Miracle-dz/UniFAR-FTUs huggingface dataset:
Expansion of Feature Information for Korean Semantic Role Labeling

Semantic role labeling is defined as determination of the semantic relation between a predicate and various arguments that are dependent on the given predicate. In this study, automatic semantic role labeling using 10,000 sentences in a semantic role tagged corpus constructed from a Korean syntax tagged corpus was conducted. In the Korean language, the grammatical relation between particle and word ending as well as their semantic relation is very important. When features based on the affix information created in this study were added to the basic features used in previous studies on semantic role labeling of languages, an F1 score of approximately 80.83% was obtained.
Low serum vitamin D and early dental implant failure: Is there a connection? A retrospective clinical study on 1740 implants placed in 885 patients

Background. Since osseointegration depends on bone metabolism, low levels of vitamin D in the blood may negatively affect bone formation around dental implants. To date, only a few studies have investigated the possible connection between serum levels of vitamin D and early dental implant failure (EDIF), i.e. failure that occurs within 4 months after placement, before the connection of the prosthetic abutment. The aim of this study was to investigate whether there is a relationship between low serum levels of vitamin D and EDIF. Methods. Data used for this retrospective study were derived from the records of a private dental clinic. Inclusion criteria were patients who had been treated with dental implants, inserted with a submerged technique from January 2003 to December 2017. EDIF was the outcome of this study. Chi-squared test was used to investigate the effect of patient-related variables (age, gender, smoking habit, history of periodontal disease and serum levels of vitamin D) on EDIF. Results. Originally, 885 patients treated with 1,740 fixtures were enrolled in this study. Overall, 35 EDIFs (3.9%) were reported. No correlation was found between EDIF and the patients' gender (P=0.998), age (P=0.832), smoking habit (P=0.473) or history of periodontal disease (P=0.386). Three EDIFs (11.1%) were reported in 27 patients with serum levels of vitamin D <10 ng/mL, 20 EDIFs (4.4%) in 448 patients with levels between 10 and 30 ng/mL, and 12 EDIFs (2.9%) in 410 patients with levels >30 ng/mL. Although there was a clear trend toward an increased incidence of EDIF with lowering of serum vitamin D levels, no statistically significant difference (P=0.105) was found among these three groups. Conclusion. Within its limitations (retrospective design, low number of patients with severe blood levels of vitamin D enrolled), this study failed to demonstrate a significant relationship between low serum levels of vitamin D and increased risk of EDIF. However, since a dramatic increase in EDIFs with lowering of vitamin D levels in the blood has been reported, further clinical studies with appropriate design (prospective or randomized controlled studies on a larger sample of severely deficient patients) are needed to better investigate this topic
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the global convergence as well as the local search performance. The DE approach showed here uses an attractive-repulsive, diversity-guided Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in â€œfighting uncertainty with uncertaintyâ€ ​ . This book discusses theoretical aspects of many such algorithms and covers their application in various scientific
Exploring Australia's global trade potential: a gravity approach with panel data

This paper investigates Australia’s global trade potential taking data of Australia and its 57 trading partners for the period of 1972-2006. Gravity model approach and panel data estimation techniques have been used. Providing a theoretical justification for using the gravity model the paper first estimates the augmented gravity models of trade. The estimated coefficients are then used to predict Australia’s trade potential. Our results reveal that Australia’s bilateral trade is affected positively by income, openness of trading partners, common language and free trade agreement, and negatively by the per capita income differential and distance between Australia and trading partners. The results indicate Australia has notable trade potential with Mexico, Argentina, Uruguay, Austria, Peru, India, the Philippines, Brazil, Chile, the USA, New Zealand, Greece, Japan, Turkey, Nepal, Kenya, Spain, Hungary, Brunei, Hong Kong, South Africa, Pakistan and Canada.
Applications of Microfluidics in the Agro-Food Sector: A Review

Background: Microfluidics is of considerable importance in food and agricultural industries. Microfluidics processes low volumes of fluids in channels with extremely small dimensions of tens of micrometers. It enables the miniaturization of analytical devices and reductions in cost and turnaround times. This allows automation, high-throughput analysis, and processing in food and agricultural applications. Purpose: This review aims to provide information on the applications of microfluidics in the agro-food sector to overcome limitations posed by conventional technologies. Results: Microfluidics contributes to medical diagnosis, biological analysis, drug discovery, chemical synthesis, biotechnology, gene sequencing, and ecology. Recently, the applications of microfluidics in food and agricultural industries have increased. A few examples of these applications include food safety analysis, food processing, and animal production. This study examines the fundamentals of microfluidics including fabrication, control, applications, and future trends of microfluidics in the agro-food sector. Conclusions: Future research efforts should focus on developing a small portable platform with modules for fluid handling, sample preparation, and signal detection electronics.
Theoretical Modeling and Analysis of Fiber-pull-out Behaviour from a Bonded Fibrous Matrix: The Elastic-bond Case

