Miracle-dz/UniFAR-FTUs huggingface dataset:
Field Performance of Trichoderma harzianum AAUT14 and Bacillus subtilis AAUB95 on Faba Bean (Vicia faba L.) Growth Promotion and Management of Chocolate Spot (Botrytis fabae Sard.)

Aims: This study was done to evaluate the effects of T. harzianum AAUT14 and B.subtilis AAUB95 on chocolate spot (B. fabae) and growth promotion of faba bean. Study Design: A completely randomized block design was utilized. Place and Duration of Study: The study was conducted at Kulumsa Agricultural Research Center, 8o2'N and 39o10’E, Kulumsa, June-November, 2018. Methodology: Two trails (Trial-1 Ashebeka and Trial-2 Hachalu) were employed. We included T1Control (B.f only); T2-T. harzianum AAUT14+ B.f; T3-B. subtilis AAUB95+B.f; T4-T. harzianum AAUT14+B. subtilis AAUB95+B.f; T5MORE 720 WP+B.f; T6ORZEB+B.f as treatments of the study. The disease development was assessed together with yield and related parameters. Original Research Article Firdu et al.; IJPSS, 32(12): 35-45, 2020; Article no.IJPSS. 60837 36 Results: In trail 1, a reduction that varied from 31-61% for disease incidence and 13-33% of severity over T1 upon 70 days after sowing. Upon 90 days, the disease incidence and severity was reduced to 20-50% and 36-51%, respectively. Trichoderma harzianum AAUT14+B.subtilis AAUB95 (T4) reduced the disease incidence and severity showing no significance difference (P=.05) with the chemical fungicide, MORE 720 WP (T5) upon 70 and 90 days of sowing. In trial 2, the microbial inoculants reduced the disease incidence and severity to 28-63% and 17-30% upon 70 days. Likewise, the disease incidence and severity was reduced to 23-51% and 37-54% upon 90 days. In addition, the AUDPC ranged from 1586.1-2250.0%DSU in trial 1 and 1382.0-2454.5%DSU in trial 2. Moreover, leaf area of 68.95cm 2 was displayed by T4 in trial 1 and 54.14cm 2 in trial 2. In addition, T 4 indicated, 62% and 49% increment of hundred seed dry weight and grain yield estimate in trial 1, and 56% and 55%, increase in trial 2 compared to the uninoculated control. The percentage of healthy pods was 90% and 88.87% in trial 1 and 2, respectively, in the treatment that received T4 and followed by T2 that showed 70.40 and 78.86% in trial 1 and 2, respectively. T4 resulted 4391.45kg/ha and 4378.12kg/ha, that followed by T2 with 3764.58kg/ha and 3654.17kg/ha of yield estimate in trial 1 and 2, respectively. 27-42% and 26-41% of harvest index was exhibited in trial 1 and trial 2, respectively. Furthermore, the seed nitrogen content increased from 33-70% in trial 1 and 29-62% in trial 2. The seed nitrogen content showed 33-70% and 29-62% increment in trial 1 and 2, respectively. Even though the fungicides (T5 and T6), protected the faba bean plants from chocolate spot, there was <10% of seed nitrogen and crudeprotein content increment over the untreated control plants in both varieties. Conclusion: The mixture of T. harzianum AAUT14 and B. subtilis AAUB95 or in some cases T. harzianum AAUT14 performed best on controlling chocolate spot, growth promotion and yield increment of faba bean.
Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach

Abstract Access to high-dimensional data has made the use of machine learning in causal inference more common in recent years. The double/debiased machine learning (DML) estimator for the treatment effect is designed to obtain a valid inference when nuisance functions in the treatment and outcome equations, are estimated using machine learning methods. However, when some covariates in the treatment equation do not appear in the outcome equation, the inclusion of such covariates in the propensity score estimation will result in the increasing bias and variance of the DML estimator. To solve this issue, we introduce an outcome-adaptive DML estimator, which incorporates the outcome-adaptive lasso for the variable selection in the propensity score estimation. We evaluate the performance of the proposed method using Monte Carlo simulation. The results indicate that our proposed method in many cases outperforms other methods.
Corporate Social Responsibility Practices in the Aviation Industry

