Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological and Biochemical Response of Wild Olive (Olea europaea Subsp. europaea var. sylvestris) to Salinity

In the face of climate change, water deficit and increasing soil salinity pose an even greater challenge to olive cultivation in the Mediterranean basin. Due to its tolerance to abiotic stresses, wild olive (Olea europaea subsp. europaea var. sylvestris) presents a good candidate in breeding climate-resilient olive varieties. In this study, the early response of the native Croatian wild olive genotype (WOG) to salinity was evaluated and compared with that of well-known cultivars (cv.) Leccino and Koroneiki. Potted olive plants were exposed either to 150 mM NaCl or 300 mM mannitol for 3 weeks to distinguish between the osmotic and ionic components of salt stress. To determine the impact of the plant age on salinity, 1-, 2-, and 3-year-old WOG plants were used in the study. The growth parameters of both the cultivars and WOG of different ages decreased in response to the mannitol treatment. In contrast to cv. Leccino, the NaCl treatment did not significantly affect the growth of cv. Koroneiki or WOG of any age. The contents of Na+ and Cl− were considerably higher in the salt-treated WOG, regardless of age, compared with the cultivars. However, while both treatments significantly reduced the K+ content of cv. Koroneiki, that nutrient was not significantly affected in either cv. Leccino or WOG. Unlike the cultivars and older WOG, the NaCl treatment caused a significant decline of photosynthetic pigments in the 1-year-old WOG. The cultivars and WOG of different ages experienced a similar drop in the chlorophyll a content under the isotonic mannitol treatment. The absence of lipid peroxidation, modulation of superoxide dismutase, and guaiacol peroxidase activity were noted in all WOG ages under both stressors. These data suggest that WOG resilience to salinity is associated with its large leaf capacity for Na+ and Cl− accumulation, K+ retention, and its adaptable antioxidative mechanisms. The results are promising with regard to obtaining a new olive cultivar with better resilience to soil salinity.
A Review of CYP3A Drug-Drug Interaction Studies: Practical Guidelines for Patients Using Targeted Oral Anticancer Drugs

Many oral anticancer drugs are metabolized by CYP3A. Clinical drug-drug interaction (DDI) studies often only examine the effect of strong CYP3A inhibitors and inducers. The effect of moderate or weak inhibitors or inducers can be examined using physiologically based pharmacokinetic simulations, but data from these simulations are not always available early after approval of a drug. In this review we provide recommendations for clinical practice on how to deal with DDIs of oral anticancer drugs if only data from strong CYP3A inhibitors or inducers is available. These recommendations were based on reviewed data of oral anticancer drugs primarily metabolized by CYP3A and approved for the treatment of solid tumors from January 1st, 2013 to December 31st, 2015. In addition, three drugs that were registered before the new EMA guideline was issued (i.e., everolimus, imatinib, and sunitinib), were reviewed. DDIs are often complex, but if no data is available from moderate CYP3A inhibitors/inducers, a change in exposure of 50% compared with strong inhibitors/inducers can be assumed. No a priori dose adaptations are indicated for weak inhibitors/inducers, because their interacting effect is small. In case pharmacologically active metabolites are involved, the metabolic pathway, the ratio of the parent to the metabolites, and the potency of the metabolites should be taken into account.
Optimization and Evaluation of Calibration for Low-cost Air Quality Sensors: Supervised and Unsupervised Machine Learning Models

With the advancement of air pollution management, low-cost sensors are increasingly being used in air quality monitoring, but the data quality of these sensors is still a major source of concern. In this paper, data from five air monitoring stations in Sofia were compared to data from fixed low-cost PM sensors. The values of atmospheric pressure from low-cost sensors and the effects of relative humidity were investigated. A two-step model was created to refine the calibration process for low-cost PM sensors. At first, we calibrated the sensors with five separate supervised machine learning models and then the ANN-final model with anomaly detection completed the results. The ANN-final model improved the R2 values of the PM10 determined by low-cost sensors from 0.62 to 0.95 as compared to standard instruments. In conclusion, the two-step calibration model proved to be a positive solution to addressing low-cost sensor efficiency issues.
Critical Success Factors for Digitalization Projects

