Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of perfectionism trait on anxiety and academic procrastination among international Saudi Arabian students studying in United States Universities

Objectives: To investigate the relationship of perfectionist personality traits to anxiety traits and academic procrastination. Also to examine the possibility of predicting trait anxiety and academic procrastination by measuring perfectionist personality traits. Methods/statistical analysis: the study followed the survey methodology. The sample consisted of 521 Saudi students studying in United States universities during the 2017-2018 academic year. Students participants were invited to complete an electronic survey that incorporated the: 1) Multidimensional Perfectionism Scale; 2) State-Trait Anxiety Inventory - Form Y; and 3) Tuckman Procrastination Scale. Regression analysis was utilized for study findings. Findings: the results of the study showed a significant positive linear relationship among trait anxiety with the three perfectionism dimensions: socially-prescribed perfectionism, self-oriented perfectionism, and other-oriented perfectionism. The findings also revealed a significant positive correlation between self-oriented perfectionism and academic procrastination. Novelty/applications: this study is the first of its kind to investigate the perfectionist trait and its relationship to academic procrastination and anxiety among Saudi students studying in the US. Keywords: perfectionism; anxiety trait; procrastination; Saudi students; Unites States universities
Exogenous melatonin-stimulated defense responses in tomato plants treated with polyethylene glycol

ABSTRACT Melatonin found in tomato (Solanum lycopersicum L.) has important roles in its metabolism. Effects of exogenously applied melatonin on tomato plants under drought are unclear. Exogenous melatonin was applied to leaves of tomato seedlings treated with polyethylene glycol (PEG), which simulates drought, to evaluate the potential role of melatonin in drought tolerance. Total chlorophyll, some phenolic compounds, malondialdehyde (MDA) contents, and activities of antioxidant enzymes were measured. Exogenous melatonin increased total chlorophyll content and inhibited MDA content under PEG application. There was an increase in p-coumaric acid content and antioxidant enzyme activities (superoxide dismutase, glutathione reductase, ascorbate peroxidase) due to melatonin application. Exogenous melatonin could help alleviate deleterious effects of PEG by enhancing antioxidant mediated defense and by increasing contents of some phenolics.
Wound-induced polyploidization is driven by Myc and supports tissue repair in the presence of DNA damage

ABSTRACT Tissue repair usually requires either polyploid cell growth or cell division, but the molecular mechanism promoting polyploidy and limiting cell division remains poorly understood. Here, we find that injury to the adult Drosophila epithelium causes cells to enter the endocycle through the activation of Yorkie-dependent genes (Myc and E2f1). Myc is even sufficient to induce the endocycle in the uninjured post-mitotic epithelium. As result, epithelial cells enter S phase but mitosis is blocked by inhibition of mitotic gene expression. The mitotic cell cycle program can be activated by simultaneously expressing the Cdc25-like phosphatase String (stg), while genetically depleting APC/C E3 ligase fizzy-related (fzr). However, forcing cells to undergo mitosis is detrimental to wound repair as the adult fly epithelium accumulates DNA damage, and mitotic errors ensue when cells are forced to proliferate. In conclusion, we find that wound-induced polyploidization enables tissue repair when cell division is not a viable option. Summary: Wound-induced polyploidization in the Drosophila epithelium is dependent on Myc and enables tissue repair in the presence of DNA damage when cell division is not a viable option.
Stacked Attention Recurrent Relational Networks for Question Answering

Relational reasoning is ability to reason about entities and their interactions, which is not applicable for many deep neural networks. Recurrent relational networks, introduced by Palm et al. (2017), increase the complexity of the reasoning tasks they can address [1]. In this paper, we introduce Stacked Attention Recurrent Relational Networks (SARRN) to answer natural language questions from facts, which fundamentally hinge on multiple steps of relational reasoning and improve the ability of reasoning. Our model is a stacked attention model that use recurrent attention to focus on fine-grained parts of the documents. We apply our model to the BaBi tasks, which have a set of proxy tasks that evaluate reading comprehension via question answering. Our model solve 19/20 tasks and the experimental results on the test sets tasks show that our model yields great improvement. We also use qualitative analysis to show the result intuitively.
Research on Construction Technology of Multi Heterogeneous Data Resource Graph of Power Grid Corporation