Over the past 30 years, there has been a significant increase in the awareness of the environmental impacts of human activity. People have become tougher about social and environmental impacts of their consuming habits. There is a greater interest for the tourism industry, which on one side can offer economic and social benefits while on the other side can have a significant negative impact. In the tourism industry, the aviation industry is receiving considerable attention because of its decisive impact on the environment and society. Traditionally, corporate managers had a very difficult but limited responsibility which is to direct the enterprise toward money. But companies today usually have commitments that go beyond generating profits and include the wider community. In this case, Corporate Social Responsibility (CSR) can be rated as an essential component of the enterprise, providing the company with both image advantages as well as operational efficiency.
Spectral-designed depthwise separable graph neural networks

This paper aims at revisiting Convolutional Graph Neural Networks (ConvGNNs) by designing new graph convolutions in spectral domain with a custom frequency proﬁle while applying them in the spatial domain. Within the proposed framework, we propose two ConvGNNs methods: one using a simple single-convolution kernel that operates as a low-pass ﬁlter, and one operating multiple convolution kernels called Depthwise Separable Graph Convolution Network (DSGCN). The latter is a generalization of the depthwise separable convolution framework for graph convolutional networks, which allows to decrease the total number of trainable parameters while keeping the capacity of the model unchanged. Our proposals are evaluated on both transductive and inductive graph learning problems, demonstrating that DSGCN outperforms the state-of-the-art methods.
SOLVING POLYNOMIAL SYSTEMS BY POLYHEDRAL HOMOTOPIES

In the last two decades, the homotopy continuation method has been developed into a reliable and efficient numerical algorithm for solving all isolated zeros of polynomial systems. During the last few years, a major computational breakthrough has emerged in the area. Based on the Bernshtein theory on root count, the polyhedral homotopy is established to considerably reduce the number of homotopy paths that need to be traced to find all the isolated roots, making the method much more powerful. This article reports the most recent development of this new method along with future considerations.
A POWER-BALANCED DYNAMIC MODEL OF FERROMAGNETIC COILS

This paper proposes a new macroscopic physical model of ferromagnetic coils used in audio circuits. To account for realistic saturation and hysteretic phenomena, this model combines statistical physics results, measurement-driven refinements and portHamiltonian formulations that guarantee passivity, thermodynamic consistency and composability according to both electric and thermal ports. As an illustration, the model is used to simulate a passive high-pass filter. Different types of audio inputs are considered and simulations are compared to measurements.
Socioeconomic Status and Glycaemic Variability in People with Type 2 Diabetes: A Baseline Analysis of the GP-OSMOTIC Study

Diabetes kan anses vara en av var tids globala halsoutmaningar. Det uppskattas att drygt 420 miljoner manniskor over hela varlden lever med diabetes och den siffran beraknas fortsatta stiga. Diabetes karaktariseras av forhojda plasmaglukosnivaer (”blodsocker”), vilket har visats kunna ha flertalet skadliga effekter pa kroppen med risk for komplikationer sasom njursjukdom, synpaverkan och hjartinfarkt pa langre sikt. Ett viktigt matt for att folja plasmaglukosnivaerna hos individer med diabetes ar HbA1c. Det ar ett blodprov som speglar medelvardet av blodsockret under en 3-manadersperiod och anvands for att utvardera effekten av behandling samt risken for diabetes-relaterade komplikationer. Tidigare forskning har visat att individer med lagre socioekonomisk status generellt sett har hogre HbA1c-nivaer (och darmed hogre plasmaglukosnivaer) an individer med hogre socioekonomisk status. Forskning har dessutom forslagit att snabba variationer i blodsockret, som pa facksprak kallas glykemisk variabilitet, kan ha skadliga effekter och ge okad risk for diabeteskomplikationer. Med anledning av detta, samt vad som ar kant kring kopplingen mellan socioekonomi och HbA1c, var malsattningen med detta projekt att undersoka det eventuella forhallandet mellan glykemisk variabilitet och socioekonomisk status. Denna studie baserades pa data ifran 279 personer med typ 2-diabetes i den australiensiska delstaten Victoria, som hade samlats in av en storre studie. Varje deltagare i studien fick bara en sa kallad FreeStyle Libre under 2 veckor. Det ar en liten sensor som fasts pa baksidan av armen och som mater glukosnivaerna kontinuerligt, var femtonde minut, utan att individen behover gora nagot. Utifran dessa matningar kunde sedan ett matt pa den glykemiska variabiliteten beraknas. For att uppskatta den socioekonomiska statusen hos deltagarna anvandes tva olika matt. Dels ett index som jamfor den socioekonomiska standarden mellan alla omraden i Australien med hjalp av ett poangsystem, baserat pa information om flera olika faktorer som erhallits fran den australiensiska folkrakningen ar 2011 och dels deltagarnas utbildningsniva. Studien kunde inte pavisa nagot samband mellan glykemisk variabilitet och en individs socioekonomiska status i den undersokta populationen. Det behovs emellertid fler och eventuellt storre studier som undersoker forhallandet mellan dessa tva viktiga faktorer innan nagra slutsatser kan dras.
The Relationship between Psychological Well-Being and University Students Academic Achievement

