Miracle-dz/UniFAR-FTUs huggingface dataset:
Is the Tunisian Central Bank Following a Linear or a Nonlinear Augmented Taylor Rule ?

The political transition in the Arab Spring countries has been accompanied by a deterioration of economic and financial indicators like the Tunisian case. Therefore, this paper aims to get a deeper understanding the nature of the rule that reflects the behavior of the Tunisian monetary authority in the current dominance of economic and financial instability. In particular, this paper assesses whether the Tunisian Central Bank is indeed following a linear or a non linear augmented Taylor rule. For our purpose, we use a forward looking version of Taylor rule augmented by including the effect of exchange rate to estimate the linear and the nonlinear models. A smooth transition regression model is employed to estimate the nonlinear rule. The results obtained imply that the Tunisian Central Bank follows a nonlinear Taylor rule in the conduct of monetary policy. In addition, our evidence suggests that the reaction of monetary authority in Tunisia to the deviation of forecasts of inflation rate, output gap and exchange rate changes in terms of magnitude and statistical significance across the high and low interest rate regimes. In particular, when the lagged interest rate is above the threshold level of 4.76%, the main objective of the policy makers is to fight the inflation rate and to limit the depreciation of exchange rate rather than to boost the economic activity.
It's a Wrap: Mapping On-Skin Input to Off-Skin Displays

Advances in sensing technologies allow for using the forearm as a touch surface to give input to off-skin displays. However, it is unclear how users perceive the mapping between an on-skin input area and an off-skin display area. We empirically describe such mappings to improve on-skin interaction. We collected discrete and continuous touch data in a study where participants mapped display content from an AR headset, a smartwatch, and a desktop display to their forearm. We model those mappings and estimate input accuracy from the spreads of touch data. Subsequently, we show how to use the models for designing touch surfaces to the forearm for a given display area, input type, and touch resolution.
Maximization Of Network Reliability Using Ann Under Node-Link Failure Model

: The network Reliability optimization problem for any type of interconnection network is to maximize the network reliability subjected some constraint such as the total cost of the network. Even though, the problem is NP-Hard, many researchers have solved this problem in different ways but with a common assumption that nodes are perfect. But, this assumption is quite unrealistic in nature. In this paper, a new method based on artificial network is proposed to solve the network reliability optimization problem considering both the nodes and links of the interconnection network to be imperfect. The problem is mapped onto an optimization Artificial Neural Network by constructing an energy function whose minimization process drives the neural network into one of its stable states. This stable state corresponds to a solution for the network reliability problem. Some standard methods are studied and modified approximately so that they would work considering the node failure. The results of these methods are compared against the result of the proposed method. The comparison strongly supports that the proposed method provides a better maximization of network reliability than its counterparts under same working environment.
EFFECTS OF SILICON SUPPLEMENTATION ON WHEAT PLANTS UNDER SALT STRESS

Silicon (Si) is the second most abundant element in the earth’s crust and is regarded as a beneficial element for higher plants. In this study, the effect of Si supplementation (1 and 4 mM) was studied on wheat (Triticum aestivum cv. Homa) plants grown hydroponically under salinity stress (50 and 150 mM NaCl) for two weeks under controlled environmental conditions. Plant biomass was found to decrease at both salinity levels. Contrary to our expectation, Si supplementation failed to mitigate the salt stress effects on dry matter production. However, the majority of biochemical parameters related to salt tolerance showed improvements as a result of Si application, particularly at 1 mM. Under both control and salinity conditions, Si treatment resulted in higher leaf contents of chlorophyll and carotenoids, soluble sugars, proteins, and free amino acids, particularly proline. Osmotic potential, however, declined in the roots, suggesting that Si supplementation might contribute to plant’s higher water uptake capacity. Si treatment diminished leaf concentration of Na + in the cell sap but increased it in the cell wall-bound fraction, indicating a Na + detoxification mechanism mediated by Si. Our results suggest that a short-term Si treatment affects biochemical indicators of salt tolerance but that long-term exposure to Si is needed for a significantly alleviating effect on plant biomass.
LOCAL ICE LOAD ON A SHIP WITH PODDED PROPULSORS

A series of model tests in ice for a ship equipped with twin podded propulsors was made at the ice model basin of National Maritime Research Institute. A tactile sensor system was used to measure local ice loads on the model. The model was tested in a free-running mode. Turning tests as well as straight-going tests were performed. In the straight-going test local ice load was dominant in the forward region of the model. In the turning test significant ice loading occurred in the aft-ship region. The load in the aft-ship region increased with the increase of lateral force of the pods.
Ananke: A Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows

