Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies concerning the production of volatile oil, rhizomes and roots, to different genotypes of Valeriana officinalis L.

Valeriana officinalis L. is considered to pertain to European species, with great ecologic plasticity, which allows its adaptation to climate conditions characteristics to plain areas and also to mountain areas up to an altitude of 2400 meters. The species is a well-known curative plant, with a long history and multiple uses. Essential oils deriving from this species revealed the interest of researchers in food industry, cosmetics and officinal industry, furthermore being used as additives too. The raw material from which essential oils are being extracted is represented mainly by rhizomes and roots. This study has the purpose to emphasize the differences of essential oils production registered based upon the genotypes diversity. Thus, 11 experimental variants have been used, with biologic material of different origin, from Romania, Poland, Germany and Russia; they have been measured in relation to their production of rhizomes, roots and volatile oil, in the ecological conditions of Brasov,
Social Involvement, Disclosure of Sexual Orientation, and the Quality of Lesbian Relationships

Gay and lesbian political activists encourage lesbians to “come out” and get involved. Is this good advice for lesbian couples? In these secondary analyses of data collected from 784 lesbian couples in 1979 for the American Couples Study (Blumstein & Schwartz, 1983), we examined whether social involvement and disclosure of sexual orientation were systematically associated with the quality of women's relationships. We predicted and found that partners matched on level of social involvement were happier than mismatched couples. The extent of women's social involvement had a curvilinear association with relationship quality: moderately involved couples were the most satisfied. Contrary to predictions, lesbians who had disclosed to a greater number of important others did not have more satisfying romantic relationships, nor did partner matching on self-disclosure enhance relationship quality.
Meat Demand in the UK: A Differential Approach

Abstract A differential approach is employed to analyze demand for meat in the United Kingdom during 1989-99. Differential demand systems with fixed price effects (Rotterdam and CBS) better explain consumers' retail purchase allocation decisions for beef, lamb, pork, bacon and poultry compared with models containing variable price effects (NBR and differential AIDS). The real expenditure and the Hicksian demand elasticities are generally found to be quite different from earlier studies using AIDS models. A quality change index of meat consumption is constructed from the estimated CBS model estimation results and decomposed into real expenditure, substitution, trend, seasonal and residual effects.
Does borrowers' impatience disclose their hidden information about default risk?

This chapter provides new evidence on borrowers' hidden information about their riskiness and its link to their impatience. To do so, I analyze consumer loans on the German platform Smava, which has a unique peer-to-peer lending process. Observationally identical but unobservably riskier borrowers offer investors a higher interest rate. This helps them to obtain their loan faster and with a higher probability. Very impatient borrowers who use Smava's instant loan service pay a higher interest rate and have a higher default risk than less impatient borrowers. These findings suggest that borrowers' impatience can be used to screen their riskiness.
REAL-TIME HEAD POSE ESTIMATION WITH SVM MODEL FOR FRONTAL FACE CLASSIFICATION

Head pose estimation (HPE) has been widely studied in the last years due to its many applications in face analysis systems. The use of such systems ranges from the analysis of focus of attention, social interactions or the use in mobile applications in the realization of the currently popular facial animations and / or in face recognition process, where the frontal faces are especially important. Many approaches were proposed focusing mainly in Random Forests and Convolutional Neural Networks (CNN). In this paper, a framework for estimation of the head pose was proposed computing the degrees of freedom (DOF) of the human head using 2D images data only. The framework implements some computer vision algorithms available in publicly machine learning libraries such as OpenCV and Dlib, which allows easy application and re-implementation. In addition, a Support Vector Machine (SVM) model with Radial Basis Function (RBF) kernel was developed for frontal face classification. Experiments conducted on 2D image datasets in constrained environment show that the approach is capable of real-time performance. Were designed three protocols of experiments with two databases for testing the SVM model. Values of 100% and 98% for precision and recall, respectively, were achieved classifying frontal faces. Significant results were obtained measuring yaw rotation with 4.24 of mean absolute error for frontal face.
Satellite Image Classification with Deep Learning: Survey

