Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Architecture of Grain Quality Traits in Nutrient Rich Rice (Oryza sativa L.) Crosses

The precise knowledge of the nature of gene action for quality traits in rice helps to choose an effective breeding strategy to accelerate the pace of genetic improvement of quality traits in rice. The Generation mean analysis indicated absence of epistasis in case of certain quality characters like hulling percent, milling percent, kernel length, breadth and kernel elongation ratio in first and second crosses. The interaction was of duplicate epistasis for milling percent and kernel length, therefore, in addition to the main genetic effects, ([d], [h] components), for development of a variety / hybrid from the cross (RP-Bio-5478 -185 x NH-686), the interaction components also have to be taken into consideration in breeding. Selection was recommended for head rice recovery improvement, for two crosses viz., MTU 1010 x NH-686, WGL-32100 x RP-Bio-5478-166, due to presence of fixable genetic variation. The interaction components are highly variable, therefore, a cross and trait specific breeding strategy is required for quality improvement in three crosses.
Lasiodiplodia theobromae and L. pseudotheobromae causing leaf necrosis on Camellia sinensis in Fujian Province, China

Abstract Camellia sinesis (tea) is an economically important crop in China, especially in Fujiang Province. Leaf necrosis symptoms were observed on C. sinensis in Fujian Province, China, during 2015. Initial symptoms were small brown, irregular lesions on young and mature leaves, which become necrotic with a brown margin. Isolations from diseased leaves yielded several pycnidial-forming fungi, including two Lasiodiplodia species which were characterized based on morphology and combined analysis of the internal transcribed spacer (ITS) and translation elongation factor 1-α (tef) gene sequences. The isolates obtained from symptomatic leaves from C. sinensis were identified as Lasiodiplodia theobromae and L. pseudotheobromae. Pathogenicity of both species was demonstrated and symptoms which developed on inoculated C. sinensis leaves were similar to those observed on diseased plants in the field. This is the first report of L. theobromae and L. pseudotheobromae causing leaf necrosis on Camellia sinensis in China.
On the Security of Practical Mail User Agents against Cache Side-Channel Attacks

Mail user agent (MUA) programs provide an integrated interface for email services. Many MUAs support email encryption functionality to ensure the confidentiality of emails. In practice, they encrypt the content of an email using email encryption standards such as OpenPGP or S/MIME, mostly implemented using GnuPG. Despite their widespread deployment, there has been insufficient research on their software structure and the security dependencies among the software components of MUA programs. In order to understand the security implications of the structures and analyze any possible vulnerabilities of MUA programs, we investigated a number of MUAs that support email encryption. As a result, we found severe vulnerabilities in a number of MUAs that allow cache side-channel attacks in virtualized desktop environments. Our analysis reveals that the root cause originates from the lack of verification and control over the third-party cryptographic libraries that they adopt. In order to demonstrate this, we implemented a cache side-channel attack on RSA in GnuPG and then conducted an evaluation of the vulnerability of 13 MUAs that support email encryption in Ubuntu 14.04, 16.04 and 18.04. Based on our experiment, we found that 10 of these MUA programs (representing approximately 77% of existing MUA programs) allow the installation of a vulnerable version of GnuPG, even when the latest version of GnuPG, which is secure against most cache side-channel attacks, is in use. In order to substantiate the importance of the vulnerability we discovered, we conducted a FLUSH+RELOAD attack on these MUA programs and demonstrated that the attack restored 92% of the bits of the 2048-bit RSA private key when the recipients read a single encrypted email.
Complete plastid genome of Gracilaria bailiniae (Rhodophyta) and phylogenetic analysis

Abstract The complete Gracilaria bailiniae plastid genome was determined and analyzed in this work. It had a circular mapping molecular with the length of 185,129 bp and contained 231 genes including 193 protein-coding, 3 rRNA, 1 tmRNA, 29 tRNA genes, and 5 unidentified open reading frames. Phylogenetic analysis showed that G. bailiniae clustered together with Gracilaria chorda and Gracilariopsis lemaneiformis. The complete plastid genome provided in this work would be useful for elucidation of Gracilaria evolution.
Proposed Nonparametric Tests Using Moses Test For Location and Scale Testing

Three nonparametric tests are proposed for the simple tree alternative to test for differences in location and/or scale. These tests are combinations of the Fligner-Wolfe test and a modified Moses test. A simulation study is conducted to determine how well the proposed tests maintain their significance levels. Powers are also estimated for the proposed tests under a variety of conditions for three and four populations. Three different types of variable parameters vectors are considered with each vector containing a location and a scale parameter. The first type of parameter vectors considered include different location parameters and equal scale parameters. The second type include different scale parameters and equal location parameters, and the third type include both different location parameters and different scale parameters. Results are given as far as which test does better under certain conditions.
Knowledge-Aided Adaptive Detection of Radar Target in Gaussian Clutter