Complex workflows that process sensor data are useful for industrial infrastructure management and diagnosis. Although running such workflows in clouds promises reduced operational costs, there are still numerous scheduling challenges to overcome. Such complex workflows are dynamic, exhibit periodic patterns, and combine diverse task groupings and requirements. In this work, we propose ANANKE, a scheduling system addressing these challenges. Our approach extends the state-of-the-art in portfolio scheduling for datacenters with a reinforcement-learning technique, and proposes various scheduling policies for managing complex workflows. Portfolio scheduling addresses the dynamic aspect of the workload. Q-learning, allows our approach to adapt to the periodic patterns of the workload, and to tune the other configuration parameters. The proposed policies are heuristics that guide the provisioning process, and map workflow tasks to the provisioned cloud resources. Through real-world experiments based on real and synthetic industrial workloads, we analyze and compare our prototype implementation of ANANKE with a system without portfolio scheduling (baseline) and with a system equipped with a standard portfolio scheduler. Overall, our experimental results give evidence that a learning-based portfolio scheduler can perform better and consume fewer resources than state-of-the-art alternatives, in particular for workloads with uniform arrival patterns.
Amelioration of the effects of polyethylene glycol-induced drought stress on six cultivars of Alfalfa by paclobutrazol treatment

This investigation was carried out with the aim of determining the effect of polyethylene glycol 6000 (PEG; 0, 3%, 6% and 9% w/v) treatment on six cultivars of Medicago sativa. PEG treatment significantly decreased the germination index in cultivars of Isfahani, Hamedani, Bami, Baghdadi, Yazdi and Ghare-Medicago, with the highest reduction in Ghare-Medicago and Hamedani cultivars by about 50% and 44%, respectively. PEG treatment significantly decreased germination percentage and germination rate in two cultivars of Ghare-Medicago (22% and 20%, respectively) and Hamedani (28% and 27%, respectively) while increased mean germination time. The seedlings height, fresh weight and water content significantly decreased in all PEG-treated cultivars. The highest reduction in the mentioned factors was observed in Ghare-Medicago (43%, 24% and 55%, respectively) and Hamedani (57%, 23% and 53%, respectively), while the lowest reduction was observed in Yazdi, by about 28%, 9% and 17%, respectively. Data showed that PEG treatment reduced seedling dry mass only in Ghare-Medicago and Hamedani cultivars by about 16% in both cultivars. PEG treatment significantly decreased the chlorophyll a, b and total, anthocyanins, phenols and α-tocopherol contents of six Medicago cultivars. The results of greenhouse experiments showed that PEG treatments resulted in a significant reduction in fresh weight, dry weight and water content of all studied cultivars. In conclusion, the findings of both petri dish and greenhouse experiments suggested that Ghare-Medicago and Hamedani cultivars can be regarded as drought sensitive cultivars while Yazdi is a drought tolerant cultivar. It should be noticed that Bami and Baghdadi cultivars were also relatively drought tolerant. Also, our study on paclobutrazol (PBZ) treatment showed that PBZ effectively decreased the negative effect of drought stress on growth of all cultivars of Medicago.
Performance of Mild Steel in Nitric Acid/ Carica Papaya Leaf Extracts Corrosion System

Corrosion inhibition efficiency of leaf extracts from Carica papaya has being investigated using gravimetric, gasometric and thermometric techniques. Carica papaya leaf extracts reduced the corrosion rate of mild steel in 2.5M nitric acid by interfering with both anodic and cathodic reactions. The corrosion rate in the absence of the inhibitor was 840 mmpy while at 60% inhibitor concentration, it was 60 mmpy. The corrosion rate decreased rapidly initially as the concentration of the inhibitor increased and attained an efficiency of 93% at a concentration of 60%. The complimentary inhibition efficiencies, E%, obtained from thermometric and gasometric techniques are similar in values and range from about 93% to 96%. The adsorption of the active components of the leaf extracts follows the Langmuir adsorption isotherm
Adjuvantes e qualidade da água na aplicação de agrotóxicos