Satellite imagery is important for many applications including disaster response, law enforcement and environmental monitoring etc. These applications require the manual identification of objects in the imagery. Because the geographic area to be covered is very large and the analysts available to conduct the searches are few, thus an automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a part of broader family of machine learning methods that have shown promise for the automation of such tasks. It has achieved success in image understanding by means that of convolutional neural networks. The problem of object and facility recognition in satellite imagery is considered. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features.
Understanding Personal Productivity How Knowledge Workers Define, Evaluate, and Reflect on Their Productivity

Productivity tracking tools often determine productivity based on the time interacting with work-related applications. To deconstruct productivity’s diverse and nebulous nature, we investigate how knowledge workers conceptualize personal productivity and delimit productive tasks in both work and non-work contexts. We report a 2-week diary study followed by a semi-structured interview with 24 knowledge workers. Participants captured productive activities and provided the rationale for why the activities were assessed to be productive. They reported a wide range of productive activities beyond typical desk-bound work—ranging from having a personal conversation with dad to getting a haircut. We found six themes that characterize the productivity assessment— work product , time management , worker’s state , attitude toward work , impact & benefit , and compound task —and identified how participants interleaved multiple facets when assessing their productivity. We discuss how these findings could inform the design of a comprehensive productivity tracking system that covers a wide range of productive activities.
Metal nanoparticles for optical limiting : comparision of reduction with sodiumborohydrate and citrate

Lasers radiation can damage eyes or sensors, a possibility to protect them are optical limiting devices. They attenuate intense laser beams while showing high transmittance at low intensity levels. The optical limiting is based on nonlinear optic. Metal nanoparticles act optically linear. The extent of their optical limiting ability is influenced by their shape and their size. So we compared the reduction with citrate with the reduction with sodium borohydride to see, which results in the best optical limiting.
Low Reynolds Number Airfoil Evaluation for the Mars Helicopter Rotor

The present research is aimed at providing a performance model for the Mars Helicopter (MH), to understand the complexity of the flow, and future regions of improvement. The Martian atmosphere's low density and the MH's relatively small rotor result in very low chord-based Reynolds number flows, Rec = O(10(exp 3)-10(exp 4)). The low density and subcritical Reynolds number reduce the lifting force and lifting efficiency, respectively. The high drag coefficients in subcritical flow, especially for thicker sections, are attributed to laminar separation from the rear of the airfoil. The goal is to generate a performance model for the MH rotor for a free wake analysis, since the computational budget for a complete Navier-Stokes solution for a rotating body-fitted rotor is substantial. In this study, a RANS-based approach is used to generate the airfoil deck using OVERFLOW with stitched experimental data for very high angles of attack. The model is presented through airfoil data tables (C81 files) that are used by comprehensive rotor analysis codes such as CAMRADII, or the mid-fidelity CFD solver RotCFD. These codes have proven to provide accurate performance predictions for all rotor operations at only a fraction of the computational expense of three- dimensional body-fitted viscous grids.
Electroless cu bottom-up filling using 3-N,N-dimethylaminodithiocarbamoyl-1-propanesulfonic acid

Cu bottom-up filling in Cu electroless deposition was attempted using 3-N,N-dimethylaminodithiocarbamoyl-1-propanesulfonic acid DPS . An accelerating effect on the planar surface was observed at low DPS concentrations, while a suppressing effect was found at higher concentrations. Co-addition of 2,2 -dipyridyl enhanced the surface morphology. Filling profiles were dependent on the concentration of DPS added, and Cu bottom-up filling was achieved due to a concentration gradient by diffusion of DPS between the top and bottom of the trench. © 2005 The Electrochemical Society. DOI: 10.1149/1.2063291 All rights reserved.
Public Investment in R&D in Reaction to Economic Crises - A Longitudinal Study for OECD Countries

