Miracle-dz/UniFAR-FTUs huggingface dataset:
FATTY ACID COMPOSITION AND ANTIBACTERIAL ACTIVITY OF CLOVE (Syzygium aromaticum) AND CARROT (Daucus carota) AS CANDIDATE OF ADDITIVE FOR BROILER CHICKENS

This study aimed to evaluate the fatty acids and to determine the antibacterial activity of clove (Syzygium aromaticum) and carrot (Daucus carota) against the pathogen Escherichia coli and Staphylococcus aureus. Clove and carrot were prepared from fresh plants in three concentrations (2.5%, 5% and 10%), the positive control of chloramphenicol and the negative control of DMSO. Disc diffusion method has been used to determine the antimicrobial activities of clove and carrot. The results showed that dominant fatty acids in flower of clove were Linoleic acid, Palmitic acid, Cis-13,16Docosadienoic acid, Stearic acid, Oleic acid, Linolenic acid, -Linolenic acid, and fatty acid total was 9.59% w/w, whereas fat content of clove was 4.19 %w/w. Moreover, in carrot, Linoleic acid was the major component followed by Palmitic acid, Linolenic acid, Oleic acid, Stearic acid, -Linolenic acid, and fatty acid total was 37.65% w/w, whereas fat content of carrot was 2.21% w/w. Inhibition zone of clove for E. coli at concentration between 2.5%, 5%, and 10% showed the same response (6 mm),and was lower than that of positive control chloramphenicol (7.69 mm), however, for S. aureus inhibition zone at concentration 10% (8.64 mm) was almost the same with positive control (10.52 mm). Inhibition zone of carrot for E. coli at concentration 2.5% was the same with positive control (9.75 mm), moreover, for S. aureus at concentration 10% was lower than positive control but still proved effective against bacteria. It can be concluded that clove flower was better as natural antimicrobials for E. coli and carrot was for S. aureus. So, clove and carrot can be used as feed additives in broiler diet
A Comparison of Single and Multi-View IR image-based AR Glasses Pose Estimation Approaches

In this paper, we present a study on single and multi-view image-based AR glasses pose estimation with two novel methods. The first approach is named GlassPose and is a VGG-based network. The second approach GlassPoseRN is based on ResNet18. We train and evaluate the two custom developed glasses pose estimation networks with one, two and three input images on the HMDPose dataset. We achieve errors as low as 0.10° and 0.90mm on average on all axes for orientation and translation. For both networks, we observe minimal improvements in position estimation with more input views.
INTERNATIONAL REMITTANCES AND ECONOMIC GROWTH OF NIGERIA Key Words: Remittances, Economic growth, ARDL cointegration, Investment, and Nigeria. ONYIKE, SIXTUS CHIMEZIE

The world is becoming a global village, with the concept of globalization having a firm grip on the world; easy migration is the resultant effect. International migration has turned into a veritable measure adopted by persons families and communities from mostly less developed countries like Nigeria in order to survive and overcome the harsh then unfavourable economic realities manifested in high level of poverty and economic uncertainty. After the recent global recession occasioned by the global financial downturn, the amount of foreign direct investment and foreign aid flowing into developing countries dwindled causing a serious gap and crisis in government finance and economic outlook but foreign remittance inflow seems to be a cushion. Even as various researchers have maintained that over the recent decades, remittances have turned out to be a veritable, reliable and secured source of foreign finances inflow given a drastic reduction in FDI and foreign aid, especially in the less developed economies (Giuliano & Ruiz-Arranz, 2009; Adenutsi, 2011; Rao & Hassan, 2011). The World Bank (2003) further asserted that remittances inflow happen to be reliably secured and a constant source of inflow than the traditional sources of foreign financial inflows which many experts argue that it tends to be pose a countercyclical effect on the economy.
Using Language Technologies to Automate the UNDP Rapid Integrated Assessment Mechanism in Serbian

Rapid Integrated Assessment (RIA) is a United Nations Development Programme procedure involving a comparison between a country’s development policy documents and the UN-defined Sustainable Development Goals. In this paper, we present the Serbian AutoRIA system that automates this procedure in Serbian, a resource-limited yet morphologically rich language. We discuss the issues regarding the preprocessing of data for this task, and the general architecture and language-related specificities of the system. We also evaluate the performance effects of various system settings using the results of a previous, manually completed RIA procedure for Serbia.
A New Clustering Algorithm for Attributive Graphs through Information Diffusion Approaches

