Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographic variation in the duets of the Rufous-naped Wren (Campylorhynchus rufinucha) complex

ABSTRACT Acoustic signals used in animal communication play a key role in mate attraction, species recognition, and territory defense. Variation in acoustic signals may reflect population structure, lack of gene flow, and phylogenetic relationships. In birds, the study of geographic variation in acoustic signals has been useful for elucidating potential factors involved in phenotypic divergence and for establishing species limits. However, most of the studies on geographic variation have focused on calls and solo songs, with few studies focusing on complex behaviors such as duets. In this study, we investigated the variation in the duets of the Rufous-naped Wren (Campylorhynchus rufinucha). Our results suggest that duets vary across the distribution range of the species complex, matching the 3 taxonomic groups previously suggested (rufinucha, humilis, and capistratus). We also found a marked song sexual dimorphism in 2 groups, rufinucha and humilis, that is lacking in capistratus. At the local level, we found that duets also vary among sites within groups, but less than between groups. Our results suggest that groups rufinucha and humilis sing the most similar duets, whereas capistratus performs the most divergent duets of the 3 groups. Further, when comparing duet variation across geography, we found that birds living closer to each other sing more similar duets. We suggest that the existence of 3 vocal lineages is probably the result of historical geographic isolation followed by other selective (i.e. sexual selection, social selection, habitat structure) and/or non-selective factors (i.e. drift, isolation by distance), and that variation found within groups may be the result of cultural drift or social selection.
The Comparative Analysis of Personality and Beliefs About Knowledge Towards Academic Performance at Three Levels Accreditation of Universities in Indonesia : Evidence from Accounting Students in Indonesia

The development of the accountancy profession in Indonesia is not as fast as other Southeast Asian countries. Accounting graduates should be able to fill in the accountancy profession but a little interested. This problem started out of the process of accounting education especially output of the learning process namely Academic Performance. The objective of the study is to investigate the comparative influence of personality and beliefs about knowledge on Academic Performance among on accounting student in Indonesia. The participants of this research were 750 Indonesian accounting students at three level accreditation. Data were analyzed with Regression and path analysis to examine the relationship among the variables. The result showed that personality influenced the academic performance at level B accreditation and beliefs about knowledge influenced the Academic Performance at level A accreditation, and the two of independent influenced the Academic performance at level C accreditation. Furthermore, accounting students at C accreditation university has the largest influence of personality towards Academic Performance. In the other hand, accounting students at A accreditation university has the largest influence of beliefs about knowledge towards Academic Performance . These findings will contribute to the literary education of accounting in which psychological factors required for accounting students to get academic performance best in their studies. Next when they, they were more competent, willing and interested to join the accounting profession. Keywords—Academic Performance, Personality factors, Beliefs about knowledge, Regression Analysis, Accounting profession DOI: 10.7176/RJFA/11-6-13 Publication date: March 31 st 2020
Enhanced Visible-Light Photocatalysis of Nanocomposites of Copper Oxide and Single-Walled Carbon Nanotubes for the Degradation of Methylene Blue

We report enhanced catalytic action of a series of copper(II)-oxide-single-walled carbon nanotube (CuO-SWCNT) composite photocatalysts (abbreviated as CuO-SWCNT-0.5, CuO-SWCNT-2, and CuO-SWCNT-5, where 0.5, 2, and 5 represent the calcination time in hours) synthesized via recrystallization followed by calcination. The photocatalytic performance of the fabricated nanocomposites was examined by evaluating the degradation of methylene blue (MB) under irradiation with visible light. All of the as-fabricated nanocomposites were effective photocatalysts for the photodegradation of a MB solution; however, the CuO-SWCNT-5 displayed the best photocatalytic ability among the investigated catalysts, achieving 97.33% degradation of MB in 2 h under visible-light irradiation. The photocatalytic action of the nanocomposites was remarkably higher than that of pristine CuO nanocrystals fabricated using the same route. The recyclability of the photocatalyst was also investigated; the CuO-SWCNT-5 catalyst could be reused for three cycles without substantial degradation of its catalytic performance or morphology.
Sibling Spillovers: Having an Academically Successful Older Sibling May Be More Important for Children in Disadvantaged Families

