels of total phenols (59.8 mg GAE/g), saponins (1.77 %), flavanoids (2.3 %), as well as greater 2, 2-diphenyl-1-picrylhydrazylradical radical quenching activity (DPPH.IC50 0.86 mg/mL). Extracts of U. lactuca and H. macrobola showed promising antimicrobial activities against Vibrio parahaemolyticus. Proton nuclear magnetic resonance fingerprint analyses of the solvent extracts showed that higher proton integrals were present in the downfield regions in U. lactuca, which could be responsible for lowering DPPH activity. The present work supports that the renewable green seaweed U. lactuca could be used to isolate prospective bioactive leads.
Implementing Suicide Risk Screening in Pediatric Medical Patients: Feasible Quality Improvement Projects that Can Save Lives

ABSTRACT With rising youth suicide rates in the US and around the world, medical settings have been leveraged as partners in prevention. Suicide risk screening programs can detect youth who may otherwise pass through the health care setting with unrecognized suicide risk. Outpatient primary care clinics and inpatient medical units are well positioned to intervene to address this critical public health crisis. Through the use of the Plan-Do-Study-Act quality improvement framework, suicide risk screening can be efficiently and effectively implemented in medical settings to identify suicide risk among youth, without overburdening busy healthcare systems.
Match games: exploring the match between novice teachers and induction coaches

ABSTRACT This mixed methods study investigates novice teacher and coach survey responses from a two-year induction programme to learn more about what makes a good match. We qualitatively analyse comments from all novice teachers and coaches who were paired across years and find shared themes of structural, professional, and personality similarities as well as the importance of coaching support prominent throughout novice teacher responses. We also use logistic regression to indicate that novice teachers’ ratings of coaching skills and coaches’ beliefs about the induction programme fitting within vertical professional development were positive and significant predictors of perceptions of being well matched. Findings have implications for induction programmes on how to match their coaches with novice teachers to enhance teacher development.
Output Feedback Controller for a Class of Unknown Nonlinear Discrete Time Systems Using Fuzzy Rules Emulated Networks and Reinforcement Learning

A model-free adaptive control for non-affine discrete time systems is developed by utilising the output feedback and action-critic networks. Fuzzy rules emulated network (FREN) is employed as the action network and multi-input version (MiFREN) is implemented as the critic network. Both networks are constructed using human knowledge based on IF–THEN rules according to the controlled plant and the learning laws are established by reinforcement learning without any off-line learning phase. The theoretical derivation of the convergence of the tracking error and internal signal is demonstrated. The numerical simulation and the experimental system are given to validate the proposed scheme.
NDUFB6 Polymorphism Is Associated With Physical Activity-Mediated Metabolic Changes in Type 2 Diabetes

The rs540467 SNP in the NDUFB6 gene, encoding a mitochondrial complex I subunit, has been shown to modulate adaptations to exercise training. Interaction effects with diabetes mellitus remain unclear. We assessed associations of habitual physical activity (PA) levels with metabolic variables and examined a possible modifying effect of the rs540467 SNP. Volunteers with type 2 (n=242), type 1 diabetes (n=250) or normal glucose tolerance (control; n=139) were studied at diagnosis and subgroups with type 1 (n=96) and type 2 diabetes (n=95) after 5 years. Insulin sensitivity was measured by hyperinsulinemic-euglycemic clamps, oxygen uptake at the ventilator threshold (VO2AT) by spiroergometry and PA by questionnaires. Translational studies investigated insulin signaling and mitochondrial function in Ndufb6 siRNA-treated C2C12 myotubes, with electronic pulse stimulation (EPS) to simulate exercising. PA levels were 10 and 6%, VO2AT was 31% and 8% lower in type 2 and type 1 diabetes compared to control. Within 5 years, 36% of people with type 2 diabetes did not improve their insulin sensitivity despite increasing PA levels. The NDUFB6 rs540467 SNP modifies PA-mediated changes in insulin sensitivity, body composition and liver fat estimates in type 2 diabetes. Silencing Ndufb6 in myotubes reduced mitochondrial respiration and prevented rescue from palmitate-induced insulin resistance after EPS. A substantial proportion of humans with type 2 diabetes fails to respond to rising PA with increasing insulin sensitivity. This may at least partly relate to a polymorphism of the NDUFB6 gene, which may contribute to modulating mitochondrial function. Clinical Trial Registration ClinicalTrials.gov, identifier NCT01055093. The trial was retrospectively registered on 25th of January 2010.
Reflecting on Emotions within VR Mood Worlds

