Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study on The Correlation Between College Students' Self-Management Ability and Online Learning Effectiveness: ——A comparison based on Mainland China and Macao

With the popularity of online training mode, students' online learning effectiveness has been widely concerned by educational practitioners and scholars. From the perspective of college students' self-management ability, this study discussed the correlation between self-management ability and online learning effectiveness. It adopted the method of questionnaire survey in empirical research, with students from universities in mainland China and Macao as samples. Analysis of the questionnaire results shows that there is a positive correlation between them, but the correlation between mainland universities is stronger than it in Macao. According to the research results, this paper analyzed the reasons for the correlation differences and puts forward some suggestions to improve the self-management ability of college students.
Relationship between Trait Anxiety and Academic Procrastination: The Mediating Role of Internet Addiction

Academic procrastination has been a pervasive issue for college students worldwide. Thus, an investigation into factors of it could shed light on future prevention and intervention programs. While studies have explored the predictive role of trait anxiety and Internet addiction, no study ever looked at whether Internet addiction mediated the path between trait anxiety and academic procrastination. This study aims to fill this void. A total of 344 college students from mainland China were recruited and filled out questionnaires tapping these three variables. PROCESS with Bootstrapped mediation analysis showed that Internet addiction was a partial mediator between trait anxiety and academic procrastination. The implication of this study was also discussed.
Teaching presence on students’ perceived engagement with online tasks: Insights from two Hong Kong award-winning teachers’ assessment practices

The explosive growth of online learning has led to an increased demand for qualified teaching presence, in which assessment is a neglected yet crucial component that drives learning. This study refines indicators of assessment as teaching presence by depicting how teachers’ assessment practices of online tasks interact with students’ perceived engagement. The multiple-sourced data was derived from prolonged classroom observations, in-depth semi-structured interviews, and document analysis (syllabus, announcements, discussion postings, uploaded materials) from a purposive sample of two award-winning teachers in a university of Hong Kong. A comparative analysis between one case teacher's Moodle practice and another's Moodle-based Wiki design, revealed the importance of communicating assessment rubrics and time parameters in triggering students’ knowledge of task and strategy. Assessment practices that were interrelated (e.g., one group's change of work online impacting on others’), compulsory (e.g., counting to final grade), and blended (e.g., combining online and classroom-based teacher interventions) showed to influence students’ perceived engagement. However exploratory, the perspectives regarding various assessment indicators and dilemmas on web-based learning, and pedagogical insights that emerged from the specific setting may carry resonance for teachers and researchers in the Hong Kong context and beyond.
Calculation of 3D Coordinates of a Point on the Basis of a Stereoscopic System

Abstract The solution of three-dimensional (3D) coordinate calculation task for a material point is considered. Two flat images (a stereopair) which correspond to the left and to the right viewpoints of a 3D scene are used for this purpose. The stereopair is obtained using two cameras with parallel optical axes. The analytical formulas for calculating 3D coordinates of a material point in the scene were obtained on the basis of analysis of the stereoscopic system optical and geometrical schemes. The detailed presentation of the algorithmic and hardware realization of the given method was discussed with the the practical. The practical module was recommended for the determination of the optical system unknown parameters. The series of experimental investigations were conducted for verification of theoretical results. During these experiments the minor inaccuracies were occurred by space distortions in the optical system and by it discrecity. While using the high quality stereoscopic system, the existing calculation inaccuracy enables to apply the given method for the wide range of practical tasks.
SVEM: A Signal Variation Elimination Model for EEG Emotion Recognition

Motivated by the non-stationarity characteristics of electroencephalograph (EEG) signals, we propose a signal variation elimination model (SVEM) for emotion recognition. The proposed SVEM enables to capture the topological structures of different EEG channels due to the utilized graph neural network (GNN). Two tricks are proposed to reduce signal variations and improve the model generalization. Firstly, the proposed SVEM is pre-trained by a mask-generation supervised learning where we randomly mask several signal channels in GNN and then generate them. Secondly, the proposed SVEM is fine-tuned by incorporating a domain classifier to reduce the distribution shift between the training and testing sets. To further reduce the subject signal variations of the training set, a subject classifier is incorporated in the fine-tuning process of SVEM. The performance of SVEM is evaluated on the real-world dataset SEED. Experiment results demonstrate that the accuracy of SVEM achieves 87% and 71%, on subject-dependent and subject-independent tasks, respectively.
Machine-Learning Based Drug and Supplement Design Platform for Chronic Disease Control