The paper investigates the reaction of public R&D spending on economic crises. We are interested in two counteracting motives: On the one hand, public R&D spending can be seen as a means to fight the crisis, and governments may decide to increase their R&D budgets. On the other hand, a crisis reduces public income and urges governments to cut spending, which may negatively affect public R&D budgets. Using panel data from 26 OECD countries over the period 1995 to 2015, we investigate how public R&D expenditure changes over the business cycle for different types of government R&D expenditure. On average, we find evidence for a strong pro-cyclical effect on public R&D investments. But country heterogeneity matters. Whereas European innovation leaders and non-EU countries pursue a counter-cyclical strategy, innovation followers and moderate innovators behave pro-cyclical. This leads to an increasing innovation gap in Europe. Short-run and long-run financing conditions (budget surplus and government debt levels) also significantly affect public R&D spending. However, there is no evidence that economic crises systematically affect the composition of public R&D spending along different thematic areas or by beneficiaries.
Impact of Risk Aversion on the Operation of Hydroelectric Reservoirs in the Presence of Renewable Energy Sources

The increasing share of renewable energy sources, such as wind and solar generation, has a direct impact on the planning and operation of power systems. In addition, the consideration of risk criteria within the decision support tools used by market participants (generation companies, energy services companies, and arbitrageurs) is becoming a common activity given the increasing level of uncertainties faced by them. As a consequence, the behavior of market participants is affected by their level of risk aversion, and the application of equilibrium-based models is a common technique used in order to simulate their behavior. This paper presents a multi-stage market equilibrium model of risk-averse agents in order to analyze up to what extent the operation of hydro reservoirs can be affected by the risk-averse profile of market participants in a context of renewable energy source penetration and fuel price volatility.
Prediksi Kelas Jamak dengan Deep Learning Berbasis Graphics Processing Units

For the first time, machine learning did the classical classification process using two classes (biclass) such as class -1 and class +1, 0 and 1, or the form of categories such as true and false. Famous methods used are Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The current development was a problem with more than two classes, known as multiclass classes. For SVM sometimes the plural classes are overcome by doing a gradual process like a decision tree (DT) method. Meanwhile, ANN has experienced rapid development and is currently being developed with a large number of layers with the new activation functions, i.e. the rectified linear units (ReLu), and the probabilistic-based activation, i.e. softmax, including its optimizer methods (adam, sgd, and others). Then the term changed to Deep Learning (DL). This study aimed to compare two well-known methods (DL and SVM) in classifying multiple classes. The number of DL layers was six with the neuron composition are 128, 64, 32, 8, 4, and 3, while SVM uses a radial kernel base function with gamma and c respectively 0.7 and 5. Besides, this study intends to compare the use of the Graphics Processing Unit (GPU) available on Google Interactive Notebook (Google Colab), an online Python language programming application. The results showed that DL accuracy outperformed SVM but required large computational resources, with the accuracy for DL and SVM are 99% and 98%, respectively. However, the use of the GPU can overcome these problems and is proven to increase the speed of the process as much as 47 times. Keyword: Artificial Neural Networks, Graphics Processing Unit, Google Interactive Notebook, Rectified Linear units, Support Vector Machine.
Web-Based and mHealth Technologies to Support Self-Management in People Living With Type 2 Diabetes: Validation of the Diabetes Self-Management and Technology Questionnaire (DSMT-Q)

