Miracle-dz/UniFAR-FTUs huggingface dataset:
Using data mining technology to explore homocysteine at low levels

Abstract A high homocysteine level is known to be an independent risk factor for cardiovascular diseases; however, whether or not low homocysteine level contributes to any damage to the body has not been extensively studied. Furthermore, acquiring healthy subject databases from domestic studies on homocysteine is not trivial. Therefore, we aimed to investigate the causality between serum homocysteine levels and health status and lifestyle factors, particularly with a focus on low serum homocysteine levels. Additionally, we discussed a systematic methodical platform for data collection and statistical analysis, using the descriptive analysis of the chi-square test, t test, multivariate analysis of variance, and logistic regression. This study was a cross-sectional analysis of 5864 subjects (i.e., clients of a health examination clinic) in Taipei, Taiwan during a general health check-up in 2017. The patients’ personal information and associated links were excluded. A sample group was selected as per the health criteria defined for this research whose data were processed using SPSS for descriptive statistical analysis using chi-square test, t test, multivariate analysis of variance, and logistic regression analysis. Those working for >12 hours/day had a higher homocysteine level than those working for <12 hours/day (P < .001). The average serum homocysteine level was 7.9 and 8.6 mol/L for people with poor sleep quality and good sleep quality, respectively (P = .003). The homocysteine value of people known to have cancer was analyzed using the logistic regression analysis, revealing a Δodds value of 0.898. The percentage of subjects with a homocysteine value of ≤6.3 μmol/L, who perceived their health status as “not very good” or “very bad,” was higher than those with a higher homocysteine level. The number of subjects who perceived their health as poor was higher than expected. The results suggest that the homocysteine level could be an effective health management indicator. We conclude that normal homocysteine level should not be ≤6.3 μmol/L. Moreover, homocysteine should not be considered as harmful and its fluctuations from the normal range could be utilized to infer a person's physical status for health management.
A Janus MoSSe monolayer: a potential wide solar-spectrum water-splitting photocatalyst with a low carrier recombination rate

For realizing efficient solar to hydrogen energy conversion based on photocatalytic technology, it is important to explore a photocatalyst with wide-range solar absorption and high electron–hole separation efficiency. With a built-in electric field, the recently synthesized Janus MoSSe is intrinsically beneficial for promoting the separation of photo-generated electrons and holes. Thus in this work, we examine the possibility of MoSSe as an efficient water-splitting photocatalyst and the effects of isotropic and uniaxial strains by the first-principles calculations. It is interesting to find that MoSSe exhibits pronounced visible-light absorption efficiency, proper valence and conduction band positions for initializing the redox reactions of H2O, and high carrier mobilities. Moreover, the band gap of MoSSe is decreased and the direct–indirect band gap transition occurs upon tensile strain, which can not only extend the light absorption range, but also reduce the recombination of photo-generated carriers. Furthermore, H2O molecules adsorb more strongly on the MoSSe monolayer surface than on the MoS2 surface, which is also beneficial for the surface water-splitting reactions. These insights provide eloquent evidence that the Janus MoSSe monolayer is potentially an efficient and wide solar-spectrum water-splitting photocatalyst.
Adsorption Thermodynamics of Arsenic on Laterite Soil

Adsorption equilibrium of arsenic (in both As(III) and As(V) states) onto laterite soil was studied at the temperatures of 298, 308 and 318 K. Batch experiments were conducted to find out the arsenic removal capacity. The effects of process parameters such as contact time, adsorbent dose, adsorbent size, temperature and pH were investigated. Detailed studies on adsorption isotherms (Langmuir, Freundlich and Dubinin-Radushkevich) were conducted. By using the Dubinin-Radushkevich (D-R) isotherm, the energy (E) related with adsorption was calculated and the results showed that the adsorption was mostly physisorption. It was observed by calculating the value of the dimensionless equilibrium parameter (R L ) that the adsorption was favorable. The standard free energy change (ΔG 0 ), enthalpy change (ΔH 0 ) and entropy change (ΔS 0 ) were determined and the calculated values showed that the adsorption process for both As(III) and As(V) was exothermic and spontaneous. Regeneration of the adsorbent was possible using 1M NaOH solution and the regenerated adsorbent could further be used for arsenic removal.
LncRNA AC136007.2 alleviates cerebral ischemic-reperfusion injury by suppressing autophagy