Background and Objectives: Simplicity and flexibility constitute the two basic features for graph models which has made them functional models for real life problems. The attributive graphs are too popular among researchers because of their efficiency and functionality. An attributive graph is a graph the nodes and edges of which can be attributive. Nodes and edges as structural dimension and their attributes as contextual dimension made graphs more flexible in modeling real problems. Methods: In this study, a new clustering algorithm is proposed based on K-Medoid which focuses on graph’s structure dimension, through heat diffusion algorithm and contextual dimension through weighted Jaccard coefficient in a simultaneous matter. The calculated clusters through proposed algorithm are of denser and nodes with more similar attributes. Results: DBLP and PBLOG real data sets are applied to evaluate and compare this algorithm with new and well-known cluster algorithms. Conclusion: Results indicate the outperformers of this algorithm in relation to its counterparts as to structure quality, cluster contextual and time complexity criteria. ======================================================================================================Copyrights©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers. ======================================================================================================
Pergamon and Pergamene Influence

a visitor in athens today, in front of the reconstructed Stoa of attalos II, experiences the full effects of pergamene construction as she can nowhere else (Figure 28.1). the stoa defines the eastern edge of the athenian agora, provides versatile spaces for archaeological storage and a museum, announces its pergamene style through palm column capitals and other signs, beckons visitors to enter through its wide colonnade, shields them from the extremes of hot and cold weather once inside, and makes them think about the attalids of pergamon all the while. although attalos II obviously could not have predicted the presence of modern archaeologists and tourists in athens, the attalids did indeed plan for their construction to define space and to facilitate the goings‐on of daily life at pergamon and elsewhere.
Hot topic prediction with deep learning Short term text-based predictions of word frequencies using a deep neural network based approach

Can we predict the topics in the news that will be spoken about more tomorrow based on the text produced in the previous days? That is the question leading to this thesis report. Based on a word frequency representation of all articles from the previous days, a prediction is made about the word frequencies of the text produced tomorrow. The dimensionality of this problem (due to the number of words used in natural language) is very large. Furthermore the temporal patterns in the data are expected to be complex, making this an interesting problem. If we predict the frequency of a word based on the past frequencies of all other words, the required model becomes too large. To reduce the dimensionality we create a form of local context to base the prediction for target words on. This local context is extracted from a semantic landscape, which is generated by the Puzzle algorithm proposed in this thesis. The actual predictions are made using a deep neural network approach, which is inspired by the use of deep networks in computer vision. The quality of the resulting semantic landscape needs improvement, and the deep neural network approach to word frequency prediction seems promising.
THEORETICAL PHYSICS TOPICS IN BAYESIAN STATISTICS AND MAXIMUM ENTROPY

Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling, The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information
Totally Positive Functions in Sampling Theory and Time-Frequency Analysis

Totally positive functions play an important role in approximation theory and statistics. In this chapter I will present recent new applications of totally positive functions in sampling theory and time-frequency analysis. (i) We study the sampling problem for shift-invariant spaces generated by a totally positive function. These spaces are spanned by the integer shifts of a totally positive function and are often used as a substitute for the Paley-Wiener space of bandlimited functions. We give a characterization of sampling sets for a shiftinvariant space with a totally positive generator of Gaussian type in the style of Beurling and Landau. (ii) A related problem is the question of Gabor frames, i.e., the spanning properties of time-frequency shifts of a given function. For a subclass of totally positive functions their lattice shifts generate a frame, if and only if the density of the lattice exceeds 1. So far, totally positive functions seem to be the only window functions for which a complete characterization of the associated Gabor frames is tractable. (iii) Yet another question in time-frequency analysis is the existence of zeros of the short-time Fourier transform. So far all examples of zero-free ambiguity functions are related to totally positive functions, e.g., the short-time Fourier transform of the Gaussian is zero free.
edge2art: Edges to Artworks Translation with Conditional Generative Adversarial Networks

Abstract. This paper presents an application of the pix2pix model [3], which presents a solution to the image to image translation problem by using cGANs. The main objective of our research consists in the evaluation of several artificial artworks that were generated by the cGAN, taking a scribble of edges as input. This evaluation covers different artistic movements and art styles such as Rococo, Ukiyo-e, Fauvism and Cubism. The set of the trained models of these different styles is called edge2art. Each art style was trained over more than 2000 artworks examples taken from the wikiart dataset used in ArtGAN [4]-[5]. The experiments consists in giving scribbles images to the cGAN, and depending on the selected style, the network will give an colored and stylized artwork as output. Comparison between the generated artworks and the target artworks are measured by Mean Squared Error and Structural Similarity Measure.
The COSMO and COSMO‐RS solvation models