Our paper provides insights into which critical success factors (CSFs) for digitalization projects are seen as important from the companies’ perspective based on an online survey. The results presented in this paper show that CSFs of the dimensions of Corporate organization and Technology are considered to be of particular relevance, as stated by the companies, with Corporate culture, Top management support, and a Unified digital corporate strategy / vision as the three most important CSFs. Therefore, this paper contributes to the CSF research regarding digital transformation and enables the development of practice-oriented recommendations for action and assistance in shaping digital transformation.
Dynamic Diverse Summarisation in Heterogeneous Graph Streams: a Comparison between Thesaurus/Ontology-based and Embeddings-based Approaches

Nowadays, there is a lot of attention drawn in smart environments, like Smart Cities and Internet of Things. These environments generate data streams that could be represented as graphs, which can be analysed in real-time to satisfy user or application needs. The challenges involved in these environments, ranging from the dynamism, heterogeneity, continuity, and high-volume of these real-world graph streams create new requirements for graph processing algorithms. We propose a dynamic graph stream summarisation system with the use of embeddings that provides expressive graphs while ensuring high usability and limited resource usage. In this paper, we examine the performance comparison between our embeddings-based approach and an existing thesaurus/ontology-based approach (FACES) that we adapted in a dynamic environment with the use of windows and data fusion. Both approaches use conceptual clustering and top-k scoring that can result in expressive, dynamic graph summaries with limited resources. Evaluations show that sending top-k fused diverse summaries, results in 34% to 92% reduction of forwarded messages and redundancy-awareness with an F-score ranging from 0.80 to 0.95 depending on the k compared to sending all the available information without top-k scoring. Also, the summaries' quality follows the agreement of ideal summaries determined by human judges. The summarisation approaches come with the expense of reduced system performance. The thesaurus/ontology-based approach proved 6 times more latency-heavy and 3 times more memory-heavy compared to the most expensive embeddings-based approach while having lower throughput but provided slightly better quality summaries.
StarCraft strategy classification of a large human versus human game replay dataset

Real-time strategy games are popular in AI research and education. Among them, Starcraft: Brood War (SCBW) is particularly well known. Recently, the largest known SCBW game replay dataset STARDATA was published. We classify player strategies used in the dataset for all 3 playable races and all 6 match-ups. We focus on early to mid-game strategies in matches less than 15 minutes long. By mapping the classified strategies to replay files, we label the files of the dataset and make the labeled dataset available.
Development of pineapple and wine flavored fermented dairy product

The objective of this work was to take advantage of buttermilk and whey for the development of a pineapple and wine flavored fermented dairy product and to characterize the product in physicochemical, microbiological and sensorial terms. The experiment was carried out through four treatments with the following formulations: T0 (70% UHT skim milk and 30% whey), T1 (70% buttermilk and 30% whey), T2 (60% buttermilk and 40% whey), T3 (50% buttermilk and 50% whey). The average treatment values did not differ among themselves, at a 5% level of significance, regarding pH, acidity, viscosity and color. The protein and fat levels decreased with the increase of the amount of buttermilk in the treatment formulations. There was no significant difference, at a 5% level of significance, in the average acceptance values of the 57 judges for flavor, texture, aroma, color and overall impression, with values above 8.0 (9 point scale) for all attributes. In the purchase intention test, there also were no differences, with averages superior to 5.4 (7 point scale) for all formulations. All formulations had counts of total coliforms and E. Coli within the acceptable range, taking into consideration the legislation for fermented dairy beverages. Regarding the lactic acid bacteria, the counts were superior to 1.3 x 10 CFU g and also in accordance with the legislation. It was concluded that the use of buttermilk in the production of fermented dairy products is technologically feasible.
A Bayesian framework for adaptive learning in educational games