Differential expression and diagnostic significance of the long noncoding RNA (lncRNA) AC136007.2 has been reported in patients with acute ischemic stroke (AIS). However, its role on disease progression and outcome remains unclear. Here, we employed an oxygen-glucose deprivation/reperfusion (OGD/R) model in neuronal SH-SY5Y cells and performed middle cerebral artery occlusion (MCAO) surgery in rats to investigate the function of AC136007.2 in ischemia-reperfusion (I/R) injury. AC136007.2 expression was determined by RT-qPCR and cell viability was examined using CCK-8, Edu, LDH, and apoptosis assays. Pro-inflammatory cytokine expression was assessed using ELISA. OGD/R downregulated AC136007.2 expression in SH-SY5Y cells, decreased viability by inducing apoptosis, and stimulated secretion of TNF-α, IL-6, and IL-1β. In turn, lentivirus-mediated AC136007.2 overexpression significantly reversed these phenomena. LC3 immunofluorescence and western blotting analyses of LC3-I/II and Beclin-1 expression and AMPK/mTOR phosphorylation status showed that AC136007.2 suppressed autophagy in SH-SY5Y cells via inactivation of AMPK/mTOR signaling. Notably, incubation with the AMPK activator AICAR abolished the pro-survival effect of AC136007.2 upon OGD/R treatment. Importantly, intraventricular injection of AC136007.2 significantly reduced cerebral infarction and brain edema in MCAO rats, as shown by TTC staining and water content measurements. We conclude that AC136007.2 alleviates cerebral I/R injury by suppressing AMPK/mTOR-dependent autophagy.
Social-belonging Intervention in a Computer Science Systems Course

A brief social-belonging intervention was implemented in an upper level computer science (CS) course. This intervention uses storytelling to help improve a sense of belonging and establish the importance of persistence in the classroom. In previous experiments using this one-time intervention, there were significant results (Walton & Brady, 2017). Recent CS graduates were interviewed about their own struggles and failures in their computer science courses. These interviews were videotaped and edited to follow the storytelling pattern of a struggle, followed by an attribution, and concluding with redemption. Interviewees were selected to represent a diverse group of students including both dominant majority and under-represented minority populations. There were two groups of students, the first group took a systems course without seeing the storytelling videos and second group took a systems course where the storytelling videos were featured. There were approximately 390 computer science students enrolled over both semesters. Survey data was received from approximately 212 students which measured student’s perception of their own belonging to the field of CS. Additionally, students were asked to respond to mock scenarios, gathering data on their attitudes and beliefs on how much other students belong in CS. Results are presented that describe how the control and intervention groups responded to the questions about their own sense of belong, as well as their opinion about who belongs in CS.
Hierarchical learning of Hidden Markov Models with clustering regularization

Hierarchical learning of generative models is use-ful for representing and interpreting complex data. For instance, one application is to learn an HMM to represent an individual’s eye ﬁxations on a stimuli, and then cluster individuals’ HMMs to discover common eye gaze strategies. However, learning the individual representation models from observations and clustering individual models to group models are often considered as two separate tasks.In this paper, we propose a novel tree structure variational Bayesian method to learn the individual model and group model simultaneously by treating the group models as the parents of individual models, so that the individual model is learned from observations and regularized by its parents, and conversely, the parent model will be optimized to best represent its children. Due to the regularization process, our method has advantages when the number of training samples decreases. Experimen-tal results on the synthetic datasets demonstrate the effectiveness of the proposed method.
Pre-processing techniques for end-to-end trainable RNN-based conversational AI

Even though communicating knowledge has always been considered as a primary purpose of language, current data-driven language generation models for dialogue systems still face limitations in encoding and decoding knowledge. This is mainly due to the usage of shallow knowledge representations which is reduced in many cases to a mere form of statistical co-occurrences in the training data set. The approaches thus lack a more profound knowledge integration model with semantically-structured information such as knowledge graphs and description logic. In this work, we evaluate and compare three approaches of pre-processing methods that involve knowledge base integration, and discuss their applicability in the context of interactive dialogue systems. The latter are prerequisites to generating semantically-structured text integrated at training time. The strength of such approaches is that they can be trained end-to-end and are adaptable to multiple domains depending on the extent of coverage of knowledge graphs and the related conversation data samples.
Learning Causal Explanations for Recommendation

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide explanations to humans due to the usage of black-box prediction models. The lack of transparency has highlighted the critical importance of improving the explainability of recommender systems. In this paper, we propose to construct causal explainable recommendation which aims to provide post-hoc explanations for the recommendations by answering “what if” questions, e.g., “what would the recommendation result change if the user’s behavior history had been different? ” Our approach first obtains counterfactual user histories and counterfactual recommendation items with the aid of a perturbation model, and then extracts personalized causal relationships for the recommendation model through a causal rule mining algorithm. Different from some existing explainable recommendation models that aim to provide persuasive explanations, our model aims to find out the true explanations for the recommendation of an item. Therefore, in addition to evaluating the fidelity of discovered causal explanations, we adopt the average causal effect to measure the quality of explanations. Here by quality we mean whether they are true explanations rather than their persuasiveness. We conduct experiments for several state-of-the-art sequential recommendation models on real-world datasets to verify the performance of our model on generating causal explanations.
The Influence of Coping Skills on Psychological Distress and Suicide Attitude among University Students in Malaysia

