Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Phylogeny and Evolution of Amazon Parrots in the Greater Antilles

Amazon parrots (Amazona spp.) colonized the islands of the Greater Antilles from the Central American mainland, but there has not been a consensus as to how and when this happened. Today, most of the five remaining island species are listed as endangered, threatened, or vulnerable as a consequence of human activity. We sequenced and annotated full mitochondrial genomes of all the extant Amazon parrot species from the Greater Antillean (A. leucocephala (Cuba), A. agilis, A. collaria (both from Jamaica), A. ventralis (Hispaniola), and A. vittata (Puerto Rico)), A. albifrons from mainland Central America, and A. rhodocorytha from the Atlantic Forest in Brazil. The assembled and annotated mitogenome maps provide information on sequence organization, variation, population diversity, and evolutionary history for the Caribbean species including the critically endangered A. vittata. Despite the larger number of available samples from the Puerto Rican Parrot Recovery Program, the sequence diversity of the A. vittata population in Puerto Rico was the lowest among all parrot species analyzed. Our data support the stepping-stone dispersal and speciation hypothesis that has started approximately 3.47 MYA when the ancestral population arrived from mainland Central America and led to diversification across the Greater Antilles, ultimately reaching the island of Puerto Rico 0.67 MYA. The results are presented and discussed in light of the geological history of the Caribbean and in the context of recent parrot evolution, island biogeography, and conservation. This analysis contributes to understating evolutionary history and empowers subsequent assessments of sequence variation and helps design future conservation efforts in the Caribbean.
Word recognition with deep conditional random fields

Recognition of handwritten words continues to be an important problem in document analysis and recognition. Existing approaches extract hand-engineered features from word images--which can perform poorly with new data sets. Recently, deep learning has attracted great attention because of the ability to learn features from raw data. Moreover they have yielded state-of-the-art results in classification tasks including character recognition and scene recognition. On the other hand, word recognition is a sequential problem where we need to model the correlation between characters. In this paper, we propose using deep Conditional Random Fields (deep CRFs) for word recognition. Basically, we combine CRFs with deep learning, in which deep features are learned and sequences are labeled in a unified framework. We pre-train the deep structure with stacked restricted Boltzmann machines (RBMs) for feature learning and optimize the entire network with an online learning algorithm. The proposed model was evaluated on two datasets, and seen to perform significantly better than competitive baseline models. The source code is available at this https URL
The role of fibroscan in assessment of liver cirrhosis in patients with chronic liver disease

Objective The aim of this study is to evaluate the accuracy of fibroscan in the assessment of liver cirrhosis in chronic liver disease. Background Fibroscan is a noninvasive imaging study for measuring liver stiffness by transducer probe-induced elastic share wave that propagates through liver tissue to measure its velocity. Patient and methods The present study was conducted on 50 patients with chronic liver disease. There were 32 males and 18 females, and their ages ranged from 34 to 75 years, with mean age of 51 years. The study was conducted in the hepatology units of Menoufiya University Hospitals, and the disease was confirmed by standard diagnostic criteria. All cases were subjected to the following protocol: full history, clinical examination, laboratory investigation, and ultrasound examination. The patients were subjected to fibroscan examination. The elasticity is directly correlated with the degree of hepatic fibrosis, and 7 kPa has been proposed as a cut-off for fibrosis equal to or greater than F2, and 12.5 kPa for cirrhosis. Results Liver stiffness was significantly correlated with liver cirrhosis. The fibroscan technique has high sensitivity and high specificity of 100%, with the area under the receiver operating characteristic curve (95% confidence interval) of 1.00, at the cut-off level of 14.5 kPa. Conclusion Transient elastography is a promising noninvasive method for detection of cirrhosis in patients with chronic liver disease. Therefore, fibroscan can be used regarding the decision of treatment and follow-up of patients with cirrhosis for screening and detection of the complications.
Age-Related Changes in Neural Networks Supporting Complex Visual and Social Processing in Adolescence