Virtual reality applications can be an effective tool for emotion regulation and therefore support emotional well-being and mental health. However, many VR applications focus on inducing (positive) emotions and there is little research on how users can express their emotions autonomously at home. Thus, there is a need to inquire how emotions can be expressed independently in virtual environments. To that end, this position paper presents VR Mood Worlds, a VR application for ER intervention, that allows users to visualise their emotions and thus supports emotion regulation and emotional well-being.
Dried Robusta Coffee Bean Quality Classification Using Convolutional Neural Network Algorithm

Coffee is a major commodity traded internationally. Most coffee is grown in developing countries and consumed in the industrialized world. The objective of this study is to develop a device that can classify the coffee beans according to its standards, perform the function test specifically in quality classification and determine the cost of materials to be used in this project. Image processing is analyzing and manipulating a digitized photo and a convolutional neural network is an algorithm used in this system to classify each coffee bean. developing an image processing system that classify/s the images of Robusta dried coffee beans into which type of quality. Coffee is still one of the top-tier gross agricultural products in terms of replenishers that is still on-demand on the global market. So as to maintain its peaking momentum various improvements have been made to pick the best quality by image processing innovations of its classifications. With this innovative selection, it boosts more the picking of great quality and classifications of coffee beans. The overall result of function tests showed 93.33% accuracy in classification.
Students’ Debugging Behavior Analysis in Game-Based Learning

As programing became more and more important, people are taking a large amount of work to help students to learn programming skills effectively. This paper applies a programming learning game called May's Journey to fit 5 debugging types including syntax, logical, structure, reasoning, and undefined debugging errors into programming levels. Then we can find out the reason why students make mistakes, and which debugging type would cause the mistakes of other debugging types. And we have 6 findings, (1) This paper proposes a student debugging model to describe how students make debugging errors, which is used for further analysis on student debugging behaviors. (2) This paper proposes to use group mean and with-in group variance based on student debugging model, which finds out the common debugging errors and personal debugging errors. (3) This paper proposes to extract student debugging patterns using Random forest, which identifies student debugging behaviors, so that students who have the same debugging pattern can be trained together. (4) This paper also proposes to use student debugging model-based SVM to extract student performance patterns, which identifies student performance changing over programming levels in terms of a specific debugging type. (5) This paper proposes to apply mean decrease accuracy and mean decrease Gini to identify the effectiveness of debugging types; and (6) this paper proposes to use a classification-based LSTM algorithm to predict debugging errors, which improves the predication accuracy a lot. Experiments and results are also provided to prove that our methods are valid and better.
POLYHYMNIA Mood – Empowering people to cope with depression through music listening

Depression is one of the largest sources of burden of disease in the worldwide and the development of flexible, timely and easily accessible interventions is considered to be a critical direction for the future. Mood Regulation (MR) via music listening may be a viable tool support these aims if people have adequate support to make music selections that underpin healthy MR strategies. We developed a new app (POLYHYMNIA Mood) that automatically generates personalised music playlists for mood elevation and the reduction of depression symptoms and here we provide an overview of POLYHYMNIA Mood and report the results of a preliminary evaluation of its effectiveness and acceptability. Results show that listening to POLYHYMNIA Mood playlists over a period of 4 weeks led to a large reduction in negative affect and a clinically significant reduction in depression symptoms. Whereas these results should be interpreted cautiously due to the small sample size and the lack of control conditions, they provide strong support to our approach.
A Convolution Neural Network with Mixed-Size Kernels for Time-Frequency Characteristics of Motor Imagery