The conductor‐like screening model COSMO, a variant of the dielectric continuum solvation models, has become very popular due to its algorithmic simplicity, numerical stability, and its great insensitivity with respect to outlying charge errors. The advanced model COSMO‐RS, i.e., COSMO for realistic solvation, is a statistical thermodynamics theory based on COSMO polarization charge densities, which overcomes many of the limitations and theoretical shortcomings of dielectric continuum models. Due to its ability to treat mixtures at variable temperatures, it has become very popular in chemical engineering and in wide areas of physical and medicinal chemistry. COSMO‐RS currently may be considered as the most accurate model for the prediction of solvation energies. This article provides a short description of the basic concepts of both the models, of the differences with other solvation models and of their application areas. Finally, direct COSMO‐RS, a recent direct integration of the COSMO‐RS concept into quantum chemical calculations, is briefly described. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 699–709 DOI: 10.1002/wcms.56
Estimate of Symmetric 2-adic Complexity of Ding-Helleseth Generalized Cyclotomic Sequences of Order Four With Period pn

In this paper, the symmetric 2-adic complexity of Ding-Helleseth generalized cyclotomic sequences of order four with period equal to a power of an odd prime is studied. The estimate of symmetric 2-adic complexity of these sequences is obtained. It is shown that above sequences have large symmetric 2-adic complexity.
THE INFLUENCE OF HAND GUARDS ON EXPLOSIVE FORCE AND PAIN AND EXERTION PERCEPTION IN A HANG HOLDING TASK

We investigated whether hand guards (HG) influence the perception of pain and exertionduring the execution of a standardized task on high bar to induce forearm muscle fatigue aswell as a decline in grip strength after the task. Design: A cross-over study design wasemployed 15 healthy and physically active volunteers completed static bodyweight holds (8cycles of 20 second load in hang and 10 second rest), on a high bar. The exercise protocolwas performed with and without HG. Perception of pain and exertion during the task wererecorded. Peak handgrip force and explosive force parameters (i.e., rate of force development[RFD] and contractile impulse [CI] at 30 to 200 ms) were obtained from force-time curves.Peak force and explosive force parameters were normalized (i.e., POS/PRE) for statisticalanalysis. The use of a HG significantly attenuates pain perception (p < 0.05), with amoderate to large effect size (d = 0.52), but did not alter the perception of exertion during thetask, nor did it alter peak force, RFD, or CI. The use of HG reduces the perception of localpain during static holds. However, HG do not alter the perception of exertion during the tasknor do they alter the gripping force ability immediately afterwards. This research did notreceive any specific grant from funding agencies in the public, commercial, or not-for-profitsectors.
Impacts of fertilization on grassland productivity and water quality across the European Alps under current and warming climate: insights from a mechanistic model

Abstract. Alpine grasslands sustain local economy by providing fodder for livestock. Intensive fertilization is common to enhance their yields, thus creating negative externalities on water quality that are difficult to evaluate without reliable estimates of nutrient fluxes. We apply a mechanistic ecosystem model, seamlessly integrating land-surface energy balance, soil hydrology, vegetation dynamics, and soil biogeochemistry, aiming at assessing the grassland response to fertilization. We simulate the major water, carbon, nutrient, and energy fluxes of nine grassland plots across the broad European Alpine region. We provide an interdisciplinary model evaluation by confirming its performance against observed variables from different datasets. Subsequently, we apply the model to test the influence of fertilization practices on grassland yields and nitrate ( NO 3 - ) losses through leaching under both current and modified climate scenarios. Despite the generally low NO 3 - concentration in groundwater recharge, the variability across sites is remarkable, which is mostly (but not exclusively) dictated by elevation. In high-Alpine sites, short growing seasons lead to less efficient nitrogen (N) uptake for biomass production. This combined with lower evapotranspiration rates results in higher amounts of drainage and NO 3 - leaching to groundwater. Scenarios with increased temperature lead to a longer growing season characterized by higher biomass production and, consequently, to a reduction of water leakage and N leaching. While the intersite variability is maintained, climate change impacts are stronger on sites at higher elevations. The local soil hydrology has a crucial role in driving the NO 3 - use efficiency. The commonly applied fixed threshold limit on fertilizer N input is suboptimal. We suggest that major hydrological and soil property differences across sites should be considered in the delineation of best practices or regulations for management. Using distributed maps informed with key soil and climatic attributes or systematically implementing integrated ecosystem models as shown here can contribute to achieving more sustainable practices.
Chapter 12: TRAINING RECURRENT NETWORKS FOR FILTERING AND CONTROL