The emerging modern medical technology has provided treatment for a variety of major human diseases (including cancer, cardiovascular and cerebrovascular diseases, diabetes, etc.). However, the long-term control of these chronic diseases remains a challenge. These chronic diseases are the main cause of death and a major burden on public health. One key question in chronic disease management is how to develop efficacious and reliable drugs based on our emerging understanding of disease-related gene expression (from genes to drugs). In contrast, specific medical treatments such as surgery or chemotherapy of cancer often possess a small period over the entire course of chronic disease management, meanwhile the therapy outcomes of chronic diseases also largely depend on the long-term lifestyles of the patients, such as mood and diet. Given the large quantity and complexity of traditional medical data, it is difficult to manually evaluate the impact of diets or nutrition on chronic disease control (from drugs to supplements/nutrition). This project aimed to systematically analyze disease-related gene expression data for drug development and to specify a dietary plan for specific chronic disease recovery from health big data and individualized data of patients. Using multiple gene-to-drug algorithms combined with Random Forest machine learning tools, I have discovered decamethonium bromide as a broad-spectrum anti-cancer drug targeting currently “undruggable” oncogenes and tumor suppressors and investigated honeysuckle as a potential supplement. CCS Concepts· Applied computing · Life and medical sciences · Computational biology
A Survey on Mental Health Detection in Online Social Network

Mental health detection in Online Social Network (OSN) is widely studied in the recent years. OSN has encouraged new ways to communicate and share information, and it is used regularly by millions of people. It generates a mass amount of information that can be utilised to develop mental health detection. The rich content provided by OSN should not be overlooked as it could give more value to the data explored by the researcher. The main purpose of this study is to extract and scrutinise related works from related literature on detection of mental health using OSN. With the focus on the method used, machine learning algorithm, sources of OSN, and types of language used for the mental health detection were chosen for the study. The basic design of this study is in the form of a survey from the literature related to current research in mental health. Major findings revealed that the most frequently used method in mental health detection is machine learning techniques, with Support Vector Machine (SVM) as the most chosen algorithm. Meanwhile, Twitter is the major data source from OSN with English language used for mental health detection. The researcher found a few challenges from the previous studies and analyses, and these include limitations in language barrier, account privacy in OSN, single type of OSN, text analysis, and limited features selection. Based on the limitations, the researcher outlined a future direction of mental health detection using language based on user’s geo-location and mother tongue. The use of pictorial, audio and video formats in OSN could become one of the potential areas to be explored in future research. Extracting data from multiple sources of OSNs with new features selection will probably improve mental health detection in the future. In conclusion, this research has a big potential to be explored further in the future.
Contextualising gender policies: encouraging parity of participation through applied theatre

ABSTRACT International development partnerships are characterised by inherent power relations with community partners often seen as needing assistance in fixing their problems. In this paper, we discuss the limitations of this perspective on the example of gender equality in the Pacific. We explore applied theatre tools to work with a variety of stakeholders to problematise policy issues while exploring different worldviews and knowledges. Our work demonstrates how the voices of community-based organisations can be recognised by governments and donor agencies and how practices of applied theatre can provide tools for designing, reflecting, and advocating for change in joint partnerships.
The Prediction of Urban Road Traffic Congestion by Using a Deep Stacked Long Short-Term Memory Network

Traffic congestion is an overwhelming problem faced by road travelers all over the world. A time-efficient and accurate prediction of upcoming traffic congestion can reduce this problem through enabling the proactive planning of routes. Recent research suggests that prediction accuracy requires the extraction of hidden features of the road network from the historical traffic data. In general, this data is either limited (with a longer sampling time) or not provided by providers. In urban areas, traffic lights, weather conditions, city events, accidents, and people’s habits significantly influence the traffic flow according to the structure of road network. Therefore, a mechanism is required to extract traffic data by scraping images from the route planners’ websites to predict traffic congestion. In this article, we devise such a method and introduce a fuzzy logic and stochastic estimation algorithm to detect congestion levels at the intersections of the road network. We then build a deep stacked long short-term memory network, in combination with online training, for the multipoint future prediction of congestion. We name the proposed model a fuzzy logic and deep learning-based traffic congestion predictor (FDLTCP) and compare the proposed predictor with the gated recurrent unit and stacked auto-encoders. Experimental evaluations demonstrate the effectiveness of FDLTCP, in terms of mean square error and other critical performance metrics, to perform future predictions.
Optimal Time Trajectory Generation and Tracking Control for Over-actuated Multirotors with Large-angle Maneuvering Capability