Despite our differences, there is much about the natural visual world that almost all observers apparently perceive in common. This coherence across observers is evidenced by the finding that, across adults, approximately 30% of the brain is activated in a consistent fashion in response to viewing naturalistic input. The critical question addressed here is how does this consistency emerge and is this pattern of coherence apparent from early in development or does it evolve with time and/or experience? We focused our investigation at a key developmental juncture that might bridge the child and adult patterns, namely, the period of adolescence. We acquired fMRI BOLD data evoked by an 11-minute age-appropriate movie in younger (age 9-14 years) and older adolescents (age 15-19 years) and in adults. Using an intra-subject correlation approach, we characterized the consistency of the neural response within-individual (across two separate runs of the movie), and then, using an inter-subject correlation approach, evaluated the similarity of the response profile within individuals of the same age group and between age-groups. In primary sensory areas (A1+, V1) the response profiles in both groups of adolescents were highly similar to those of the adults, suggesting that these areas are functionally mature at earlier stages of the development. In contrast, some other regions exhibited higher within-age correlations in the adolescent groups than in the adult group. Last, we evaluated the brain responses across the whole cortex and identified the different patterns of maturation as reflected in different inter-subject correlations across the age groups. Together, these findings provide a fine-grained characterization of functional neural development. The approach offers the potential for careful tracking of the development of widespread cortical networks that support the emerging stereotypical responses to naturalistic visual and social stimuli and has important implications for future studies of cortical development.
Comparative study of the tribological behaviour of 3D-printed and moulded ABS under lubricated condition

In recent times, 3D printing has been extensively used for design and fabrication of different components of certain mechanisms and systems. The orientation of the component during 3D printing has its effect on the surface texture, and thus it impacts the tribological properties of tribopairs. Nonetheless, there is not much work done to comprehend the tribological properties of the components of 3D printing, which may cause uncontrolled wear of tribopairs surfaces. Therefore, the objective of this research is to examine the tribological behaviour of a regularly utilised polymer in 3D printing, namely acrylonitrile butadiene styrene (ABS). The outcomes of this study were evaluated against the moulded ABS. The sample of ABS was fabricated by using the technique of fused deposition modelling (FDM) 3D printing and compression moulding. The tribology analysis was conducted by utilising a ball-on-disc tribometer under paraffin oil lubrication. The analysis was conducted by applying different loads varying from 20∼55 N, sliding distance of 1000 m and sliding speed of 100 rpm at room temperature. Irrespective of the manufacturing process, coefficient of friction (COF) increases as the applied load is increased. Nonetheless, 3D-printed ABS has greater COF (0.040∼0.055) compared to the moulded ABS (0.009∼0.025), though the test running-in period before the steady-state condition is less compared to the moulded ABS. Meanwhile, the wear rate of the 3D-printed ABS decreases as the applied load is increased. In case of moulded ABS, at first, the wear rate increases as the load increases but gets decreased at higher applied load. The inherent surface roughness of 3D printed surfaces and the high COF due to it have still to be overcome. Betterments in the FDM technique can be carried out. The surfaces could also be treated physically or chemically so that they can be smoothened.
DETERMINANTS OF CAPITAL STRUCTURE AND THE 2015 FINANCIAL CRISIS: EVIDENCE FROM KAZAKHSTAN

This study aims to investigate the determinants of capital structure in Kazakhstan and to analyze the effects of the 2015 financial crisis in Kazakhstan on the determinants of the capital structure of large firms. The sample used for the following study includes 4000 to 7000 firms from 2009 to 2017. Results obtained show significance of tangibility, growth, size and liquidity variables on the leverage and increase in the significance of profitability in the post-crisis period.
A decentralized model of Wind turbine optimization

The decoupling of energy prices from fossil fuel is slowly making its way as investment is poured into renewable energy sources. Small Island Developing States are gaining in both stability and cost from this momentum but face threat from the same unsustainable centralization practices. A decentralized framework is proposed for Small Island Developing States aimed at achieving grid stability and in attracting independent financing mechanisms. This framework is applied from a Wind perspective and to ensure replicability on all types of terrains, and the model is analysed through three case studies: high-rise buildings, flat terrains and Gaussian terrains. This study provides a novel framework and a general solution for Wind farming over different terrain layouts including forbidden regions and complex topography.
HOP: Achieving Efficient Anonymity in MANETs by Combining HIP, OLSR, and Pseudonyms

Offering secure and anonymous communications in mobile ad hoc networking environments is essential to achieve confidence and privacy, thus promoting widespread adoption of this kind of networks. In addition, some minimum performance levels must be achieved for any solution to be practical and become widely adopted. In this paper, we propose and implement HOP, a novel solution based on cryptographic Host Identity Protocol (HIP) that offers security and user-level anonymity in MANET environments while maintaining good performance levels. In particular, we introduce enhancements to the authentication process to achieve Host Identity Tag (HIT) relationship anonymity, along with source/destination HIT anonymity when combined with multihoming. Afterward we detail how we integrate our improved version of HIP with the OLSR routing protocol to achieve efficient support for pseudonyms. We implemented our proposal in an experimental testbed, and the results obtained show that performance levels achieved are quite good, and that the integration with OLSR is achieved with a low overhead.
Semantic Matching in Search