This article examines causal sibling spillover effects among students from different family backgrounds in elementary and middle school. Family backgrounds are captured by race, household structure, mothers’ educational attainment, and school poverty. Exploiting discontinuities in school starting age created by North Carolina school-entry laws, we adopt a quasi-experimental approach and compare test scores of public school students whose older siblings were born shortly before and after the school-entry cutoff date. We find that individuals whose older siblings were born shortly after the school-entry cutoff date have significantly higher test scores in middle school and that this positive spillover effect is particularly strong in disadvantaged families. We estimate that the spillover effect accounts for approximately one-third of observed statistical associations in test scores between siblings, and the magnitude is much larger for disadvantaged families. Our results suggest that spillover effects from older to younger siblings may lead to greater divergence in academic outcomes and economic inequality between families.
On the Development of Tools for Modelling Dynamic Beliefs Based on Past Data

In order to develop effective ubiquitous computing systems, we often need to predict an agent’s behaviour based on past data. One way to do this is to maintain a model of what the agent believes at any point in time, as well as a mechanism for changing the beliefs to incorporate new information. In the knowledge representation community, this process is captured through formal belief revision operators. In this paper, we assume that we are monitoring the behaviour of an agent that uses a belief revision operator to incorporate new information; but we do not know exactly which operator is being used. Given past data about the beliefs of the agent, we propose two approaches for predicting future changes in belief. In the first approach, we simply search for all revision operators consistent with the data. In the second approach, we use machine learning to predict if a certain formula will be believed based on past data. We describe work in progress on prototype software to experiment with both approaches, and discuss when each is appropriate. We argue that modelling the dynamic beliefs of an agent in this way can be a useful component of a software system tasked with predicting behaviour when new information is received.
First-principles study of n- and p-type doping opportunities in LiGaO2

First-principles calculations are presented for Si, Ge, Sn doping of LiGaO2. Both Si Ga and Ge Ga are found to be shallow donors. Sn Ga is found to be a somewhat deeper donor. The site preference is studied and all three group-IV dopants are found to favor the Ga over the Li site when the Fermi level is in the upper part of the gap, corresponding to n-type doping and for Li-rich growth conditions. The prospects for n-type doping are thus favorable. N O is found to be amphoteric with both deep donor and acceptor type transition levels. Zn has a significant site competition between Zn Li (donor) and Zn Ga (acceptor) levels, which would pin the Fermi level in the middle of the gap. Thus, p-type doping cannot be achieved with these dopants.
Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction

Student grade prediction (SGP) is an important educational problem for designing personalized strategies of teaching and learning. Many studies adopt the technique of matrix factorization (MF). However, their methods often focus on the grade records regardless of the side information, such as backgrounds and relationships. To this end, in this paper, we propose a new MF method, called graph regularized robust matrix factorization (GRMF), based on the recent robust MF version. GRMF integrates two side graphs built on the side data of students and courses into the objective of robust low-rank MF. As a result, the learned features of students and courses can grasp more priors from educational situations to achieve higher grade prediction results. The resulting objective problem can be effectively optimized by the Majorization Minimization (MM) algorithm. In addition, GRMF not only can yield the specific features for the education domain but can also deal with the case of missing, noisy, and corruptive data. To verify our method, we test GRMF on two public data sets for rating prediction and image recovery. Finally, we apply GRMF to educational data from our university, which is composed of 1325 students and 832 courses. The extensive experimental results manifestly show that GRMF is robust to various data problem and achieves more effective features in comparison with other methods. Moreover, GRMF also delivers higher prediction accuracy than other methods on our educational data set. This technique can facilitate personalized teaching and learning in higher education.
Contribution to the Knowledge of the Phytocenotic Diversity of the Lesser Antilles Revisiting Some Old and More Recent Floristic Data

The complex structure of the abiotic factors in the Lesser Antilles leads to singular vegetation ranging from the dry to the humid. Geomorphology by means of the wide variety of topographic facets is the determining parameter which leads to gradients of mesological factors. Among the latter, both on the windward and leeward facades, the precipitation is distinguished by bioclimatic staging associated with plant stagings which consist of a mosaic of phytocenoses of variable sizes, ages, floristic compositions and structures as well as of different architecture. Based on examples of old and recent floristic surveys by authors which indicate ecotones, types and inversions of vegetation, we have shown the great phytocenotic diversity of the plant cover in the Lesser Antilles which are all biosystemic responses to the effects of natural and anthropogenic hazards. Thanks to this significant ecosystemic plasticity, the Lesser Antilles represent veritable laboratories for autoecological and synecological studies as well as of floristic succession.
Prospect and Potential of Adansonia digitata L. (Baobab) Bast Fiber in Composite Materials Reinforced with Natural Fibers. Part1: Fiber Characterization