Suicide is a leading cause of death among university students worldwide. The reduction of psychological distress and suicidality is related to the adoption of effective coping strategies. This study sought to explore the associations between psychological distress, coping style and suicide attitude among university students in Malaysia. This study was conducted upon 178 students in two universities from public and private settings. Participants completed four self-report questionnaires, namely the Kessler Psychological Distress Scale (K10), Brief COPE and Yatt Suicide Attitude Scale (YSAS). Students from the private university showed significantly higher levels of psychological distress compared to students from the public university, but their suicide attitude did not differ significantly. There is a significant relationship between psychological distress and suicide attitude participant (r= .263, p < .001) and the most commonly employed coping strategies among them were the Religion and Positive Reframing domains. Behavioral Disengagement and Self-Blame predicted higher psychological distress, whereas participant were Substance Use, Planning and Self-Blame predicted higher suicide attitude. The suicide rate is rising and young adults need to adopt appropriate and effective coping strategies, especially among university students. Both public and private university students should be targeted for regular screening for psychological distress and suicidality as they are a psychologically vulnerable population.
Classifying Mobile Applications Using Word Embeddings

Modern application stores enable developers to classify their apps by choosing from a set of generic categories, or genres, such as health, games, and music. These categories are typically static—new categories do not necessarily emerge over time to reflect innovations in the mobile software landscape. With thousands of apps classified under each category, locating apps that match a specific consumer interest can be a challenging task. To overcome this challenge, in this article, we propose an automated approach for classifying mobile apps into more focused categories of functionally related application domains. Our aim is to enhance apps visibility and discoverability. Specifically, we employ word embeddings to generate numeric semantic representations of app descriptions. These representations are then classified to generate more cohesive categories of apps. Our empirical investigation is conducted using a dataset of 600 apps, sampled from the Education, Health&Fitness, and Medical categories of the Apple App Store. The results show that our classification algorithms achieve their best performance when app descriptions are vectorized using GloVe, a count-based model of word embeddings. Our findings are further validated using a dataset of Sharing Economy apps and the results are evaluated by 12 human subjects. The results show that GloVe combined with Support Vector Machines can produce app classifications that are aligned to a large extent with human-generated classifications.
Understanding levels and motivations of rural women engaging in Forest Management Institutions: Evidence from developing countries

Although studies emphasize that rural women are persistently being marginalized in forest governance in the Global South, some feminist scholars contend that women are still joining and engaging in forest management institutions. Drawing on the ‘Typology of Participation’ model and the ‘Gender Box’ framework, this article intends to widen our understanding of the levels and motivations of rural women participating in forest management institutions in developing countries. Published journal articles, theses, organizational reports, and conference papers were reviewed. The review demonstrates that some of the rural women who joined forest management institutions attended forest meetings, spoke during the meetings, performed some specific tasks, participated in various forest conservation initiatives, took leadership positions, and influenced forest use decision making. Existing literature further advances that the factors operating at micro-scale, meso-scale, and macro scale levels enabled, motivated and forced women to join and continue engaging in forest management institutions. This article proposes that forest policy makers, practitioners, and academicians should re-focus their interests on examining the influence of: rural men, women empowerment, adoption of information technology, and gendered sharing of roles and benefits on the continuity of women involvement in forest management institutions. Key words: Forest governance, forest management institutions, ‘gender box’ framework, rural women, ‘typology of participation’ model.
Visual Analytics for Integrated Evolution of Physical and Cyber-Events: A Case of the World Cup in Social Media

Significant events, such as large sport tournaments or presidential elections, develop over a long time in the physical world. In parallel, people’s reactions to these events expressed in social media evolve in the cyber world. We are interested in exploring dynamic relationships between these two processes. We propose a DualFlow visualization, which uses a storyline metaphor for presenting the evolution of a real-world event arousing discussions in social media together with the evolution of these discussions. We illustrate our approach by example of social media discussions of the football World Cup 2018. DualFlow visualizes the course of the whole championship including games of all 32 teams and shows the evolution of the related reactions of social media users, i.e., how the interests of the users were distributed among the teams and how this distribution changed over time. Analysts can brush a period and select teams to explore the changing focus patterns of social media users as the tournament goes on. Major keywords of the discussion are shown on demand for explaining the content of the discussion. *S. Chen, G. Andrienko, N. Andrienko are with Fraunhofer Institute IAIS, Germany. {siming.chen, gennady.andrienko, natalia.andrienko} @iais.fraunhofer. de. S. Chen is also with Bonn University, Germany. G. Andrienko, N. Andrienko are also with City University London, UK. †X. Gao and J. Li are with Tianjin University, China. jie.li@tju.edu. cn ‡The work is supported by National NSFC project (Grant number 61602340) and it is also supported by German Priority Research Program SPP 1894 on Volunteered Geographic Information, EU project SoBigData, and Fraunhofer Cluster of Excellence Cognitive Internet Technologies.
The Influence of Music Education on Children with Autism