Relevance is the most important factor to assure users' satisfaction in search and the success of a search engine heavily depends on its performance on relevance. It has been observed that most of the dissatisfaction cases in relevance are due to term mismatch between queries and documents (e.g., query "NY times" does not match well with a document only containing "New York Times"), because term matching, i.e., the bag-of-words approach, still functions as the main mechanism of modern search engines. It is not exaggerated to say, therefore, that mismatch between query and document poses the most critical challenge in search. Ideally, one would like to see query and document match with each other, if they are topically relevant. Recently, researchers have expended significant effort to address the problem. The major approach is to conduct semantic matching, i.e., to perform more query and document understanding to represent the meanings of them, and perform better matching between the enriched query and document representations. With the availability of large amounts of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. This survey gives a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. The ideas and solutions explained may motivate industrial practitioners to turn the research results into products. The methods introduced and the discussions made may also stimulate academic researchers to find new research directions and approaches. Matching between query and document is not limited to search and similar problems can be found in question answering, online advertising, cross-language information retrieval, machine translation, recommender systems, link prediction, image annotation, drug design, and other applications, as the general task of matching between objects from two different spaces. The technologies introduced can be generalized into more general machine learning techniques, which is referred to as learning to match in this survey.
Persistence of Low Pathogenic Influenza A Virus in Water: A Systematic Review and Quantitative Meta-Analysis

Avian influenza viruses are able to persist in the environment, in-between the transmission of the virus among its natural hosts. Quantifying the environmental factors that affect the persistence of avian influenza virus is important for influencing our ability to predict future outbreaks and target surveillance and control methods. We conducted a systematic review and quantitative meta-analysis of the environmental factors that affect the decay of low pathogenic avian influenza virus (LPAIV) in water. Abiotic factors affecting the persistence of LPAIV have been investigated for nearly 40 years, yet published data was produced by only 26 quantitative studies. These studies have been conducted by a small number of principal authors (n = 17) and have investigated a narrow range of environmental conditions, all of which were based in laboratories with limited reflection of natural conditions. The use of quantitative meta-analytic techniques provided the opportunity to assess persistence across a greater range of conditions than each individual study can achieve, through the estimation of mean effect-sizes and relationships among multiple variables. Temperature was the most influential variable, for both the strength and magnitude of the effect-size. Moderator variables explained a large proportion of the heterogeneity among effect-sizes. Salinity and pH were important factors, although future work is required to broaden the range of abiotic factors examined, as well as including further diurnal variation and greater environmental realism generally. We were unable to extract a quantitative effect-size estimate for approximately half (50.4%) of the reported experimental outcomes and we strongly recommend a minimum set of quantitative reporting to be included in all studies, which will allow robust assimilation and analysis of future findings. In addition we suggest possible means of increasing the applicability of future studies to the natural environment, and evaluating the biological content of natural waterbodies.
TLR4-Induced Inflammation Is a Key Promoter of Tumor Growth, Vascularization, and Metastasis

Toll-like receptor-4 (TLR4) is a powerful pathway best known for inducing inflammation in response to bacteria-produced lipopolysaccharide. TLR4 is also activated by endogenous ligands produced by host-damaged cells and a chemo-drug paclitaxel. Under normal conditions, TLR4 is expressed mainly in macrophages and, at a lower level, in epithelial, endothelial, and stromal cells. Activated TLR4 significantly increases inflammatory cytokines and enhances cell proliferation, migration, invasion, and survival. While these functions in normal cells are essential for host defense and tissue repair, TLR4 overexpression in malignant cells promotes tumor growth and metastasis. This is because pro-oncogenic effects of activated TLR4 in tumor cells are amplified by similar event in TLR4-positive tumor-associated cells including endothelial cells and their mobilized progenitors. The collective activation of multiple cell types within the tumor promotes chemoresistance and metastasis. Here, we summarize the current knowledge of the TLR4 pathway and its functional outcomes in normal and tumor cells. We also discuss its underap-preciated role in supporting tumor progression through vascular activation and recruitment of endothelial progenitors. The review considers several open questions regarding the impact of TLR4-mediated pro- and antitumor effects, structural requirements for recognition of the TLR4 complex, and a potential contribution of chemotherapy to tumor spread.
Impact of Employees Core Self-Evaluations on Employee Engagement: Moderating Role of Organizational Culture