This study aims to measure psychological well-being and its relationship with academic achievement of the students from Sultan Idris Education University. Information gained through questionnaire distributed to 542 second year undergraduate students in Sultan Idris Education University. Psychological well-being is measured based on Psychological Well-Being Instrument (Ryff,1989). Academic achievement is measured based on Culmulative Grade Point Average (CGPA). Decriptive analysis has shown that most students indicate intermediate and high level psychological well-being. The analysis of psychological well-being dimension shows that most students have high purpose in life meanwhile another psychological well-being dimensions which are autonomy, environmental mastery, personal growth, positive relationship with others and self-acceptance are at intermediate level. Correlation analysis indicates that there is a significant positive relationship between psychological well-being and academic achievement. The result of this study shows that there is a significant positive correlation between the dimensions of psychological well-being which are environmental mastery, personal growth, positive relationship with others, purpose in life and selfacceptance with academic achievement. In conclusion, this study shows that most students potrays positive attitude as well as accepting themselves, have control over their environment, selfautonomous, able to maintain positive relationship with others, have clear life meaning and goals as well as the presence of continuous personal growth and development. This study implies that university need to design continuous program to help students especially to those whom have low level of psychological well-being due to its relationship with academic achievement.
Study on Machine Learning Algorithms for the Epilepsy Automatic Diagnosis

Epilepsy is a chronic disorder of the brain caused by excessive discharge of brain cells. Currently, doctors are manually diagnosing by visual judgment based on long-term intracranial electroencephalogram (iEEG) data. This diagnostic procedure is very time consuming and relies on experience. In order to reduce the workload of the doctor, an automatic diagnosis system with high accuracy is required. In recent years, machine learning methods are often applied to medical diagnosis field. In this thesis, we proposed several methods using conventional machine learning and deep learning. Conventional machine learning methods use filters, entropy and short time Fourier transform (STFT) to extract features. Next, based on the labeled data, the support vector machine (SVM), neural network, and convolutional neural network (CNN) classification models were used for focal localization problems and achieve high performance. However, when these methods are applied to practical clinical patient data, it is difficult to acquire a large amount of iEEG data with high quality labels. Aim at this problem, this study uses a weakly supervised learning method (PU learning) to learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. The labeling work can be greatly reduced. In addition, a data augmentation method is used based on the discrete cosine transform (DCT) to generate a large amount of artificial data. Combine the raw data and artificial data as a training data set, the performance
Multiscale Adaptive Gabor Expansion (MAGE): Improved Detection of Transient Oscillatory Burst Amplitude and Phase

