Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Egocentric Distance and Screen Size on Virtual Presence: Implications for the Design of Virtual Reality Environments in Large-Screen Displays

This study examined the effects of egocentric distance and screen size on learners’ perceived virtual presence in a virtual reality environment with a large-screen display. Sixty-four undergraduate students participated in the study, which used a 3 × 2 randomized-block factorial design with repeated measures. Two independent variables were included: 1) egocentric distance, or the physical distance between the viewer's position and a screen display, and 2) screen size, or different screen heights with fixed width. Learners’ perceived virtual presence, comprising involvement, spatial presence, and realness, was the dependent variable. Results showed that egocentric distance had significant effects on virtual presence, while screen size had none. A detailed discussion and implications are provided.
Climate impacts of short-lived climate 1 forcers versus CO 2 from biodiesel : A 2 case of the EU on-road sector 3

10 Biofuels are proposed to play an important role in several mitigation strategies to meet future 11 CO2 emission targets for the transport sector, but remain controversial due to significant 12 uncertainties in net impacts on environment, society and climate. A switch to biofuels can also 13 affect short-lived climate forcers (SLCFs), which provide significant contributions to the net 14 climate impact of transportation. We quantify the radiative forcing (RF) and global-mean 15 temperature response over time to EU on-road fossil diesel SLCFs, and the impact of 20% 16 (B20) and 100% (B100) replacement of fossil diesel by biodiesel. SLCFs are compared to 17 impacts of on-road CO2 using different approaches from existing literature to account for 18 biodiesel CO2. Given the best estimates for changes in SLCFs when replacing fossil diesel 19 with biodiesel, the net positive RF from EU on-road fossil diesel SLCF of 3.4 mW/m is 20 reduced by 15% and 80% in B20 and B100, respectively. Over time the warming of SLCFs is 21 likely small compared to biodiesel CO2 impacts. However, SLCFs may be relatively more 22 important for the total warming than in the fossil fuel case if biodiesel from feedstock with 23 very short rotation periods and low land-use-change impacts replaces a high fraction of fossil 24
H YBRID M UTUAL I NFORMATION L OWER - BOUND E STIMATORS FOR R EPRESENTATION L EARNING

Self-supervised representation learning methods based on the principle of maximizing mutual information have been successful in unsupervised learning of visual representations. These approaches are low-variance mutual information lower bound estimators, yet the lack of distributional assumptions prevent them from learning certain important information such as texture. Estimators that are based on distributional assumptions bypass this issue with autoencoders but they tend to have worse performance on downstream classiﬁcation. To this end, we consider a hybrid approach that incorporates both the distribution-free contrastive lower bound and the distribution-based autoencoder lower bound. We illustrate that with one set of representations, the hybrid approach is able to achieve good performance on multiple downstream tasks such as classiﬁcation, reconstruction, and generation.
A Knowledge Graph Based Health Assistant

With the rapid development of artiﬁcial intelligence, especially natural language processing technologies, chatbots have been designed and implemented for various use cases, ranging from e-commerce customer service to travel reservation agents. In the area of healthcare, it is of great importance to develop an intelligent personalized health assistant chatbot that can help people analyze their medical symptoms and pay more attention to their health. In this paper, we introduce a medical knowledge-based dialogue system that acts as a health assistant which provides medical advice and nutrition suggestions based on user’s disease symptoms reported via chat. With the construction of a medical knowledge graph, our chatbot system can ask about relevant symptoms and achieve a more accurate search range of possible diseases. With well-designed algorithms, it efﬁciently matches symptoms against its medical knowledge base and recommend a suitable diagnosis. Our health assistant chatbot was launched in 2018 and has served hundreds of thousands of users who are drivers working in Didi Chuxing (DiDi) online ride-sharing platform.
Generation Scheduling of Active Distribution Network with Renewable Energy Resources Considering Demand Response Management

The scheduling of electricity distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these resources in the active distribution network (ADN) has several advantages, accordingly, the undesirable effects of these resources on ADN need to be analyzed and recovered. In this paper, a hybrid ADN, including wind, PV, and ESS, is investigated in 33 buses IEEE standard system. First of all, optimal energy management and sizing of the RES and ESS are the purposes. Secondly, as demand response (DR) is another substantial option in ADNs for regulating production and demand, an incentive-based DR program is applied for peak shaving. Forasmuch as this method has uncertainty, due to its dependence on customer consumption patterns, the use of inappropriate incentives will not be able to stimulate customers to reduce their consumption at peak times. Accordingly, the climatic condition uncertainty, which is another factor of variability on the production side, is minimized in this paper by relying on the Monte Carlo estimation method. Besides, the optimization problem, which is formulated as optimal programming, is solved to calculate the optimal size and place of each RESs and ESS conditions regarding power loss, voltage profile, and cost optimization. Furthermore, a geometric, energy source and network capacity, and cost constraints, are considered. The results confirm the effectiveness of proposed energy management and cost reduction in the studied test system.
Capital flows and geographically uneven economic dynamics: A monetary perspective