The development of new-generation artificial intelligence technologies such as deep learning, knowledge graphing, and enhanced learning is driving the “Internet + Smart Energy” into a new era of “Internet of Energy. ” The data structure, type, and format of the data between the main body of the energy and power system, various departments of the enterprise, and various types of users are all constructed at different times using different standards. The data is huge and complex. Establishing a company-level data center for power grid companies to resolve the standardized management and fast and reliable exchange of heterogeneous data under massive data is an opportunity and a challenge for the company to build an energy Internet. As the core foundation of integrating heterogeneous data resources of power grid, data resource graph can provide effective support for the management of heterogeneous data of power grid enterprises under the energy Internet. From the multi-dimensional perspective of data modeling, resource management, data navigation, learning cognition, database, this paper focuses on the diversified and heterogeneous data resources of power grid companies, and explores from the specific technical aspects of data ontology modeling, data naming and identification, data relationship mining, data fusion and exchange technology, aiming to provide technical support and reference for power grid companies to build data resource graph.
Research on User Behavior with Collaborative Recommendation Based on Social Network

With the rise of social networks and the existence of a large number of active users, a large number of data recording daily life are constantly generated. This kind of data has the characteristics of interactivity, real-time and sociality, which implies a lot of valuable information. Therefore, social network data has a very positive role in promoting the construction of smart city. In this paper, users’ annotation information of the item in social network is used. Similarity model is established with appropriate similarity calculation method. Users’ social relationship in social network is used to establish trust relationship and model for users. Personalized recommendation algorithm is improved to dig user behavior data, analyze users’ interests and hobbies. Information of interest or item can be recommended to meet the needs of users to the maximum extent. And the stickiness of users to the website is increased. Based on the widely used recommendation algorithm, this paper analyzes the user behavior with trust-based collaborative recommendation algorithm based on user relationship of social network.
Screening potential inhibitors of lactate dehydrogenasefrom Plasmodium knowlesi via virtual screeningapproaches

Lactate dehydrogenase from Plasmodium knowlesi (Pk-LDH) has been suggested as a potential therapeutic target for the development of drugs against malaria disease. This paper reported the screening of compounds which have potentials to be developed as drugs specific for malaria caused by P. knowlesi via in silico screening. Due to the unavailability of Pk-LDH crystal structure, a protein model was built based on the crystal structure of the closest similar protein, lactate dehydrogenase from Plasmodium falciparum (Pf-LDH) with 91% sequence identity between the two enzymes. The model was developed using MODELLER program and verified in Structural Analysis and Verification Server. Primary and secondary structure features were determined and based on Globplot, two disordered regions were predicted at amino acid numbers 85-95 and 269-281. Meanwhile, results of PPCpred server predicted that Pk-LDH is crystallisable with predicted crystallisation propensity of 0.766. Verification of the model was performed with the ERRAT quality factor of 92.2% while Verify 3D gave the percentage of 85.76%. Ligand-based drug design was performed using Ultra-Fast Shape Recognition with Atom Types (USFRAT) with scores for compounds most resemble oxamate ranged from 0.832-0.914. Meanwhile, the results from structure-based screening using Autodock4 and Cygwin gave minimum binding energies ranged from -3.59 to -0.07. Taken together, this study has successfully generated a verified model structure of Pk-LDH and yielded a list of compounds that have potentials to be developed as antimalarial drugs.
A Review on the Preparation Method of Fruit Leathers

Fruit leathers are dried sheets of fruit pulp which have a soft, rubbery texture and a sweet taste (Diamante et al., 2014). Leathers can also be made from a mixture of fruits and also eaten as snack foods instead of boiled sweets. It is chewy and flavourful, naturally low in fat and high in fiber and carbohydrates; it is also lightweight and easily stored and packed (Ayotte, 1980; Diamante et al., 2014). Consuming fruit leather is an economic and convenient value-added substitute for natural fruits as a source of various nutritional elements. They are produced by dehydrating of fruit puree into a leathery sheet (Raab and Oehler, 1999). The edible portion of fruit (one or more types) is pureed, mixed with other ingredients to improve its physicochemical and sensory characteristics, heated, formed International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 5 (2020) Journal homepage: http://www.ijcmas. com
Foraging Habitat Selection and Seasonality of Breeding in Germain's Swiftlet (Aerodramus inexpectatus germani) in Southern Thailand

Habitat degradation is major threat that limits animal populations and knowledge of foraging habitat selection is important to support the survival of organisms. Germain’s swiftlet (Aerodramus inexpectatus germani) produces highly valuable nests and has an important ecological role in ecosystems as an insect predator. However, little is known about the foraging habitat preferences, vertical distribution and factors affecting the timing of breeding, knowledge which may be helpful for managing swiftlet populations. We aimed to determine 1) the preferred foraging habitats, diurnal and seasonal variation on foraging, 2) vertical foraging pattern and 3) the variation in breeding chronology between the west and east of peninsular Thailand, areas with relatively large swiftlet populations. The intensity of foraging within five major habitats was investigated from prey capture attempt and related to insect availability (estimated as biomass). Breeding activities of total 85 breeding pairs were observed using an IP camera at four colonies for three breeding cycles and related to insect biomass in its major foraging habitats. The highest foraging intensity was over water bodies, forest and open paddy land where insect abundance was higher compared to tree-dominated agriculture and urban areas. Foraging intensity was greater in twilight period than midday, the wet season showed higher intensity of foraging compared to the dry season and related to insect biomass. Vertical foraging height influenced by vegetation foliage height where were rich of insect prey. This suggested that food supply affected habitat use and it can affect swiftlet breeding strategies. Breeding time in the western colonies was earlier than the eastern colonies of peninsular Thailand probably due to differences in prey availability which is influenced by the timing of rainfall. Consequently, better protection and management of wetlands, forest and open paddy land within their
Modeling Primary Fields of TMS Coils with the Fast Multipole Method