A theory to characterize the single-fibre-pull-out behaviour of bonded fibrous systems is presented. Based on the shear-lag mechanism and the statistical-mean microstructural elements in the systems, the theory is developed for structures in which fibers are bonded with each other at contact points, and, as a result, the fiber-bonding with the matrix (the other fibers) is effected through the discrete points instead of a continuous interface. A theory aimed at one single bond is first derived, and it is then extended to the whole fiber by adding up the contributions from all bonds on this fiber into the result so as to predict a stepwise curve of the pull-out force and the embedded fiber length. This theory can also be used, combined with experimental results, to estimate the important bond properties, such as the bond shear strength Tband the shear modulus Gb. A parametric study is carried out and illustrated in this study to reveal the effects of the important factors involved during the fiber-pull-out .
Pear ester based lures for the codling moth Cydia pomonella L. - A summary of research efforts in Hungary

For acceptable capture efficiency it was necessary to add acetic acid to pear ester for successful trapping of codling moth populations in Hungary. The activity of pear ester on its own was very weak and unsatisfactory. Pear ester+acetic acid baited traps caught on an average 25% (mean of 6 tests) of the catch in pheromone traps. Traps with pear ester+acetic acid were clearly advantageous as compared to pheromone traps in that they caught not only males but also females (both virgins and mated) in a high percentage. Traps baited with pear ester+acetic acid clearly outperformed high-load pheromone lures in orchards with mating disruption and should be the right choice for the grower for sampling populations of codling moth in a mating disruption situation. In orchards with no mating disruption the relative inefficiency of pear ester+acetic acid baited traps as compared to pheromone traps can easily be overcome by applying more traps than usual. Thus the overall codling moth numbers caught will become highe.
Effect of cement kiln dust on Geopolymer composition and its resistance to sulphate attack

Utilization of cement kiln dust (CKD) with its high alkali content in the activation of geopolymer specimens to create nonconventional cementitious binders was investigated. Relatively high alkaline content of CKD is predominant factor preventing its recycling in cement manufacture. It was observed that depending on the water-soluble alkalis and sulfate compounds, CKD could provide the necessary environment to activate geopolymer materials. Materials used in this investigation are ground granulated blast furnace slag (GGBFS), air cooled slag (ACS), cement kiln dust (CKD) and calcined kaolin. Calcinations process was done by kaolin firing at 750°C for 3 hours. Alkaline activation by 2% NaOH along with the added cement dust was studied as compared with that not activated by sodium hydroxide. Curing was performed at 38°C under 100% relative humidity. Results showed that 25% CKD is the optimum ratio for geopolymer formation and activation of ggbfs by 2% NaOH along with 25% CKD results in best enhancement in mechanical as well as microstructural characteristics. Activation of low iron metakaolin by 50% CKD and 2% NaOH has the lowest mechanical properties due to calcium deficiency that bind geopolymer matrix. Effect of 5% magnesium sulfate on alkali activated water cooled slag and metakaolin (high iron content) by cement kiln dust results in an enhancement in their mechanical properties up to three months, while subjected to a strength loss up to six months.
Review on WEBV-RTPN : Retrieve Person Names from Web Video

The Internet hosts vast amounts of content of different types including text, images, and video. Leveraging this content requires the content to be searchable and organized. Images are generally searched and organized based on identifiers that are manually assigned by users. The distinguishing proof of superstar face pictures are given that create a name rundown of unmistakable famous people, get an arrangement of pictures and comparing highlight vectors for every name, identify confronts inside the arrangement of pictures, and evacuate non-confront pictures. An examination of the pictures is performed utilizing an intrademonstrate investigation, a between model investigation, and an unearthly examination to return very exact biometric models for each of the people exhibit in the name list. Recognition is then performed based on precision and recall to identify the face images as belonging to a celebrity or indicate that the face is unknown. In particular, when an image is that of a person's face, the recognition of that face by a person can be done with extremely high accuracy despite large variations in appearance, lighting, and expressions. Computer vision systems, on the other hand, have had a difficult time in performing recognition at the level of accuracy of a human being. Datasets of famous people have become available, an effort to recognize celebrities in the news has also occurred. Algorithms for face identification, verification, and recognition have been developed that typically contain datasets constrained to news pictures that are usually of high quality, taken in controlled environments, and in controlled poses. In contrast, generic images of people of interest in uncontrolled environments lack the ability to be automatically recognized and verified. Partner confronts showing up in Web recordings with names introduced in the encompassing setting is a vital assignment in numerous applications. Be that as it may, the issue is not all around researched. The assignment of unsupervised face-name affiliation has gotten extensive interests in sight and sound and data recovery groups. The errand of face-name affiliation ought to comply with the accompanying three limitations: (1) a face must be relegated to a name showing up in its related subtitle or to invalid; (2) a name can be doled out to at most one face; and (3) a face can be appointed to at most one name.
On the Distinction of Subjectivity and Objectivity of Emotions in Texts