ABSTRACT Sustainability and environmental consciousness directed attention toward research and innovation in the field of natural fiber composites. Baobab (Adansonia digitata L.) is tropical tree of the silk-cotton family that is native to Africa. The aim of this study is to pore over the use of baobab bast fibers as potential reinforcement in polymer composites. The baobab bast fibers were extracted from the bark of baobab tree in Sudan. Fibers were investigated by chemical analysis, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and single fiber tensile test. Further, the density and relative humidity of fibers were determined.The cellulose, hemicellulose, lignin, ash, and moisture contents present in the fiber are 60.70%, 21.98%, 5.91%, 5.32%, 13%, respectively. The fiber has a density of 1.1041 g/cm3. FTIR spectra for cellulose and nanocellulose confirms absorption bands characteristic of pure celluloses at 3330, 2919, 1640, 1028. Scanning electron micrographs showed roughening of the fiber surface and crack-like lines indicate fiber clusters being held together. The baobab fiber possessed a crystallinity index of 48.01% with thermal stability up to 250°C. The results revealed that baobab fiber can be utilized as reinforcement in polymer composites.
Effects of ants (Hymenoptera: Formicidae) on flying insect visitor behaviour and fruit production in açaí palm (Euterpe oleracea Martius)

Açaí palm (Euterpe oleracea) production in the Amazon region has grown rapidly in recent decades to meet both domestic and international demand for the fruit. Understanding functional roles of different insects in açaí fruit production is essential for the development of sustainable management practices and the conservation of associated biodiversity in plantations. Ants play a variety of key roles in agroecosystems, particularly as predators, but may also influence crop pollination, either directly (as legitimate pollinators) or indirectly by altering behaviour/density of other flower visitors. Here, we compare flower visitor communities, behavioural interactions between ants and flying insects and fruit production in açaí inflorescences, under the experimental exclusion of ants. Flying insects differed in their response to experimental exclusion of ants, with bees and flies found in higher abundance on inflorescences where ants had been excluded. In contrast, beetles and wasps were unaffected by ant exclosure. Among bees, only medium‐sized species, but not small‐sized and large‐sized species, were affected by the presence of ants. However, fruit production on inflorescences did not differ among ant exclosure treatments. We found clear evidence that ants interfere with flying insects visiting açaí inflorescences, but these changes do not affect açaí fruit production, likely due to the large diversity of flying insects that contribute to pollination services. Therefore, given their potential role as pest natural enemies, control of ant colonies in plantations is not recommended.
Finding the Sweet Spot: Batch Selection for One-Class Active Learning

Active learning methods collect annotations in the form of class labels, often from human experts, to improve upon some classiﬁcation task. In many cases, one can collect annotations for a batch of observations at a time, e.g., when several annotators are available. This can make the annotation process more eﬃcient, both regarding human and computational resources. However, selecting a good batch is dif-ﬁcult. It requires to understand several trade-oﬀs between the costs of classiﬁer training, batch selection, annotation eﬀort, and classiﬁcation accuracy. For one-class classiﬁcation, a very important application of active learning, batch selection has not received any attention in the literature so far. In this article, we strive to ﬁnd a sweet spot between the costs of batch-mode learning and classiﬁcation accuracy. To this end, we ﬁrst frame batch selection as an optimization problem. We then propose several strategies to identify good batches, discuss their properties, and evaluate them on real-world data. A core result is that a sweet spot indeed exists, with active learning costs reduced by up to an order of magnitude compared to a sequential procedure, without sacriﬁcing accuracy.
The impact of wheelchairs driving support systems on the rolling resistance coefficient

The 21stcentury is characterized by an increase in the obesity of society, including people with disabilities. The increase in the weight of people in wheelchairs requires them to have more power to drive these types of vehicles. Diseases and a varied level of disability or infirmity may lead to the inability to overcome the resistance of a vehicle driven by muscle power, such as a wheelchair. The solution to this problem are systems supporting the motion of this type of vehicles among which we can distinguish electric-manual hybrid drives that add energy to the drive system and systems that reduce the amount of force necessary for the movement of the cart lowering at the same time of its moving speed. Systems and mechanisms that have been developed for this purpose contain an increased number of cooperating elements than in a classic wheelchair, leading to an increase in rolling resistance. The article presents the methodology and test results of rolling resistance coefficients of wheelchairs with innovative systems supporting their propulsion showing that the tested auxiliary drives generate an increase in rolling resistance of vehicles for the disabled from 30 % to 75 %.
The complete mitochondrial genome of Neoheterobothrium hirame (Platyhelminthes: Monogenea) from olive flounder (Paralichthys olivaceus)