This paper presents an adaptive detection method of radar target using the prior knowledge of Gaussian clutter. It is assumed that the clutter covariance matrix is random and obeys the inverse complex Wishart distribution. Based on the prior knowledge, we propose an adaptive detector via utilizing the generalized likelihood ratio test. The proposed adaptive detector does not need training data and detection performance can be achieved due to the use of a priori knowledge about the clutter. Finally, the detection performance of the proposed detector is evaluated and the results illustrate that the proposed detector is superior to the conventional counterparts, particularly for small sample number of the received signal.
DOWNSCALING BUDYKO EQUATION FOR MONTHLY ACTUAL EVAPOTRANSPIRATION ESTIMATION OVER THE EMILIA-ROMAGNA REGION

: This paper presents a modified Budyko equation (Budyko DOWNSCALED) for assessing actual evapotranspiration (AET0). The approach is tested by using 100 controlled and homogeneous meteorological stations located in the Emilia-Romagna region from North of Italy. A period of 55 years, from 1961-1990 and 1991-2015, was analyzed as long-term datasets of monthly values of precipitations, maximum and minimum temperatures. These data have allowed AET0 to be computed both at the yearly and the monthly scale with the Budyko ORIGINAL and Budyko DOWNSCALED formula, respectively. Results of both methods have been compared at the yearly scale, demonstrating that the Budyko DOWNSCALED approach almost correctly reproduces the annual AET0 values (R 2 equal to 0.77 and 0.73 for 1990s and 2015s, respectively) even if slightly underestimated (by 119 mm for 1990s and by 136 mm for 2015s). Further, monthly AET0 values were aggregated over a baseline period (between 1961 and 1990: 1990s) and a recent period (between 1991 and 2015: 2015s). In both baseline and recent periods, AET0 is higher in the summer months (May to September), while it is almost nil in winter season (January, February, and December). Monthly values of AET0 did not increase over the recent period as a result of increased temperatures. Further, this study contributes to the future management of water resources in the region.
The implementation of lesson study as a tool to improve the students’ higher order thinking skill on trigonometric material regarding creative thinking skill

The development of lesson study-based learning tool where students are able to involve directly in the learning so it is expected to improve their creative thinking skill. This research aimed at developing lesson study-based learning tool to help the students improve their creative thinking skill at SMK Darussalam in Trigonometry material validly, practically and effectively. This research used Plomp development model research design consisting of 3 phases covering: (i) Preliminary Research, (ii) Prototyping Phase and (iii) Assessment Phase. This research was the learning tool development research and experimental research by using Quasi Experimental Design method and Non-equivalent Post-test Only Control Group Design. In this research, the sampling technique used was simple random sampling. Out of 7 population classes, 2 sample classes were selected namely class X TKJ 1 as the experimental class which was given the treatment by using teaching material development and class X TKJ 2 as the control class by using direct learning. The data analysis technique used descriptive analysis to test the validity, practicality and effectivity as well as through mix method analysis to analyze the results of the students' learning achievement between the experimental and control classes. The results of this research revealed that: in the pre-test phase, from the experimental class, there were 3 students who belonged to L4 level, 1 student from the control class and in the post-test phase, from the experimental class, there were 8 students who were at L4 and only 1 student from the control class. With a significance of [t=1.15 and df = 61, p>0.05] in the pre-test phase and the value of [t=4.04 and df = 61, p<0.05] in the post-test phase and in the regression test, the values of tcount was 4.649 and ttable in degrees of freedom (df) = N − 2 = 33 − 2 = 31 with a significance of 95% as much as 1.196 with a determination of tcount > ttable (8.296 > 1.196) therefore Ho was accepted and Ha was rejected with a provision that there was a significant influence of lesson study method on the students' creative thinking skill.
EPIDEMIOLOGY, PATHOGENESIS AND SOCIO-ECONOMIC IMPACT OF SARS-CoV-2: AN INVESTIGATIONAL STUDY ACROSS THE BOARD

As per the reports of World Health Organisation (WHO), the first positive case of novel coronavirus infection (COVID-19) triggered on 8 of January 2020 in China and the disease as on 2020 May 13, prevalent over 215 countries. During this period, the virus have claimed over two lac lives and infected over twenty nine lac people around the globe despite substantial prophylactic measures and modernized health-care facilities. The virus causing COVID-19 (Corona virus disease-19) pandemic outbreak was named as severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) and is believed to be transmitted initially from animals to human and later occurrence of person-to-person transmission is reported. India adopted “Lock Down” throughout the country as a strategic measure, well in advance to combat the disease like most of the other foreign countries. As a consequence, all types of large and small industrial sectors have been badly affected, resulting in a major socio-economic impact. The pandemic has created an unprecedented level of risk, causing investors to suffer significant losses in a very short period of time which is the worst crash since 1987 and there is a knock of $1.1 trillion, off the expected growth of global Gross Domestic Product (GDP). The government of India and most of the other countries has declared special packages for the workers of unorganized sectors, women, small and medium sized industries to deal with the situation. This review will focus on the overall information regarding the virus, global transference pattern analysis, socioeconomic impact, treatment and management strategies by different countries and strategies to prevent future viral pandemics like this COVID-19 outbreak.
Collaborative Recommendation System in Users of Anime Films