Neural networks can be classified into recurrent and nonrecurrent catego Nonrecurrent (feedforward) networks have no feedback elements; the outp calculated directly from the input through feedforward connections. In recur networks the output depends not only on the current input to the network, bu on the current or previous outputs or states of the network. For this rea recurrent networks are more powerful than nonrecurrent networks and important uses in control and signal processing applications. This chapter introduces the Layered Digital Recurrent Network (LDR develops a general training algorithm for this network, and demonstrates application of the LDRN to problems in controls and signal processing. In Sec II we present the notation necessary to represent the LDRN. Section III con a discussion of the dynamic backpropagation algorithms that are require compute training gradients for recurrent networks. The concepts underlying backpropagation-through-time and forward perturbation algorithms are prese in a unified framework and are demonstrated for a simple, single-loop recu network. In Section IV we describe a general forward perturbation algorithm computing training gradients for the LDRN. Two application sections follow discussion of dynamic backpropagation: neurocontrol and nonlinear filter These sections demonstrate the implementation of the general dyn backpropagation algorithm. The control section (Section V) applies neurocontrol architecture to the automatic equalization of an acoustic transm The nonlinear filtering section (Section VI) demonstrates the application recurrent filtering network to a noise-cancellation application.
Performance Evaluation according to RSU Range in Vehicle-to-Vehicle Communications

Recently, wireless communication technology and Intelligent Transportation System (ITS) technology in vehicles have been developed. Therefore, it becomes a vehicle and the necessary communication skills for improving safety and traffic flow for drivers has been the study of the Vehicular Ad-hoc Network (VANET) based vehicle communication technology progress. This study aims to implement a simulation of a VANET. It is a special kind of Mobile Ad-hoc Network (MANET) adapted to the communication between vehicles. It moves at high speed and is further classified into vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication methods. To develop a VANET-based vehicle wireless communication technology, it is necessary to establish a test environment and synchronize vehicle mobility in various road scenarios. However, the equipment is expensive for the built experiment environment, and the road traffic situation is difficult to predict. In this study, the simulation of VANET is intended to be tested in an urban environment, and the result can be used also to develop the Media Access Control (MAC) protocols. This paper proposes a scheme for experiments in a similar environment to actual urban traffic and analyzes the performance by dividing it into V2I and V2V nodes according to the Road-Side Unit (RSU) coverage.
Nearest Target for Retina Images Based On Canny Edge Detector and Ensemble Classifiers

For retina problems diagnosing, valuable information to ophthalmologists are provided by Retinal fundus images. Curing rate can be enhanced using early detection and it may prevent blindness. Using retinal fundus images, medical experts diagnosis retinal problems like retinitis pigmentosa and diabetic retinopathy. Mutual Information (MI) optimization is initialized as coarse localization process, where optimization domain is narrowed and local optima are avoided in recent works. In addition, Improved Support Vector Machine (ISVM) technique is used for performing retina image’s nearest template and it is used with registration based on area, provides a robust technique. However, proper detection of retina image’s edges are not done using these techniques and concerned region’s visibility and perceptibility. Performing Retina image’s nearest template according to single classifiers leads to degradation in classifier accuracy. So, for avoiding this issue, a four stage framework is proposed in this work. Fuzzy clustering algorithm based noise removal is done in the first phase, where similarity among un-noisy and noisy pixels are computed. Canny edge detection operator based edge detection and enhancement are done at the second stage. Dimension reduction is done in the third stage, where Mutual Information (MI) optimization is initialized as coarse localization process, where optimization domain is narrowed and local optima are avoided. Fuzzy neural network (FNN), Probabilistic neural network (PNN), Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensemble is used for computing retina image’s nearest template in fourth stage. For STARE dataset, with respect to run time rate, success rate and mean error rate, proposed model’s robustness is demonstrated using experimental results.
Pleistocene and Holocene history of Rhyacophila aquitanica (Insecta: Trichoptera) in the Carpathian Mountains, potential speciation centers

The present study was carried out to investigate genetic relationships among populations of a mountain Trichoptera species. While the European mountains harbor an important part of European biodiversity, mountain aquatic and semi-aquatic species are still not sufficiently investigated. We chose Rhyacophila aquitanica as model organism: the populations of this species are known from several European mountains, but the distribution of the species is very fragmented, raising a number of questions about the origin and identity of different populations. Previous work describing taxonomic structures within R. aquitanica also supported our choice. Specimens were obtained from almost all major distributional areas of the species. A number of morphometric and genetic analyzes were carried out on collected and museum specimens. Based on the results of morphometric and genetic studies Carpathian and Southern Alps populations of what was previously considered R. aquitanica are now considered as distinct taxonomic entities. The study revealed important aspects of speciation events among the investigated species and thus presented evidence for allopatric differentiation in the European mountain systems. The study proves that many of the observed present-day structures of the European mountain diversity may be much older than the last Pleistocene glaciation. In the case of the R. aquitanica complex the differentiation events probably happened several hundreds of thousand years earlier than the last glacial maxima among the Carpathian, western European and Southern Alps clades. The present study also points towards the importance of mountain aquatic habitats as biodiversity hotspots and emphasizes their role in the preservation of the European mountain biodiversity.