As a paradigm of spontaneous Brain-Computer Interface (BCI), motor imagery electroencephalogram (EEG) has always been a hot topic in BCI and clinical rehabilitation. Many algorithms have been proposed for decoding the motor imagery signals. The algorithm based on the convolutional neural network has shown excellent potential in the task of motor imagery signal classification. However, the existing models are not specific to the characteristics of motor imagery signals, and so, they cannot fully extract the signal features of different rhythms. Moreover, limited by difficulties in the acquisition, the classification effect of the model is network based on mixed-size convolution kernel is designed. The time-frequency graph obtained by Short-time Fourier transform (STFT) is used as input, and Deep Convolutional Generative Adversarial Networks (DCGANs) is used for data enhancement. The results show that the average classification accuracy is 85.7%. Compared with current mainstream classification algorithms, the model presented in this paper has shown high classification accuracy and good robustness.
Participants’ Experience and Adherence in Repeated Measurement Studies Among Office-Based Workers

While diary studies, especially when applying ecological momentary assessment (EMA), are a great way to capture self perceptions to later use as labels for other data, they can be a burden for study participants. To increase their adherence to the study design, it is important to tailor it to their needs and take their feedback into account. This paper reports on a data collection process in a study focused on occupational stress. The data collection is briefly described and the participants’ responses are analysed in terms of adherence. Participants’ feedback was collected at the end of the study and its main themes are summarized. These experiences are compared to the ones in another study focusing on stress and burnout, with a very similar methodological design. Some general conclusions are drawn from both with suggestions on how to best carry out an EMA study.
Detecting Single-Hand Riding with Integrated Accelerometer and Gyroscope of Smartphone

Single-hand cycling poses a safety threat with the decrement of riders’ response capacity. Recognizing risky behavior by prevalently used smartphones could lead to enhanced riding safety. In this work, we propose a single-hand cycling recognition method based on motion data acquired from the three-axis accelerometer and gyroscope integrated into a handlebar-installed smartphone. We conducted a 4-person experiment. The data result demonstrates that motion data of double-hand cycling clearly distinguishes from that of single-hand, revealing the chance to materialize a robust detection tool in smartphones to enable safer biking. For future work, we prepare to redesign the experiment under more sophisticated circumstances with an improved platform, thus scaling this sensing method for real-life usage.
Enhanced Velocity-Driven Particle Swarm Optimization for Evolving Artificial Neural Network

∗ Neural network has been considered as an effective tool for predicting chaotic time series and approximating functions. However, traditional methods for training neural networks have some short-comings, such as slow convergence speed, easy to fall into local optimum and so on. To obtain an efficient performance, an enhanced velocity-driven particle swarm optimization, called EVD-PSO, is proposed in this paper. The proposed algorithm introduces an adaptive method for the parameter φ used in the velocity-driven mechanism, which helps to achieve an effective trade-off between exploration and exploitation. To verify the effectiveness of the proposed algorithm, the algorithm is employed as a new optimize method to train artificial neural networks for nonlinear function approximation problems and chaotic time series prediction. The experimental results demonstrate the effectiveness of the proposed algorithm over other traditional optimization algorithms.
Intelligent Traffic Signal Control with Deep Reinforcement Learning at Single Intersection

In this paper, we apply the Proximal Policy Optimization (PPO) algorithm in intelligent traffic signal control at a single intersection with eight lanes and four signal phases. The optimization goal is to minimize the average waiting time of vehicles so as to improve the traffic efficiency of the intersection. Extensive experiments are conducted in Simulation of Urban MObility (SUMO) to evaluate the performance of the proposed algorithm, and compare it with other classic algorithms including Deep Q-network (DQN), Advantage Actor Critic (A2C) and Fixed Time. Simulation results show that the proposed PPO algorithm outperforms the others under various traffic scenarios to different extent. The performance gain is significant under unbalanced traffic where one direction is saturated while the other is not, and becomes marginal when all the directions are saturated or unsaturated. PPO also demonstrates good portability and robustness over time-varying traffic patterns, while implies it could be a preferable option for implementation in real world intelligent traffic signal control systems.
Detecting forward leaning posture using eSense and developing a posture improvement promoting system

In this study, we propose a method using eSense for detecting forward leaning posture during computer work, and verify its effectiveness. We will also develop a system that prompts the user to improve posture when a forward leaning posture is detected, and verify its effectiveness. In the future, we aim to develop a system that targets not only forward leaning posture but also posture improvement in other parts of the body by utilizing the contents verified in this research.