The recommendation system is one method to know the preference consumer by showing the potential object. This recommendation also helps the consumer gets the preference object. One of the popular objects in the recommendation is anime film. In this case, we conduct research to recommend anime films based on ratings of previously watched films. Collaborative filtering is a technique that consists of calculating similarities, predictions, and recommendations. This study is taken from dataset Kaggle which consists of 73,516 users and 12,294 anime. A user’s history will be matched with the whole user’s history with alternating least squares (ALS) method. The anime will be recommended based on that results. This method is expected to help millions of users find the desired anime.
Big data and machine learning in health

Introduction Big data is defined as the amount of data that once organized and analysed, can make a value, make decisions, make predictions and discover patterns in order to reduce costs, avoid risks and optimize services. Machine Learning (ML) is a field of artificial intelligence and is characterized as a method of machine learning, which uses algorithms that learn from data analysis, allowing computers to find patterns, draw conclusions and make predictions. These tools can be used in different areas of human knowledge, particularly in the health sector which are generated daily a huge amount of information, allowing the creation of algorithms that learn and gain understanding to assist in various clinical practices. Objectives The purpose of this paper is to analyse the benefits of Big Data and Machine Learning in providing overall health care. Methodology We conducted a review of the scientific literature published in the electronic databases PubMed/MEDLINE and Google Scholar, according to specific criteria, using keywords: Big Data”, “Machine Learning". Results In the field of oncology (skin cancer, breast, lung, leukaemia) ML and Big Data have contributed to early diagnosis of different pathologies and their evolution, as well as optimizing therapies. In ophthalmology (diabetic retinopathy and congenital cataract) has shown high efficacy in rapid diagnosis and appropriate treatment crucial to prevent the progress of the disease. The tested algorithms achieved very favourable results in cases of Parkinson’s and cardiovascular diseases. In the pharmaceutical industry these computer and digital tools have contributed to the optimization of clinical trials, genome sequencing of tumours to then identify and develop specific drugs to fight it. Conclusion Advances of MIL and Big data are notorious and development opportunities are immense and can come to revolutionize tasks such as diagnosis, treatment and health care in general.
Genome Resequencing Reveals Genetic Variation between the Parents of An Elite Hybrid Upland Cotton

Cotton is one of the most important economic crops worldwide. As the global demands rising, cotton yield improvement is the most important goal of cotton breeding. Hybrids have great potential for increasing yield, however, the genetic mechanism of hybrids is still not clear. To investigate the genetic basis of cotton hybrids, we resequenced 9053 and sGK9708 with 62.13x coverage depth, the parents of the elite hybrid cotton CCRI63 that has obvious heterosis in lint percentage (LP) and boll weight (BW). Based on the cotton reference genome (TM-1), 1,287,661 single nucleotide polymorphisms (SNPs) and 152,479 insertions/deletions (InDels) were identified in 9053, and 1,482,784 SNPs and 152,985 InDels in sGK9708. Among them, 8649 SNPs and 629 InDels in the gene coding regions showed polymorphism between parents. Moreover, these variations involved 5092 genes, and 3835 of these genes were divided into 10 clusters based on the gene expression profiles. The genes in Cluster 3 and 7 were specifically expressed in the ovule and fiber development stage, suggesting that they might relate to LP and BW. We further co-localized the polymorphic SNPs and InDels with the reported quantitative trait loci (QTLs) of LP and BW, and identified 68 genes containing the polymorphic SNPs or InDels within these QTL intervals and as being related to fiber development. This suggested that the outstanding traits of CCRI63 such as LP and BW might be generated by accumulating the favorable variations from the parents. The results generated herein provide a genetic basis for cotton hybrids and genetic markers for marker-assisted selection breeding of cotton.
Gentlemen on the Road: Understanding How Pedestrians Interpret Yielding Behavior of Autonomous Vehicles using Machine Learning