As technology advances, educational games and adaptive learning methods are increasingly implemented to enhance education. Adaptive learning promises to help educators support the wide variety of learning needs and goals in our current educational system, and it has been shown that personalization improves learning (e.g., Connor, Morrison, Fishman, Schatschneider, & Underwood, 2007). In addition, there is ample evidence that game-based learning improves engagement (Steinkuehler, Squire, & Barab, 2012). An educational game can be adaptive in many ways. Learners may be assessed initially and then assigned to a fixed learning path, or their path may change after each “level” of the game. At the extreme is real time adaptivity, where the game adapts every time a learner completes a challenge. This high level of adaptivity is intended to keep the game at a“Goldilocks” level of difficulty for the learner (neither too easy nor too hard) to optimize engagement and learning. Real time adaptivity of educational games, when combined with a psychometric framework, can help educational games meet two goals: (a) adapt to the level of the learner to keep the game motivating and challenging (b) involve parents and teachers in children’s digital learning by giving them feedback on how a learner is doing. One way to achieve the continuous assessment required for real time adaptivity is to use item response theory (IRT) models as developed for computerized adaptive testing (CAT, e.g., van der Linden & Glas, 2010; Wainer et al., 2000). IRT provides the tools to estimate item (i.e., game challenge) difficulty and learner ability on a single scale. Hence, the difficulty of the next game challenge can easily be matched to the estimated ability level of the learner. In addition, the estimated ability level can be used to report on the learner’s progress to parents or teachers. Games differ in important ways from tests, however. Tests consist of a considerable number of test items, administered at one specific point in time. They generate a lot of information about the ability of a learner at that moment, resulting in a highly accurate measurement. Educational games, however, typically consist
Word Order-Aware Text Processing: A Third Generation of Text as Data in Political Science

Text has always been a key source of data for political scientists. In the past five to ten years, new techniques have automated some components of text analysis, allowing researcher to classify, categorize, and find meaning in large collections of text. I argue that text as data in political science has moved through two distinct approaches and is on the cusp of a third. The first generation consisted of simple text matching operations, where documents are searched for keywords or phrases were checked against hand-built dictionaries. The second generation introduced machine learning methods, operating on documents represented as “bags-of-words”, where word order is discarded and documents are treated as counts of the words they use. The second generation has given us the staple techniques of text analysis in political science, including supervised document classification and topicmodeling through latentDirichlet allocation (Blei, Ng, and Jordan 2003) or the structural topicmodel (Roberts et al. 2013). The third generation will be characterized by word order-aware machine learning models, informed by research in natural language processing. These two techniques may perform better than the second generation on some existing tasks, but more importantly, this “linguistic turn” in political text analysis will enable wholly new methods of extracting meaning from text.
An Optimized Stochastic Techniques related to Option Pricing

Recently stochastic methods have become very important tool for high performance computing of very high dimensional problems in computational finance. The advantages and disadvantages of the different highly efficient stochastic methods for multidimensional integrals related to evaluation of European style options will be analyzed. Multidimensional integrals up to 100 dimensions related to European options will be computed with highly efficient optimized lattice rules.
Evaluation of the new capture vaporizer for aerosol mass spectrometers: Characterization of organic aerosol mass spectra