In this study, an accurate TMS-coil modeling approach based on conductor’s cross-section representation with many distributed current filaments coupled with an efficient fast multipole method (FMM) accelerator is developed and tested. Uniform (Litz wire) or skin-effect based current distributions are included into consideration. Speed and accuracy estimates as well as two application examples are given, which indicate that this approach is potentially capable of rapid and accurate evaluation of various detailed TMS coil designs and arrays of such coils. The MATLAB-based wire and CAD mesh generator for the coil geometry is interfaced with the FMM FORTAN program, which is also compiled within the MATLAB shell. No extra MATLAB toolboxes are necessary. The CAD model of the coil can be imported into any other computational software package in STL format. The algorithm is organized in the form of a MATLAB-based toolkit. First, a coil model is generated using a dedicated script. Then, we compute high-resolution 2D contour plots for any component of the electric and/or magnetic field in coronal, sagittal, and transverse planes via FMM. These two scripts may be further augmented with a parametric loop to enable rapid analysis. Objective We show how the fast multipole method can be applied to compute primary electric and magnetic fields for detailed TMS coil models
Sampling the feasible sets of SDPs and volume approximation

We present algorithmic, complexity, and implementation results on the problem of sampling points in the interior and the boundary of a spectrahedron, that is the feasible region of a semidefinite program. Our main tool is random walks. We define and analyze a set of primitive geometric operations that exploits the algebraic properties of spectrahedra and the polynomial eigenvalue problem and leads to the realization of a broad collection of efficient random walks. We demonstrate random walks that experimentally show faster mixing time than the ones used previously for sampling from spectrahedra in theory or applications, for example Hit and Run. Consecutively, the variety of random walks allows us to sample from general probability distributions, for example the family of log-concave distributions which arise frequently in numerous applications. We apply our tools to compute (i) the volume of a spectrahedron and (ii) the expectation of functions coming from robust optimal control. We provide a C++ open source implementation of our methods that scales efficiently up to dimension 200. We illustrate its efficiency on various data sets.
Learning Robust Scene Classification Model with Data Augmentation Based on Xception

Scene classification technology based on computer vision has been widely applied in many fields. However, with the increasing complexity of images, many computer vision classification models are difficult to meet requirements of current scene classification tasks, as they not only require considering the object, background, spatial layout and other information, but also many relationships in the image. Based on the analysis of existing scene classification algorithms and Xception model, an approach that adds optimization from two aspects of data set processing is proposed to solve complicated scene classification tasks. Combined with the image enhancement technology, the serialized image enhancement method is used to expand the dataset and enhance the image features, and takes advantage of the Xception model to extract the image features to obtain the scene classification model with high robustness. The experimental results showed that Xception model was able to deal with scene classification efficiently by making up for the shortcomings of traditional Convolutional Neural Networks (CNN) models in feature extraction and generalization ability.
A Decision-making Method for Self-driving Based on Deep Reinforcement Learning

L5-level autonomous driving is the development trend of the future in the automotive industry, and the realization of autonomous driving through deep reinforcement learning algorithms are one of the research directions. Soft Actor-Critic the algorithm adds the maximum entropy term to the original deep reinforcement learning the objective function, and it shows great advantages in continuous control problems. Here, based on the open-source platform TORCS, this algorithm will be used to conduct automatic driving simulation experiments, design a reasonable reward function, add relevant constraints, use vehicle radar sensor information to make automatic driving decisions, and compare experiments with the Deep Deterministic Policy Gradient Algorithm. SAC can effectively extend training time, improve stability, and improve generalization ability.
Discovery of Meaningful Rules by using DTW based on Cubic Spline Interpolation