Background A growing number of web-based and mobile health (mHealth) technologies have been developed to support type 2 diabetes self-management. Little is known about individuals’ experiences with these technologies and how they support self-management. Appropriate tools are needed to understand how web-based and mHealth interventions may impact self-management. Objective This study aimed to develop an instrument, the Diabetes Self-Management and Technology Questionnaire (DSMT-Q), to assess self-management among people living with type 2 diabetes who use web-based and mHealth technologies. Methods A total of 36 candidate questionnaire items, drafted previously, were refined using cognitive debriefing interviews (n=8), expert consultation, and public patient involvement feedback. Item reduction steps were performed on survey data (n=250), and tests of validity and reliability were subsequently performed. Results Following amendments, patients and experts found 21 items relevant and acceptable for inclusion in the instrument. Survey participants included 104 (41.6%) women and 146 (58.4%) men. Two subscales with high construct validity, internal consistency, and test-retest reliability were identified: “Understanding individual health and making informed decisions” and “Confidence to reach and sustain goals. ” Conclusions Analyses confirmed good psychometric properties in the DSMT-Q scales. This tool will facilitate the measurement of self-management in people living with type 2 diabetes who use web-based or mHealth technologies.
Bayesian Learning of Non-Negative Matrix / Tensor Factorizations by Simulating Pólya Urns

We introduce a dynamic generative model, Bayesian Allocation Model (BAM) that makes the connection among nonnegative tensor decompositions, topic models, and Bayesian networks explicit. Our construction is based on marking the events of a Poisson process using a Bayesian network and integrating out the parameters analytically where the resulting marginal process is a Pólya urn. The marginal likelihood can then be characterized as the probability that an urn process hits a certain small set. This alternative random process perspective allows us to develop sequential Monte Carlo (SMC) algorithms for computing nonnegative tensor decompositions and the marginal likelihood whose computational complexity is independent from the tensor dimensions. Our analysis and simulation results suggest that the SMC algorithms have favorable properties in the sparse data regime, but our construction also provides additional justification for the popular variational algorithms in the dense/large data limits.
THE INFLUENCE OF ROOT CANAL TAPER AND CURVATURE ON THE STRESSES GENERATED DURING VERTICAL COMPACTION OBTURATION TECHNIQUE

Objective: The aim of this study was to evaluate the effect of different taper on stresses generated within root canals that have varying degree of curvature when subjected to vertical compaction obturation technique. Methods: Thirty extracted teeth were divided into three groups, group1: teeth with straight roots, group 2: teeth with moderate curvature roots, group3: teeth with severe curvature. Each group was divided into two subgroups: A or B according to the preparation taper of root canal either 2% or 6% taper. Molds were constructed to simulate the presence of root within artificial socket, then vertical compaction obturation technique is performed with force equal to 1kg at three levels apical, middle and coronal, then the samples were subjected to loading up to fracture. Results: The result showed that there is no significance difference between the mean of fracture load of straight, moderate and severe curvature among the same group taper but 6% groups showed higher fracture resistance than 2% taper groups. Conclusion: Increasing the canal taper will decrease the internal stresses within the root canal
Institutional Constraints for the Success of Agricultural Biotechnology in Developing Countries: The Case of Bt-Cotton in Shandong Province, China

The use of genetically engineered crop varieties has recently become one option to prevent pest damage in agriculture. The promoters of biotechnology stress the great potential for yield increase and pesticide reduction while the critics point out the potential risks for biodiversity and human health as well as institutional problems for implementation especially in developing countries. The objective of this paper is an in-depth economic analysis of Btcotton production in North East China under small-scale conditions and several years after technology introduction. Data were collected in 2002 (March - October) in Linqing County, a major cotton growing area of Shandong Province, China. Data collection comprised a seasonlong monitoring of Bt-cotton production with 150 farmers from five villages, and three complementary household interviews. In addition, plot-level biological testing was carried out to determine the actual Bt toxin concentration in the varieties that were used by the farmers. All farmers in the case study were growing insect resistant Bt-cotton varieties in 2002. Nevertheless, they sprayed high amounts of chemical pesticides that were almost entirely insecticides. A proportion of 40% of the pesticides applied belonged to the categories extremely or highly hazardous (WHO classes Ia and Ib). The paper reviews methodological issues inherent to impact assessment of crop biotechnology and identifies market and institutional failure as possible reasons for continued high pesticide use. The production function methodology with damage control function was applied and it was found that for both damage control inputs, i.e. Bt and insecticides the coefficients were not significantly different from zero. In contrast to studies that treat Bt varieties as dummy variable in economic models, in this research it was possible to specify Bt toxin concentration in cotton leaf samples as a continuous variable. The results of this study support the notion that introducing Biotechnology in developing countries without enabling institutions that assure proper use of the technology can considerably limit its benefits. Hence it is important to include institutional criteria in the evaluation of agricultural biotechnology especially in developing countries.
EFFECT OF 4% ARTICAINE INFILTRATION VERSUS 2% LIDOCAINE NERVE BLOCK AFTER PREMEDICATION BY IBUPROFEN ON ANESTHETIC EFFICACY IN ENDODONTIC TREATMENT OF ACUTE IRREVERSIBLE PULPITIS: A RANDOMIZED CLINICAL TRIAL

