Miracle-dz/UniFAR-FTUs huggingface dataset:
Public Safe Assets Determination

ABSTRACT On a sample of 150 economies, we characterise the safety of public debt by both ordinary least square and instrument variable regressions. For demand analysis, the public debt is safer for a larger financial market size, a higher financial development level, a lower inflation rate and greater political stability. For supply analysis, the safety of debt improves for a huger debt stock in economies with high income per capita but deteriorates in economies with low income per capita. Cases studies record that, compared with the prediction by economic fundamentals, the investors overestimate the debt safety of China but underestimate that of Greece and Japan.
Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).
Molecular Basis for Endocrine Disruption by Pesticides Targeting Aromatase and Estrogen Receptor

The intensive use of pesticides has led to their increasing presence in water, soil, and agricultural products. Mounting evidence indicates that some pesticides may be endocrine disrupting chemicals (EDCs), being therefore harmful for the human health and the environment. In this study, three pesticides, glyphosate, thiacloprid, and imidacloprid, were tested for their ability to interfere with estrogen biosynthesis and/or signaling, to evaluate their potential action as EDCs. Among the tested compounds, only glyphosate inhibited aromatase activity (up to 30%) via a non-competitive inhibition or a mixed inhibition mechanism depending on the concentration applied. Then, the ability of the three pesticides to induce an estrogenic activity was tested in MELN cells. When compared to 17β-estradiol, thiacloprid and imidacloprid induced an estrogenic activity at the highest concentrations tested with a relative potency of 5.4 × 10−10 and 3.7 × 10−9, respectively. Molecular dynamics and docking simulations predicted the potential binding sites and the binding mode of the three pesticides on the structure of the two key targets, providing a rational for their mechanism as EDCs. The results demonstrate that the three pesticides are potential EDCs as glyphosate acts as an aromatase inhibitor, whereas imidacloprid and thiacloprid can interfere with estrogen induced signaling.
Modified Modeling Method of Quartz Crystal Resonator Frequency–Temperature Characteristic With Considering Thermal Hysteresis

The frequency–temperature (<inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula>) characteristic of quartz crystal resonators is an important topic closely related to the frequency-deviation compensation in the design of cost-effective oscillators. Traditional studies depict the <inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> characteristic as a polynomial function (usually a cubic function). However, this omits the thermal hysteresis phenomenon and cannot provide a very accurate frequency compensation. To handle this issue, this article is to propose two modified <inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> characteristic modeling methods with considering the thermal hysteresis. First, to reflect the thermal hysteresis property of quartz crystal resonators, a two-directional <inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> model is designed by introducing two individual submodels for describing the increasing and decreasing temperature stages, respectively. Furthermore, to integrate the submodels and provide the more accurate frequency-deviation estimation, a holistic <inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> characteristic model based on double-hidden layer extreme learning machine (DHL-ELM) is presented. Different from the basic ELM model, two hidden layers, including one deterministic nonlinear mapping and one random nonlinear activation layer, are constructed for a better description of <inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> characteristic. To validate our studies, an experiment system is applied to obtain the testing data of the real crystal resonators, and the applications demonstrate the effectiveness of the proposed methods.
Factors influencing trust-supporting mechanisms in European agri-food chains.

he role of trust in eight selected agri-food chains in four EU countries is explored. The empirical analysis is based on a qualitative assessment of 28 expert interviews conducted in summer/autumn 2005. The findings suggest that trust is more pronounced among SMEs which are characterised by the existence of personal relationships between business partners. However, as is clear for pork breeders, if a source of distrust exists, trust may not fully develop. Also, if the general economic situation is difficult, the development of trust may be hampered, because all chain participants are struggling to command a share of a diminishing margin within the chain. Finally, if economic power is distributed unevenly, as is the case in Germany, Ireland, Spain and the UK where retailers dominate most chains, trust towards the more powerful may be limited.
Reactions and continuous adaptation in collaborative robots