Core self-evaluations have predictive value for crucial work outcomes. However, less attention is given to examine the relationship between employee core self evaluations and employee engagement. Based on trait activation theory present study propose a research framework that examines the relationship of employee core self evaluations with employee engagement accompanied by organizational culture as a moderator. It was assumed that employees core self evaluation traits significantly interacts with their perception about the organizational culture and produce valuable work outcome like employee engagement. Confirmatory factor analysis was performed to demonstrate validity and reliability of proposed model and structural equation modeling was used to examine hypothesized model. The proposed model was supported empirically by data collected from 537 employees working in different branches of Pakistan Telecommunication Company limited (PTCL). The result indicated core self evaluations of employees as predictor of their engagement levels, also innovative and supportive organizational culture were found to have positive moderating effect on the relationship of employee core self evaluations with employee engagement.
ORGANIZATIONAL CULTURE OF ENTERPRISES IN THUA THIEN HUE PROVINCE WITH DENISON MODEL

Abstract: Nowadays, businesses recognize that organizational culture has played a particularly important role in the organization's success. This study aims to identify the cultural traits of enterprises in Thua Thien Hue province by applying the Denison organizational culture model. The results show that the organizational culture of enterprises in Thua Thien Hue province is an internal focus and in a stable and flexible balance. In addition, “Involvement” is the strongest trait, and “Adaptability” is the weakest trait of Thua Thien Hue’s enterprises. Among the 12 indices of the Denison organizational culture model, the index “Team orientation” has the highest score, while “Creating change” and “Agreement” have the lowest. These are important findings for managers to identify the strengths and weaknesses of the organizational cultural system. They can set priorities for adjustment to improve the organizational culture and enhance the competitiveness of enterprises in Thua Thien Hue province.Keywords: organizational culture, Denison model
Comparison of two individual-based models for simulating HIV epidemics in a population with HSV-2 using as case study Yaoundé-Cameroon, 1989-1998

Mathematical modelling has been widely applied to better understand the transmission, treatment and prevention of infectious diseases. The comparison of different models is crucial for providing robust evidence for policy-makers because differences in model properties can influence their prediction. In this study, two individual-based models for simulating HIV epidemics and sexually transmitted infection (STI) co-factor effects, i.e. models developed with the Simpact Cyan 1.0 and StepSyn 1.0 frameworks, were compared. Simpact Cyan 1.0 uses a continuous-time implementation and is updated each time an event happens, while StepSyn 1.0 uses discrete daily or weekly time-steps and updates population, sexual links, and STI states at each time-step. Furthermore, there are differences in how stochastic processes are described, how individuals enter and leave the population, and in the formation and break-up of ties in the sexual network. We evaluated how these differences would affect survival and HIV prevalence curves during the course of a heterosexual HIV epidemic with Herpes simplex virus-2 as contributing STI, using as case study Yaoundé (Cameroon), 1989-1998. To allow such direct comparison, neither modelling framework used its full potential. For each model, 100 simulations were performed with parameters calibrated to HIV prevalence data from Yaoundé between 1989-1998. The median time profile for both models matched the data equally well, but a slight difference in variability among simulations could be observed. This can be explained by differences in model initialization and the generation of the sexual network. Moreover, our example shows that similar HIV prevalence curves can be simulated using a different combination of sexual network and HIV transmission parameters. We conclude that it is important to carefully consider differences in model properties, here in particular their initialization and the generation of sexual networks. We strongly recommend using different models to assess the robustness of evidence provided by mathematical modelling.
On the contribution of yarn–yarn slippage to woven fabric failure

Abstract Failure of woven fabrics involves an interaction between yarns, which can be predicted by yarn slippage. To investigate the contribution of yarn slippage to the failure, we analyzed the relation between various failure properties and critical slippage force (FS) using plain and twill weaves of fabrics with different characteristics. Results show that FS increases with yarn density and yarn linear density. The properties in tension for fabrics with different weaves, yarn density, and yarn linear density superimpose on master curves described by a linear relationship when expressed as a function of FS. The same linear behavior is observed when the critical jamming force of yarns expressed as a function of FS. For the tearing energy, a master curve is also obtained when expressed as a function of FS; however, the curve has a polynomial shape. FS thus appears as a valuable tool to predict the onset of damage in fabrics.
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP

We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at https://github.com/zalandoresearch/flair .