This paper presents an optimal time trajectory generation method for over-actuated multirotors. Different from underactuated multi-rotors that can only track a 4-D trajectory, over-actuated multi-rotors have the ability to track a 6-D trajectory. The proposed method can generate a 3-degree of freedom(DoF) position trajectory and a 3-DoF attitude trajectory. The method is composed of two steps: first, on the basis of polynomials, position and attitude trajectories satisfying geometric constraints are generated by solving unconstrainted problem in the spatial domain. Second, attitude and position dynamics constraints are added in the temporal domain, in order to achieve large-angle maneuvers. Afterwards, optimal time allocation is achieved by solving a mapping function in the temporal domain. The mapping function describes the mapping relationship between spatial variables and temporal variables. A comparative experiment is presented to verify that time allocated to trajectory by the proposed algorithm is shorter compared to other algorithms, while a tracking controller is constructed for overactuated multirotors. The tracking controller is verified to be feasible for the experimental platform through flight experiment.
Sensor-guided gait-synchronization lower-extremity-exoskeleton for potential application on unilateral knee-injured people

This paper presents a sensor-guided gait-synchronization system to help potential unilateral knee-injured people walk normally with a weight-supported lower-extremity-exoskeleton (LEE). This relieves the body weight loading on the knee-injured leg and synchronizes its motion with that of the healthy leg during the swing phase of walking. The sensor-guided gait-synchronization system is integrated with a body sensor network designed to sense the motion/gait of the healthy leg. Guided by the measured joint-angle trajectories, the motorized hip joint lifts the links during walking and synchronizes the knee-injured gait with the healthy gait by a half-cycle delay. The effectiveness of the LEE is illustrated experimentally. We compare the measured joint-angle trajectories between the healthy and knee-injured legs, the simulated knee forces, and the human-exoskeleton interaction forces. The results indicate that the motorized hip-controlled LEE can synchronize the motion/gait of the combined body-weight-supported LEE and injured leg with that of the healthy leg.
Reconstructing protein interactions across time using phylogeny-aware graph neural networks

Motivation Genes which are involved in the same biological processes tend to co-evolve. Thus, metabolic pathways, protein complexes, and other kinds of protein-protein interactions can be inferred by looking for correlated patterns of gene retention and loss across the tree of life—a technique called phylogenetic profiling. Recent methodological developments on phylogenetic profiling have focused on scalability improvements to take advantage of the rapidly accumulating genomic data. However, state-of-the-art methods assume that the correlation resulting from co-evolving proteins is uniform across all species considered. This is reasonable for interactions already present at the root of the species considered, but less so for ones that emerge in more recent lineages. To address this challenge and take advantage of recent developments in deep learning methods, we introduce a phylogenetic profiling method which processes large gene co-phylogenies using neural networks. Results We show that post-processing conventional phylogenetic profiles using deep neural networks can improve predictions, but requires onerous training on specific phylogenies. Overcoming this limitation by taking the topology of the species tree as an input, Graph Neural Networks are shown to outperform all other methods when interaction detection is not centered on just one species of interest, while also predicting when interactions appeared and in which taxa they are present. Conclusion Graph Neural Networks constitute a promising new approach for phylogenetic profiling. Our work is a first foray into “dynamic phylogenetic profiling”—the reconstruction of pairwise protein interaction across time. Availability All of the code is available on the project Git at https://github.com/DessimozLab/HogProf/tree/master/pyprofiler/notebooks/Graphnet. Datasets used are hosted at http://humap2.proteincomplexes. org/download and https://string-db.org/cgi/download. Contact dmoi@unil.ch
ZAXINONE SYNTHASE 2 regulates growth and arbuscular mycorrhizal symbiosis in rice