Emotion classification in texts is an instance of the text classification problem. It therefore could apply some existing text classifiers by considering each emotion as a label of the text. However, most of recent works does not differentiate the subjectivity and objectivity of the same emotion in the text. This paper firstly builds some datasets whose labels are emotion, in which the subject and object of the same emotion are considered as two separated labels. Secondly, this paper evaluates some existing classifiers via some scenarios on the built datasets. The results are then discussed on some difficulties of these kinds of problem.
Mapping landscape scale variations of forest structure, biomass, and productivity in Amazonia

Abstract. Landscape and environmental variables such as topography, geomorphology, soil types, and climate are important factors affecting forest composition, structure, productivity, and biomass. Here, we combine a network of forest inventories with recently developed global data products from satellite observations in modeling the potential distributions of forest structure and productivity in Amazonia and examine how geomorphology, soil, and precipitation control these distributions. We use the RAINFOR network of forest plots distributed in lowland forests across Amazonia, and satellite observations of tree cover, leaf area index, phenology, moisture, and topographical variations. A maximum entropy estimation (Maxent) model is employed to predict the spatial distribution of several key forest structure parameters: basal area, fraction of large trees, fraction of palms, wood density, productivity, and above-ground biomass at 5 km spatial resolution. A series of statistical tests at selected thresholds as well as across all thresholds and jackknife analysis are used to examine the accuracy of distribution maps and the relative contributions of environmental variables. The final maps were interpreted using soil, precipitation, and geomorphological features of Amazonia and it was found that the length of dry season played a key role in impacting the distribution of all forest variables except the wood density. Soil type had a significant impact on the wood productivity. Most high productivity forests were distributed either on less infertile soils of western Amazonia and Andean foothills, on crystalline shields, and younger alluvial deposits. Areas of low elevation and high density of small rivers of Central Amazonia showed distinct features, hosting mainly forests with low productivity and smaller trees.
Performance Evaluation of Different Pattern Matching Algorithms of Snort

Snort is the most widely deployed Network Intrusion Detection System (NIDS) whose performance is dominated by the pattern matching of packets in the network. In this paper, we present an experimental evaluation and comparison of the performance of different pattern matching algorithms of Snort NIDS namely ac-q, ac-bnfa, acsplit, ac-banded and ac-sparsebands on Linux Operating System (Ubuntu Server 16.04). Snort's performance is measured by subjecting the server running Snort v2.9. 9.1 to live malicious traffic and a standard dataset. The performance is calculated and compared in terms of throughput, memory utilization and CPU utilization.
2 Gene Regulatory Network Inference : Problem Statement and Available Literature

High-throughput omics data offers measurements for cell components at different stages of the central dogmatic view of information flow. This data can be used to construct a network level understanding of biological systems and functions such as inferring a gene regulatory network. Many computational techniques are readily available to infer gene regulatory networks from high-throughput experiments. One suggested way of improving the performance of these techniques is the use of domain-specific prior information such as the distribution of the number of regulatory links in biological networks, which is known to be restricted exponential for indegree and scale-free for outdegree. We perform a systematic study of three different ways of incorporating this prior information in the network inference task. The analysis shows that the prior helps both globally and locally in the performance. We further explore how the prior affects the prediction confidence for different motif structures.
Comparison of 3 Level and 5 Level NPC Inverter using SPWM

Multilevel inverters are becoming increasingly popular for high and medium power applications. The diode clamped multilevel inverter (DCMLI) is an attractive high voltage multilevel inverter due to its robustness. For pulse generation the Sinusoidal Pulse Width Modulation (SPWM) technique is used. In this paper a comparative study of 3level multilevel inverter and 5 level multilevel inverter using Sinusoidal Pulse Width Modulation is done. The total harmonic distortion present in the output waveform of 5 level multilevel inverter is reduced compared to 3 level inverter. The inverter is connected to a 1HP induction motor and its performance analysis is done. Torque ripple is reduced in a 5 level inverter compared to 3 level inverter. The result is verified using Matlab simulation.
An Improved Mining of Biomedical Data from Web Documents Using Clustering