Autonomous vehicles (AVs) can prevent collisions by understanding pedestrian intention. We conducted a virtual reality experiment with 39 participants and measured crossing times (seconds) and head orientation (yaw degrees). We manipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and the AV size (small, medium, and large). Using machine learning approach, we classified head orientation change of pedestrians by time into 6 clusters of patterns. Results indicate that pedestrian head orientation change was influenced by AV yielding behavior as well as the size of the AV. Participants fixated on the front most of the time even when the car approached near. Participants changed head orientation most frequently when a large size AV did not yield (no-yield). In post-experiment interviews, participants reported that yielding behavior and size affected their decision to cross and perceived safety. For autonomous vehicles to be perceived more safe and trustful, vehicle-specific factors such as size and yielding behavior should be considered in the designing process.
An Application of Positive Psychology in an Inclusive Primary School in Slovakia

In recent years, there has been an increasing interest to identify and assess indicators of school quality in Slovakia. Social-emotional health of pupils and teachers has been identified as one of the most important criterion of school quality. This chapter reports on social-emotional health of pupils and teachers in a Slovak inclusive primary school. The main organizational principles of an inclusive primary school in Bratislava, Slovakia are presented. The social-emotional health of teachers and students was assessed via an adapted Social-Emotional Health Survey-Secondary (SEHS-S). Findings of this pilot project indicate that teachers and pupils reported high level of social-emotional health associated with school’s core organizational principles guided by positive education frameworks.
How Situational Factors at the Workplace Affect Work Engagement among Public Administrators: A mediation moderated framework

Situational factors at work may influence work engagement among employees. With 169 respondents from public administrators in the Johor Bahru Tengah Municipal Council, Malaysia, the findings were derived. The survey data from respondents were analyses using hierarchical multiple regression analysis. Findings showed that ambiguity and imbalance impaired work engagement. Meanwhile, distributive fairness significantly influences work engagement directly and indirectly. The results suggest that the influence of an ambiguous situation at work decreased work engagement, but can be improved, mainly through distributive fairness. Distributive fairness also moderated the linkage between ambiguous situational at work and work engagement. Keywords: Work engagement, Public administrators, Situational factors, Ambiguity, Imbalance, Distributive fairness. eISSN: 2398-4287 © 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5iSI1. 2330
Optimization of heterotrophic feeding of the soft coral Sarcophyton cf. glaucum

Heterotrophic feeding has an important role in the processes of growth and reproduction of mixotrophic corals. The soft coral Sarcophyton cf. glaucum is a good candidate for aquaculture due to its economic interest for the marine aquarium trade and for the bioprospection of marine natural products. The lack of information on heterotrophic feeding of this species with preserved microalgae conducted to development of this work. The present study aimed to evaluate the effect of the conservation processes of microalgae in its suitability as heterotrophic feeding for the mixotrophic coral S. cf. glaucum. Additionally, we aimed to identify the most suitable freeze-dried microalgae species and cell density to be employed in the culture of this mixotrophic coral species. Two experiments were performed: in the first experiment the microalgae Nannochloropsis oculata was supplied to coral fragments in three different preservation forms (live paste, frozen and freeze-dried) at the concentration of 106 cell mL-1; in the second experiment three different microalgae species (Nannochloropsis oculata, Isochrysis galbana and Phaeodactylum tricornutum) were tested in two different amounts: 7.33 mg L-1 (corresponding to the concentration of 106 cell mL-1 of Nannochloropsis oculata) and 3.66 mg L-1. Growth rate, survival, organic weight and photobiology of coral fragments, as well as water quality in culture tanks, were evaluated in both experiments. Preserved forms of microalgae did not demonstrated differences in growth rate, organic weight and survival rate of coral fragments, but affected water quality. Freeze-dried microalgae seems to be a good feed supply for coral aquaculture, as it has the best results and it has the higher shell-life time and the lower associated costs. Between the species evaluated in second experiment, Isochrysis galbana promoted higher specific growth rate and higher percentage of organic weight in the coral fragments; additionally the culture tanks supplied with this microalgae species also presented a better water quality in the end of the experiment.
An Intelligent Learning and Ensembling Framework for Predicting Option Prices

ABSTRACT Estimating option prices and implied volatilities are critical for option risk management and trading. Common strategies in previous studies have relied on parametric models, including the stochastic volatility model (SV), jump-diffusion model (JD), and Black-Scholes model (BS). However, these models are built on several strict and idealistic assumptions, including lognormality and sample-path continuity. In addition, previous studies on option pricing mainly relied on its own market-level indicators without considering the effect of other concurrent options. To address these challenges, we propose an intelligent learning and ensembling framework based on convolutional neural network (CNN). Specifically, the customized nonparametric learning approach is first utilized to estimate option prices. Second, several traditional parametric models are also applied to estimate these prices. The estimated prices are combined by a CNN to obtain the final estimations. Our experiments based on Chinese SSE 50 ETF options demonstrate that the proposed intelligent framework outperforms the traditional SV model, JD model, and BS model with at least 41.52% performance enhancement in terms of RMSE.