Carotenoid cleavage, catalyzed by CAROTENOID CLEAVAGE DIOXYGENASES (CCDs), provides signaling molecules and precursors of plant hormones. Recently, we showed that zaxinone, a novel apocarotenoid metabolite formed by the CCD Zaxinone Synthase (ZAS), is a growth regulator required for normal rice growth and development. The rice genome encodes three OsZAS homologs, called here OsZAS1b, OsZAS1c, and OsZAS2, with unknown functions. Here, we investigated the enzymatic activity, expression pattern, and subcellular localization of OsZAS2, and generated and characterized loss-of-function CRISPR/Cas9-Oszas2 mutants. We show that OsZAS2 formed zaxinone in vitro. OsZAS2 is a plastid-localized enzyme mainly expressed in the root cortex under phosphate starvation. Moreover, OsZAS2 expression increased during mycorrhization, specifically in arbuscule-containing cells. Oszas2 mutants contained lower zaxinone content in roots and exhibited reduced root and shoot biomass, less productive tiller, and higher strigolactone (SL) levels. Exogenous zaxinone application repressed SL biosynthesis and partially rescued the growth retardation of Oszas2 mutant. Consistent with the OsZAS2 expression pattern, Oszas2 mutants displayed a lower frequency of AM colonization. In conclusion, OsZAS2 encodes a further zaxinone-forming enzyme that determines rice growth and architecture and strigolactone content and is required for optimal mycorrhization.
3D Centroidnet: Nuclei Centroid Detection with Vector Flow Voting

Automated microscope systems are increasingly used to collect large-scale 3D image volumes of biological tissues. Since cell boundaries are seldom delineated in these images, detection of nuclei is a critical step for identifying and analyzing individual cells. Due to the large intra-class variability in nuclei morphology and the difficulty of generating ground truth annotations, accurate nuclei detection remains a challenging task. We propose a 3D nuclei centroid detection method by estimating the “vector flow” volume where each voxel represents a 3D vector pointing to its nearest nuclei centroid in the corresponding microscopy volume. We then use a voting mechanism to estimate the 3D nuclei centroids from the “vector flow” volume. Our system is trained on synthetic microscopy volumes and tested on real microscopy volumes. The evaluation results indicate our method outperforms other methods both visually and quantitatively.
AIFS: A novel perspective, Artificial Intelligence infused wrapper based Feature Selection Algorithm on High Dimensional data analysis

Background Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper approaches primarily focus on shortening the path to find an optimal feature set. However, these approaches underutilize the capability of feature subset models, which impacts feature selection and its predictive performance. Method and Results This study proposes a novel Artificial Intelligence infused wrapper based Feature Selection (AIFS), a new feature selection method that integrates artificial intelligence with wrapper based feature selection. The approach creates a Performance Prediction Model (PPM) using artificial intelligence (AI) which predicts the performance of any feature set and allows wrapper based methods to predict and evaluate the feature subset model performance without building actual model. The algorithm can make wrapper based method more relevant for high-dimensional data and is flexible to be applicable in any wrapper based method. We evaluate the performance of this algorithm using simulated studies and real research studies. AIFS shows better or at par feature selection and model prediction performance than standard penalized feature selection algorithms like LASSO and sparse partial least squares. Conclusion AIFS approach provides an alternative method to the existing approaches for feature selection. The current study focuses on AIFS application in continuous cross-sectional data. However, it could be applied to other datasets like longitudinal, categorical and time-to-event biological data.
The presence of BBB hastens neuronal differentiation of cerebral organoids – the potential role of endothelial derived BDNF

Despite remaining the best in vitro model to resemble the human brain, a weakness of human cerebral organoids is the lack of the endothelial component that in vivo organizes in the blood brain barrier (BBB). Since the BBB is crucial to control the microenvironment of the nervous system, this study proposes a co-culture BBB and cerebral organoids. We utilized a BBB model consisting of primary brain microvascular endothelial cells and astrocytes in a transwell system. Starting from induced Pluripotent Stem Cells (iPSCs) we generated human cerebral organoids which were then cultured in the absence or presence of an in vitro model of BBB. We evaluated if the presence of the BBB influences the maturation of cerebral organoids. By morphological analysis, it emerges that in the presence of the BBB the cerebral organoids are better organized than controls in the absence of the BBB. This effect seems to be driven by Brain Derived Neurotrophic Factor (BDNF), a neurotrophic factor released by the endothelial component of the BBB, which is involved in neurodevelopment, neuroplasticity and neurosurvival. Graphical Abstract The current culture model of human cerebral organoids does not require the presence of a BBB (left side). However, the BBB is an important source of BDNF, which is crucial for neurodevelopment and brain health. The cerebral organoids co-cultured for 4 days in the presence of the BBB show a higher cortical organization than the organoids cultured in the absence of the BBB, as illustrated on the right.