Children with autism have different degrees of mental development disorders, which are specifically manifested as sluggish behavior and lack of language. Music has many similarities with language, which can slowly open up the heart of children, reorganize and improve their emotions and interpersonal skills. The essay explores the influence of music activities on children with autism through literature review, visits and surveys, questionnaire surveys.
THE SOCIAL CLASS MYTH OF COLLECTIVISM: A QUALITATIVE STUDY OF THE IMPACT OF SOCIAL CLASS ON FAMILIES MEAL INTERACTION BEHAVIOUR

Dr. Sheku Kakay University of Hertfordshire, United Kingdom. Manuscript Info Abstract . Manuscript History Received: 25 April 2021 Final Accepted: 28 May 2021 Published: June 2021 The class distinction in the Sierra Leonean society is the primary determinant of families’ access to a balanced diet as well as the level of social interaction at mealtimes. The income earned by families, their status in society, level of education and the type of job they do, significantly determines the type of food they consume. This implies that, social class can act as the arbiter to families’ access not only to adequate, but quality food. It also influences the food variety available at mealtimes. The study shows that, many Sierra Leonean families experience the problem of daily food affordability challenges, which limits social interaction at the dinner table at mealtimes. Nevertheless, the findings also show that, irrespective of the social standing of families, table etiquette are important to the different social classes, as it provides the foundation for training and socialising children into becoming responsible adults.
In vitro Evaluation of Selected Bio-agents, Neem Oil and Amendments against Stem Rot (Sclerotium rolfsii Sacc.) Disease of Groundnut (Arachis hypogea L.) Waldron and Kohler

Groundnut (Arachis hypogea L.) is one of the important economic oilseed crops of the world. It is commonly called peanut, goober pea, jack nut, manila nut and monkey nut (Rathnakumar et al., 2013). It is known as king of oilseeds (Aycock, R. 1966). In world groundnut is grown in an area of about 21.7m. ha with a production of 38.6 tones (FAOSTAT 2011). Oilseed crops have a specific place in Indian agriculture because edible oil is the next to food grain in Indian diet. The Groundnut seed contains moisture (5.52%), lipid (46.22%), calcium (0.087%), Phosphorous (0.29%), carbohydrate (21.26%), ash (2.57%) and energy (601.85%), saturated fatty acid (10.44%) and unsaturated fatty acid (33.51%) (Ingale and Shivastava, 2011).
AUDIO TAGGING AND DEEP ARCHITECTURES FOR ACOUSTIC SCENE CLASSIFICATION: UOS SUBMISSION FOR THE DCASE 2020 CHALLENGE Technical

In this technical report, we address the UOS submission for the Detection and Classiﬁcation of Acoustic Scenes and Events 2020 Challenge Task 1-a. We propose to utilize the representation vectors, extracted from a pre-trained audio tagging system, for the acoustic scene classiﬁcation task. Audio tagging denotes the existence of various sound events and is known to help the classiﬁcation of acoustic scene. To select suitable feature for the acoustic scene clas-siﬁcation task, we also explore deep architectures such as light convolutional neural networks and convolutional block attention module. Experiments are conducted using the ofﬁcial fold-1 conﬁgura-tion test set. Results using audio tagging representation and deep architectures demonstrate accuracies of 68.8% and 70.5%, compared to that of 65.3% of the baseline. Additionally, score-sum ensemble of the two proposed systems has an accuracy of 71.9% which shows 10.1% relative improvement.
Internal Enforcement

Over the past several years, Americans have become more aware and more vocal regarding the number of illegal aliens who have taken up residence in the United States. While this issue—and a resolution of this issue—is still being debated, many have questioned why current enforcement efforts are so lax. The focus of this paper is on the government agency responsible for the enforcement of our immigration laws, and in particular how the actions of this agency are influenced by political interests. This paper fills a gap in the literature-to-date by examining the enforcement of immigration laws within the interior of the nation. While other studies put border enforcement efforts in a political framework, this analysis is the first, to the authors’ knowledge, to place interior enforcement within the interest-group theory of government framework. Our findings indicate that pressure groups shape the pattern of enforcement that emerges. Despite polls that indicate a majority of Americans favoring stricter enforcement, government enforcement agencies charged with this responsibility apparently succumb to the wishes of those that matter most politically.