This paper provides a monetary perspective on capital flows and their effects on geographically uneven economic dynamics. Contributing to debates on global imbalances and the international dimension of financialisation, the paper applies heterodox theories of endogenous money creation, asset pricing, and balance sheet fragility to capital flows across regions. Three theoretical claims are made and illustrated through coherent balance-sheet accounting and empirical examples. First, trade imbalances usually are financed endogenously by net inflows that need not originate from surplus regions. Second, bank inflows are not a precondition for local credit creation, but certain types of gross financial flows can contribute to destabilising financial booms through exchange rate appreciation and asset price inflation. Third, sudden stops in capital flows can be entirely unrelated to current account deficits but may trigger financial instability, resulting in negative gross flows rather than increased outflows. For debates in economic geography and heterodox economics, the arguments imply that the focus on surplus countries as originators of destabilising flows can be misleading and that global financial centres are likely to be more important. More attention is needed to gross portfolio and FDI flows into asset markets rather than bank flows and net flows.
From JSON to JSEN through Virtual Languages

In this paper we describe a data format suitable for storing and manipulating executable language statements that can be used for exchanging/storing programs, executing them concurrently and extending homoiconicity of the hosting language. We call it JSEN, JavaScript Executable Notation, which represents the counterpart of JSON, JavaScript Object Notation. JSON and JSEN complement each other. The former is a data format for storing and representing objects and data, while the latter has been created for exchanging/storing/executing and manipulating statements of programs. The two formats, JSON and JSEN, share some common properties, reviewed in this paper with a more extensive analysis on what the JSEN data format can provide. JSEN extends homoiconicity of the hosting language (in our case JavaScript), giving the possibility to manipulate programs in a finer grain manner than what is currently possible. This property makes definition of virtual languages (or DSL) simple and straightforward. Moreover, JSEN provides a base for implementing a type of concurrent multitasking for a single-threaded language like JavaScript. TYPE OF PAPER AND
Strategies Teachers Use in the Management of Inclusive Classrooms in Primary Schools: Lessons from Kazungula and Livingstone Districts, Zambia

The article examined the strategies teachers used to manage learners and learning process in selected inclusive primary classrooms in Kazungula and Livingstone Districts of Southern Province of Zambia. An interpretive research design supported by qualitative data collection approaches were used in the study. The sample comprised of 52 participants: two District Standard Education Officers, two Education Standard Officers-Special Education, two members of staff from Community Based Rehabilitation Project, eight head teachers handling inclusive classes, two members of the multidisciplinary team, four parents of learners with disabilities and thirty-two classroom teachers. Purposive sampling was used to select the participants while, data was collected through use of interview and focused group discussion guides. Thematic analysis was used to analyse qualitative data. This involved grouping and categorising data in order to establish the emerging themes. Use of open, axial and selective coding process helped to clearly identify emerging themes which were presented descriptively. The findings revealed that teachers were either oriented or trained in inclusive education methodologies before they were assigned to teach and manage learners and learning process in inclusive classes; a variety of strategies were being used to manage the learners and learning process. These ranged from: use of multi-tasking approaches; leaner-preparatory approaches; attention seeking mechanism, use of user-friendly curriculum; increased talking walls to tolerance and patience building approaches among learners. The study recommended the strengthening of school-based in-service training of teachers with a focus on strategies for managing learners and learning process in inclusive classrooms in the study districts.
UWS Academic Portal Re-oxygenation after anoxia induces brain cell death and memory loss in the anoxia- tolerant crucian carp Lefevre,