Collaborative robotics aims to bring humans and robots together to work as teams whose combined contributions and skills–superior perception and generalized dexterity in humans, and superior repeatability and precision in robots– surpass either party alone. But so far, collaborative robots in production environments have been largely agnostic of their surroundings, running through meticulously pre-programmed motions blindly, forcing their human collaborators to adapt and preventing the type of close proximity work required to fully leverage the benefits of human-robot teamwork. Perceptual systems improve at an aggressive pace, fueled by multidisciplinary incentives across both industry and academia. Developing new motion behavior and control technology that can anticipate and incrementally leverage the steady stream of advances in perception will have tremendous value, and can drive an important conversation around new research directions in vision that address issues around the innately sequential nature of active decision systems. Tight perceptual integration would enable safe close quarter collaboration in unstructured and uncertain environments while maintaining the precision and repeatability advantages of robotic systems. We have developed a system for behavior generation from visual stimuli for this purpose, leveraging a multi-time-scale architecture combining continuous motion optimization with reactive local controllers. Our system turns the typically sequential sense-plan-act elements into a collection of parallel continuously running and continuously communicating processes. Vision provides a continuous stream of world state updates. That information is consumed by motion optimization, which continuously processes it and communicates desired behavior as a steady stream of reactive kinematic LQR policies to control, treating them as infinite collections of motion trajectories (their integral curves) rather than just a single trajectory. At the lowest level it combines these motion policies with other acceleration policies using quadratic programming, obviating the need to bend or otherwise adapt a reference trajectory to perturbations or environmental changes between re-optimizations. We will be demonstrating the current state of our system live (in simulation) and use that to motivate discussions around further research, especially into issues around the integrating noisy and uncertain perception systems into physical real-time systems. See our demo video (https://youtu.be/
Nanocomposites by Surface-Initiated Living Cationic Polymerization of 2-Oxazolines on Functionalized Gold Nanoparticles

End-functionalized 3D self-assembled monolayers (SAMs) on gold nanoparticles were used to initiate the living cationic ring-opening polymerization reaction directly on gold nanoparticle surfaces ("grafting-from technique"). In this manner, dense polymer brushes were prepared in a "one-pot multistep" reaction. ER FTIR spectroscopy confirms the successful polymerization reaction as well as the introduction of a terminal functional group. The resulting gold/polymer nanocomposite was very stable. The combination of a "grafting-from reaction" resulting in polymer "brush-type" shells of linear macromolecules and the introduction of a terminal mesogen by means of a quantitative termination reaction resulted in an amphiphilic core-shell material with a well-defined hydrophilic/lipophilic balance (HLB). Ex-situ kinetic studies of the polymerization of 2-phenyl-2-oxazoline using FTIR spectroscopy and MALDI TOF mass spectrometry resulted in a linear relationship between the reaction time and degree of polymerization of the grafted polymer. This as well as the successful end-functionalization by termination with secondary amines corroborates our view of a well-defined living polymerization mechanism.
A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.
Differential Expression of Trichomes on the Leaves of Upland Cotton ( Gossypium hirsutum L . ) .

The close relationship between Gossypium species (Malvales) and Arabidopsis (Brassicales) determined by molecular phylogeny might facilitate the identification of regulatory mechanisms for trichome development in cotton leaves. Focus of this study is on the evaluation and quantification of differences in trichomes on cotton leaves with varying degrees of leaf pubescence. Trichomes were evaluated using scanning electron and light microscopy on both fixed and live samples. Trichome quantity and complexity were measured on mainstem leaves and usually found to increase on new leaves as they develop progressively up the plant. Glandular trichomes (GTs) were found on all mainstem leaves examined and were not affected by various loci that either increased or inhibited the expression of non-GTs (NGTs or hairs). Neither GTs nor NGTs on Pilose leaves were affected by the expression of N1N1 genotype, which removes approximately 75% of all lint and 100% of all fuzz on cotton ovules. The abaxial side of the leaf usually had more GTs and NGTs. These data indicate that at least two regulatory networks exist in cotton leaves for trichome initiation, one for GTs and the other for NGTs. A third regulatory network is likely for the initiation of ovule trichomes; however, this third network might share genetic components with the NGTs. Future studies will use four very pubescent cotton lines identified in this survey for further genetic evaluations by crossing with three fiberless ovule lines.
Comparison of Observed and Simulated Drop Size Distributions from Large-Eddy Simulations with Bin Microphysics