: Now a day’s web is the main source of information in every field. Web is also expanding exponentially day by day. To get the relevant information is very time consuming and is not a very easy task. Mainly users go for the various search engines to search any information. But sometimes search engines are not able to give the useful results as most of the web documents are present in unstructured manner. Data mining is extraction of information from large database. Text mining uses the many techniques of data mining. In web, biomedical documents are also increasing at a very fast pace but most of them are unstructured text. These documents can be very helpful in diagnostics, treatment and prevention of any disease. There are we millions of documents on internet about a specific term so to obtain a relevant document is very difficult. The goal of this is to apply text mining techniques to retrieve useful biomedical web documents. Here a more efficient mechanism is proposed which uses the optimised K-means clustering algorithm where it can group the similar documents in one place. This approach will help the user to get all the relevant biomedical documents at one place. On comparing our approach with the original k-means algorithm and found that our algorithm on an average giving 99.06 % F-measure.
Graphene plasmons embedded in a gain medium

Graphene plasmonics has attracted much attention because of its remarkable properties such as tunable conductivity and extreme confinement. However, one of the major drawbacks to develop more efficient devices based on graphene plasmons remains losses. Here we study how the introduction of a gain medium surrounding graphene can affect plasmonic modes, both in 1D and 2D settings. We show the presence of a critical gain in 1D structures when the losses are entirely compensated. Furthermore, using numerical simulations and analytical models, we demonstrate a resonant gain leading to enhanced absorption in 2D graphene ribbons.
Nanosponges: A Novel Drug Delivery System

The invention of nanosponges (NSs) has become a significant step toward overcoming the problems associated with conventional drug delivery. NS drug delivery system has emerged as one of the most promising fields in life science. These small sponges circulate around the body until they encounter the target site and stick on the surface and began to release the drug in a controlled and predictable manner which is more effective for a particular given dosage. The development of new colloidal carrier called NSs has the potential to solve these problems. Both lipophilic and hydrophilic drugs can be loaded in NSs. NS is a novel and emerging technology which offers controlled drug delivery for topical use. They can be crafted for targeting drugs to a specific site, prevent drug and protein degradation, and prolong the drug release in a controlled manner. NSs play a key role in targeting drug delivery in a controlled manner. In this review article, application of NSs, its preparation methods, and evaluation have been discussed.
Tests for Symmetric and Asymmetric Nonlinear Mean Reversion in Real Exchange Rates

New tests, based on smooth transition autoregressive models, for mean reversion in time series of real exchange rates are proposed. One test forces mean reversion to be symmetric about the integrated process central case, while the other permits asymmetry. The tests are applied to monthly series of seventeen real exchange rates against the U.S. dollar and fourteen against the deutsche mark. They reveal stronger evidence against the unit root null hypothesis than does the usual Dickey-Fuller test.
High-level Decision Making for Safe and Reasonable Autonomous Lane Changing using Reinforcement Learning

Machine learning techniques have been shown to outperform many rule-based systems for the decision-making of autonomous vehicles. However, applying machine learning is challenging due to the possibility of executing unsafe actions and slow learning rates. We address these issues by presenting a reinforcement learning-based approach, which is combined with formal safety verification to ensure that only safe actions are chosen at any time. We let a deep reinforcement learning (RL) agent learn to drive as close as possible to a desired velocity by executing reasonable lane changes on simulated highways with an arbitrary number of lanes. By making use of a minimal state representation, consisting of only 13 continuous features, and a Deep Q-Network (DQN), we are able to achieve fast learning rates. Our RL agent is able to learn the desired task without causing collisions and outperforms a complex, rule-based agent that we use for benchmarking.
Phylogenetic Analysis of Anaerobic Co-Digestion of Animal Manure and Corn Stover Reveals Linkages between Bacterial Communities and Digestion Performance

Over 3 million tons of manures are produced annually in the United States and pose environmental and health risks if not remediated. Anaerobic digestion is an effective method in treating organic wastes to reduce environmental impacts and produce methane as an alternative energy. Previous studies suggested that optimization of feed composition, hydraulic retention time, and other operational conditions can greatly improve total solids removal and increase methane productivity. These environmental factors improve functionality by altering the microbial community structure but explicit details of how the bacterial community shifts are poorly understood. Our investigations were conducted to investigate the relationship between environmental factors, microbial community structure and bioreactor efficiency by using metagenomic analysis of the microbial communities. Our results indicated that the bioreactor with the greatest methane production, digestion efficiency and reduced levels of E. coli/Shigella had a distinctive community structure at the genus level with unique and abundant uncultivated strains of Bacteroidetes. Moreover the same bioreactor was enriched in Aminomonas paucivorans and Clostridia populations that can utilize secondary metabolites produced during cellulose/hemicellulose degradation to generate hydrogen and acetate. Hence specific digestion conditions that enrich for these populations may provide a route to the optimization of co-digestion systems and control the variability in reactor performance.