30 Crucian carp (Carassius carassius) survives without oxygen for several months, but it is unknown 31 if they are able to protect themselves from the cell death normally caused by absence, and 32 particularly re-entrance, of oxygen. Here, we quantified cell death in brain tissue from crucian carp 33 exposed to anoxia and re-oxygenation using the terminal deoxy-nucleotidyl transferase dUTP nick34 end labelling (TUNEL) assay, and cell proliferation by immunohistochemical staining for 35 proliferating cell nuclear antigen (PCNA) as well as PCNA mRNA expression. We also measured 36 mRNA and protein expression of the apoptosis executer protease caspase 3, in laboratory-exposed 37 fish and fish caught in their natural habitat over the year. Finally, a behavioural experiment was 38 used to assess the ability to learn and remember how to navigate in a maze to find food, before and 39 after exposure to anoxia and re-oxygenation. The number of TUNEL-positive cells in the 40 telencephalon increased after one day of re-oxygenation following 7 days of anoxia, indicating 41 increased cell death. However, there were no consistent changes in whole-brain expression of 42 caspase 3 in either laboratory or naturally exposed fish, indicating that cell death might occur via 43 caspase-independent pathways or necrosis. Re-oxygenated crucian carp appeared to have lost the 44 memory of how to navigate in a maze (learnt prior to anoxia exposure), while the ability to learn 45 remained intact. PCNA mRNA was elevated after re-oxygenation indicating increased 46 neurogenesis. We conclude that anoxia tolerance involves not only protection from damage but also 47 repair after re-oxygenation. 48
A Toolkit for Practice-Based Learning of Mechanisms in Industrial Design Education: An Application of a Method Combining Deductive and Inductive Learning

Industrial design education is focused on teaching a combination of various interdisciplinary competencies. One of these projected learning outcomes is to be able to design mechanisms in order to fulfil certain mechanical constraints in products. Studies show that theoretical knowledge supported by practice helps to teach industrial design students the mechanisms. In the current situation in Turkey, practice-based courses are designed with a similar purpose. However, graduates severely lack mechanical design skills. In this study, a two-staged toolkit of a holistic flow is introduced to prevent the deficiency mentioned above. In the toolkit, mechanisms are taught by combining deductive and inductive approaches, instead of a directly inductive conventional approach. The toolkit is applied to 36 sophomore year students. Assessments of the students and their self-evaluations are collected and analysed. Findings show that the toolkit can be beneficial for teaching of mechanisms to ID students through some revisions.
Effect of high alcohol intake on heavy metal levels in the blood, urine cotinine metabolism, and pulmonary function according to the severity of smoking

The relationship between alcohol intake, heavy metal levels in the blood, urine cotinine (uCot), and pulmonary function in smokers has seldom been studied. This study aimed to investigate the effect of high alcohol consumption on heavy metal levels in the blood, cotinine metabolism, and lung function according to the severity of smoking. A total of 3,262 adult participants were evaluated by measuring heavy metal levels in the blood, uCot, and spirometric indices. The degree of alcohol intake was assessed by the alcohol use disorders identification test (AUDIT) score. In heavy smokers, levels of lead, cadmium, and mercury in the blood were significantly higher in subjects with AUDIT score ≥ 8, than in those with AUDIT score < 8. However, in light smokers, there were no significant differences in heavy metal levels in the blood between the two groups. AUDIT score was significantly associated with mercury level in the blood, whereas uCot was correlated with cadmium levels in the blood. In nonsmokers, men with AUDIT score ≥ 8 demonstrated a significantly higher forced expiratory volume in the first second/forced vital capacity ratio than those with AUDIT score < 8 (0.81 ± 0.09 versus 0.76 ± 0.08, P < 0.001). However, no significant difference was observed in uCot levels between the groups. In conclusion, co-use of cigarette smoking and high alcohol intake leads to elevated heavy metal levels in the blood in heavy smokers. High alcohol consumption has some positive effects on pulmonary function in nonsmokers but has no significant influence on cotinine metabolism.
Quality Assurance and Safety of Crops & Foods

Effects of some enzymes (transglutaminase; TG and glucosoxidase; GO), emulsifiers (sodium stearoyl-2-lactylate; SSL, diacetyl tartaric acid esters of mono and diglycerides; DATEM, and SSL+DATEM combination) and oxidant (ascorbic acid; AA) on the properties of dough and bread made of a wheat flour (WF) and lupin flour (LF) blend were determined. LF replaced with WF at a 15% level in all dough formulation. All additives except TG increased development time and stability of dough containing 15% LF. While the highest dough energy obtained with AA addition, maximum resistance value increased from 217 BU up to 434 BU with TG usage in dough formulation. SSL+DATEM combination gave highest bread volume and lowest crumb hardness at 24 and 72 h. Generally, SSL+DATEM and AA had a more positive effect on symmetry, pore structure and crumb texture of the bread than the other additives. For improvement of the properties bread prepared with WF-LF blend, SSL+DATEM combination as emulsifier and AA as oxidant can be recommended. Effect of TG and GO on bread properties were found limited compared to AA.
Identifying Emerging Trends in Scientific Texts Using TF-IDF Algorithm: A Case Study of Medical Librarianship and Information Articles