Two case studies of marine stratocumulus (one nocturnal and drizzling, the other daytime and nonprecipitating) are simulated by the UCLA large-eddy simulation model with bin microphysics for comparison with aircraft in situ observations. A high-bin-resolution variant of the microphysics is implemented for closer comparison with cloud drop size distribution (DSD) observations and a turbulent collision–coalescence kernel to evaluate the role of turbulence on drizzle formation. Simulations agree well with observational constraints, reproducing observed thermodynamic profiles (i.e., liquid water potential temperature and total moisture mixing ratio) as well as liquid water path. Cloud drop number concentration and liquid water content profiles also agree well insofar as the thermodynamic profiles match observations, but there are significant differences in DSD shape among simulations that cause discrepancies in higher-order moments such as sedimentation flux, especially as a function of bin resolution. Counterintuitively, high-bin-resolution simulations produce broader DSDs than standard resolution for both cases. Examination of several metrics of DSD width and percentile drop sizes shows that various discrepancies of model output with respect to the observations can be attributed to specific microphysical processes: condensation spuriously creates DSDs that are too wide as measured by standard deviation, which leads to collisional production of too many large drops. The turbulent kernel has the greatest impact on the low-bin-resolution simulation of the drizzling case, which exhibits greater surface precipitation accumulation and broader DSDs than the control (quiescent kernel) simulations. Turbulence effects on precipitation formation cannot be definitively evaluated using bin microphysics until the artificial condensation broadening issue has been addressed.
A Study On Resource Allocation Aspects For Wireless Networks

Resource allocation is one of the most challenging tasks in the wireless networks. Many schemes exist in literature to allocate the available resources to users. Different schemes employ different algorithms to allocate the subcarrier, bits, data rate, transmit power, bandwidth etc. to different users in the network while satisfying the desired constraints posed by the users like utility maximization, power reduction, throughput maximization and so on. Adaptive modulation is also used in order to effectively handle the network resources. In this paper, some of the existing resource allocation schemes and their performance is
In vitro and in silico analysis of L. donovani enoyl acyl carrier protein reductase - A possible drug target

Abstract The emergence of increased resistance to the available drugs has created a situation that demands to find out more specific molecular drug targets for Leishmaniasis. The enoyl acyl carrier protein reductase (ENR), a regulatory enzyme in type II fatty acid synthesis, was confirmed as a novel drug target and triclosan as its specific inhibitor in many microorganisms. In this study, the triclosan was tested for the leishmanicidal property against Leishmania donovani (L. donovani) and the results of in vitro and ex vivo drug assays on promastigotes and amastigotes showed that triclosan possessed antileishmanial activity with a half minimal inhibitory concentration (IC50) of 30 µM. Consequently, adopting in silico approach, we have tested the triclosan’s ability to bind with the L. donovani enoyl acyl carrier protein reductase (LdENR). The 3D structure of LdENR was modelled, triclosan and cofactors were docked in LdENR model and molecular dynamic simulations were performed to observe the protein-ligands interactions, stability, compactness and binding energy calculation of the ligands-LdENR complexes. The observation showed that triclosan stably interacted with LdENR in presence of both the cofactors (NADPH and NADH), however, simulation results favor NADH as a preferred co-factor for LdENR. These results support that the reduction of L. donovani growth in the in vitro and ex vivo drug assays may be due to the interaction of triclosan with LdENR, which should be confirmed through enzymatic assays. The results of this study suggest that LdENR could be a potential drug target and triclosan as a lead for Leishmaniasis. Communicated by Ramaswamy H. Sarma
Novel Toilet Paper–Based Point-Of-Care Test for the Rapid Detection of Fecal Occult Blood: Instrument Validation Study