Abstract The mitogenome of Neoheterobothrium hirame from olive flounder (Paralichthys olivaceus) was obtained and analyzed in this study. The N. hirame circular mitogenome was 14,588 bp in size, consisting of 12 protein-coding genes (PCGs) lacking atp8 gene, 22 tRNA genes, 2 rRNA genes, 1 repeat region and several non-coding regions. The arrangement of PCGs was identical with those of other polyophistocotylean mitogenomes. The phylogenetic tree revealed that N. hirame clustered with Pseudochauhanea macrorchis, Neomazocraes dorsomatis, forming a sister group relationship to microcotylids.
The Everyday Communication of Commitment: Testing an Integrated Model of Self-Construal, Cognition, Affect, Motivation and Communication

People build and sustain their relationship commitment through their everyday communication. Two studies tested an integrated motivational model exploring the multiple ways relational self-construal, cognition, affect, and goals promote commitment-related communication in romantic relationships. Mediational analyses of survey data showed that when participants had higher relational self-construal, stronger commitment, more positive commitment-related affect, and stronger relationship approach goals, they (a) expressed their commitment to their partners more frequently (Study 1, N = 405) and (b) more often monitored their partner’s expressions of commitment (Study 2, N = 154). The findings underscore the integrated, holistic nature of the everyday communication of commitment.
Reconsidering Multilevel Latent Class Models: Can Level-2 Latent Classes Affect Item Response Probabilities?

Abstract Multilevel latent class analysis (MLCA) has been increasingly used to investigate unobserved population heterogeneity while taking into account data dependency. Nonparametric MLCA has gained much popularity due to the advantage of classifying both individuals and clusters into latent classes. This study demonstrated the need to relax the assumption in specifying the nonparametric MLCA: item response probabilities varied only across level-1 latent classes, but not level-2 latent classes. An empirical demonstration with data from the Trends in International Mathematics and Science Study (TIMSS) 2011 showed that item response probabilities could vary across both level-1 and level-2 latent classes. This relaxed MLCA yielded better model fit and provided more nuanced understanding of the heterogeneous response patterns. Monte Carlo simulation was conducted to evaluate class enumeration and assignment accuracy of the relaxed MLCA. Based on the simulation results, we recommended the use of AIC in class enumeration and highlighted the benefits of having larger cluster size.
An Overview of the Anticoagulant Drugs Used in Routine Clinical Practice

Anticoagulant drugs directly or indirectly influence coagulation factors preventing fibrin formation thus preventing blot clotting. They are classified into two groups according to the mode of application, namely parenteral and oral drugs. Among the latter, vitamin K antagonists (most often warfarin) were the only available oral drugs and were widely used for almost a century. In the recent years, new oral anticoagulant drugs became available that directly target either factor IIa or Xa. This chapter provides an overview of both parenteral and oral anticoagulant drugs used in clinical practice with description of the mode of action and management of therapy in different clinical settings.
Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery

Abstract Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.
Cloud Forensic Challenges and Recommendations: A Review

Cloud computing becomes more popular since the emergence of the Fourth Industrial Revolution (IR 4.0) as almost all internet services are highly dependent on high-end networks of server computers. The large-scale used on the internet around the world may cause the cloud server to be highly exposed to cyber threats and it is very difficult to apply forensic method specifically in conducting cloud forensic investigation. Subsequently, the lack of digital investigation may increase the threats towards cloud environment. Consequently, the cloud forensic investigation needs to be recognized for any incident happened in cloud services. Thus, this paper will review the the challenges in conducting a forensic investigation on cloud computing and the challenges are described according to cloud forensic investigation phase, which are identification, collection, examination and analysis, and lastly reporting and presentation. Moreover, recommendation to overcome current cloud forensic challenges which were specified by previous researches also being provided. This review will be beneficial to the community in order to overcome the challenges of cloud forensic investigation in the future.
Teacher’s Belief about the Implementation of Curriculum 2013 in English Language

This research was conducted to investigate teacher`s belief about the implementation of curriculum 2013 in English language teaching. The research method used in this research is descriptive qualitative involving four teachers of junior high schools as the subjects. The data were collected using open-ended interview as the instrument. The result of this research showed that there were many kinds of activities which were mostly done by all participants; (1) teachers’ beliefs about curriculum 2013 understanding, (2) teachers’ beliefs about reaction of the learners, and (3) teachers’ beliefs about assessment in curriculum 2013. The teachers believed that curriculum changes brought positive, innovative, and creative impacts on English for future education. Besides, changes in the current curriculum also give influence on the way teachers and students understand English learning from previously traditionally based learning to modern pedagogical dimension. Therefore, it can be concluded that teachers` belief about curriculum 2013 has an impact on its implementation in English language teaching.