— Since Denis Gabor’s pioneering paper on the discrete Gabor Expansion (Gabor, 1946), time-frequency signal analysis has proven to be an important tool for many fields. In neurophysiology, time-frequency analysis has often been used to characterize and describe transient bursts in local field potential data. However, these transient bursts have a wide range of variable durations, suggesting that a time-frequency-scale dictionary composed of elementary signal “atoms” may prove useful to more accurately match recorded bursts. While overcomplete multiscale dictionaries are useful, generating a sparse code over such dictionaries is a difficult computational problem. Existing adaptive algorithms for discovering a sparse description are slow and computationally intensive. Here we describe the Multiscale Adaptive Gabor Expansion (MAGE), which uses an implicit dictionary of parametric time-frequency-scale Gabor atoms to perform fast parameter reassignment to accelerate discovery of a sparse decomposition. Using analytic expressions together with numerical computation, MAGE is a greedy pursuit algorithm similar to Matching Pursuit, restricted to a dictionary of multiscale Gaussian-envelope Gabor atoms. MAGE parameter reassignment is robust in the presence of moderate noise. By expressing a unknown signal as a weighted sum of Gabor atoms, MAGE permits a more accurate estimate of the amplitude and phase of transient bursts than existing methods. Since Denis Gabor’s pioneering paper on the discrete Gabor Expansion (Gabor, 1946), time-frequency signal analysis has proven to be an important tool for many fields. In neurophysiology, time-frequency analysis has often been used to characterize and describe transient bursts in local field potential data. However, these transient bursts have a wide range of variable durations, suggesting that a time-frequency-scale dictionary composed of elementary signal “atoms” may prove useful to more accurately match recorded bursts. While overcomplete multiscale dictionaries are useful, generating a sparse code over such dictionaries is a difficult computational problem. Existing adaptive algorithms for discovering a sparse description are slow and computationally intensive. Here we describe the Multiscale Adaptive Gabor Expansion (MAGE), which uses an implicit dictionary of parametric time-frequency-scale Gabor atoms to perform fast parameter reassignment to accelerate discovery of a sparse decomposition. Using analytic expressions together with numerical computation, MAGE is a greedy pursuit algorithm similar to Matching Pursuit, restricted to a dictionary of multiscale Gaussian-envelope Gabor atoms. MAGE parameter reassignment is robust in the presence of moderate noise. By expressing a unknown signal as a weighted sum of Gabor atoms, MAGE permits a more accurate estimate of the amplitude and phase of transient bursts than existing methods. A
ADVERSARIAL SYNTHESIS OF DRUM SOUNDS

Recent advancements in generative audio synthesis have allowed for the development of creative tools for generation and manipulation of audio. In this paper, a strategy is proposed for the synthesis of drum sounds using generative adversarial networks (GANs). The system is based on a conditional Wasserstein GAN, which learns the underlying probability distribution of a dataset compiled of labeled drum sounds. Labels are used to condition the system on an integer value that can be used to generate audio with the desired characteristics. Synthesis is controlled by an input latent vector that enables continuous exploration and interpolation of generated waveforms. Additionally we experiment with a training method that progressively learns to generate audio at different temporal resolutions. We present our results and discuss the benefits of generating audio with GANs along with sound examples and demonstrations.
Stay Positive: Knowledge Graph Embedding Without Negative Sampling

Knowledge graphs (KGs) are typically incomplete and we often wish to infer new facts given the existing ones. This can be thought of as a binary classification problem; we aim to predict if new facts are true or false. Unfortunately, we generally only have positive examples (the known facts) but we also need negative ones to train a classifier. To resolve this, it is usual to generate negative examples using a negative sampling strategy. However, this can produce false negatives which may reduce performance, is computationally expensive, and does not produce calibrated classification probabilities. In this paper, we propose a training procedure that obviates the need for negative sampling by adding a novel regularization term to the loss function. Our results for two relational embedding models (DistMult and SimplE) show the merit of our proposal both in terms of performance and speed.
Supplementary : Learning to Detect Open Classes for Universal Domain Adaptation

Confidence has contour lines where all class distributions on the line share the same confidence. In the paper, we claim that confidence has high discriminability for extremely confident and uncertain predictions, meaning that the contour line for extremely high and low confidence should be short. For each confidence, the length of contour line is proportional to the number of possible value combinations of probabilities, i.e. the size of solution space, satisfying the confidence. If the confidence is larger than 0.5, meaning that the highest probability is higher than 0.5, and the sum of the rest probabilities is smaller than 0.5. Let the three probabilities pi, (i = 1, ., n) without loss of generality, we let p1 be the largest probabilities. Then we have
INCREASING THE BIOAVALILABILITY OF MEBENDAZOLE I. INFLUENCE OF CROSCARMELLOSE ON DISSOLUTION RATE, EXTENT AND MECHANISM IN SIMULATED GASTRIC MEDIUM