The river water presents clay and organic particles in suspension. Clay and organic matter in suspension in the water can adsorb herbicides and reduce their activity. In water quality, more specifically physical quality, a very important factor to be considered is the amount of sediments in suspension. These sediments obstruct filters and spray nozzles, reduce the operational capacity of the sprayers, reduce the useful life of pumps, nozzles and other sprayer components and can bind to the pesticide added to the tank, inactivating or reducing its effectiveness. Water quality can affect the biological efficiency of the herbicides; it can reduce the half-life of herbicides. The pesticides, in general, are deposited on the plant surface, such as branches, leaves and fruits. Some of them should remain on those surfaces, while Adjuvantes e qualidade. QUEIROZ, A. A. ; MARTINS, J. A. S. ; CUNHA, J. P. A. R. Biosci. J., Uberlândia, v. 24, n. 4, p. 8-19, Oct./Dec. 2008 16 others should be absorbed, to perform their effect inside the plant tissue. Plant surfaces present a barrier for the penetration of liquids, denominated cuticle. Its characteristics vary from species to species and depend on the age of the plant organ and of the climate conditions. Thus, inert substances, denominated addictive or adjuvants, are used to pass these barriers. They are capable of modifying the activity of the applied pesticides and the spray characteristics. These products can be mixed to the formulation of the pesticide products by the manufacturing companies, or be added to the tank in the moment of the spray.
A Research On Bitcoin Price Prediction Using Machine Learning Algorithms

--In this paper, we proposed to predict the Bitcoin price accurately taking into consideration various parameters that affect the Bitcoin value. By gathering information from different reference papers and applying in real time ,I found the advantages and disadvantages of bitcoin price prediction. Each and every paper has its own set of methodologies of bitcoin price prediction. Many papers has accurate price but some other don’t, but the time complexity is higher in those predictions, so to reduce the time complexity here in this paper we use an algorithm linked to artificial intelligence named LASSO(least absolute shrinkage selection operator. The other papers used different algorithms like SVM(support vector machine),coinmarkupcap, Quandl, GLM, CNN(Convolutional Neural Networks)and RNN(Recurrent neural networks) etc. which do not have a great time management, but in LASSO finding of the results from a larger database is quick and fast.so for this purpose we draw a comparison between other algorithms and the LASSO algorithm, this survey paper helps the upcoming researchers to make an impact in the their papers. The process happens in the paper is first moment of the research, we aim to understand and find daily trends in the Bitcoin market while gaining insight into optimal features surrounding Bitcoin price. Our data set consists of various features relating to the Bitcoin price and payment network over the course of every years, recorded daily. By preprocessing the dataset, we apply the some data mining techniques to reduce the noise of data. Then the second moment of our research, using the available information, we will predict the sign of the daily price change with highest possible accuracy.
The Effect Of Nano Cellulose Synthesized From Rice Straw On The Performance Of Cement Composite Reinforced With Carbon Nano Tubes

Recently, Nano materials showed great potential in enhancing the cement matrices. The major problem in utilizing Nano-particles are their tendency to agglomerate especially after being added to water. Effective de-agglomeration and dispersion for the Nano-particles is needed to overcome the bonding Van Der Waals forces after wetting. One of the most challenging Nano particles to be utilized within the cement matrix is carbon Nano tubes (CNTs) due to their high aspect ratio. The purpose of the proposed research case study is to investigate the effectiveness of Nano cellulose particles (NCL) synthesized from Rice husk straw to replace partially the CNTs with much greener material, as well as studying their possible enhancement to the dispersion of the multi walled carbon Nano Tubes. The results revealed that the presence of CNT's or NCL's separately had shown significant enhancement in the compressive strength at a late age without any remarkable increase at an early age. However, the utilization of CNTs in the presence of NCL particles highly, and positively affected the mechanical properties of the cement composites after early and late curing ages. The utmost gain in the early age and the late age compressive strengths reached 21%, and 60% respectively, these values were reported for the mix contained 0.125% NCL, and 0.01% CNTs as compared with the control mix.
Amylin Structure, Aggregation and Pancreatic β Cell Toxicity

To better understand the structural basis for the interactions between amylin and membranes, we determined the NMR structure of human amylin bound to membrane mimetic SDS micelles. The first four residues in the structure are constrained to form a hairpin loop by the single disulfide bond in amylin. The last nine residues near the C terminus are unfolded. The core of the structure is an α-helix that runs from about residues 5-28. A distortion or kink near residues 18-22 introduces pliancy in the angle between the Nand C-terminal segments of the α-helix. Mobility, as determined by N relaxation experiments, increases from the N to the C terminus and is strongly correlated with the accessibility of the polypeptide to spin probes in the solution phase. The spin probe data suggest that the segment between residues 5 and 17 is positioned within the hydrophobic lipid environment, whereas the amyloidogenic segment between residues 20 and 29 is at the interface between the lipid and solvent. This orientation may direct the aggregation of amylin on membranes, whereas coupling between the two segments may mediate the transition to
Machine Learning Based Pricing Model For International Football Players