ABSTRACT The Aerosol Mass Spectrometer (AMS) and Aerosol Chemical Speciation Monitor (ACSM) are widely used for quantifying submicron aerosol mass concentration and composition, in particular for organic aerosols (OA). Using the standard vaporizer (SV) installed in almost all commercial instruments, a collection efficiency (CE) correction, varying with aerosol phase and chemical composition, is needed to account for particle bounce losses. Recently, a new “capture vaporizer” (CV) has been shown to achieve CE∼1 for ambient aerosols, but its chemical detection properties show some differences from the SV due to the increased residence time of particles and vaporized molecules inside the CV. This study reports on the properties and changes of mass spectra of OA in CV-AMS using both AMS and ACSM for the first time. Compared with SV spectra, larger molecular-weight fragments tend to shift toward smaller ions in the CV due to additional thermal decomposition arising from increased residence time and hot surface collisions. Artifact CO+ ions (and to a lesser extent, H2O+), when sampling long chain alkane/alkene-like OA (e.g., squalene) in the CV during the laboratory studies, are observed, probably caused by chemical reactions between sampled OA and molybdenum oxides on the vaporizer surfaces (with the carbon derived from the incident OA). No evidence for such CO+ enhancement is observed for ambient OA. Tracer ion marker fractions (fm/z =, i.e., the ratio of the organic signal at a given m/z to the total OA signal), which are used to characterize the impact of different sources are still present and usable in the CV. A public, web-based spectral database for mass spectra from CV-AMS has been established. Copyright © 2018 American Association for Aerosol Research
Triterpenoid saponins from Eryngium agavifolium

Aerial parts of E. agavifoliumG. yielded 3-O-(iÂ¢-D-glucopyranosyl) betulinic acid 28-O-(iÂ¢-D-glucopyranosyl) ester, 3-O-(iÂ¢-D-glucuronopyranosyl) betulinic acid 28-O-(iÂ¢-D-glucopyranosyl) ester, 3-O-(iÂ¢-D-galactopyranosyl) betulinic acid 28-O-[iÂ¢-D-glucopyranosyl (1iÂ®3)-iÂ¢-D- glucopyranosyl] ester, 3-O-[3-iÂ¢-D-glucopyranosyl(1iÂ®2)-iÂi-L-arabinopyranosyl] betulinic acid 28-O-(iÂ¢-D-glucopyranosyl) ester and the already known 3-O-iÂ¢-Dglucopyranosyl betulinic acid. The structures of the isolated compounds were determined using spectroscopic methods.
Degradation of ferrate species produced electrochemically for use in drinking water treatment applications

Stability of ferrate species, produced electrochemically for on-site treatment of drinking water, has been thoroughly studied for a number of important conditions including: pH, temperature, initial concentration effects, and the presence of impurities in order to assess the degradation process quantitatively. The limited number of ferrate degradation studies reported to this date, have all focused on the chemically-produced ferrate species and have examined conditions which are unsuitable for drinking water treatment. Degradation of electrochemical ferrates over a wide temperature range of 5 °C to 80 °C shows an Arrhenius-type reaction behaviour with an activation energy of 27.3 ±0.6 kJ mol−1. Initial rate analysis of ferrate degradation reveals a reaction order of about 1. The impacts of potential water impurities, such as salts and natural organic matter, and the source water (e.g. tap, deionized, and natural lake) have been studied and are presented. Degradation of ferrates in the pH range of 5 to 13 appear to have a first-order kinetic behaviour with rates constants ranging from 1.02 × 10−3 s−1 to 6.67 × 10−5 s−1. Understanding the stability properties is important for further development and deployment of on-site generated ferrates for the purposes of drinking water treatment. This article is protected by copyright. All rights reserved
Combined impacts of climate and air pollution on human health and agricultural productivity

Climate change and air pollution can interact to amplify risks to human health and crop production. This has significant implications for our ability to reach the Sustainable Development Goals (e.g. SDGs 2, 3, 13, 15) and for the design of effective mitigation and adaptation policies and risk management. To be able to achieve the SDG targets, closer integration of climate change and air pollution both in terms of impact assessment for human health and agricultural productivity and respective policy development is needed. Currently, studies estimating the impacts of climate and air pollutants on human health and crops mostly treat these stressors separately, and the methods used by the health and agricultural science communities differ. Better insights into the methods applied in the different communities can help to improve existing and develop new methods to advance our knowledge about the combined impacts of climate change and air pollution on human health and crops. This topical review provides an overview of current methodologies applied in the two fields of human health and agricultural crop impact studies, ranging from empirical regression-based and experimental methods to more complex process-based models. The latter are reasonably well developed for estimating impacts on agricultural crops, but not for health impacts. We review available literature addressing the combined effects of climate and air pollution on human health or agricultural productivity to provide insights regarding state-of-the-art knowledge and currently available methods in the two fields. Challenges to assess the combined effect of climate and air pollution on human health and crops, and opportunities for both fields to learn from each other, are discussed.
Transformation of the flow of matter in valley reservoirs as a factor of improving their ecological condition