The objective of the research presented in the paper was the obtaining of mebendazole tablets with increased bioavailability. Five solid formulations containing croscarmellose, an internally cross-linked sodium carboxymethylcellulose as superdisintegrant in 1%, 2 %, 3 %, 4 % and 5 % concentrations were prepared by direct compression and characterized in accordance with USP specifications. For the mebendazole quantitative analysis a HPLC method with UV detection was developed and validated. The effect of croscarmellose on rate, extent and mechanism of release of mebendazole from resulted formulations was studied. In vitro release kinetics was evaluated using USP Apparatus 2, in acidic medium (0.1N HCl) containing 1% sodium lauryl sulfate (SLS). The release of mebendazole was complete within 120 minutes. Rate and extent of release increased with croscarmellose concentration. The release mechanism was diffusion controlled, application of Higuchi law being excellent both in pre and post-disintegration phases. Peppas and Weibull models proved to be also applicable.
Describing Artworks Using Convolutional Neural Networks

In this paper, we describe an approach based on Convolutional Neural Networks (CNNs) to classify artworks by artistic technique, time period, genre, and produce image captions. We assemble a large dataset of high-resolution images ( 40,000) and use it to train several neural network models, achieving promising results. We also propose to use the features we learn to illustrate various categories.
The Influential Role of Robot in Second Language Classes Based on Artificial Intelligence

In the fast development of technology, robot has played many vital roles in different fields of life. Robot is also used in education as a teaching assistant in arts, science and language course. In Viet Nam, the secondary schools or high schools spend a lot of money on hiring native speakers for teaching English classes. Currently, in most Asian countries where English is taught as a foreign language, therefore many various instructional methods are being used for teaching English at school as well as English Language Centers. As a result of this paper approach to English teaching robots, the use of robots like as foreign language instructors has been referred to as a robotic revolution in Industrial Revolution 4.0. In this study, we will describe applications of several robots have been conducting the English teaching instructors tutoring some extra-class’s programs in Viet Nam. The results shown the potentials of individualized interaction in language education are substantial. These performances give the positively contributions to improve of motivations of children studying English as well as other languages. This paper will show the effective tools for language education of robot. From experiments in reality, using robot being an instructor in English class has proven to enhance the language teaching for children effectively. The children, who learn English with an instructor like as robot have shown better learning results in speaking, as well as more confidence and much motivation in studying. 
Ursolic acid induces apoptosis of lung cancer cells by regulating miR-21/KLF6 axis

Background: Ursolic acid (UA) is considered a potential drug for the treatment of carcinoma due to its ability to inhibit the growth of carcinoma cells. However, the antitumor mechanism of UA has not been clarified. Objective: To understand the effects of UA on the biological behavior of lung cancer (LC) cells and its related mechanisms. Methods: Human LC cells were placed in a culture medium containing multiple incubation concentrations of UA at 0, 5, 10, 20 and 40 μM, and miRNA-21 (miR-21) and Krupple-like factor 6 (KLF6) over-expression or inhibition vectors were transfected into LC cells. CCK-8, Transwell and flow cytometry were used to detect the changes in cell behavior after treatment. The relationship of miR-21 with KLF6 was analyzed by dual luciferase reports. Results: UA could inhibit the growth and invasion of LC cells and promote apoptosis. In addition, UA could also reduce miR21 in LC cells. Down-regulation of miR-21 or increase of KLF6 could inhibit the proliferation, invasion and promote apoptosis of LC cells. miR-21 could negatively regulate KLF6. Both increasing miR-21 and inhibiting KLF6 could reduce the influence of UA on the biological behavior of LC cells. Conclusion: UA plays an anti-cancer role in LCs by regulating the miR-21/KLF6 axis.