Background Colorectal cancer screening by fecal occult blood testing has been an important public health test and shown to reduce colorectal cancer–related mortality. However, the low participation rate in colorectal cancer screening by the general public remains a problematic public health issue. This fact could be attributed to the complex and unpleasant operation of the screening tool. Objective This study aimed to validate a novel toilet paper–based point-of-care test (ie, JustWipe) as a public health instrument to detect fecal occult blood and provide detailed results from the evaluation of the analytic characteristics in the clinical validation. Methods The mechanism of fecal specimen collection by the toilet-paper device was verified with repeatability and reproducibility tests. We also evaluated the analytical characteristics of the test reagents. For clinical validation, we conducted comparisons between JustWipe and other fecal occult blood tests. The first comparison was between JustWipe and typical fecal occult blood testing in a central laboratory setting with 70 fecal specimens from the hospital. For the second comparison, a total of 58 volunteers were recruited, and JustWipe was compared with the commercially available Hemoccult SENSA in a point-of-care setting. Results Adequate amounts of fecal specimens were collected using the toilet-paper device with small day-to-day and person-to-person variations. The limit of detection of the test reagent was evaluated to be 3.75 µg of hemoglobin per milliliter of reagent. Moreover, the test reagent also showed high repeatability (100%) on different days and high reproducibility (>96%) among different users. The overall agreement between JustWipe and a typical fecal occult blood test in a central laboratory setting was 82.9%. In the setting of point-of-care tests, the overall agreement between JustWipe and Hemoccult SENSA was 89.7%. Moreover, the usability questionnaire showed that the novel test tool had high scores in operation friendliness (87.3/100), ease of reading results (97.4/100), and information usefulness (96.1/100). Conclusions We developed and validated a toilet paper–based fecal occult blood test for use as a point-of-care test for the rapid (in 60 seconds) and easy testing of fecal occult blood. These favorable characteristics render it a promising tool for colorectal cancer screening as a public health instrument.
Special Section : Advancing Soil Physics for Securing Food , Water , Soil and Ecosystem Services

Crop canopy CO2 exchange rate (CER) includes crop photosynthesis and soil/plant respiration. A portable canopy chamber is effective in determining crop CER values at a relatively small spatial (m2) scale. The objectives of this study were to use a canopy chamber to measure CO2 fluxes in corn (Zea mays L.) and soybean [Glycine max (L.) Merr. ]. Chamber measurements were performed for 18 and 15 d in 2013 and 2014, respectively. The canopy chamber measures instantaneous CER fluxes, and daily and daytime cumulative CO2 values were calculated from the instantaneous CER. The chamber CER results were compared with nearby eddy covariance (EC) flux tower measurements at a variety of time scales, i.e., instantaneous, daily, and daytime cumulative (multiple months). The daily and daytime cumulative chamber CER values were within 5% of the EC results, providing evidence for the effectiveness of the portable canopy chamber method. In conclusion, the portable canopy chamber provides reliable CO2 flux measurements despite the small size of field plots.
Oil Reservoir Properties Estimation by Fuzzy-Neural Networks

Porosity and permeability are two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its ability to flow. These properties have a significant impact on petroleum fields operations and reservoir management. Up to now, more than twenty reservoirs have been found in basement rocks all over the world, which were known as un-usual reservoir. They were named un-usual reservoir because of the small number are compared with clastic and carbonate reservoirs. Study on basement reservoir always is difficult task, especially estimation of reservoir properties due to complex nature of the geological model. In this paper, we suggest an efficient method to determine reservoir properties from well log by using fuzzy logic and neural networks. The ranking technique based on fuzzy logic is used for noise rejection of training data for neural networks. By learning the nonlinear relationship between selected well logs and core measurements, the neural network can perform a nonlinear transformation to predict porosity or permeability with high accuracy. The approach is demonstrated with an application to the well data in A2-VD prospect, Southern offshore Vietnam. The results show that this technique can make more accurate and reliable reservoir properties estimation than conventional computing methods. The study plays an important role in projects of development of basement reservoirs in the future.