: At international level players are picked up by clubs at various prices ranging from very low amounts to very high prices. Datasets of recent past on vital statistics and playing skills of international soccer players along with their price worth are available on various portals for analysis. Authors have built a decision tree regressor based upon past data to predict price of player based upon player‘s attributes. Fitted Decision tree regressor is tested to produce results with high accuracy. Authors have also reduced dimensionality by finding correlation among independent variables. Dataset obtained after dimensionality reduction has been used to construct again a decision tree regressor, that has been tested and found to produce high accuracy without loss due to dimensionality reduction. Authors have used Football analytics dataset of FIFA 2019 and machine learning using Python to develop the regressor.
Ensemble Learning for Relational Data

We present a theoretical analysis framework for relational ensemble models. We show that ensembles of collective classiﬁers can improve predictions for graph data by reducing errors due to variance in both learning and inference. In addition, we propose a relational ensemble framework that combines a relational ensemble learning approach with a relational ensemble inference approach for collective classiﬁcation. The proposed ensemble techniques are applicable for both single and multiple graph settings. Experiments on both synthetic and real-world data demonstrate the eﬀectiveness of the proposed framework. Finally, our experimental results support the theoretical analysis and conﬁrm that ensemble algorithms that explicitly focus on both learning and inference processes and aim at reducing errors associated with both, are the best performers
Performance and uncertainty analysis in machine-assisted design of photonic devices

Machine-assisted design of integrated photonic devices (e.g. through optimization and inverse design methods) is opening the possibility of exploring very large design spaces, novel functionalities and non-intuitive geometries. These methods are generally used to optimize performance figures-of-merit. On the other hand, the effect of manufacturing variability remains a fundamental challenge since small fabrication errors can have a significant impact on light propagation, especially in high-index-contrast platforms. Brute-force analysis of these variabilities during the main optimization process can become prohibitive, since a large number of simulations would be required. To this purpose, efficient stochastic techniques integrated in the design cycle allow to quickly assess the performance robustness and the expected fabrication yield of each tentative device generated by the optimization. In this invited talk we present an overview of the recent advances in the implementation of stochastic techniques in photonics, focusing in particular on stochastic spectral methods that have been regarded as a promising alternative to the classical Monte Carlo method. Polynomial chaos expansion techniques generate so called surrogate models by means of an orthogonal set of polynomials to efficiently represent the dependence of a function to statistical variabilities. They achieve a considerable reduction of the simulation time compared to Monte Carlo, at least for mid-scale problems, making feasible the incorporation of tolerance analysis and yield optimization within the photonic design flow.
Role of variant allele fraction and rare SNP filtering to improve cellular DNA repair endpoint association

Background Large cancer genome studies continue to reveal new players in treatment response and tumorigenesis. The discrimination of functional alterations from the abundance of passenger genetic alterations still poses challenges and determines DNA sequence variant selection procedures. Here we evaluate variant selection strategies that select homozygous variants and rare SNPs and assess its value in detecting tumor cells with DNA repair defects. Methods To this end we employed a panel of 29 patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines, of which a subset harbors DNA repair defects. Mitomycin C (MMC) sensitivity was used as functional endpoint of DNA crosslink repair deficiency. 556 genes including the Fanconi anemia (FA) and homologous recombination (HR) genes, whose products strongly determine MMC response, were capture-sequenced. Results We show a strong association between MMC sensitivity, thus loss of DNA repair function, and the presence of homozygous and rare SNPs in the relevant FA/HR genes. Excluding such selection criteria impedes the discrimination of crosslink repair status by mutation analysis. Applied to all KEGG pathways, we find that the association with MMC sensitivity is strongest in the KEGG FA pathway, therefore also demonstrating the value of such selection strategies for exploratory analyses. Variant analyses in 56 clinical samples demonstrate that homozygous variants occur more frequently in tumor suppressor genes than oncogenes further supporting the role of a homozygosity criterion to improve gene function association or tumor suppressor gene identification studies. Conclusion Together our data show that the detection of relevant genes or of repair pathway defected tumor cells can be improved by the consideration of allele zygosity and SNP allele frequencies.
Analysis of Human Gait for Person Identiﬁcation and Human Action Recognition

The gait sequence analysis ﬁnds applications in several ﬁelds such as, person identiﬁcation, human action recognition and event clas-siﬁcation. The extraction of gait sequences is the initial step in gait sequence analysis. The Latent Dirichlet Allocation (LDA) a type of ’Topic Model’ is used to analyse the gait sequences for person identiﬁcation and human action recognition. This is achieved by proposing a novel method that transforms the gait sequences into words representation. Finally, the generated words are then analysed using LDA for person identiﬁcation and human action recognition. The proposed person identiﬁcation algo-rithm is tested using CASIA gait dataset A and human action recognition algorithm is tested using Weizmann action recognition dataset, resulting in correct classiﬁcation rate of 85 % using CASIA Dataset A and 85 % using Weizmann action recognition dataset.