The ability to make short or long term predictions is at the heart of much of science. In the last decade, the data science community have been highly interested in foretelling real life events, using data mining techniques to discover meaningful rules or patterns, from different data types, including Time Series. Short-term predictions based on “the shape” of meaningful rules lead to a vast number of applications. The discovery of meaningful rules is achieved through efficient algorithms, equipped with a robust and accurate distance measure. Consequently, it is important to wisely choose a distance measure that can deal with noise, entropy and other technical constraints, to get accurate outcomes of similarity from the comparison between two time series. In this work, we do believe that Dynamic Time Warping based on Cubic Spline Interpolation (SIDTW), can be useful to carry out the similarity computation for two specific algorithms: 1- DiscoverRules() and 2- TestRules(). Mohammad Shokoohi-Yekta et al developed a framework, using these two algoritghms, to find and test meaningful rules from time series. Our research expanded the scope of their project, adding a set of well-known similarity search measures, including SIDTW as novel and enhanced version of DTW.
Size and Shape Design Optimization of Truss Structures Using the Jaya Algorithm

The metaheuristic algorithm is proposed to solve the weight minimization problem of truss structures, considering the shape and sizing design variables. Design variables are discrete and/or continuous. The design of truss structures is optimized by an efficient optimization algorithm called Jaya. The main feature of Jaya is that it does not require setting algorithm-specific parameters. The algorithm has a very simple formulation in which the basic idea is to approach the best solution and escape from the worst solution [6]. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of the Jaya algorithm is demonstrated using two benchmark examples: planar truss 18-bar and spatial truss 39-bar, and compared with results in references.
Nutritional potential of dehydrated residues from rice milk production

The aim of this work was to verify the influence of drying on physical and chemical characteristics of residues from rice milk production. Residues were from the production of white, red and black rice. They were dehydrated in an oven with air circulation at 60ºC for 8h. Characterization of them were carried out by physical and chemical analyses. Residues have presented statistical difference for all parameters measured. Its characterization is necessary to justify its use in other process or application on new products.The residues from rice milk production have high content of nutrients and phenolic compounds, even after dehydration. Keywords: Nutrients; Oryza sativa; waste.
Performance Evaluation of ASCO-OFDM Based LiFi

Light fidelity (LiFi) is a means of high speed wireless data transmission along with room illumination. As a data encoder for LiFi, different variants of orthogonal frequency division multiplexing (OFDM) such as asymmetrically clipped optical OFDM (ACO-OFDM), asymmetrically clipped DC biased optical OFDM (ADOOFDM) and asymmetrically and symmetrically clipped optical OFDM (ASCO-OFDM) have been considered. This paper provides a framework using pulse-width modulation (PWM) for dimming control of ASCO-OFDM based LiFi. In this framework, the generated ASCO-OFDM signal in the electrical domain is multiplied with the PWM signal, and the resultant signal is converted to the optical signal by optical modulators. The pulse width of the PWM based ASCO-OFDM signal is varied accordance with the dimming or brightness level. Next, the bit error rate (BER) performance is evaluated for PWM based ASCO-OFDM. Finally, results show that with PWM dimming, ASCO-OFDM is more electrical power efficient than others for a given data rate. Results indicate that for low data rates, both ASCO-OFDM and ACO-OFDM, and for higher data rates, ASCO-OFDM and ADO-OFDM are good choices in terms of optical power efficiency. Moreover, 4-QAM ASCOOFDM system ensures low BER even for large dimming or lower signal power level.
Does mobile money promote financial inclusion in Eswatini?

PurposeMobile money, a service permitting monetary value to be digitally stored in a mobile phone and transacted to others through text messaging, is increasingly becoming available in several African countries including Eswatini. This study examines the factors associated with mobile money usage and the extent to which mobile money accelerates financial inclusion in Eswatini.Design/methodology/approachData were collected from the nationally representative FinScope Consumer Survey for Eswatini conducted in 2014. The authors use a quasi-experimental method in propensity score matching (PSM) with bootstrapped standard errors to alleviate the possibility of selection bias associated with mobile money use and bank account ownership. As a sensitivity check, the authors calculate the average treatment effect (ATE) using kernel-based matching methods, as well as estimate a multilevel model that accounts for the hierarchical structure of data.FindingsThe authors found that higher education, entrepreneurship, being female, improvement in work situation in the past year and living in urban area and in the Lubombo region all positively influence the probability to use mobile money. The results also show that individuals who use mobile money are 19% more likely to own a bank account at a formal financial institution with a higher probability estimate observed amongst rural residents.Originality/valueThis study examines whether mobile money accelerates financial inclusion in Eswatini. On analysing data from the 2014 FinScope Consumer Survey, the results show that mobile money does not seem to be accelerating the reach of financial services to those who are structurally excluded from the formal financial system and suggest the need for ongoing review of the financial inclusion strategies of the country to enhance access to financial services in underserved areas.Peer reviewThe peer review history for this article is available at:https://publons.com/publon/10.1108/IJSE-12-2019-0723