Introduction: The purpose of the present study was to assess effect of 4% articaine infiltration versus 2% lidocaine nerve block after premedication by ibuprofen on anesthetic efficacy in endodontic treatment of first permanent molar with acute irreversible pulpitis, in a randomized, single-blind study. Subjects and Methods: Fifty two patients were included in the study with 26 patients in lidocaine group and 26 patients in articaine group. Patients were given one 600 mg tablet of ibuprofen as a premedication one hour before anesthetic administration. Electric pulp tester reading was recorded before and after anesthesia. Then root canal treatment was done in single visit. Patient rated pain on Visual Analogue Scale (VAS) preoperatively, during access cavity, cleaning and shaping and postoperatively after 6, 12 and 24 hours. Number of analgesic tablets taken was recorded. Chi square test was used to compare between categorical data. Results: There was no statistically significant difference in percent of successful cases between lidocaine (53.8%, 76.9%) and articaine groups (50%, 57.7%) in access and cleaning and shaping, respectively. There was no statistically significant difference in number of patients needed supplemental intrapulpal anesthesia. Most patients reported no or mild pain after 24 hours in both groups and only one tablet was taken when needed. Conclusions: lidocaine inferior alveolar nerve block (IANB) is similar to articaine infiltration in mandibular molars with acute irreversible pulpitis, thus making infiltration with articaine a viable alternative to IANB. Furthermore, supplemental intrapulpal anesthesia proved to be an effective method in controlling pain and finalizing treatment successfully.
Surgical tactics with acute pancreatitis complicated ascites-peritonitis

Fluid accumulation in the peri-pancreatic region and the distal areas of the abdominal cavity is widespread in the early stage of acute severe pancreatitis. The fluid is mainly hemorrhagic ascites, rich in activated lipolytic and proteolytic enzymes, vasoactive substances and inflammatory cytokines, and is aseptic at the onset of the disease. Evacuation of a fluid rich in potentially toxic mediators from the abdominal cavity is supported in a number of publications as a rational measure to alleviate the burden of disease in critically ill patients.
Fabrication of paper micro-devices with wax jetting

Paper microfluidic devices are a promising technology in developing analytical devices for point-of-care diagnosis in the developing world. This article describes a novel method of wax jetting with a PZT (piezoelectric ceramic transducer) actuator and glass nozzle for the fabrication of paper microfluidic devices. The hydrophobic fluid pattern was formed by the permeation of filter paper with wax droplets. Results showed that the size of the wax droplet, which was determined by the voltage of the driving signal and nozzle diameter, ranged from 150 μm to 380 μm, and the coefficient of variation of the droplet diameter was under 4.0%. The smallest width of the fluid channel was 600 μm frontside and 750 μm backside. The patterned filter paper was without any leakage, and multi-assay of glucose, protein, and pH on the paper microfluidic device, and laminar diffusion flow with blue and yellow dye were realized. The wax jetting system supplied a low-cost, simple, easy-to-use and fast fabrication method for paper microfluidic devices.