Background: Nowadays, due to the increased publication of articles in various scientific fields, identifying the publishing trends and emerging keywords in the texts of these articles is essential. Objectives: Thus, the present study identified and analyzed the keywords used in the published articles on medical librarianship and information. Methods: In the present investigation, an exploratory and descriptive approach was used to analyze librarianship and information articles published in specialized journals in this field from 1964 to 2019 by applying text mining techniques. The TF-IDF weighting algorithm was applied to identify the most important keywords used in the articles. The Python programming language was used to implement text mining algorithms. Results: The results obtained from the TF-IDF algorithm indicated that the words “Library”, “Patient”, and “Inform” with the weights of 95.087, 65.796, and 63.386, respectively, were the most important keywords in the published articles on medical librarianship and information. Also, the words “Catalog”, “Book”, and “Journal” were the most important keywords used in the articles published between the years 1960 and 1970, and the words “Patient”, “Bookstore”, and “Intervent” were the most important keywords used in articles on medical librarianship and information published from 2015 to 2020. The words “Blockchain”, “Telerehabilit”, “Instagram”, “WeChat”, and “Comic” were new keywords observed in articles on medical librarianship and information between 2015 and 2020. Conclusions: The results of the present study revealed that the keywords used in articles on medical librarianship and information were not consistent over time and have undergone a change at different periods so that nowadays, this field of science has also changed following the needs of society with the advent and growth of information technologies.
Improving protein docking with binding site prediction

Protein-protein and protein-ligand interactions are fundamental as many proteins mediate their biological function through these interactions. Many important applications follow directly from the identification of residues in the interfaces between protein-protein and protein-ligand interactions, such as drug design, protein mimetics engineering, elucidation of molecular pathways, and understanding of disease mechanisms. The identification of interface residues can also guide the docking process to build the structural model of protein-protein complexes. This dissertation focuses on developing computational approaches for proteinligand and protein-protein binding site prediction and applying these predictions to improve protein-protein docking. First, we develop an automated approach LIGSITE to predict proteinligand binding site, based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 boundstructures. LIGSITE performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively. Second, for protein-protein binding site, we develop metaPPI, a meta server for interface prediction. MetaPPI combines results from a number of tools, such as PPI Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme-inhibitor interfaces with sucess rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. After refinement, metaPPI significantly improves prediction success rates to 70% for enzyme-inhibitor and 44% for other interfaces. Third, for protein-protein docking, we develop a FFT-based docking algorithm and system BDOCK, which includes specific scoring functions for specific types of complexes. BDOCK uses family-based residue interface propensities as a scoring function and obtains improvement factors of 4-30 for enzyme-inhibitor and 4-11 for antibodyantigen complexes in two specific SCOP families. Furthermore, the degrees of buriedness of surface residues are integrated into BDOCK, which improves the shape discriminator for enzyme-inhibitor complexes. The predicted interfaces from metaPPI are integrated as well, either during docking or after docking. The evaluation results show that reliable interface predictions improve the disrimination between near-native solutions and false positive. Finally, we propose an implicit method to deal with the flexibility of proteins by softening the surface, to improve docking for non enzyme-inhibitor complexes.
Dynamic Strategic Informed Trading with Risk-averse Market Makers

This paper presents an infinite horizon asset-pricing model to explain several financial anomalies. In our model, private information and pubic dividend (earnings) announcements follow a VAR process. There are risk-averse market makers who set prices competitively, a risk-averse informed trader whose trading incurs trade-size related costs and liquidity traders with possible overreaction (underreaction) to dividend announcements. The paper shows that trading cost and market impact cost lead to the short-term positive autocorrelation of stock returns (momentum), that the underreaction of liquidity traders to public news and the momentum trading of the informed trader lead to short-term post-earnings drift, and that the risk-aversion of the informed trader and the mean-reverting properties of private information contribute to the long-term negative autocorrelation of stock returns (reversal) and to long-term opposite post-earnings drift. In addition, our model shows that the variance of order flow contributes to the high volatility of stock returns relative to fundamental values. The model also has implications for market liquidity, trading volume, price volatility, and the incentives for traders to collect private information.
Transforming Perspective Teachers' Beliefs about Teaching: What Teacher Educators do in a Teacher Education Institution in Pakistan?

Taking the lenses of cultural perspectives, this study has investigated the role of teacher educators in transforming the teaching and learning beliefs of prospective teachers in a formal teacher education program in a remote mountainous region of Pakistan. We used a qualitative exploratory approach to conduct the study and collected data from eight teacher educators through semi-structured interviews, non-participatory observations of the classroom practices, as well as researchers' field, notes Findings, showed that the changing culture of teacher education in Pakistan seem to redefine and reinterpret teacher educators' roles in transforming prospective teachers' beliefs. As a result, teacher educators saw themselves as role models, counselors, mentors, storytellers, and listeners. These findings have pertinent implications for the teacher educators' roles in similar settings.