The results of perennial studies of the valley reservoirs of the Moskva River (Mozhaysk, Ruza and Ozerna reservoirs) have shown that the influx of river waters bringing nutrients leads to eutrophication of the upper parts of the reservoirs. However, as water moves, this matter is extracted by phytoplankton and zooplankton and undergoes sedimentation. The content of the suspended materials, iron, true colour of water near the dam are two times lower than in the upper reaches of the reservoirs; the concentration of mineral silicon and phosphorus decreases by nearly one-fourth, and that of organic matter, by 10-15%. As a result, in the near-dam zones of the water reservoirs biological productivity decreases: the biomass of phytoplankton and bacteria in water decreases by nearly 40%. Thus, transformation of the flow of matter along the length of the valley reservoirs results in essential improvement of the quality of water and reduction of the degree of eutrophication, which is especially important for the reservoirs which serve as sources of water supply for the city of Moscow.
Full InGaN red (625 nm) micro-LED (10 μm) demonstration on a relaxed pseudo-substrate

The full InGaN structure was grown on two different InGaNOS substrates from Soitec. An electron blocking layer was inserted in the full InGaN light emitting diode (LED). Enhanced internal quantum efficiency of red emitting InGaN/InGaN quantum wells was measured with a value above 10% at 640 nm. 10 μm diameter circular micro-LEDs are emitted at 625 nm with an external quantum efficiency of 0.14% at 8 A cm−2 with an estimated light extraction efficiency below 4%. With a a lattice parameter of 3.210 Å, InGaN based red LED can also emit up to 650 nm.
Preparation and characterization of PLA/NCC nanofibers

ABSTRACT By electronspinning PLA/NCC nanofibers were prepared with polylactic acid (PLA) and nanocellulose whiskers (NCC) which obtained from microcrystalline cellulose (MCC) by sulfuric acid hydrolysis. The morphologies and physical states were investigated using scanning electron microscopy (SEM), thermogravimetric analysis (TG-DTG) and differential scanning calorimetry (DSC). When the mass fraction of NCC was 5%, scanning electron microscopy observations revealed that the diameter of PLA/NCC nanofibers was the smallest and the distribution was uniform and results from TG-DTG and DSC suggested that thermal decomposition temperature of composite films was approximately increased by 20°C than pure PLA film, at the same time mechanical and hydrolysis property of composite films were obviously improved.
Fake Followers Detection on Twitter

: Twitter is one of the most popular social networking platforms that people use to communicate and interrelate. Organization and companies use Twitter and other social media platforms, for the marketing and promotions of their products or services. To achieve this goal they seek to partner with Twitter influensers, as a part of their marketing strategy. Influencer marketing is considered more effective than traditional marketing. Influencers are more honest than a business due to the truth that they have developed close relationship with their followers.
Matching with Subjective and Objective Evaluations

There are matching markets in which objective evaluations such as exam scores, skill qualifications, and priorities, are available in addition to subjective evaluations over agents. To examine these situations, we extend a college admission model by allowing that colleges have two different types of ordinal rankings over students, i.e., common priority order and individual preferences. A matching is called double stable if it is both priority stable and preference stable. While the existence of a double stable matching is not always guaranteed, we provide its characterization through the existing well-known mechanisms in the literature; a double stable matching exists if and only if the resulting outcome of the serial dictatorship mechanism coincides with that of the student-proposing deferred acceptance mechanism.